Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters  to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media. Several factors are contributing globally to declining social sustainability including people’s health, economic issues, global events such as the COVID-19 pandemic and environmental disasters, and increased social division and polarization [Mental health is related to social sustainability because it is an important aspect of overall health and well-being, and mental health issues can have significant impacts on individuals and communities. Mental health issues such as depression and anxiety can lead to decreased productivity, absenteeism, suicides, and other negative impacts on social and economic well-being. For example, according to the World Health Organization (WHO), there is a suicide every 40 s, totaling more than 700,000 per year. This high rate of suicide highlights the deteriorating social conditions around the world .Moreover, addiction is often related to mental health in that it can be a symptom of, or a response to, underlying mental health issues. For example, people may turn to substances or behaviors such as drugs, smoking, alcohol, gambling, or internet use as a way to cope with mental health issues such as depression, anxiety, or stress. However, addiction can also contribute to, or exacerbate, mental health problems, as the use of substances or engagement in certain behaviors can have negative impacts on mental well-being . The CenThere are several challenges facing mental health treatment, including a lack of access to care, stigma, a shortage of mental health professionals, limited treatment options, co-occurring disorders, and a lack of integration with physical health care. These challenges can make it difficult for people to receive the mental health treatment they need, which can have negative impacts on their well-being and overall quality of life. Addressing these challenges is important for promoting mental health and improving the well-being of individuals and communities.However, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring, improve the effectiveness of treatment, and reduce the need for ongoing care. Root causes of mental health issues can include trauma, genetics, environmental factors, and physical health issues. A multifaceted approach that addresses social, economic, and environmental factors as well as individual needs is needed to remove the root causes of mental health issues effectively.There is a significant body of research on the relationship between physical and psychological health. Studies have explored the connection between mental stress and physical diseases such as cancer, lung disease, and kidney disease ,7,8, as A holistic approach to understanding mental health issues is missing from the existing research. What is needed is to understand mental health and illnesses in the context of socio-economic and environmental contexts, create awareness for the people of the causes and effects of mental illnesses, and develop interventions to bring social behaviors, lifestyle, and root cause changes.This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters (or factors) related to mental health  from Twitter data. The parameters are discovered from three different perspectives Drugs and Treatments, Causes and Effects, and Drug Abuse. Moreover, we automatically discover associations between the parameters and drugs. We gathered from Twitter 1,048,575 tweets in Arabic about psychological health in Saudi Arabia during the month of October 2022. The tweets were retrieved using various keywords and hashtags related to mental health. We built a machine learning software tool for this work  using different data sources. See for instance ,7,8,17. Discussing works that used machine-learning methods in studying topics related to mental health, for instance, Iram et al.  utilizedSeveral studies have utilized Twitter data for studying psychological health. Zhang et al.  developeRegarding research on mental health using Arabic Twitter data, we have found only a limited number of studies. Alabdulkreem  proposedOur work differs from previous research studies from a variety of perspectives including its particular focus, the nature of the dataset , the software design (the pipeline and approach for machine learning), the innovative methodology of using AI for discovering parameters, and the innovative methodology and design of finding associations between parameters and drugs.In this section, our methodology and the design of our tool are explained. Note that we have used contextual translations and made some adjustments to the translations of original Arabic tweets in order to make them more understandable to English readers. This may include changes to the order of the information in the tweet, the removal of unnecessary or redundant information, and the provision of summaries for tweets that are too long or contain unnecessary information. We have also sometimes omitted parts of the original tweets in order to protect the privacy of the tweeters. Note that Arabic tweets  tend to be written in an informal style, so a literal translation may not always be clear or convey the intended meaning. Note also that in some tables in the paper, some search terms, or key terms detected by our machine learning models, may appear multiple times. This is because the original terms in Arabic may be different, but their English translations may be identical. The Arabic content  is not presented in this paper due to the publisher’s requirements. It can be found in an extended version of this paper made available on a preprint server .This study proposes a big data and machine learning-based approach for the automatic discovery of parameters related to psychological health from Twitter data. The proposed approach focuses on psychological disorders in Saudi Arabia but can be applied to other diseases and languages. There are five components in the proposed approach: data collection and storage, data preprocessing, drugs for psychological health parameters discovery, validation, and visualization and reporting. The first step was to use a Python script with a specified search query and a set of keywords and Twitter hashtags related to psychological health in Saudi Arabia. A discovery module was then constructed for data analysis and detection of parameters using Latent Dirichlet Allocation (LDA) and the scikit-learn library. We discovered the parameters from three different perspectives . The results for each perspective are discussed in detail in We collected Arabic tweets that are related to psychological health in Saudi Arabia using Twitter REST API and Tweepy. The data was obtained using various key terms and hashtags related to psychological health. For instance, the following key terms were used: depression, sadness, panic, mental illness, and others. Additionally, we used various hashtags such as depression month, social anxiety, social phobia, and others. A sample of the keywords used for data collection is as follows: suicide, social phobia, depression, depressed, sadness, fear, anxiety, obsessive, incantation, envy, panic, neurology, psychotherapy, mental health, psychological counseling, and mental illness. Some of the hashtags used include World Suicide Prevention Day, Suicide Awareness Month, suicide prevention, social anxiety, social phobia, depression, depression month, and seasonal depression. The list of Arabic key terms used in data collection can be found in an extended preprint version of this paper . The datData analytics requires the preparation of data as a critical ingredient. Data preprocessing involves a number of methods for cleaning, eliminating noise, improving quality, and, eventually, increasing accuracy. One of the libraries available for preparing textually based data is Natural Language Toolkit (NLTK). Preprocessing includes a number of steps including tokenization, normalization (replacing letters), stop word removal, and the elimination of irrelevant words and characters. Our first step in the preprocessing was eliminating all irrelevant characters and words such as numbers, URLs, different symbols , English alphabets, emojis, etc. Moreover, we eliminated non-Arabic characters, repeating characters, and all various forms of punctuation symbols such as brackets and mathematical notations. The next step was tokenization and normalization in which we removed all different types of Arabic diacritics including single marks such as Fatha, Dammah, Kasra, Tashdid, and Sukun as well as double marks such as Tanwin Damm, Tanwin Kasr, and Tanwin Fath. Moreover, we used the normalizer to convert all different shapes of Alif, Yaa, and Taa Murbutah to the basic form bare Alif, dotless Yaa, and Haa, respectively. After that, we removed the list of stop words provided by the NLTK library with an additional list of words in dialectical Arabic developed by us; further details of data-preprocessing of Arabic tweets including a list of Arabic stop words can be found in our earlier work .In this section, we discuss the methodology for identifying psychological health parameters through topic modeling analysis of Twitter data. Modeling of topics is a frequently employed AI approach for data analysis and topic discovery, and it contains various algorithms that identify patterns and themes in a collection of documents by clustering word terms . For topWe modeled the data from three perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used a list of keywords to create a subset of the dataset and discover the parameters for each perspective. For instance, for the Drugs and Treatments perspective, we used names of antidepressants, painkillers, and medicines . For the Causes and Effects perspective, we used multiple keywords such as side, effects, and cause. For the Drug Abuse perspective, we used multiple keywords such as abuse and extra. Most of the keywords are in Arabic and few in English because some tweets use some terms in English such as medicine names. We modeled each perspective into different clusters. After extracting the clusters, we allocated each tweet to its cluster based on the highest probability of the tweet association with a cluster. After that, we performed an analysis of the tweets and keywords in each cluster in which we looked at the keywords and examined the context of the keywords in each parameter. This enabled us to name each cluster based on the keywords and tweets using our domain knowledge. We iteratively refined clusters’ names using our domain knowledge and other quantitative measures. The process enabled us to eliminate irrelevant clusters and combine clusters that were similar. We eventually aggregated the parameters based on their common themes into macro-parameters that represent broader areas. This is done separately for each perspective.The discovered parameters were validated internally and externally. For external validation of the data and parameters extracted from the Twitter data, we utilized academic papers, news articles, and online reports. To assess the validity of the discovered data and parameters, internal validation was carried out utilizing tweets from the gathered dataset.In this study, we provide a variety of visualization methods of the parameters we have discovered. These are intertopic distance maps, taxonomies, and keyword frequency diagrams (both cluster-specific and corpus-wide). Python pyLDAvis package was used to compute and depict the terms frequency diagrams and distance maps ,51. The This section focuses on the parameters discovered for the Drugs and Treatments perspective. As noted in the Introduction Section, we have translated the Arabic content (words and tweets) contextually and made adjustments to the original text, including changes to the information order and the removal of unnecessary or redundant information. We have also omitted parts of the original text that were not useful.We used a list of Arabic and English keywords to create a subset of the dataset and discover the parameters for the Drugs and Treatments perspective. The subset contains 6717 tweets. The LDA algorithm detected 30 clusters from the subset of the dataset. We merged similar clusters, discovered parameters, and categorized them into five macro-parameters.The keywords used (translated into English) are as follows: medicine, drugs, pharmaceutical, medicinal, prescribe, prescription, dose, antidepressant, as anti (depression), anti (depressants), tranquilizer, milligrams, pill, pills, reliever, melatonin, Panadol, Rufenac, Celebrex, Ibuprofen, Acetaminophen, Brintellix, Duloxetine, Faverin, Seroxat, Lyrica, Remeron, Cipralex, Xanax, Benzodiazepine, Valium, Escitalopram, Leponex, Paroxetine, Bupropion, Imipramine, Haloperidol, Reserpine, Tetrabenazine, Clonazepam, Lorazepam, Diazepam, Amitriptyline, Nortriptyline, Mirzagen, Prozac, Serotonin, Cyproheptadine, Salipax, Tramadol, Wellbutrin, Letrozole, Cabergoline, Tranylcypromine, Gomood, Rhodiola, Ashwagandha, Duspatalin, and Omeprazole.A taxonomy see  illustraIn this section, we discuss the parameters related to the macro-parameter Diseases and Disorders. This parameter is about postpartum depression which is a form of depression that develops in women after giving birth to a child. The parameter is represented by keywords such as depression, birth, gloom, death, mother, afflict, women, sadness, husband, hate, and postpartum. Several tweets in this parameter discuss the symptoms of this disease such as exhaustion and lack of energy, sleep disturbance, anorexia disorder, weakness in concentration, and thinking about death.In this section, we discuss the parameters related to the macro-parameter Individual Factors including anxiety, sadness, poor concentration, poor memory, loss of appetite, and fear of medicine. This parameter relates to anxiety, a common emotion characterized by worry, nervousness, and unease about an uncertain outcome. It is a normal reaction to stress that everyone experiences at some point, but anxiety disorders are more than short-term worries or fears. Anxiety can have a negative impact on daily activities such as work, school, and interpersonal relationships . Common This parameter relates to sadness. It includes the following keywords: depression, treatment, sadness, time, psychological, anti (depression), symptoms, psychological, pills, treatment, deep, disappointed, hopes, wound, and heal. Many tweets in this parameter initiate sad thoughts. Some of the tweets are poems. We found several tweets that contain poems focusing on sadness due to love. They initiate sad thoughts in people; although people may enjoy it first, it can lead to severe depression and suicide like any other intoxication. It is well known that sad songs may give enjoyment to lovers, but they may also become a source of depression.The poor concentration parameter regards the difficulties in concentration and the issues related to it. This parameter contains the following keywords: depression, medication, treatment, disorder, anti (depression), self, causes, pills, diabetes, deficiency, anxiety, prescription, treatment, psychological, and dangerous. Although these keywords do not directly mention concentration, they are about diseases related to concentration. Most of the tweets are about causes of poor concentration including depression and anxiety.The poor memory parameter discusses the negative effects of depression on memory and focus. People and experts discussed how depression affects memory and one’s ability to concentrate and remember. Moreover, some tweets have highlighted that some people fear using antidepressants because they think that it will cause issues with memory and concentration.This parameter is about loss of appetite, which can happen because of depression. Some of the keywords in this parameter are biscuits, psychological, treatment, medication, depression, eating, taking, alone, light, coffee, chocolate, and food. Some people have mentioned loss of appetite among the negative effects of antidepressants.This parameter regards fear of medicine. Individuals with this fear may feel intense fear and worry when considering taking medication or going to a doctor or hospital. The parameter covers some factors of fear, including the length of medicine (duration of treatment), side effects, fear about taking the incorrect medication, worry about being incorrectly diagnosed, concern about becoming dependent on medication, and fear about medical procedures or treatments.Here, we cover the parameters related to the macro-parameter Social and Economic Factors including poverty, unemployment and insufficient finances, high cost of healthcare, loss of loved ones, forensic psychiatry, and social depression. This parameter relates to poverty as an economic factor that can cause mental health issues. This parameter captures various dimensions such as low household income, medical care, children, stealing, sadness, fear, depression, and psychological diseases. Some tweets under this parameter have highlighted how stealing money from poor people can have a devastating impact on their mental health. It can have a disastrous effect on the livelihoods of those affected, depriving them of the basic necessities such as food, shelter, and medical care that their families and children require for survival. Eventually, it can lead to feelings of despair, depression, and mistrust in others.This parameter discusses inadequate finances and unemployment as social and economic factors for depression and mental health issues. This parameter includes various dimensions detected by our model such as work conditions, unemployment status, children, prison, depression, and committing suicide.This parameter relates to the high cost of healthcare as one of the socioeconomic causes of depression. This parameter involves various dimensions such as low income; healthcare expenses; chronic diseases; healthcare expenses for elderly parents; and stress and pressure. Some tweets discussed how low-income elderly people with chronic illnesses, such as diabetes or hypertension, tend to incur greater expenses for medical services. This may result in inadequate housing and nutrition, which may worsen health outcomes. All of these factors may lead to higher healthcare costs for those who are poor. This can contribute to feelings of stress and pressure.This parameter highlights one of the social causes of depression, the loss of loved ones. The parameter is represented by keywords such as pills, depression, period, feeling, lost, most important, depression, best, sleep, matter, medicine, even, life, living, death, friend, desire, and Iniesta. Some tweets related to the depression experience of the football player Iniesta who got depressed from the death of his close friend. The following tweet is an example: “When I was fighting depression, my best time was when I swallowed pills and went to sleep. Even hugging my wife was like hugging a pillow, without feeling.”.This parameter is about forensic psychiatry, defined as “the branch of psychiatry that deals with issues arising in the interface between psychiatry and the law, and with the flow of mentally disordered offenders along a continuum of social systems” . It is “This parameter is about social depression. This parameter highlights various dimensions such as stressful life events, stigmatization of medicines, weight increase, and lifestyle. This parameter emphasizes the fact that society is living in a time when the cost of living and healthcare has increased, and high achievement has become a necessity leading to social depression and anxiety.The parameters associated with Treatment Options macro-parameter are discussed in this section. This parameter discusses walking as a treatment for psychological diseases. It is represented by keywords such as prescribe, body, walking, negativity, psychological, energy, nature, anxiety, needs, medications, diseases, fear, equivalent, work, painkillers, emptying, endorphins, sedatives, and reduce. The tweets in this parameter discuss a range of benefits of walking such as triggering the body’s whole muscular system and reducing relapses of mental illnesses.This parameter is regarding optimism, which is a psychological approach or outlook that emphasizes the beneficial aspects of life and anticipates positive results. Optimism is often regarded as a form of resilience, allowing individuals to manage challenging circumstances and recover from adversity . This paThis parameter is about good company and the importance of good friends for mental health issues. Some of the keywords for the parameter are depression, anti (depression), best, friend, anti (depressants), normal, good, and defect. People discussed how good friend can be as an antidepressant for depression. There are many tweets in this parameter such as the following, “Best antidepressant: (a good) Friend”.This parameter focuses on the pendulum technique. It contains the following keywords: fear, then, question, pendulum, yourself, effectiveness, know, answer, write, ask, feelings, attachment, ready, mention, answer, sharp, intention, depression, anti (depressants), and sun. It was detected as a treatment for psychological issues.This parameter covers spirituality as a treatment for sadness and depression. People discussed how spirituality is used to treat people from sadness and depression. For example, doing good actions, remembering God, and praying the morning prayer.The antioxidants parameter focuses on the role of antioxidants in fighting depression and mental illnesses. It is represented by key terms such as coffee, psychological, depression, oxidation, treatment, anti (depression), condition, people, helps, most, moods, relieve, improve, anti (oxidants), richness, fruits, combined, plus, and vegetables. The tweets in this parameter have discussed natural sources of antioxidants. Moreover, many tweets have mentioned how coffee is rich in antioxidants and how it can help relieve depression and improve mood state.The painkillers and antidepressants parameter highlights the difference between painkillers and antidepressants in terms of their use. This parameter contains the following keywords: depression, medications, disease, treatment, patient, psychiatric, medication, psychological, anti (depression), instead of, doctor, depression, for a patient, Cipralex, painkiller, body, give, Celebrex, and hurt. Some tweets have mentioned that antidepressants can be prescribed for physical illnesses. It is not clear from the tweets why an antidepressant is prescribed. Doctors may see some symptoms of depression.This parameter is about community-supported therapies. This parameter includes various dimensions such as community support groups, society programs, faith, successful treatment factors, etc. Here is an example of a related tweet: “Psychological diseases involve a set of genetic, familial and social factors, and therefore recovery from them also requires a combination of all these factors, such as regular use of medication, adherence to healthy habits and lifestyle “sports”, family support, community awareness and embrace and not to reject those who suffer from it, or to stigmatize them as weak or lacking in faith!”.This parameter is about the types of medical treatments for psychological illnesses, which are Cogitative Behavioral Therapy (CBT) and the use of medications/drugs. A number of tweets discuss psychotherapy and medication. For example, a tweet stated that “in mental illness, each case is different from the other. And depends on the condition and depression degrees. Medicines are used in severe cases and behavioral therapy benefits most people. Therefore, first, you must visit a doctor, who will examine you and let you know whether you need to take medicine or undergo behavioral therapy”. In addition, some tweets highlighted the importance of lifestyle for mental health.The parameters related to the macro-parameter Treatment Limitations are covered in this section. This parameter discusses antidepressant limitations. It is represented by keywords including depression, medicine, truth, relieve, reality, natural, dealing, mind, crises, right, exaggerating, delight, emotion, happiness, nervousness, etc.The parameter is about the negative effects of antidepressants. It is represented by keywords such as depression, medicine, anti (depressants), people, psychological, sadness, possible, pill, condition, psychological, actually, disease, nervousness, causes, etc. There are many tweets that mention the side effects of antidepressants. As previously noted, we have translated the Arabic content (words and tweets) contextually and made adjustments to the original text, including changes to the information order and the removal of unnecessary or redundant information. We have also omitted parts of the original text that were not useful. Here is an example tweet: “If the psychiatrist is incompetent, he will give the patient pills that ruin a person’s life”. A tweeter stated that one of her siblings committed suicide after a doctor convinced him that his depression doesn’t have a solution and there is no treatment for it. In addition, in another case, it was reported in a tweet that someone’s close relative was prescribed so many strong pills that if he forgot to take them for a day or two he would have a bout of screaming and crying.A number of tweets mentioned some diseases that were detected by our tool as side effects of antidepressants such as obesity, drowsiness, bruxism, and attention-deficit/hyperactivity disorder (ADHD). Some tweets have discussed the positive sides of using antidepressants. For example, the following tweet: “… some mental illnesses are chronic like some physical illnesses such as diabetes and high blood pressure. Therefore, you may need to take antidepressants for long periods or all of your life”. Several tweets have mentioned other ways of treating depression such as electroconvulsive therapy (ECT), St. John’s wort, and magnesium.In this section, we provide the associations between the detected parameters and drugs for the Drugs and Treatments perspective. These are shown in These parameter-drug associations can be discovered automatically as follows: we built a vocabulary of all medicines used for the treatment of psychological illnesses, and we searched for these medicines against tweets in each parameter and recorded the associations with the drugs found through the search for each parameter.This section discusses the parameters discovered for the Causes and Effects perspective. An overview of parameters and macro-parameters is provided in We created a list of Arabic keywords to build a subset of the dataset and identify the parameters for the Causes and Effects perspective. The dataset obtained contains 88,566 tweets. The parameters and other information on the Causes and Effects perspective are provided in The keywords employed are side, effects, because of, cause it, it causes, caused by, cause, brought, result, weight, my weight, cholesterol, disorders, lethargy, migraine, appetite, metabolism, memory, concentration, dizziness, sleep, insomnia, headache, crying, stomach, hyperactivity, attention, deficit, depression, and addiction.In this section, the parameters that belong to the macro-parameter Diseases and Disorders are discussed including attachment disorder, insomnia, and obsessive-compulsive disorder (OCD). This parameter is about attachment disorder, which is a form of mental illness or behavioral condition that interferes with a person’s capacity to establish and sustain relationships. It relates to the challenges involved in understanding emotions, expressing affection, and placing one’s trust in others. The parameter is represented by keywords such as psychological, health, family, live, your life, hospital, person, reality, well-being, success, locked up, lost, attachment, money, etc. People discussed that someone should avoid excessive attachment to loved ones as it can destroy person’s life. Furthermore, a case of a celebrity who was deceived by a loved one was discussed.This parameter focuses on insomnia, which can be a cause or an effect of other psychological issues. This parameter is characterized by keywords such as sleep, sadness, anxiety, doctor, eye, fear, depression, symptoms, fear, diaspora, matter, etc. People discussed different reasons for insomnia such as excessive worry, fear, depression, anxiety about events or people, sadness, excessive thinking, exhaustion, or loss and nostalgia.This parameter regards obsessive-compulsive disorder (OCD), which is a prevalent mental health problem characterized by compulsive behaviors and obsessive thoughts. People discussed the symptoms of OCD, the causes and treatment. For example, someone tweeted: “Obsessive-compulsive disorder is the control of an idea that its owner knows is absurd, forcing him to repeat actions, such as making sure the door is locked, cleanliness, or purity, to a degree that may affect the productivity of the individual. This indicates underlying anxiety and can be treated with some medication and dialogue...”.This cluster is about post-surgery depression, and it focuses on surgeries as a cause of depression. The tweets associated with this parameter are mostly related to the depression that occurs after Sleeve gastrectomy surgery because the stomach is restricted to a certain food, and this has a negative effect, such as feeling lonely or that the person cannot go out and eat a variety of foods like before. However, depression can happen as a side effect of any other surgery.The chronic physiological diseases parameter discusses various diseases that could lead to depression. The following keywords were detected by our model: depression, depression, cause, psychological, sick, chronic, king, medical, brain, fear, diseases, Salman, suffering, surgical, cause, city, relationship, psychological, nerves, and compensate. When a person suffers from a disease that affects his ability to move and could lead to some changes in lifestyle, this could result in depression. A tweet mentioned five chronic diseases which cause depression and sadness including diabetes mellitus, arthritis, heart disease, kidney failure, and thyroid gland. Some other tweets linked COVID-19 infection to a range of chronic neuropsychiatric disorders, including depression, memory problems, and Parkinson’s disease-like disorders.We highlight here the parameters under the macro-parameter Individual Factors. There are eight parameters. This parameter is about fear as a cause or effect of psychological illnesses. Our model detected the following keywords: leave, care, fear, weight, gain, about you, subject, sleep, diseases, poverty, keep away, think, difference, fear, doctor, health, face, your fear, and sources. The tweets highlighted different kinds of fear including fear of losing persons, fear of diseases, fear of poverty, and others.This parameter is about sadness which could be a symptom, a cause, or an effect of psychological diseases. It is represented by keywords including world, depression, wish, real, people, complete, me, normal, age, try, need, needs, work, fear, person, years, time, stay, etc. This parameter is similar to a parameter covered in the previous perspective. For more details see This parameter is about loneliness, which is characterized by keywords such as wish, heart, alone, sadness, complete, pass, loneliness, mind, stage, fear, focus, nights, human, thinking, anxiety, unknown, details, compensate, trust, and calm down. Someone tweeted: “I hope that God will compensate me for all the nights of loneliness, sadness and misery, and reassure my heart …”.This parameter is about people who lost their sense of value and pleasure in everything and wish for death. This parameter includes the following keywords: depression, want, need, myself, times, moment, desire, overwhelming, disappear, the world, have, presence, heavy, exist, feel, want, depression, sadness, and view. People discussed different symptoms associated with lacking passion such as a feeling of helplessness, low energy and exhaustion, constant pain, and the feeling of guilt. Other tweets have mentioned other symptoms including a lack of self-esteem, self-loathing, lack of focus, loss of hope, and the desire to disappear.This parameter is about the suppression of emotions either positive or negative, which can lead to depression and other psychological illnesses. Based on our model, the following keywords were detected: sadness, sorrow, physical, cause, after, able, personality, disease, experience, sleep, possible, upset/angry, need, your chest, was not, wish, tell, say, inside, and live. Some tweets highlighted some of the effects of suppressed emotions such as anxiety, depression, and other stress-related illnesses. Other tweets mentioned the importance of discussion and expressing emotions for psychological health.The negative emotions parameter is about people who talk about and share their personal negative experiences and generalize them, causing depression for themselves and others in society. It is represented by keywords such as depression, condition, people, friendliness, because, human, life, depression, psychological, crying, sleep, conversation, life, yourself, have, sadness, anxiety, permanent, phrase, and love.This parameter regards the devil and negative thoughts. It is characterized by keywords such as most important, sadness, anxiety, whirlpool, fear, heart, devil, life, comfortable, bad, sorrows, stable, caused, current, last, past, make, tense, and destroy. People discussed how the devil negatively affects people’s mental health. The following tweet is an example: “Remember that one of devil’s most important goals is to cultivate sadness and fear in the heart, so that he does not make you stable or comfortable, but rather discontented, anxious, and pessimistic. He links you to the past, its pains, and the sorrows it causes you, and links you to the future, its fears and anxieties; To make you always in a tense spiral and mistrust, and his goal is to destroy your current moment and spoil your life.”.This parameter is about lacking inner peace. The following keywords were detected by our model: life, peace, anxiety, insomnia, stay away, in you, people, many, things, topic, anger, inside me, focus, your Lord, struggle, urgency, fear, anxiety, psychological, and joy. This parameter focuses on the importance of inner peace for fighting depression. People discussed different things such as how to get inner peace by avoiding passing judgment on people. Following is an example tweet: “If you do not feel peace within you, you will find many things in life that cause you anger, chaos, grumbling, anxiety, and conflict. How do I find peace inside me? get closer to your Lord; avoid passing judgment on people; stay away from focusing on any disturbing topic; live life with grace, not with complexity.”.There are five parameters under the Social and Economic Factors macro-parameter. This parameter covers various study-related issues which could cause psychological illnesses such as studying for long hours, studies-related depression, and bullying in schools. The parameter contains the following keywords: concern, problem, subject, permission, fear, cause, psychological, lead, schools, academic, level, impact, delay, space, going, coming, elite, to school, disability, and counsellors. The tweets discussed the causes of psychological illness. For instance, the following tweet highlights different causes of psychological illnesses and some solutions which don’t lie in drugs: “When the psychologist’s tweets highlight how some psychological disorders, such as depression, anxiety, etc., develop as a result of people’s exposure to psychological trauma, abused childhood, or some social problems such as divorce and others. It is natural to find that the solution to these problems does not lie in drug treatment”.Furthermore, a tweet stated a list of disorders which are related to certain causes. These disorders include anxiety disorders, especially panic attacks and anxiety about disease, depression and mood disorders, traumatic disorders, personality disorders, dissociative disorders, and internal psychological struggle due to social pressure. Another tweet highlighted various socioeconomic causes of depression and psychiatric disturbances. A tweet reported that “the poor economic state of the family may cause social problems and bad psychological effects that lead to excessive thinking and eventually lead to mental illnesses”.A number of tweets reported that universities and schools cause fear and depression. Moreover, several tweets discussed the issues of bullying in schools and how it affects the academic progress of students. For example, a tweet mentioned that bullying in schools can cause depression, anxiety, social shyness, social phobia, and eventually delay in the academic level.This parameter focuses on work as a cause of psychological issues. Among the keywords that our model detected are depression, limit, need, possible, permanence, depressed, length, fear, offender, no one, praise be to God, literally, still, life, sufficiency, society, coming, and deficiency. People discussed how long working hours affect mental health and how leaving very little time for family and social relationships can result in depression and family breakup.This parameter is about the lifestyle as a cause of psychological illness including eating and thinking patterns. The parameter is represented by keywords such as time, depression, sadness, cause, grace, speech, problems, know, silence, understood, inside, pretended, stupid, committed, smiled, answered, wellness, weight, in relation to, and hospital. Here are some example tweets about maintaining a good lifestyle. For example, the following tweet: “Most people write about pain and talk about fatigue until their minds are programmed to be depressed and think negatively which cause them illnesses”.Following is another example tweet: “Malnutrition is the cause of mental illness, which can be treated with diet, exercise, cupping, and good company rather than medicine. The consumption of indomie, soft drinks, and drinks containing stimulants causes fear. Alcohol, smoking, and sweets cause anxiety and depression.”.This parameter is about the high cost of healthcare as a socioeconomic factor for psychological illnesses. This parameter includes the keywords depression, knew, make, have, session, psychological, period, good, for depression, seasons, diseases, suffering, fear, difficult, home, street, family, and life. This parameter is similar to a parameter covered in the previous dimension. For further details see This parameter is about seasonal depression which is a type of depression that occurs as a result of the change of seasons. This parameter is represented by keywords such as depression, Saturday, gloom, depression, severe, I have, birth, feel, winter, weather, spray, period, know, month, offender, people, cause, feel, atmosphere, and inside. In tweets and keywords, different types of depression were mentioned such as post-weekend depression, postpartum depression, and winter depression.This parameter regards emotional release (catharsis) as part of psychotherapy. The following keywords were detected by our model: depression, life, fear, hair, remove, cut, winter, sleep, name, family, wake up, satiate, inside, side, entered, smell, bring, come, answer, and people. Many tweets have talked about cutting hair as a way of emotional release. The following tweet, for example: “Cutting hair removes 100% of life’s depression”.This parameter is about good friends and is described by the following keywords: depression, better, anxiety, person, can, deeper, seriously, kidding, inside you, collect, spontaneity, quest, reach, continuity, wonderful, include you, the two things, the mother, cause, and not happened. People discussed the importance of good friends for psychological health. Following is an example tweet: “When you talk to good friend while you are in a state of anxiety and fear, you become reassured because of his deep words and great actions”.This parameter covers spirituality as a treatment for psychological illnesses. This parameter is similar to a parameter covered in the previous perspective. For further details see This parameter is about surgery as a treatment for psychological diseases. Among the keywords that our model detected are operation, depression, sadness, hours, surgery, success, suffering, future, sleep, psychological, medical, patient, mood, Salman, natural, first, thinking, anxiety, excess, and permanent. Several tweets have talked about the success of a surgical operation to treat a patient suffering from chronic depression.Similar to Furthermore, we found that some painkillers, such as Panadol, are associated with the Insomnia parameter. The research highlights that the use of painkillers may be associated with an increased risk of developing insomnia. Opioid use is likely to be a contributing factor to insomnia due to the sedative effects of opioids, which can make it difficult to fall asleep and stay asleep . FurtherIt is possible that individuals who experience pain or headaches and have difficulty sleeping may benefit from the use of painkillers. Research has demonstrated that individuals suffering from chronic pain often experience sleep disturbances , includiIn this section, we discuss the parameters discovered for the Drug Abuse perspective. An overview of parameters and macro-parameters is provided in In this section, we discuss the Drug Abuse perspective. We employed a list of keywords to build a subset of the dataset and identify the parameters for the Drug Abuse perspective. The following is a translation of the list of keywords utilized: abuse, mood, trance, without a recipe, pill, and extra. The dataset that we got after filtering data contains 2,701 tweets.The LDA algorithm detected 30 clusters for the Drug Abuse perspective. We excluded twenty clusters from the results as they were irrelevant to the focus of this perspective. We merged similar clusters, discovered parameters, and categorized them into five macro-parameters. Using the discovered parameters for Drug Abuse perspective, a taxonomy was created see .In this section, we discuss the parameters related to the macro-parameter Drug Abuse. This parameter relates to bipolar disorder, known as manic depression, which is defined as “a mental illness that causes unusual shifts in a person’s mood, energy, activity levels, concentration, and ability to carry out day-to-day tasks” . It invoThis parameter regards university exams and is described by the following keywords: depression, condition, fine, pills, work, tried, unfortunately, help, peace, family, suicide, prison, answer, mercy, cut, tired, have, difficult, and see.This parameter relates to the death of loved ones as a cause of drug abuse. The following keywords were detected by our model: pills, depression, matter, feeling, period, depression, life, better, more important, medicine, even, death, lived, lost, sleep, go, desire, resistance, Kharkhi, etc. Some tweets relate to the depression of football player Iniesta and the death of his close friend which caused him depression.This parameter discusses addiction, known as substance use disorder. It is a condition characterized by an impaired ability to control the use of legal or illicit drugs, alcohol, nicotine, or other substances, resulting in changes in brain function and behavior . This paThis parameter is about abusing drugs and committing suicide as an effect of psychological issues. The following keywords were detected by our model: psychological, potion, treatment, heart, one, long, love, doctor, take, pass, fear, depression, bad, etc. Some people mentioned that they have tried to commit suicide by using an overdose of medicine.This parameter is about the Flakka drug. The following keywords were detected by our model: depression, fear, love, potion, intensity, newness, intake, feeling, problem, desire, alone, therefore, withdrawal, dope, lethargy, drug, to withdraw, attempt, symptoms, and depression. Many tweets mentioned that this drug is spread among young people and the reason for its spread is that it is cheap. People have also discussed the effects of using the Flakka drug such as hallucinations, madness, strange behavior, loss of control over mental abilities, and a mad start to a dark path. Many tweets have also mentioned the withdrawal symptoms of the drug such as feeling lethargy and suffering severe depression.This section highlights the associations between the detected parameters and drugs for the Drug Abuse perspective. In this research, we proposed a big data and machine learning-based approach for the automatic discovery of parameters related to psychological health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. Moreover, we automatically discovered associations between the parameters and drugs. The parameters were discussed in detail in We discovered twenty-four parameters from the Drugs and Treatments perspective and grouped them into five macro-parameters: Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, and Treatment Limitations. A total of twenty-two parameters were detected from the Causes and Effects perspective and we grouped them into four macro-parameters: Diseases and Disorders, Individual Factors, Social and Economic Factors, and Treatment Options. We detected six parameters from Drug Abuse perspective, namely, bipolar disorder, university exams, death of loved ones, addiction, suicide, and flakka drug.A multi-perspective view of psychological health data is depicted in This work makes important theoretical and practical contributions to the area. The earlier research see  has lookThis work sheds new light on the social and environmental factors that impact mental health and makes significant contributions to the field of toxics. Through the use of big data and machine learning, the study was able to identify the root causes of mental health issues from Twitter data and analyze parameters related to drugs and treatments, causes and effects, and drug abuse. As a result, this work provides a more comprehensive understanding of the complex social and environmental factors that contribute to mental health issues. This research seeks to identify and address the harmful social and environmental factors that can cause or worsen mental health issues, making it an important contribution to the field of toxics. Furthermore, it provides valuable insights into the relationship between mental health and various factors, which has the potential to inform the development of effective interventions that can improve public health and social sustainability. Our approach of focusing on different perspectives and aggregating identified parameters into macro-parameters is a powerful method that allows for a more nuanced and detailed understanding of mental health and its contributing factors. By identifying these factors, this study has the potential to contribute to the development of strategies to reduce exposure to harmful substances and mitigate the negative impacts of social, economic, and environmental factors on mental health. Therefore, the findings of this study have significant implications for the improvement of public health and social sustainability.The findings have the potential to open new avenues for identifying drug use and abuse for mental health, as well as other micro and macro factors related to mental health through social media. The methodology can also be applied to other diseases and may have the potential for forensic toxicology research. However, more research is needed to fully explore the potential of social media for forensic purposes. Moreover, our approach can serve as an autonomous real-time surveillance system that captures crucial system parameters related to mental health, socio-economic factors, and the environment. By detecting opportunities, challenges, and risks, we can take proactive steps toward optimizing society for better, sustainable health outcomes. Moreover, our system’s ability to monitor and analyze a variety of factors in real-time allows for swift and effective action to be taken in response to potential threats to mental health. By continually tracking and assessing these parameters, we can proactively identify potential risks and take steps to mitigate them. This not only improves individual well-being but also helps to create a healthier, more resilient society.The work presented in this paper is the beginning, many more works are needed to investigate the potential of social media for forensic and other purposes. This research is part of our broader work on data-driven parameter discovery from Twitter and other data sources applied previously to different research areas including the education sector in KSA during COVID-19 , the disThis work is scientifically valid as it uses a systematic approach to gather data from Twitter and applies big data and machine learning techniques to analyze the data. The study collected a large sample of over one million tweets in Arabic about psychological health in Saudi Arabia. The use of a large sample increases the statistical power of the study, making it more representative of the population of interest. The study used machine learning algorithms to discover parameters related to mental health from the tweets, which ensures a consistent and replicable approach to the data analysis. The methodology used in the study is comprehensive and transparent. The paper provides a detailed account of how the study was conducted, including the data collection process, machine learning algorithms, and the parameters discovered. This allows other researchers to replicate the study and verify the results. Furthermore, this study identifies the potential for extending the methodology to other diseases and forensic toxicology, further demonstrating the robustness of the methodology. Our methodology and results are applicable to other research domains beyond mental health; therefore, our findings have a level of generality and transferability. This implies that results are not limited to the specific sample, population, or context used in this paper but can be extended to other contexts or populations. This study’s results provide a comprehensive account of mental health from different perspectives, including drugs and treatments, causes and effects, and drug abuse. This study identified 52 parameters from different perspectives related to mental health, which were further aggregated into six macro-parameters. This study’s findings were based on social media discussions by both the public and health professionals, which provides insight into the social and environmental factors that impact mental health. Furthermore, this study identified the associations between different drugs and mental health, which has implications for the effectiveness and risks of medication for treating mental health issues. In conclusion, this study’s comprehensive methodology, large sample size, and use of machine learning algorithms make it scientifically valid. This study’s results provide a valuable contribution to understanding mental health issues from a social and environmental perspective, and the potential for extending the methodology to other diseases and forensic toxicology provides a promising avenue for future research.Mental health issues can have significant impacts on individuals and communities, and addressing root causes can help prevent mental health problems. The big data and machine learning approach proposed in this paper can be used to automatically discover parameters related to mental health from Twitter data, including information on drugs and treatments, causes and effects, and drug abuse. This can provide a comprehensive understanding of mental health as seen on social media, discussed by the public and health professionals, and can also identify associations with different drugs. The methodology can be extended to other diseases and has the potential for discovering evidence for forensic toxicology from social and digital media. Additional research is necessary to fully explore the potential of social media for forensic purposes; this paper is just the beginning and it will form our future work.The conclusions drawn in this work are confirmed by scientific data and objectivity in several ways. First, the paper identifies mental health as a significant issue with impacts on individuals and communities and highlights the need for a holistic approach to understand its root causes. This understanding is supported by scientific research on mental health and the impacts of social and environmental factors on mental health outcomes. Second, we propose a data-driven approach for discovering parameters related to mental health from Twitter data. The use of big data and machine learning tools is a well-established methodology for data analysis and can provide insights into complex problems, including mental health. Third, we report the results of this study that collected over one million Arabic tweets about psychological health in Saudi Arabia. The use of Twitter data provides an objective and real-time view of mental health issues, as discussed by the public and health professionals. The analysis of the Twitter data provides a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse.Although our work on using big data and machine learning to automatically discover parameters related to mental health from Twitter data is innovative and insightful, there are limitations to the study that must be considered. Firstly, the study solely relies on Twitter data, which may not represent the entire population and may suffer from selection bias. Additionally, the study only focuses on Arabic tweets related to mental health in Saudi Arabia during a specific period, which may limit the generalizability of the findings. To address this limitation, more data should be collected and analyzed in Saudi Arabia and internationally. Secondly, the study collected a substantial number of tweets, but further research is needed to capture relevant tweets comprehensively, such as through additional keywords and hashtags. Thirdly, the research focused on three perspectives, and more research is needed to ensure that all perspectives are captured to provide more comprehensive information. Fourthly, there is a need to incorporate other sources of digital media and scientific data to enhance the diversity and richness of the discovered information. Finally, while machine learning algorithms are powerful tools for data analysis, they are not infallible and may produce inaccurate or biased results. We are confident that all the limitations we have identified can be overcome in our future work. As we move forward, we invite the community to join us in improving the proposed approach and enhancing its robustness and impact. Working together, we can achieve our goals and realize the full potential of our efforts.


The last five decades have witnessed an inordinate number of advances in the diagnosis and management of congenital heart defects (CHDs), as reviewed elsewhere . These aIn the first paper, Vecchiato and associates from Padova and Naples, Italy, discuss the overshoot of the respiratory exchange ratio during recovery from maximal exercise testing in young patients with congenital heart disease (CHD) . The autIn the second paper, Sganga and her colleagues from Harvard Medical School, Boston, MA, and Stanford University School of Medicine, Palo Alto, CA, USA review quality improvements in a pediatric echocardiography laboratory . The autIn the third paper, Dr. Pop from the University of Medicine Pharmacy Sciences and Technology of Tirgu Mures and the Tirgu Mures Emergency Institute for Cardiovascular Diseases and Heart Transplant, Tirgu Mures, Romania, compares different contrast agents  used for computed tomographic angiography (CTA) studies in infants referred for aortic arch evaluation . Dr. PopIn the paper to follow, I review clinical aspects of mitral atresia with normal aortic root . Mitral In the next paper, Dr. Singh from the Baylor College of Medicine/The Children’s Hospital of San Antonio, San Antonio, TX, USA, reviews “as low as reasonably achievable” (ALARA) in pediatric electrophysiology laboratory . Dr. SinIn the sixth paper, Dr. Divekar and associates from the University of Texas Southwestern Medical Center/Children’s Medical Center, Dallas, TX, USA, and the Cleveland Clinic Children’s Hospital, Cleveland, OH, USA, review transcatheter device therapy and how to integrate advanced imaging with invasive procedures in the management of CHD . These aIn the subsequent paper, Dr. Arar and his colleagues at the University of Texas Southwestern Medical Center/Children’s Medical Center, Dallas, TX, USA, review the role of cross-sectional imaging in pediatric interventional cardiac catheterization . This paIn the next paper, Dr. Betancourt and her associates from The Children’s Hospital of San Antonio/Baylor College of Medicine, San Antonio, TX, USA, review the utility of a three-dimensional printed model in the biventricular repair of complex CHDs . They rep = 0.27). In addition, no difference in time to first oral feed (2.0 days (1.1–4.5) vs. 3.1 days (1.8–4.4), p = 0.34) and time to goal feed (6.0 days (3.2–8.3) vs. 6.9 days (5.0–9.0), p = 0.15) was detected. Furthermore, no variance in establishing all oral feeds in one year: 88% vs. 98%, p = 0.16 was identified. However, the delayed group performed substantially worse on all the above parameters. The authors conclude that immediate and early groups demonstrated no variations in feeding outcomes or length of stay in their investigation, while the delayed group performed worse on all measures. Consequently, they recommend that caregivers focus on extubating the babies within 3 days following surgery to enhance feeding outcomes while reducing hospitalization duration.In a subsequent paper, Kepple and his associates from Creighton University School of Medicine and University of Nebraska Medical Center, Omaha, NE, USA, review the impact of extubation time on feeding outcomes after neonatal cardiac surgery . The autIn the final paper of this series, Dr. Sharma and associates from The Children’s Hospital of San Antonio/Baylor College of Medicine, San Antonio, TX, USA, and the Cohen Children’s Medical Center, New York, NY, USA, discuss advances in the prenatal management of fetal cardiac disease . They stThe papers included in this Special Issue, while not addressing all the advances that occurred in the last 50 years , have in


Incorrect AffiliationIn the published article, there was an error in affiliation 7. Instead of “Department of Chemistry, College of Science, Princess Noura bint Abdulrahman University, Riyadh, Saudi Arabia”, it should be “Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia”.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Correction: Liu et al. BMC Medical Imaging (2023) 23:14710.1186/s12880-023-01077-4Following the publication of the original article [1], it was brought to our attention that an error had been introduced during typesetting. In the article [1], it was stated that the Corresponding Author, Zhentao Li, was affiliated with United Imaging Intelligence (Beijing) Co., Ltd, located at Yongteng North Road, Haidian District, Beijing 100,094, People’s Republic of China.The correct affiliation for Zhentao Li should have been the Department of Radiology, Peking University People’s Hospital, located at 11 Xizhimen Nandajie, Xicheng District, Beijing, 100,044, People’s Republic of China.The author group information has been updated accordingly, and the original article  has been


Patient blood management (PBM) is a multidisciplinary and patient-centered treatment approach, comprising the detection and treatment of anemia, the minimization of blood loss, and the rational use of allogeneic transfusions. Pregnancy, delivery, and the puerperium are associated with increased rates of iron deficiency and anemia, which correlates with worse maternal and fetal outcomes and places pregnant women at increased risk of obstetric hemorrhage.off-label use of human recombinant erythropoietin in selected patients. This regimen should be tailored to the needs of each individual patient. Postpartum hemorrhage (PPH) accounts for up to one-third of maternal deaths in both developing and developed countries. Bleeding complications should be anticipated and blood loss reduced by interdisciplinary preventive measures and individually tailored care. It is recommended that facilities have a PPH algorithm, primarily focusing on prevention through use of uterotonics, but also incorporating early diagnosis of the cause of bleeding, optimization of hemostatic conditions, timely administration of tranexamic acid, and integration of point-of-care tests to support the guided substitution of coagulation factors, alongside standard laboratory tests. Additionally, cell salvage has proven beneficial and should be considered for various indications in obstetrics including hematologic disturbances, as well as various forms of placental disorders.Early screening for iron deficiency before the onset of anemia, as well as the use of oral and intravenous iron to treat iron deficiency anemia, has been shown to be beneficial. Anemia in pregnancy and the puerperium should be treated according to a staged regimen, administering either iron alone or in combination with an This article reviews PBM in pregnancy, delivery, and the puerperium. The concept comprises early screening and treatment of anemia and iron deficiency, a transfusion and coagulation algorithm during delivery, as well as cell salvage. Patient blood management (PBM) is a multidisciplinary and patient-centered treatment approach, comprising the detection and treatment of anemia, the minimization of blood loss, and the rational use of allogeneic transfusions. So far, the focus of PBM has been on major elective surgeries. Given the success of PBM initiatives , it seemPregnancy, delivery, and the puerperium are associated with increased rates of iron deficiency and anemia , 3, 4, wAnemia in pregnancy is defined by the WHO as a hemoglobin level <110 g/L. Worldwide, up to 40% of pregnant women receive a diagnosis of anemia . In EuroThe leading cause of anemia in pregnancy as well as postpartum is iron deficiency , 14. TheIron deficiency has detrimental effects on physical and mental performance, thermoregulation, immune function, neurologic function, and enzymatic function , with the consequence that maternal mortality rises with increasing severity of iron deficiency anemia . In geneThe administration of iron without actual knowledge of iron status is considered controversial , 27. AccBy contrast, diagnosed iron deficiency should be treated, as even mild anemia can be unpredictable, suddenly worsening over the course of a pregnancy with attendant risks to the mother and fetus. The mode of therapy should take into account various factors, including time remaining until delivery, the severity of anemia, the likelihood of additional complications , maternal comorbidities, and the patient's wishes   Fig. 2)2).Oral iron is the gold standard for the treatment of mild to moderate iron deficiency anemia . Higher Gastrointestinal side effects such as constipation, heartburn, and nausea, which occur in up to 30% of patients and limit the dose which can be administered, are a major disadvantage of oral iron preparations , 38, 39.Intravenous iron preparations are an important adjunct or alternative to oral iron therapy and allogenic blood transfusion. Indications for intravenous iron therapy include inadequate therapeutic response to oral iron therapy, anemia requiring rapid normalization, or simultaneous use of erythropoiesis stimulating proteins  .Intravenous iron administration bypasses the intestinal mechanism of iron absorption and therefore often results in an increase in free iron in serum after intravenous iron administration . DespiteA first, hemoglobin and ferritin measurement is normally performed in the first trimester and an oral iron therapy is usually prescribed in case of hemoglobin <100 g/L and/or ferritin <30 g/L. In the case of a lack of response to oral iron (Hb levels rising by less than 10 g/L within 14 days), severe iron-deficiency anemia, intolerance of oral iron, or clinical need for rapid and efficient treatment of anemia , intravenous iron therapy should be administered in the absence of contraindications but not before 16 weeks of gestation .The intravenous iron preparations used today differ in terms of pharmacokinetics, molecular mass, toxicity, and side effects . Meta-anSeveral studies have confirmed the efficacy and safety of parenteral iron as ferrous saccharate in iron deficiency and anemia in pregnancy , 47, 48.Study data are available for other intravenous iron preparations too, for example, iron polymaltose, iron gluconate, and low molecular weight iron dextrans . In geneThe growth factor rhEPO, a glycoprotein , is identical to endogenous erythropoietin and serves as a selective growth and survival factor for erythroid cells . Of releMeanwhile, there is also increasing experience using rhEPO in obstetrics , 64, 65.According to present results, the combination of rhEPO and parenteral iron is superior to iron therapy alone in terms of raising hemoglobin concentration and may be considered as an alternative in severe anemia or rejection of allogenic blood transfusion . The effThe coagulation cascade as classically taught, with extrinsic and intrinsic pathways converging on a final common pathway of thrombin formation, does not fully capture the true nature of in vivo clot formation . A more In this model, the exposure of TF-bearing cells to factor VII activates the extrinsic pathway leading to the formation of a small amount of thrombin. This small amount of thrombin then amplifies the pro-coagulant response by activating cofactors, factor XI, and platelets. Following that, a large burst of thrombin is formed on the platelet surface via the intrinsic pathway. Thus, the extrinsic and intrinsic pathways are not redundant but rather work in concert to bring about adequate secondary hemostasis ending in conversion of fibrinogen in fibrin monomers .3, due to a combination of hemodilution, and rapid turnover in the uteroplacental unit [Pregnancy is characterized by multiple changes to this system  Fig. 3)3). Firsttal unit , 76.Third, and perhaps most importantly, there is an altered baseline coagulation status in pregnancy. The most notable change with respect to the nonpregnant state is a progressive rise in fibrinogen (factor I) concentration, peaking at 24 h postpartum , 79, 80.Standard laboratory tests of coagulation do not reliably reflect the hypercoagulable state of pregnancy , 89, 90.Coagulation disorders in the setting of preeclampsia, HELLP syndrome, or disseminated intravascular coagulation often result in high blood loss. Other, more common, situations with a risk for massive hemorrhage arise from the placenta accreta spectrum, or placental abruption. 90% of patients with placenta accreta spectrum have blood loss >2,000 mL, which is associated with an increased risk of allogeneic blood transfusion. PPH is defined as blood loss in excess of 500 mL independent of delivery mode .The four leading causes of PPH are uterine atony, accounting for up to 80% of cases, as well as trauma, placental disorders, and coagulation defects, although PPH can also occur in women without any of these risk factors. These are also known as the “four Ts”: tone, trauma, tissue, and thrombin , 105. AsMeasures for the prevention of PPH suggested by the World Health Organization (WHO) include the administration of uterotonics, primarily oxytocin, late cord clamping , controlled cord traction for placental removal, and assessment of uterine tone in all women . The treConsidering the importance of rapid correction of hemostasis during PPH, viscoelastic point-of-care tests, such as ROTEM and TEG, have been proven to be of great value in the assessment of bleeding, by allowing for individualized coagulation management according to each patient's needs . PPH algFocusing on the second pillar of PBM, the reduction of perioperative red blood cell loss, the use of a cell salvage can also be applied in a peripartum setting. Historically, the idea of collecting a patient's own blood was born in obstetrics when the case of a woman who died of PPH provoked William Highmore to write a letter to The Lancet in 1874, stating that re-transfusing the lost blood could have saved the patient's life. In the past few decades, cell salvage has been implemented in various surgical settings. Despite this auspicious start, clinicians were initially cautious in obstetrics, due to concerns regarding amniotic fluid embolism and the re-transfusion of fetal debris potentially causing maternal alloimmunization. However, a focused review by Goucher et al.  of over Within obstetrics, cell salvage should be considered for various indications, especially various forms of placental disorders . FurtherPregnancy, delivery, and the puerperium are associated with increased rates of iron deficiency and anemia, which correlates with worse maternal and fetal outcomes and places pregnant women at increased risk of obstetric hemorrhage. Iron deficiency and anemia in pregnancy and the puerperium should be treated according to a staged regimen, administering either iron alone or in combination with human recombinant erythropoietin in selected patients. This regimen should be tailored to of the needs of each individual woman. It is recommended that obstetric facilities have a PPH algorithm, which incorporates early diagnosis of the cause of bleeding, optimization of hemostatic conditions, timely administration of TXA as well as oxytocin, and integration of point-of-care tests to support the guided substitution of coagulation factors, alongside standard laboratory tests. Cell salvage has been shown to be safe and beneficial and should be considered for various indications in obstetrics.David Hencker and Clara Castellucci have no conflicts of interest to declare. Alexander Kaserer received honoraria for lecturing from Bayer AG, Zürich, Switzerland. Christian Breymann is medical advisor in the field of iron therapy in OBGYN and received honoraria for lecturing from Vifor International and Pierre Fabre Switzerland. Donat R. Spahn's academic department is receiving grant support from the Swiss National Science Foundation, Berne, Switzerland, the Swiss Society of Anesthesiology and Perioperative Medicine (SSAPM), Berne, Switzerland, the Swiss Foundation for Anesthesia Research, Zurich, Switzerland, Vifor SA, Villars-sur-Glâne, Switzerland, and Vifor  AG, St. Gallen, Switzerland. Dr. Spahn is co-chair of the ABC-Trauma Faculty, sponsored by unrestricted educational grants from Novo Nordisk Health Care AG, Zurich, Switzerland, CSL Behring GmbH, Marburg, Germany, LFB Biomédicaments, Courtaboeuf Cedex, France, and Octapharma AG, Lachen, Switzerland. Dr. Spahn received honoraria/travel support for consulting or lecturing from Danube University of Krems, Austria, European Society of Anesthesiology and Intensive Care, Brussels, BE, Korean Society for Patient Blood Management, Seoul, Korea, Korean Society of Anesthesiologists, Seoul, Korea, Network for the Advancement of Patient Blood Management, Haemostasis and Thrombosis, Paris, France, Alexion Pharmaceuticals Inc., Boston, MA, AstraZeneca AG, Baar, Switzerland, Bayer AG, Zürich, Switzerland, B. Braun Melsungen AG, Melsungen, Germany, CSL Behring GmbH, Hattersheim am Main, Germany and Berne, Switzerland, Celgene International II Sàrl, Couvet, Switzerland, Daiichi Sankyo AG, Thalwil, Switzerland, Haemonetics, Braintree, MA, USA, Instrumentation Laboratory (Werfen), Bedford, MA, USA, LFB Biomédicaments, Courtaboeuf Cedex, France, Merck Sharp & Dohme, Kenilworth, NJ, USA, Novo Nordisk Health Care AG, Zurich, Switzerland, PAION Deutschland GmbH, Aachen, Germany, Pharmacosmos A/S, Holbaek, Denmark, Pfizer AG, Zürich, Switzerland, Pierre Fabre Pharma, Alschwil, Switzerland, Portola Schweiz GmbH, Aarau, Switzerland, Roche Diagnostics International Ltd, Reinach, Switzerland, Sarstedt AG & Co., Sevelen, Switzerland and Nümbrecht, Germany, Shire Switzerland GmbH, Zug, Switzerland, Takeda, Glattpark, Switzerland, Tem International GmbH, Munich, Germany, Vifor Pharma, Munich, Germany, Neuilly sur Seine, France and Villars-sur-Glâne, Switzerland, Vifor  AG, St. Gallen, Switzerland, and Zuellig Pharma Holdings, Singapore, Singapore.Concept and design: Donat R. Spahn and Alexander Kaserer. Drafting of the manuscript: Alexander Kaserer, Clara Castellucci, David Henckert, and Christian Breymann. Figures: Alexander Kaserer and David Henckert. All authors critically edited the manuscript, approved the final version to be submitted, and agree to be accountable for the accuracy and integrity of the work.


Dear Editor, Leishmaniasis is a neglected tropical disease caused by a parasite transmitted by infected female sandflies. There are 3 main forms of the disease :Cutaneous leishmaniasis (Cl) is the most common form and causes skin lesions, mainly ulcers, on exposed parts of the body (see examples in Figure 1 Mucocutaneous leishmaniasis leads to partial or total destruction of mucous membranes of the nose, mouth and throat. Over 90% of mucocutaneous leishmaniasis cases occur in Bolivia, Brazil, Ethiopia and Peru.Visceral leishmaniasis, also known as kala-azar, is fatal if left untreated in over 95% of cases. Most cases occur in Brazil, east Africa and India. An estimated 50,000 to 90,000 new cases occur worldwide annually. Visceral leishmaniasis is highly endemic in Iraq, Somalia, Sudan and Yemen.Several Leishmania spp. can cause Cl, but most infections probably remain symptomless. The first sign of an infection is typically a small erythema, which develops into a papule, then a nodule that progressively ulcerates over a period of 2 weeks to 6 months. Lymphatic spread and lymph-gland involvement, which may precede lesion development, are common and there is a variable tendency for lesions to self-heal . In addiHealthy skin is essential for physical well-being and can have a significant impact on an individual's self-reliance and sexual attractiveness. Cl can make life very difficult for affected individuals, as the visible skin lesions can create emotional distress and make routine relationships with close relatives and friends difficult, particularly when the lesions are visible in exposed parts of the body.In addition to the psycho-social effects of visible lesions, the permanent scars left by the disease can negatively impact an individual's quality of life, creating social issues and psychological symptoms. As such, it is important for healthcare providers to take a holistic approach to managing and treating leishmaniasis, addressing both the physical and psycho-social aspects of the disease to improve outcomes for affected individuals .Quality of life is a subjective perception that is influenced by an individual's values, culture, goals, standards, expectations, and concerns. This concept is particularly relevant in chronic diseases like leishmaniasis, as it can help capture the disease's social and psychosomatic impact and the effectiveness of therapeutic interventions. By taking a comprehensive approach to the management and treatment of leishmaniasis, healthcare providers can improve the quality of life for affected individuals . Assessing patients’ quality of life with Cl can improve our understanding of their needs and the psycho-social consequences of the disease. This can help develop effective therapeutic solutions, improving outcomes for affected individuals. By taking a patient-centred approach, healthcare providers can optimize outcomes and improve the overall quality of life for those with leishmaniasis. This may involve tailored treatment plans and ongoing support and education for patients to manage the disease and its effects on their daily lives . Cl can have significant physical and psychological impacts on patients’ quality of life. To create a better quality of life for affected individuals, healthcare professionals should consider all aspects of the disease in conjunction with therapeutic interventions. In addition, evaluating the extent of disease involvement and characteristics such as the number, size, and duration of scars, along with potential side effects of treatment and residual lesions, is crucial for planning a successful patient care management program. By taking a comprehensive approach that addresses the disease’s physical and psycho-social aspects, healthcare providers can improve outcomes and quality of life for those with Cl.For countries where Cl is not endemic, it is important to know where leishmaniasis is endemic in order to be aware of the disease in travellers returning with symptoms. About 95% of Cl cases occur in the Americas, the Mediterranean basin, the Middle East and central Asia. Cl is highly endemic in Algeria, whereas for West Africa, the epidemiological information is scarce. In East Africa, all forms are endemic. In the following countries leishmaniasis is endemic :Afghanistan, Albania, Algeria, Argentinia, Azerbajan, Belize, Bhutan, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Burkina Faso, Cameroon, Central African Republic, Chad, China, Colombia, Costa Rica, Croatia, Cyprus, Côte d’Ivoir, Democratic Republic Congo, Djbouti, Dominican Republic, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, France, French Guiana, Gambia, Georgia, Ghana, Greece,Guatemala, Guinea, Guinea-Bissau, Guyana, Honduras, India, Iran, Iraq, Israel, Italy, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Lebanon, Libya, Malawi, Mali, Malta, Mauritania, Mexico, Monaco, Montenegro, Morocco, Namibia, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Panama, Paraguay, Peru, Portugal, Republic of North Macedonia, Saudi Arabia, Senegal, Slovenia, Spain, Sri Lanka, Siudan, Suriname, Syrian Arab Republic, Taiwan, Tajikistan, Thailand,Tunisia, Turkmenistan, Turkey, Ukraine, USA, Uzbekistan, Venezuela, Palestine, and Yemen. In northern and central Europe, no authochthonus cases have been reported. That is, in Germany, leishmaniasis occurs almost as an imported disease. In 2000, a reference centre for the diagnosis and therapy of leishmaniasis was opened at the Institute for Tropical Medicine, Berlin, Germany. During the first two years, 58 cases of leishmaniasis were imported . Up to 2The authors declare that they have no competing interests.Not applicable0000-0003-2794-1508The ORCID ID of Azami M is:


Due to the increase in life expectancy and the aging of the global population, the “Belt and Road” (“B&R”) countries are faced with varying degrees of lung cancer threat. The purpose of this study is to analyze the differences in the burden and trend of lung cancer disability in the “B&R” countries from 1990 to 2019 so as to provide an analytical strategic basis to build a healthy “B&R”.Data were derived from the Global Burden of Disease 2019 (GBD 2019). Incidence, mortality, prevalence, the years lived with disability (YLDs), and disability-adjusted life years (DALYs) of lung cancer and those attributable to different risk factors were measured from 1990 to 2019. Trends of disease burden were estimated by using the average annual percent change (AAPC), and the 95% uncertainty interval (UI) was reported.China, India, and the Russian Federation were the three countries with the highest burden of lung cancer in 2019. From 1990 to 2019, the AAPC of incidence, prevalence, mortality, and DALYs generally showed a downward trend in Central Asia (except Georgia) and Eastern Europe, while in China, South Asia (except Bangladesh), most countries in North Africa, and the Middle East, the trend was mainly upward. The AAPC of age-standardized incidence was 1.33% (1.15%–1.50%); the AAPC of prevalence, mortality, and DALYs from lung cancer in China increased by 24% (2.10%–2.38%), 0.94% (0.74%–1.14%), and 0.42% (0.25%–0.59%), respectively. A downward trend of the AAPC values of age-standardized YLD rate in men was shown in the vast majority of “B&R” countries, but for women, most countries had an upward trend. For adults aged 75 years or older, the age-standardized YLD rate showed an increasing trend in most of the “B&R” countries. Except for the DALY rate of lung cancer attributable to metabolic risks, a downward trend of the DALY rate attributable to all risk factors, behavioral risks, and environmental/occupational risks was shown in the vast majority of “B&R” countries.The burden of lung cancer in “B&R” countries varied significantly between regions, genders, and risk factors. Strengthening health cooperation among the “B&R” countries will help to jointly build a community with a shared future for mankind. The “Belt and Road” (“B&R”) Initiative refers to the “Silk Road Economic Belt” and the “21st Century Maritime Silk Road”, which was first proposed by China in 2013. “B&R” countries run through Eurasia, connecting the Asia Pacific Economic Circle in the east and the European Economic Circle in the west . Detailed methodology has been published elsewhere  study obtained from the Institute for Health Metrics and Evaluation (IHME) website. All data for this study were obtained from the Institute for Health Metrics and Evaluation (IHME) website . YLLs are calculated as the product of counts of deaths caused by lung cancer and a standard remaining life expectancy at the age of death. The age-standardized rates were corrected by the direct method and the world standard population to account for differences in the population age structure. Our study follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure transparency and replicability Table 1 The 66 members of “B&R” countries are as follows: 1) East Asia: China; 2) Central Asia: Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan; 3) South Asia: Bangladesh, Bhutan, India, Nepal, and Pakistan; 4) Southeast Asia: Cambodia, Indonesia, Laos, Malaysia, Maldives, Burma, the Philippines, Sri Lanka, Thailand, and Vietnam; 5) high-income Asia Pacific: Brunei and Singapore; 6) North Africa and the Middle East: Afghanistan, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Turkey, the United Arab Emirates, and Yemen; 7) Central Europe: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Hungary, Montenegro, North Macedonia, Poland, Romania, Serbia, Slovakia, and Slovenia; 8) Eastern Europe: Belarus, Estonia, Latvia, Lithuania, Republic of Moldova, Russia, and Ukraine; 9) Western Europe: Cyprus, Greece, and Israel. See p < 0.05 was considered statistically significant.We calculated absolute numbers and age-standardized rates of incidence, mortality, YLDs, and DALYs to quantify the burden of lung cancer, grouped by gender and age in the “B&R” countries. Age-standardized estimates allow comparisons across time, countries, and subregions and are adjusted for differences in the age distribution of the population. Age was divided into three groups: 20–54 years, 55–74 years, and ≥75 years. The three risk factors  were included in the present study. Data were stratified by region . SDI is a composite indicator of a country’s lag-distributed income per capita, educational attainment, and the total fertility rate in women younger than 25 years. Methods of SDI development and computation are detailed elsewhere . Trends Being involved in the Global Burden of Disease 2019 and other open databases rather than directly speaking to patients inspired this research. Although no patient was directly involved in this study, members of the public read our manuscript, and all agreed on the specific findings of this study.The absolute number of incidence, mortality, YLDs, and DALYs in 2019 caused by lung cancer in each member country of the “B&R” are shown in p < 0.001), 2.24% , 0.94% , and 0.42% , respectively. See From 1990 to 2019, the AAPC of age-standardized incidence, prevalence, mortality, and DALYs generally showed a downward trend in Central Asia (except Georgia) and Eastern Europe, while in China, South Asia (except Bangladesh), and most countries in North Africa and the Middle East, the trend was mainly upward Figure 3p < 0.05) (p < 0.05). For adults aged 75 years or older, the age-standardized YLD rate from 1990 to 2019 showed an increasing trend in the “B&R” countries, except Kazakhstan, Kyrgyzstan, Turkmenistan, Mongolia, Bangladesh, Maldives, Afghanistan, Bahrain, the United Arab Emirates, Belarus, Moldova, Ukraine, Greece, and the Philippines. In China, age-standardized YLDs showed an increasing trend with the increase of age, and the highest AAPC value of age-standardized YLD rate from 1990 to 2019 was in adults aged 75 years or older: 2.87% . See p < 0.05).p < 0.05).For DALYs of lung cancer attributable to behavioral risks, the age-standardized DALY rate of middle SDI regions, China, Georgia, Bhutan, Bhutan, Indonesia, Sri Lanka, Vietnam, Afghanistan, Egypt, Jordan, Lebanon, Palestine, Bulgaria, Montenegro, Macedonia, Serbia, and Cyprus showed an increasing trend in the “B&R” countries from 1990 to 2019 .For DALYs of lung cancer due to environmental/occupational risks, the age-standardized DALY rate of Georgia, Bhutan, Pakistan, Sri Lanka, Egypt, Iran, Iraq, Jordan, Lebanon, and Bulgaria showed an increasing trend in the “B&R” countries from 1990 to 2019 . See For DALYs of lung cancer attributable to metabolic risks, the age-standardized DALY rate of Kazakhstan, Kyrgyzstan, Turkmenistan, Maldives, the Philippines, Thailand, Singapore, Bahrain, Slovakia, Belarus, and Ukraine showed a decreasing trend in the “B&R” countries from 1990 to 2019 (all With an estimated 1.79 million deaths per year, lung cancer is one of the leading causes of cancer-related deaths . SmokingOur study found significant differences in the trend of age-standardized YLDs between genders. A downward trend of the AAPC values of age-standardized YLD rate in men was shown in the “B&R” countries. For women, the upward change trend of YLDs was observed in most countries. The global incidence of lung cancer in men is declining twice as fast as in women . The ageThe increase in life expectancy has led to a greater global burden of diseases. Global population aging is the principal medical and social demographic problem worldwide. In the Non-Organisation for Economic Co-operation and Development countries, the fastest-aging countries are Saudi Arabia, Brazil, and China . Since 2In recent decades, countries within the Middle East have faced social, political, and financial instability brought about by war. These conflicts have directly led to a significant decline in the overall level of local medical services and a shortage of professional experts, seriously affecting the provision of cancer diagnosis services. The cancer patients in these areas cannot be diagnosed early and cannot receive effective healthcare , 23. In With a deeper understanding of the biology of lung cancer, many advances have been made in the treatment of lung cancer, such as minimally invasive techniques, stereotactic ablative radiotherapy, targeted therapies, and ICIs . New theYLDs can reflect the amount of time lived in states of less than good health due to a specific disease or injury and are calculated as the prevalence of a sequela of any given cause multiplied by the average duration until death or remission and by the disability weight for that sequela. The YLDs are the sum of each of the sequelae associated with the disease or injury , 27. YLLGlobally, from 2010 to 2019, the number of lung cancer increased by 23.3%, and the age-standardized incidence rates decreased by 7.4% in men and increased by 0.9% in women . All theUnlike previous lung cancer burden studies based on GBD data, this study focuses on the “B&R” countries proposed by China, the world’s second-largest economy, under the global community of shared future strategy. It not only describes the changes in disease burden in a specific region or globally but also provides targeted data support for how countries with significant differences in social demographic indices but strong political and economic connections can formulate policies to reduce the burden of lung cancer. Preventive measures such as smoking control interventions and air quality management should be prioritized in low and middle SDI regions. Our research also suggested that we should pay more attention to female lung cancer patients. For women, the upward trend of YLDs was observed in the “B&R” countries, and it may continue to rise in the future . By studThis study also has several limitations. First, GBD 2019 has inherent limitations that are applicable to this study. Second, the GBD database lacks lung cancer’s pathological staging and classification. In the future, the “B&R” countries can use economic development as a link to drive the construction of information-based disease monitoring systems, providing sufficient support for the estimation of disease burden and policy adjustments.In summary, the overall burden of lung cancer in the “B&R” countries is still huge, especially in China, South Asia, North Africa, and the Middle East. There are significant differences between genders and ages. The lung cancer prevention and treatment policies in women and adults aged 75 years or older need to be improved. With the background of the health “B&R” Initiative, multi-country cooperation and experience sharing will play an important role in jointly facing the challenges caused by lung cancer and promoting the positive development of healthcare in all member countries.The original contributions presented in the study are included in the article/XL conceived and designed the study. ZZhu, WY, LZ, WJ, BC,QW, XC, SY, and ZZhang analyzed the data. ZZhu, XL, and YD provided significant advice and consultation. WY and XL wrote the manuscript. All authors contributed to the article and approved the submitted version.


In “Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson”  the authors noted two errors.1. The Authors Contributions section currently reads as:All authors contributed to the study conception; design; and acquisition, analysis, or interpretation of the data. AR, PMG, AS, LAV, DLS, GPJ, and DWB were responsible for study conception or design. PMG, HHE, DLS, and MGA developed the interview guides. MM, AS, SD, and LPN conducted participant recruitment. PMG acted as the interview moderator and had either AR or MM assisting with data collection during testing. PMG, MM, JC, and SD abstracted the data from interview recordings. Data analysis was performed by PMG, MM, and AR. The first draft of the manuscript was written by AR and PMG, with all authors reviewing the draft and providing critical feedback. All authors contributed to and approved the final manuscript.And will be changed to:RR and DWB are co-senior authors and contributed equally.All authors contributed to the study conception; design; and acquisition, analysis, or interpretation of the data. AR, PMG, AS, LAV, DLS, GPJ, and DWB were responsible for study conception or design. PMG, HHE, DLS, and MGA developed the interview guides. MM, AS, SD, and LPN conducted participant recruitment. PMG acted as the interview moderator and had either AR or MM assisting with data collection during testing. PMG, MM, JC, and SD abstracted the data from interview recordings. Data analysis was performed by PMG, MM, and AR. The first draft of the manuscript was written by AR and PMG, with all authors reviewing the draft and providing critical feedback. All authors contributed to and approved the final manuscript.2. In the original article, the ORCID number for Petra Schultz was reported as follows:0000-0001-7337-1046And has been updated to:0000-0001-7949-9243The correction will appear in the online version of the paper on the JMIR Publications website on May 18, 2023, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.