Iatrogenic ST elevation myocardial infarction (STEMI) after aortic valve surgery is a rare complication. Myocardial infarction (MI) due to mediastinal drain tube compression on the native coronary artery is also seen rarely. We present a case of ST elevation inferior myocardial infarction due to post-surgical drain tube placed after aortic valve replacement compressing on the right-sided posterior descending artery (rPDA).A 75-year-old female presented with exertional chest pain and was found to have severe aortic stenosis (AS). After a normal coronary angiogram and proper risk stratification, the patient underwent surgical aortic valve replacement (SAVR). One day after surgery in the post-operative area, the patient was complaining about central chest pain suggestive of anginal pain. Electrocardiogram (ECG) revealed that she has ST elevation myocardial infarction in the inferior wall. Immediately, she was taken to the cardiac catheterization laboratory, which revealed that she has occlusion of the posterior descending artery due to compression by a post-operative mediastinal chest tube. All features of myocardial infarction resolved after simple manipulation of the drain tube.The compression of the epicardial coronary artery after aortic valve surgery is very unusual. There are a few cases of other coronary artery compression due to mediastinal chest tube, but posterior descending artery compression causing ST elevation inferior myocardial compression is unique. Though rare, we need to be vigilant about mediastinal chest tube compression, which can cause ST elevation myocardial infarction after cardiac surgery. The iatrogenic compression of the native epicardial artery is a rare phenomenon. There are a few cases of bypass graft -4 or natA 74-year-old female with no past medical illness presented with exertional chest pain and was found to have severe aortic stenosis (AS). Electrocardiogram (ECG) shows features of left ventricular hypertrophy (LVH). On echocardiography, her mean pressure gradient across the aortic valve was 47 mmHg. As she had symptomatic severe aortic stenosis (AS) without any other significant comorbidities, the plan was made to do surgical aortic valve replacement (SAVR). She underwent preoperative left heart catheterization, which shows normal epicardial coronary arteries.Eventually, the patient underwent a surgical replacement of the stenosed aortic valve with a bioprosthetic valve. The surgery was uneventful, and the patient was sent to the cardiac ICU for monitoring. A mediastinal drain tube was put in to prevent fluid accumulation after cardiac surgery. On post-operative day 1, the patient was complaining of central chest pain with radiation to the arm. ECG was done showing ST elevation in leads II, III, and arteriovenous fistula (aVF) with baseline LVH. It was puzzling as previously, she had no ECG changes suggestive of ischemic heart disease (IHD), and left heart catheterization was also normal. Bedside echocardiogram revealed wall motion abnormality in the middle and distal inferior wall with mildly reduced LV function. STEMI alert was activated, and the patient was taken to the catheterization laboratory emergently.A coronary angiogram revealed that the chest tube is intermittently compressing on the right-sided posterior descending artery (rPDA) limiting blood flow distal to the compression Figure . The draWe present a case of posterior descending artery compression by a mediastinal drain tube placed after SAVR, which is the first reported case. In the past, there were a few reports of native coronary artery or graft compression.Most complications of chest tube placement come from closed pleural chest tube placement, which could be benign to serious complications . There hComplications from open mediastinal tube placement after cardiac surgery are usually benign and underreported. Pneumothorax after removing such tubes or patients suffering from pain due to the tube placement is most encountered by cardiac surgeons . Yet, thAfter surgical aortic valve replacement (SAVR), myocardial ischemia or infarction could be due to supply-demand mismatch as a result of aortic cross-clamping causing ischemia in hypertrophied left ventricle (LV) . STEMI aBeing vigilant about post-surgical drain tubes that may cause compression in the coronary artery can be an easy fix and prevent dreaded complications. Mediastinal chest tubes causing the occlusion of the native coronary arteries are unexpected and easily overlooked.


Objective. To evaluate the impact of setup uncertainty reduction (SUR) and adaptation to geometrical changes (AGC) on normal tissue complication probability (NTCP) when using online adaptive head and neck intensity modulated proton therapy (IMPT). Approach. A cohort of ten retrospective head and neck cancer patients with daily scatter corrected cone-beam CT (CBCT) was studied. For each patient, two IMPT treatment plans were created: one with a 3 mm setup uncertainty robustness setting and one with no explicit setup robustness. Both plans were recalculated on the daily CBCT considering three scenarios: the robust plan without adaptation, the non-robust plan without adaptation and the non-robust plan with daily online adaptation. Online-adaptation was simulated using an in-house developed workflow based on GPU-accelerated Monte Carlo dose calculation and partial spot-intensity re-optimization. Dose distributions associated with each scenario were accumulated on the planning CT, where NTCP models for six toxicities were applied. NTCP values from each scenario were intercompared to quantify the reduction in toxicity risk induced by SUR alone, AGC alone and SUR and AGC combined. Finally, a decision tree was implemented to assess the clinical significance of the toxicity reduction associated with each mechanism. Main results. For most patients, clinically meaningful NTCP reductions were only achieved when SUR and AGC were performed together. In these conditions, total reductions in NTCP of up to 30.48 pp were obtained, with noticeable NTCP reductions for aspiration, dysphagia and xerostomia . While SUR had a generally larger impact than AGC on NTCP reductions, SUR alone did not induce clinically meaningful toxicity reductions in any patient, compared to only one for AGC alone. Significance Online adaptive head and neck proton therapy can only yield clinically significant reductions in the risk of long-term side effects when combining the benefits of SUR and AGC. For this purpose, a retrospective cohort of ten head and neck squamous cell carcinoma patients with daily cone-beam CT (CBCT) was used to compare NTCP for six toxicities using three distinct scenarios. Differences in NTCP between each scenario was evaluated to assess the impact of SUR alone, AGC alone and SUR and AGC combined. Finally, the clinical significance of NTCP reductions associated with each mechanism was established using a decision tree inspired by a model-based patient selection algorithm.Our patient cohort consisted of ten head and neck squamous cell carcinoma patients treated at the Massachusetts General Hospital with volumetric modulated arc therapy, since CBCT imaging was not available for our proton therapy patients at the time of this study. Each patient dataset consisted of a planning CT acquired on a wide bore GE scanner  as well as a series of daily CBCT obtained on an Elekta XVI system  using a 100 kVp tube voltage and a 220-degree acquisition. The number of CBCTs available for each patient ranged between 30 and 35, for a total of 328 scans analyzed. No patient of this cohort had their treatment interrupted and no offline replanning was deemed necessary during the treatment courses.et alTwo clinical target volumes (CTV) were delineated on each planning CT by a trained radiation oncologist: a high-risk CTV including the primary tumor and high-risk lymph nodes, as well as a low-risk CTV, including bilateral lymph nodes considered at risk for subclinical disease. The constrictor muscles, larynx, oral cavity, spinal cord, brainstem, esophagus and both parotid glands were delineated on the planning CT. The esophagus, constrictor muscles and larynx were contoured following published guidelines for swallowing OARs . The first plan was robustly optimized to both CTVs using the minimax method  and adaptation schemes (fast daily OA versus no adaptation). As done in similar studies, our data analysis was focussed on NTCP differences between each scenario rather than absolute NTCP values (Van De Water ar et al . Differeet alFocussing on AGC alone, our results indicate that online adaptive proton therapy can induce NTCP reductions that go beyond what is achieved when applying SUR only. Indeed, total NTCP reductions per patient for xerostomia, dysphagia, aspiration and oral mucositis reported in figure et alet alThe clinical significance of the reductions in NTCP induced by SUR and AGC was assessed using a decision tree inspired by the Dutch model-based selection system (Tambas et alet alet alet alThe fact that clinically meaningful toxicity reductions were observed in five out of ten patients also illustrates the heterogeneous effect of online adaptive proton therapy on toxicity reductions across our patient cohort. This is even more apparent in figure et alet alet alet alet alet alet alet alThis study had some limitations worth mentioning. First, uncertainties in image deformation, contour propagation and residual setup errors beyond treatment adaptation were neglected. NTCP reductions achieved in this work shall therefore be interpreted as the best-case scenario for AGC and might not be fully achievable in a clinical setting. However, it is also worth noting that several strategies have been suggested to mitigate the impact of these uncertainties in the context of online adaptive proton therapy: the integration of structure uncertainties in plan re-optimization (Nenoff et al (et alAnother limitation to consider is the fact that NTCP models were derived from photon patients. Blanchard et al  showed tal et al. An evalIn conclusion, this study highlighted for the first time the impact of online adaptive proton therapy on toxicity risks. Combining effects from a reduction of the setup uncertainty setting by 3 mm and the AGC, online adaptive proton therapy based on a limited spot-intensity re-optimization workflow was shown to allow clinically meaningful toxicity reductions in 50% of our patient cohort, with total NTCP reductions up to 30.48 pp for the four main toxicities considered in this work.


POT-PUFF sign has been introduced as a potential alternative for detecting malapposition during coronary bifurcation procedures. Here, we present two clinical cases from a developing country where the POT-PUFF sign was employed to assess the result of proximal optimization therapy after stent implantation. The POT-PUFF sign exhibits potential as an affordable and feasible approach for assessing stent malapposition in settings with limited resources.Acute stent malapposition poses a significant risk for adverse cardiac events following percutaneous coronary intervention. Detection of acute stent malapposition traditionally relies on intracoronary imaging techniques, such as intravascular ultrasound and optical coherence tomography, which may be limited in developing countries due to accessibility issues. A new angiographic sign called the  Acute stent malapposition (ASM) refers to post-procedure lack of contact of stent struts with vessel walls. This condition can be detected using intracoronary imaging techniques such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Identifying and addressing malapposition is crucial as it can result in various complications, including impaired stent function, heightened risk of stent thrombosis, and potential adverse cardiac events . RecentlCase oneA 58-year-old man, hypertensive and a weaned smoker, had been suffering from exertional angina for several months. The electrocardiogram showed lateral biphasic T waves. Echocardiography revealed hypokinesia of the anterolateral wall with a moderately impaired ejection fraction of 45%. Coronary angiography revealed significant stenosis at the start of the main marginal artery Figure . After pCase twoOur second case was a 64-year-old hypertensive patient with dyslipidemia on statins. She was admitted for management of chronic coronary syndrome with New York Heart Association class 2 exertional dyspnea. The electrocardiogram was unremarkable. Echocardiography showed left ventricular hypertrophy without segmental contractility abnormalities and a preserved ejection fraction. Stress echocardiography showed ischemia in two segments of the right coronary artery territory. Coronary angiography revealed a long, significant stenosis in the middle segment of the right coronary artery Figure . After pAfter stent implantation, IVUS and OCT can identify remediable irregularities associated with both the stent and the underlying vessel wall. These irregularities include stent under-expansion, geographic plaque miss, strut malapposition, and stent edge dissection, and they have been linked to adverse percutaneous coronary intervention (PCI) outcomes . Strut mThe POT-PUFF sign presents a promising new angiographic indicator for detecting stent malapposition during POT after PCI. While further research is required to validate its effectiveness, the POT-PUFF sign shows great potential in enhancing stent apposition evaluation and improving PCI outcomes in resource-constrained settings.


Glioblastoma (GBM) is an aggressive brain tumor with limited prognosis despite multimodal treatment approaches. Various immunotherapies have been investigated to address the need for novel therapeutic options in GBM with limited success. Recently, alterations in the metabolism of cancer cells which allow for tumor proliferation, but simultaneously alter immune populations leading to an immunosuppressive tumor microenvironment, have been investigated as contributory to therapeutic resistance. This review discusses metabolic alterations in GBM tumor cells which have been investigated as contributory to immunosuppression and resistance to immunotherapies.Glioblastoma (GBM) is the most common primary brain tumor with a poor prognosis with the current standard of care treatment. To address the need for novel therapeutic options in GBM, immunotherapies which target cancer cells through stimulating an anti-tumoral immune response have been investigated in GBM. However, immunotherapies in GBM have not met with anywhere near the level of success they have encountered in other cancers. The immunosuppressive tumor microenvironment in GBM is thought to contribute significantly to resistance to immunotherapy. Metabolic alterations employed by cancer cells to promote their own growth and proliferation have been shown to impact the distribution and function of immune cells in the tumor microenvironment. More recently, the diminished function of anti-tumoral effector immune cells and promotion of immunosuppressive populations resulting from metabolic alterations have been investigated as contributory to therapeutic resistance. The GBM tumor cell metabolism of four nutrients  has recently been described as contributory to an immunosuppressive tumor microenvironment and immunotherapy resistance. Understanding metabolic mechanisms of resistance to immunotherapy in GBM can provide insight into future directions targeting the anti-tumor immune response in combination with tumor metabolism. Glioblastoma (GBM) is the most common primary brain tumor with a limited prognosis and a median survival of 15 months despite an aggressive standard of care treatment consisting of maximal safe surgical resection followed by radiation and chemotherapy with temozolomide . DevelopOne novel emerging area of cancer therapeutics is immunotherapies, which target one of the hallmarks of cancer—the ability to evade cellular immunity that would otherwise result in immunological targeting of tumor cells . While iThe tumor microenvironment is affected by unique cancer cell metabolism that not only promotes tumor cell growth but also alters the pH, oxygen, and metabolite contents that affect the survival and function of immune cells in the tumor microenvironment . MetabolGlycolysis is the most prominent metabolic pathway implicated in cancer metabolism as contributory to sustaining the energetic cost of growth and proliferation. During glycolysis, glucose is catabolized to pyruvate which is then converted to lactate to either be secreted or enter the TCA cycle generating ATP and NADH in the process. Glucose metabolism plays a significant role in the brain microenvironment given the high metabolic demand of the brain and lack of glycogen storage within the brain. High blood glucose levels and increased neuronal expression of glucose transporters have been linked to decreased survival in glioblastoma patients . HK2, PKFP, ALDOA, PGAM1, ENO1, ENO2, or PDK1 inhibits GBM tumor growth and prolongs survival in a mouse xenograft model . Gl. Gl13]. Glycolysis requires export of lactate from cells by transporters which co-transport lactate and protons (H+), leading to their accumulation in the tumor microenvironment and resulting in tumor acidosis which impacts the function of immune cells in the tumor microenvironment. Acidosis has been described in other cancers as contributory to immunosuppression . Lactic Strategies that free T cell glycolytic metabolism from the restrictions imposed on these cells by the tumor microenvironment have been evaluated in preclinical models. For example, genetic modification of tumor specific CD4 and CD8 T cells to overexpress phosphoenolpyruvate carboxykinase 1 (PCK1) increased the production of the glycolytic metabolite phosphoenolpyruvate, resulted in increased T cell glycolysis, increased T cell effector function, and restricted tumor growth and prolonged survival in a melanoma mouse model . FurtherLactic acid production has also been suggested to not only reduce anti-tumoral immune cell populations but also promote immunosuppressive populations. Notably, myeloid cells are resistant to lactic acid-induced apoptosis . In factGlycolytic alterations may also specifically impact neutrophils. While less is understood about the metabolic utilization of neutrophils in the tumor microenvironment than leukocytes, neutrophils are generally regarded as highly glycolytic. Neutrophil function has been described to highly depend on glucose availability with lack of glucose abrogating function . suggestInterestingly, the interplay between glycolytic tumor metabolism and immune cell function may be bidirectional with immune cells able to regulate metabolic pathways as well. Zhang et al. show that macrophages produce interleukin-6 which leads to downstream phosphorylation of the glycolytic enzyme phosphoglycerate kinase 1 (PGK1) and facilitates a PGK1-catalyzed reaction towards glycolysis rather than gluconeogenesis through altered substate affinity . PGK1 phENO1, promoted M2 microglia polarization promoting immunosuppression and glioblastoma cell malignancy. Another recent study utilizing differentially expressed genes between high and low glycolytic activity to assign risk scores to classify high and low risk GBM patients found differential infiltration of immune cells and immune checkpoints, suggesting a relationship between glycolytic activity and immunosuppression in patients with GBM [Studies of glycolysis in glioblastoma have paralleled the findings in other cancer types of the significance of increased glycolysis in creating an immunosuppressive tumor microenvironment. The shift to increased aerobic glycolysis from oxidative phosphorylation in glioblastoma is associated with immunosuppression and tumor progression . In GBM,with GBM . Glutamine, an amino acid highly expressed in cancer cells, plays a critical role for cellular function and the generation of energy and metabolic precursors for macromolecule synthesis which help sustain anabolic growth. Glutamine is converted by glutaminase into glutamate which is then converted to α-ketoglutarate, a critical component of the TCA cycle and in the production of metabolic intermediates utilized in the production of lipids, nucleic acids, and proteins. Upregulated glutamine metabolism in cancer cells promotes tumor growth through supporting macromolecule biosynthesis, altered signaling pathways, and cancer cell proliferation and survival. The metabolism of glutamine provides carbons for the TCA cycle to sustain accelerated anabolism in cancer cells and promotes tumor growth ,40,41.Glutamine is amongst the most prevalent amino acids in the brain as a precursor to the excitatory neurotransmitter glutamate . GlutamaGlutamine metabolic pathways are also upregulated in glioblastoma. Glutamate dehydrogenase (GDH), an enzyme which catalyzes the conversion of L-glutamate into α-ketoglutarate as part of glutaminolysis, is upregulated in many human cancers and shown to promote tumor growth . An isoeGlutamine metabolism in cancer cells impacts the tumor microenvironment and the immune populations within it in ways similar to glucose metabolism . Cancer Increased uptake of glutamine by tumor cells may result in its depletion in the tumor microenvironment and affect the function of immune cells which utilize glutamine for their own metabolic programs. Activated T cells upregulate glutamine metabolism to generate α-ketoglutarate to enter the TCA cycle and generate ATP to fulfill the energetic demands of T cell proliferation . GlutamiTargeting glutamine metabolism in a mouse model of colon cancer through a glutamine antagonist 6-Diazo-5-oxo-L-norleucine, which broadly inhibits several glutamine-using enzymes, led to suppression of both oxidative phosphorylation and glycolytic metabolism in cancer cells and decreased tumor-related changes in the microenvironment with decreased hypoxia, acidosis, and nutrient depletion . In contAdditionally, glutamine metabolism by cancer cells leads to the enrichment of various immunosuppressive populations in cancer. Notably, α-ketoglutarate generated through glutaminolysis restricts anti-tumoral macrophage M1 activation . A separTargeting glutamine metabolism in cancers with known resistance to checkpoint blockade (triple negative breast cancer and lung carcinoma) with a small molecule inhibitor led to the marked inhibition of the generation and recruitment of immunosuppressive myeloid-derived suppressor cells (MDSCs) through apoptosis of these MDSCs . AdditioTargeting glutamine metabolism may enhance endogenous anti-tumor immunity through independent mechanisms promoting the metabolic programs of cytotoxic populations while inhibiting immunosuppressive populations. Given the success of combining targeting glutamine metabolism with checkpoint inhibitors in other immunotherapy resistant tumors, it may be worthwhile to explore this combination in GBM. Tryptophan is an essential amino acid utilized for protein biosynthesis and a biochemical precursor to physiologically important compounds such as serotonin and melatonin. The majority of tryptophan which is not incorporated into proteins is broken down into degradation products (kynurenines) via the kynurenine pathway . PhysiolTryptophan catabolism and alterations in kynurenine pathway has been implicated in poor prognosis in several cancer types, including in GBM . The corIDO1- and TDO2- mediated degradation of tryptophan by cancer cells is a driver of immune suppression in the tumor microenvironment through recruitment and activation of myeloid-derived suppressor cells (MDSCs) and induction of anergy of CD8+ T cells . DegradaTryptophan utilization by tumor cells leads to metabolic starvation of T cells which are unable to utilize tryptophan for their own functions and thus promotes immunosuppression in the tumor microenvironment. Inhibition of tryptophan degrading enzymes blocks enzymatic activity and restores cytotoxic T cell activity in vitro and in vivo . T cellsGiven the role of IDO through kynurenine synthesis in generating the tumor microenvironment allowing for immune escape in cancer, IDO inhibition has been explored as an attractive therapeutic option in multiple cancers. Inhibition of IDO was found to effectively normalize plasma kynurenine levels in patients with various tumor types . InteresKynurenine metabolites activate a ligand-activated transcription factor, aryl hydrocarbon receptor (AhR) which results in increased expression of IDO1 and IDO2 in a positive feedback loop. Targeting AhR in vitro led to decreased glioma cell viability . Opitz eTryptophan catabolism and its downstream metabolic pathways are known to contribute to the immunosuppressive tumor microenvironment and contribute to resistance to novel immunotherapies for malignant gliomas. Increased expression of IDO and TDO has been suggested as an acquired resistance mechanism to PD-1 and CTLA blockade in pre-clinical models of multiple cancers, including GBM ,74. The The combination of targeting tryptophan metabolism with immune checkpoint blockade has been also explored in glioblastoma. Combining 1-methyltryptophan, which inhibits IDO, with dual immune checkpoint blockade significantly improved survival in an orthotropic mouse GBM model correlating with increased T-cell survival and synergistic decrease of Treg infiltration . LikewisPotential limitations of IDO1 inhibition that led to failure in this phase 3 trial combining this approach with a PD-1 inhibitor include: insufficient inhibition of IDO1 at the doses being used; the ability of other enzymes involved in tryptophan metabolisms such as TDO2 or pathways downstream of IDO1 inhibition to compensate and still generate immunosuppressive tryptophan metabolites such as kynurenine and its derivatives when IDO1 is inhibited; and lack of patient selection based on IDO1 expression ,78. IDO1Targeting tryptophan metabolism may also have implications for vaccine related cancer therapies which rely on T cell mediated antitumoral responses. IDO expression correlated with lack of specific T cell enrichment at the tumor site and prevented the rejection of tumor cells in mice who have been preimmunized against tumor antigens with a vaccine . This efLipid metabolism physiologically functions to allow for cellular energy storage, synthesis of cellular membranes, and cellular signaling. In cancer, alterations in lipid metabolism help meet high bioenergetic demands by generating energy through beta-oxidation. Utilization of fatty acid oxidation in addition to increased glycolysis allows for bioenergetic flexibility in promoting aggressive tumor growth and metastasis . Glioma cells utilize lipid oxidation and upregulate transport of ketones generated from lipid metabolism to sustain growth . Lipid mAltered lipid metabolism in GBM impacts immune cell function, particularly that of T cells . T cellsWhile less is understood about the metabolic requirements of Treg cells, utilizing fatty acid oxidation over glycolysis may promote Treg survival over the survival of CD4 and CD8 T cells . Lipid sGBM cells are also able to evade the anti-tumor immune response due to altered lipid metabolism impacting the function of antigen presenting cells. Exogenous induction of lipid peroxidation and ferroptosis resulted in release of damage-associated molecular patterns from glioma cells that stimulate dendritic cell activation and maturation and can lead to activation of cytotoxic T lymphocytes by dendritic cells ,95. RecuWhile initially the central nervous system was thought to be an immune-privileged site, current thought points to the presence of immune surveillance in the brain following findings revealing the presence of dedicated lymphatic channels running parallel to dural venous sinuses and allowing for lymphocyte priming from antigen presenting cells in the brain ,100. DesThe most studied immunotherapeutic approaches for GBM are vaccines, immune checkpoint inhibitors, and biologic therapies . The mosImmune checkpoint inhibitors targeting PD-1/PD-L1 and/or CTLA-4 are also being investigated in phase 3 trials in GBM, with initial results suggesting no clinical benefit ,105. WhiBiologic therapies for GBM can be viral or cellular. Oncolytic viruses have overall met with limited success in GBM, and initial anti-tumor T cell immune responses generated by viral infiltration into tumor do not persist without serial treatment . MetabolMetabolic pathways implicated in maintaining immunosuppression in the GBM microenvironment have been well elucidated. However, the potential for targeting these metabolic pathways to condition the tumor microenvironment to become more responsive to immunotherapies remains underexplored in GBM. Preclinical data targeting metabolic pathways in conjunction with immunotherapies largely come from models of more immunogenic cancers. This presents an attractive avenue for further study in GBM, in which modifying a largely immunosuppressive environment may meaningfully alter immunotherapeutic response. While this review discusses the most studied metabolic pathways with respect to immunosuppression in GBM, several other metabolic pathway alterations occur to meet the energetic demands of GBM progression. Arginine metabolism reprogramming in GBM leads to increased intake and decreased degradation of arginine by tumor cells and has been linked to impaired T cell responses due to altered bioavailablity of arginine . TargetiWhile many immunotherapies are being investigated in GBM patients, none have resulted in major improvements in survival outcomes. Negative results from phase II and phase III clinical trials of vaccines and immune checkpoint inhibitors have challenged the potential of these approaches in GBM. Future directions for immune-based strategies for glioblastoma require treatment modalities that can convert a ‘cold’ tumor with significant local immunosuppression into a ‘hot’ tumor. The uniquely immunosuppressive environment generated by tumor cellular metabolism in GBM presents an opportunity for augmenting responses to immunotherapy. Combining immunotherapy with agents that target the metabolic alterations resulting in an immunosuppressive microenvironment may have greater success in generating an antitumor immune response. These combinations should be evaluated rigorously preclinically in order to ensure that the most biologically sound combination approaches addressing the antitumor immune response along with tumor metabolism are advanced to clinical trials.


The United States FDA has approved daprodustat (DPD) as the first oral treatment option for anemia due to chronic kidney disease (CKD) in dialysis patients. Clinical trials have demonstrated DPD’s efficacy and safety, showing non-inferiority to darbepoetin and suggesting reduced IV iron usage. DPD also holds potential for treating chronic kidney disease anemia in non-dialysis patients and may have benefits for patients with coexisting renal anemia and heart failure, pending further research and trials. The United States Food and Drug Administration (FDA) has approved daprodustat (DPD) for treating anemia due to chronic kidney disease (CKD) for patients undergoing dialysis for at least 4 months . This isAnother Randomized, Double-Blind, Phase 3 Study was carried out in Japanese hemodialysis patients with anemia to compare the effects of DPD and darbepoetin. With most participants obtaining and maintaining hemoglobin levels within the target range, the results showed DPD’s non-inferiority to darbepoetin. Also, it was strongly advised to continuously check the hemoglobin response following the start of the DPD treatment. This study also suggested using less IV iron to regulate hemoglobin in individuals receiving DPD . AnotherDPD has not yet been approved for patients with CKD anemia without dialysis, but ASCEND-ND  trial has demonstrated safety and efficacy of DPD in treating anemia due to CKD in patients not undergoing dialysis. . DPD wouData from the FDA’s adverse event reporting system (FAERS) may indicate an elevated risk of GI bleeding when HIF-PHIs are used to treat renal anaemia. DPD treatment may also raise the risk of vascular calcification in CKD patients with hyperphosphatemia. Serum copper excess may also be noted in some patients in the treatment.Anemia in Heart Failure is a poor prognostic factor in both acute and chronic heart failure. Although anemia in heart failure is multifactorial, renal anaemia is one of the major causes. Coexisting renal and heart failure is reported to be associated with increased mortality . The StuProvenance and peer review: Not Commissioned, externally peer-reviewed.


Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer related-death in the worldDespite reported benefits, colonoscopy outcomes may differ depending on quality of the endoscopist performing the procedure. Among others, this relates to differences in ability of endoscopists to accurately assess polyp characteristics such as locationOver the last decade, artificial intelligence (AI) in biomedical science has received growing attention. AI can be defined as the simulation of human intelligence by computer systemsWhile evidence is currently scattered, we aimed to write a narrative review to provide a broad overview of current developments within the field of AI and computer science for computer-aided assessment of colorectal polyps. This includes assessment of polyp location, size, morphology and histology, including degree of dysplasia (low grade dysplasia [LGD] versus high grade dysplasia [HGD]) and, in case of suspected malignancy, invasion depth. Since computer-aided polyp detection concerns an already more thoroughly studied and evaluated topicthof July 2022. Key search terms used were “colorectal,” “polyp,” “artificial intelligence,” “size,” “location,” “morphology,” “histology,” “dysplasia” and “invasion depth.” Only studies published in English were screened. Reference lists of retrieved studies were manually screened to identify other relevant publications.A comprehensive literature search was performed in the MEDLINE/PubMed, Embase and Cochrane Libraries from the inception of the databases up to and including the 17Accurate determination of polyp location is important to facilitate identification of a polyp or polypectomy site during consecutive colonoscopies and/or surgical procedures. In addition, polyp location can aid in polyp histology predictionTo determine the location of the endoscope tip during colonoscopy procedures, and hence the location of observed polyps, endoscopists often rely on identification of various endoscopic anatomical landmarks and differences in colonic caliber, color tones and vasculature of different colon segmentsSeveral deep learning approaches for orientation in the colon based on analysis ofendoscopic videos and images have been proposed 45464Proposed systems could possibly aid endoscopists in orientation within the colon. However, current studies still concern feasibility studies and accuracy is mostly still limited. Besides, usage of a segment classification that assumes that all colons and segments are of similar length currently limits feasibility of the proposed motion-based localization systemToward the future, the issue of a lack of a solid reference standard could possibly be addressed by using magnetic endoscopic imaging (MEI) devices. These devices can improve accuracy of determination of location within the colon during colonoscopyPolyp size has been shown to be associated with the risk that a polyp harbors advancedhistological featuresIn daily practice, polyp size is based on visual estimation by the endoscopist. However, this strategy is prone to interobserver variabilityWhile most studies showed promising results, some issues should be addressed. Most importantly, similar to polyp location, a robust reference standard is not available for polyp size. This is illustrated by the fact that different reference standards were used in the different studies, limiting robustness and comparison of performance of the different systems. Additionally, several studies used binary polyp size classificationsNext steps for the development of more robust computerized polyp size measurement methods should include prospective evaluation of proposed systems in real-time clinical settings. Simultaneously, the problem concerning the lack of a solid reference standard might possibly be addressed through usage of recently developed endoscope-integrated or -attached polyp measurement toolsPolyp morphology is an important feature for polyp malignancy risk-assessmentColorectal polyps can generally be subdivided into neoplastic and non-neoplastic.Neoplastic lesions concerns both lesions yielding malignant potential and malignant lesions,while non-neoplastic lesions do not yield malignant potential. Hence, removal and analysisof non-neoplastic lesions is often unnecessaryA wide variety of studies describing computer systems trained to differentiateneoplastic and non-neoplastic lesions based on polyp phenotype has been published. For thisreview, we will highlight available prospective clinical trials evaluating the performanceof such systems in real-time clinical settings and using either white light, (magnified)narrow band imaging (NBI) or blue light imaging (BLI) imaging modalities 77787To facilitate implementation of optical diagnosis strategies in daily practice, the Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) initiativeFrom clinical perspective, the fact that different studies managed sessile serrated lesions (SSLs) in different ways should also be addressed. While SSLs are estimated to make up 15–30% of CRC casesDespite remaining limitations and need for further optimization of system performances to reach PIVI and SODA thresholds, most CADx systems for differentiation of neoplastic and non-neoplastic lesions showed to be able to meet expert endoscopist performance in real-time clinical settings. In addition, a significant optical diagnosis learning curve for ‘non-expert’ endoscopists was illustratedTable S1)Several studies assessing the feasibility of deep learning approaches for differentiation of polyps with different degrees of dysplasia (LGD versus HGD) are available  innovations in upcoming years will likely aid in improving accuracy of existing CADx systems, while there are also still numerous purposes for which possibilities of computer-aided diagnosis is yet to be explored. In example, besides computer systems that could aid endoscopists in assessment of polyp morphology, systems for purposes such as suggestion of appropriate polyp resection method or assessment of completeness of resection might yield significant clinical potential.The strength of this review is that, to the best of our knowledge, this is the first review to provide such a broad overview of available studies on computer-aided diagnosis of all polyp characteristics essential for clinical decision making. However, in the context of the extensive scope of the aim of this review, we decided to comply to a narrative rather than a systematic review approach. While this might have resulted in accidental miss of relevant publications, this can be considered a limitation.To conclude, with recent breakthroughs in the field of AI and computer science, a major increase in research on the topic of computer-aided colorectal polyp assessment is seen. With commercial availability of CADx systems for differentiation between neoplastic and non-neoplastic polyps, first steps toward improved endoscopy quality with use of CADx systems in daily practice might be ahead. However, optimization of performance is still required to ensure that these CADx systems meet all performance thresholds. Besides, toward the future, further innovation, exploration and clinical validation of computer-aided diagnosis approaches for diagnosis of other polyp characteristics is required for realization of complete computer-aided polyp assessment.


Pleural infection, or pleural empyema, is a severe medical condition associated with high morbidity and mortality rates. Timely and accurate prognostication is crucial for optimizing patient outcomes and resource allocation. Rapid scoring systems have emerged as promising tools in pleural infection prognostication, integrating various clinical and laboratory parameters to assess disease severity and quantitatively predict short-term and long-term outcomes. This review article critically evaluates existing rapid scoring systems, including CURB-65 , A-DROP , dehydration, respiratory failure, orientation disturbance, and low blood pressure), and APACHE II , assessing their predictive accuracy and limitations. Our analysis highlights the potential clinical implications of rapid scoring, including risk stratification, treatment tailoring, and follow-up planning. We discuss practical considerations and challenges in implementing rapid scoring such as data accessibility and potential sources of bias. Furthermore, we emphasize the importance of validation, transparency, and multidisciplinary collaboration to refine and enhance the clinical applicability of these scoring systems. The prospects for rapid scoring in pleural infection management are promising, with ongoing research and data science advances offering improvement opportunities. Ultimately, the successful integration of rapid scoring into clinical practice can potentially improve patient care and outcomes in pleural infection management. Pleural infection, commonly known as pleural empyema, is a serious medical condition characterized by the accumulation of infected fluid in the pleural space, the thin, fluid-filled cavity surrounding the lungs. It can arise as a complication of various respiratory infections, such as pneumonia, and may result from both bacterial and, less frequently, fungal pathogens. Pleural infection poses a significant health burden worldwide, with high morbidity and mortality rates, especially in vulnerable populations such as the elderly, immunocompromised individuals, and those with pre-existing lung diseases -4. ManagPrognostication is pivotal in determining the most appropriate therapeutic approach and predicting patient outcomes in pleural infection. Identifying patients with severe diseases and poor prognoses can aid clinicians in tailoring treatment plans and allocating resources more efficiently. Traditional prognostic indicators such as clinical signs, laboratory parameters, and radiological findings are valuable but may lack the desired accuracy to predict disease progression reliably -10. The Rapid scoring systems have emerged as promising tools in the prognostication of pleural infection. These scoring systems leverage clinical, radiological, and laboratory parameters to generate a quantifiable score, enabling clinicians to quickly evaluate the severity of the disease and predict the likelihood of specific outcomes, such as mortality, treatment response, and length of hospital stay ,15. By sThis review aims to comprehensively evaluate the predictive potential of rapid scoring systems in pleural infection. By critically analyzing the existing literature and summarizing the findings from various studies, we aim to provide an in-depth understanding of the utility and limitations of these scoring systems in clinical practice. We will compare their performance in predicting short-term and long-term outcomes. Additionally, we will explore the factors that may influence the accuracy of these scoring systems, such as patient demographics, microbiological and radiological findings, and the presence of comorbidities.Overview of existing rapid scoring systems for pleural infectionsIn recent years, significant advancements have been made in developing and validating rapid scoring systems tailored for pleural infection prognostication. These scoring systems represent a crucial step toward enhancing clinical decision-making and optimizing patient care in the management of this complex condition. By providing a swift and reliable method to assess disease severity and predict outcomes, these rapid scoring systems offer a valuable tool for healthcare providers . Three pThe CURB-65 ScoreOriginally devised for predicting outcomes in community-acquired pneumonia, the CURB-65 score has been adapted and validated for use in pleural infection scenarios. It is based on five readily available clinical variables: confusion, urea level, respiratory rate, blood pressure, and age ≥65 years. By incorporating these parameters, the scoring system offers a simple yet effective approach to risk stratification and prognostication in pleural infection cases .The A-DROP ScoreOriginally designed for predicting severe pneumonia, the A-DROP score has found relevance in the context of pleural infection prognostication. This scoring system comprises five essential parameters: age, dehydration, respiratory failure, orientation disturbance, and low blood pressure. By encompassing these key variables, the A-DROP score assists in identifying patients at higher risk of adverse outcomes, and guiding appropriate interventions .The APACHE II ScoreWidely employed in intensive care units, the APACHE II score is a comprehensive scoring system that incorporates various physiological parameters to assess disease severity and predict outcomes in critically ill patients, including those with pleural infection. While the APACHE II score may involve more extensive laboratory measurements and clinical evaluations, it offers a more detailed and nuanced assessment of patient status, aiding in precise prognostication .These rapid scoring systems serve as valuable tools for healthcare providers, enabling them to assess disease severity and anticipate patient outcomes rapidly. These scoring systems offer a standardized and objective approach to pleural infection prognostication by integrating key clinical and laboratory parameters. By promptly identifying patients at higher risk of adverse outcomes, clinicians can optimize treatment plans, allocate resources effectively, and potentially improve patient outcomes .Despite their effectiveness, it is essential to recognize that these scoring systems are adjunctive tools and should not replace clinical judgment. The context of each patient's presentation, comorbidities, and response to therapy remains crucial in the overall management of pleural infection. Continued research, validation, and refinement of these rapid scoring systems are vital for maximizing their clinical utility and further improving patient care in pleural infection management. With ongoing efforts and multidisciplinary collaboration, rapid scoring systems promise to positively impact pleural infection management in the future .Comparison of different scoring systemsScoring Criteria and ComponentsScoring criteria and components form the foundation of rapid scoring systems in pleural infection prognostication, as they determine the variables used to generate a numerical score reflecting disease severity. These variables' selection varies among scoring systems based on their intended scope and underlying concepts .Some rapid scoring systems like CURB-65 and A-DROP utilize more straightforward and easily accessible clinical parameters to calculate the score. These criteria typically include age, respiratory rate, blood pressure, and mental status. These variables can be quickly assessed at the bedside and are commonly available in most healthcare settings. By focusing on straightforward clinical indicators, these scoring systems offer a convenient and time-efficient approach to prognostication, particularly in resource-limited environments .On the other hand, the APACHE II score takes a more comprehensive approach by integrating a wide range of physiological and laboratory measurements. These variables encompass arterial pH, oxygenation, serum creatinine levels, and hematocrit. The APACHE II score was originally designed for assessing the severity of critically ill patients in intensive care units and has been adapted for use in various clinical settings, including pleural infection. Including a broader array of physiological parameters enhances the APACHE II score's discriminative ability, allowing for a more detailed evaluation of disease status and patient outcomes .The choice between simpler clinical variables and more complex physiological measurements depends on the scoring system's intended use and clinical context. While rapid scoring systems like CURB-65 and A-DROP offer practicality and ease of use, the APACHE II score provides a more nuanced assessment, especially for critically ill patients. When selecting the most appropriate rapid scoring approach for pleural infection prognostication, healthcare providers must consider the scoring system's applicability to their specific patient population and available resources. These scoring criteria and components are essential for risk stratification and clinical decision-making, contributing to improved patient care and outcomes .Pros and Cons of Each Scoring SystemThe CURB-65 and A-DROP scores offer practical benefits due to their straightforwardness and ease of calculation. These scores utilize easily obtainable clinical parameters, including age, respiratory rate, blood pressure, and mental status, which can be swiftly evaluated at the patient's bedside. Their limited reliance on extensive laboratory tests makes them particularly suitable for settings with limited resources, where access to advanced medical facilities might be constrained. Furthermore, their uncomplicated nature allows for rapid risk assessment, enabling timely interventions in patients with pleural infection. However, the simplicity of the CURB-65 and A-DROP scores might result in some degree of oversimplification in disease evaluation. The sensitivity, specificity, positive predictive value, and negative predictive value of CURB-65 were 71%, 69%, 35%, and 91%, respectively. The best-performing version of the systemic inflammatory response syndrome (SIRS) criteria showed rates of 62%, 73%, 35%, and 89%, respectively, while the standardized early warning scoring system (SEWS) exhibited rates of 52%, 67%, 27%, and 86%, respectively. By concentrating on a restricted set of criteria, these scoring systems may not fully encompass the intricacies and variations present in cases of pleural infection. This limitation could potentially impact their accuracy in predicting outcomes .On the contrary, the APACHE II score provides a more advanced and comprehensive evaluation of a patient's condition, which holds particular significance for those critically ill with pleural infections. The APACHE-II score demonstrated a sensitivity of 89.9% and a specificity of 97.6%. By amalgamating various physiological measurements and laboratory results, the APACHE II score delivers a thorough assessment of the severity of the disease and the dysfunction of organs. This renders it especially pertinent within intensive care units (ICUs) confines. Its capacity to consider multiple variables enables a more subtle and precise classification of risk, thereby assisting healthcare practitioners in customizing treatment strategies for critically ill patients. Nevertheless, the reliance on the APACHE II score on multiple intricate laboratory measurements and complex computations could impede its immediate application within standard clinical practice, particularly in non-ICU environments. In scenarios with limited time and resources, the extensive data collection demanded by the APACHE II score might not always be feasible or pragmatic. Consequently, this could diminish its practicality and clinical influence .Current Challenges in Prognostication Using Rapid ScoringWhile rapid scoring systems hold promise in aiding prognostication for pleural infection, several challenges must be addressed to maximize their clinical utility. Firstly, these scoring systems' validation and external applicability pose significant concerns. Many of these systems are developed and validated in specific patient populations, which may limit their generalizability to diverse cohorts or healthcare settings. To ensure their reliability across various contexts, it is crucial to conduct rigorous validation studies in different patient populations representative of real-world scenarios .Secondly, successfully integrating rapid scoring systems into routine clinical practice requires overcoming practical hurdles. Healthcare providers must receive adequate training and awareness regarding the use and interpretation of scoring results. Seamless incorporation into existing clinical workflows and overcoming potential resistance to change are essential steps in maximizing the impact of these scoring systems on patient care .Another critical challenge is centered around predicting long-term outcomes. While rapid scoring systems often concentrate on short-term outcomes like hospital mortality or length of stay, complexity arises when predicting long-term outcomes, including factors like recurrence rates and functional recovery. These aspects demand a more extensive follow-up period and meticulous data collection. The development of scoring models that can reliably forecast these long-term outcomes holds significant importance, as it can greatly enhance patient management strategies and the support provided post-infection .Addressing these challenges requires continued research and collaboration among healthcare institutions and researchers. External validation across diverse patient populations, transparent reporting of findings, and prospective studies can enhance the reliability and credibility of rapid scoring systems. Additionally, advances in data science and integrating novel biomarkers may offer opportunities to refine these scoring approaches, leading to more accurate and personalized prognostication for pleural infection .By overcoming these challenges and refining existing rapid scoring systems, clinicians can make more informed decisions, tailor treatments, and allocate resources effectively, ultimately leading to improved patient outcomes and enhanced management of pleural infection. With ongoing efforts and future advancements, rapid scoring systems have the potential to revolutionize the way pleural infection is managed, providing valuable tools for healthcare providers and better care for patients .Prognostic value of rapid scoring in pleural infectionEvaluating the Predictive Accuracy of Rapid Scoring SystemsSensitivity and specificity analysis: Sensitivity measures the proportion of true positive cases correctly identified by the scoring system, i.e., the ability to classify patients with adverse outcomes correctly. Specificity, on the other hand, measures the proportion of true negative cases correctly identified, i.e., the ability to classify patients with favorable outcomes correctly. A comprehensive analysis of sensitivity and specificity provides valuable insights into the scoring system's ability to discriminate between patients with different outcomes. High sensitivity and specificity indicate a scoring system's ability to accurately identify positive and negative cases, thereby increasing its reliability in distinguishing patients with better or worse prognoses .Receiver operating characteristic (ROC) curves: ROC curves graphically illustrate the trade-off between sensitivity and specificity at various score thresholds. By plotting sensitivity against (1 - specificity) for different threshold values, the ROC curve visually represents the scoring system's performance in distinguishing between patients with different outcomes. The area under the ROC curve (AUC) quantifies the overall discriminative ability of the scoring system. An AUC value close to 1 indicates excellent discriminative power, meaning the scoring system can effectively differentiate between patients with favorable and adverse outcomes. Conversely, an AUC value close to 0.5 suggests poor discrimination, equivalent to random chance, rendering the scoring system less effective in predicting patient outcomes . ResearcPredictive Potential for Short-Term OutcomesMortality prediction: Mortality prediction is a critical aspect of managing pleural infection, as identifying patients at higher risk of death enables healthcare providers to initiate timely interventions and allocate resources effectively. By integrating clinical and laboratory parameters, rapid scoring systems aim to quantify the mortality risk in infected patients. The scores generated by these systems can serve as an early warning system, alerting clinicians to patients with a higher likelihood of adverse outcomes. Studies that analyze the association between rapid scoring system scores and actual mortality rates are of paramount importance. Evaluating the accuracy and reliability of these scoring systems in predicting mortality is essential for their successful implementation in clinical practice. Validated scoring systems can support evidence-based clinical decision-making, guiding aggressive treatment approaches for high-risk patients and potentially improving overall survival rates in pleural infection cases .Treatment response: Assessing the predictive potential of rapid scoring systems on treatment response is equally significant. Identifying patients less likely to respond to standard therapies is crucial for early intervention and alternative treatment strategies. Rapid scoring systems can assist in predicting treatment response by considering factors such as disease severity, pathogen characteristics, and patient-specific variables. By flagging non-responsive or slow-responding cases, clinicians can promptly modify treatment regimens, thereby preventing disease progression and minimizing complications. This proactive approach to treatment tailoring may improve therapeutic efficacy and enhance patient outcomes. Furthermore, predicting treatment response can aid in the judicious use of healthcare resources, optimize medication utilization, and reduce unnecessary interventions .Length of hospital stay: Prolonged hospital stays in pleural infection cases impact patient well-being and impose a substantial economic burden on healthcare systems. Rapid scoring systems that accurately predict the length of hospitalization can provide valuable insights for resource management and discharge planning. By estimating the expected duration of hospital stay, healthcare providers can allocate resources more efficiently, including hospital beds, staff, and medications. This allows for smoother patient flow and reduces overcrowding in medical facilities. Additionally, accurate predictions of hospital stay duration enable healthcare teams to engage in proactive discharge planning, ensuring that patients receive appropriate care and support after discharge. By minimizing unnecessary hospitalization, rapid scoring systems can contribute to optimizing healthcare resources and improve overall patient care in the context of pleural infection management ,37.Predictive Potential for Long-Term OutcomesRecurrence rates: Pleural infection recurrence is a vexing issue that poses challenges in patient management. Identifying individuals at higher risk of recurrence is crucial for implementing appropriate surveillance and follow-up strategies. Rapid scoring systems, with their ability to provide a quantitative assessment of disease severity, may offer valuable insights into the likelihood of recurrence. Clinicians can tailor long-term treatment planning and preventive measures for high-risk patients by analyzing the correlation between scoring system scores and recurrence rates. A scoring system capable of predicting recurrence can prompt intensified monitoring, early interventions, or targeted therapies to reduce the burden of recurrent pleural infections and improve patient outcomes ,39.Functional outcomes and quality of Life: Beyond short-term outcomes such as mortality and recurrence rates, the impact of pleural infection on functional outcomes and quality of life is paramount. Many survivors of pleural infection experience impaired lung function, reduced exercise capacity, and diminished overall quality of life. Scoring systems that can predict long-term functional outcomes may prove instrumental in optimizing rehabilitation efforts and post-infection support. By identifying patients at risk of poor functional recovery, clinicians can initiate timely interventions such as pulmonary rehabilitation, physical therapy, and psychological support to enhance the patient's overall well-being and quality of life ,41.Understanding the predictive value of rapid scoring systems for both short-term and long-term outcomes is essential for their effective clinical application. The analysis of various studies investigating these aspects provides a comprehensive overview of the potential benefits and limitations of using rapid scoring systems in pleural infection management. Integrating rapid scoring into clinical practice can facilitate risk stratification, personalized treatment planning, and informed decision-making. However, it is crucial to recognize these scoring systems' limitations and potential biases and address them through rigorous validation and future research. By refining and enhancing the accuracy and utility of rapid scoring systems, healthcare providers can optimize patient care and improve outcomes in pleural infection management. Ultimately, the successful integration of rapid scoring into routine clinical practice can positively impact patient well-being and healthcare resource allocation, bringing us closer to more effective and patient-centered pleural infection management strategies ,21.Factors affecting the prognostic accuracy of rapid scoringAccurate prognostication is a critical aspect of managing pleural infection effectively. Rapid scoring systems offer a quantifiable and standardized approach to assessing disease severity and predicting patient outcomes. However, to ensure their reliability and usefulness in clinical practice, it is essential to consider various patient-specific factors that can influence the predictive accuracy of these scoring systems . One sucMicrobiological and radiological findings are another crucial aspect of pleural infection management. The causative microorganisms, their antibiotic sensitivity, and the extent of lung involvement and pleural effusion on imaging can significantly affect disease severity and treatment response. Rapid scoring systems incorporating these findings into their algorithms may offer more accurate prognostic predictions . ComorbiMoreover, interpreting scoring results in the context of the severity of pleural infection is crucial. In mild or moderate cases, scoring systems may have limited discriminatory ability due to the relatively uniform nature of the patient population. However, scoring systems can offer more accurate prognostic insights in severe or complicated infections, guiding clinicians towards more aggressive interventions and monitoring strategies .Impact of Patient DemographicsPatient demographics, specifically age and sex, influence disease progression and outcomes in pleural infection. Age-related factors can significantly impact the clinical course of the infection. As patients age, changes in immune function and physiological reserves may increase infection vulnerability, making elderly individuals more susceptible to severe pleural infection. Additionally, comorbidities commonly accompanying aging can complicate disease management and influence treatment response. Therefore, considering age-related variations in prognostic scoring systems is essential to accurately predict disease severity and outcomes in different age groups .Furthermore, gender-related differences in pleural infection outcomes have been observed in some studies. While the underlying reasons for these disparities are not yet fully understood, it is believed that hormonal and immunological differences between males and females may contribute to varying disease outcomes. For instance, estrogen is known to have immunomodulatory effects that might influence the body's response to infection. Some studies have suggested that female patients with pleural infection may experience milder disease courses and have better treatment responses than males. By incorporating gender as a parameter in prognostic scoring systems, it is possible to account for these gender-specific differences and improve the accuracy of predicting outcomes for both male and female patients .Addressing patient demographics in rapid scoring systems is essential for personalized medicine and individualized patient care. Tailoring treatment strategies based on age and gender considerations can help clinicians optimize therapeutic interventions and improve patient outcomes. However, it is crucial to ensure that any consideration of demographics in scoring systems is evidence-based and supported by rigorous research. Understanding the impact of age and gender on pleural infection outcomes is an ongoing area of investigation, and continued research in this domain will further enhance the accuracy and utility of rapid scoring systems in pleural infection prognostication .Role of Microbiological and Radiological FindingsMicrobiological and radiological findings are integral components in diagnosing and managing pleural infection. Identifying causative microorganisms and their susceptibility to antibiotics is crucial for guiding appropriate antimicrobial therapy, significantly impacting treatment response and overall patient outcomes. Rapid scoring systems that consider microbiological data have the potential to enhance predictive accuracy, particularly in cases where tailored antibiotic treatment is necessary .Microbiological data can reveal valuable information about the type of pathogen causing the infection, its virulence, and its antibiotic resistance. Scoring systems that incorporate microbiological findings can aid clinicians in identifying high-risk patients who may require prompt escalation to targeted and more aggressive therapies. This enables timely adjustments to treatment regimens, potentially preventing treatment failure and the development of complications .Conversely, evaluating disease severity and tissue involvement heavily relies on radiological characteristics. Essential insights regarding pleural effusion, lung parenchymal engagement, and necrosis are provided through imaging techniques like chest X-rays and computed tomography (CT) scans. These radiological attributes mirror both the local and systemic effects of the disease, thereby enhancing clinicians' comprehension of the disease's status and possible complications .Scoring systems integrating radiological parameters offer a more comprehensive and nuanced disease evaluation. Such assessments can assist in risk stratification, allowing healthcare providers to identify patients with more severe infections who may require more intensive management, such as drainage procedures or surgical interventions . By consInfluence of Comorbidities on Scoring SystemsComorbidities significantly shape the clinical course of pleural infection, influencing disease progression, treatment response, and patient outcomes. Conditions such as diabetes, chronic kidney disease, heart failure, and immunosuppressive states can weaken the immune system, impair lung function, and increase the risk of complications. Consequently, patients with comorbidities are often at higher risk of adverse outcomes, including prolonged hospital stays, treatment failure, and increased mortality .Incorporating comorbidities into rapid scoring systems can enhance their prognostic accuracy by providing a more comprehensive assessment of the patient's overall health status. Scoring models considering these additional risk factors can better identify high-risk patients requiring more aggressive interventions or closer monitoring. Tailored treatment plans based on accurate risk stratification can improve clinical outcomes and resource utilization, ensuring that the most vulnerable patients receive the necessary care . HoweverAdditionally, choosing which comorbidities to include in the scoring systems must be carefully considered. Not all comorbidities may impact pleural infection outcomes equally, and some may be more relevant in specific patient populations. Thus, the selection of comorbidities should be based on their clinical significance and the availability of data . MoreoveInterpreting Scoring Results in the Context of Pleural Infection SeverityThe interpretation of scoring results in the context of pleural infection is critical when utilizing rapid scoring systems. It is important to recognize that these scoring systems' accuracy and predictive power may vary depending on the overall severity of the pleural infection in a given patient population . The disDespite their potential usefulness, clinicians need to understand that rapid scoring systems are adjunctive tools and not definitive predictors of outcomes. These systems are designed to aid in risk stratification and decision-making but should not be solely relied upon to make critical clinical decisions. Instead, they should be used with clinical judgment and other diagnostic assessments, such as radiological findings, microbiological data, and the patient's overall clinical status . By inteClinical implications and application of rapid scoringRole of Rapid Scoring in Clinical Decision-MakingRapid scoring systems in pleural infection management have the potential to significantly impact clinical decision-making by providing a quantitative and standardized assessment of disease severity and prognostication. These scoring systems offer valuable information to healthcare providers, aiding in risk stratification, treatment tailoring, and follow-up planning .Risk stratification: Rapid scoring systems excel in identifying patients at higher risk of adverse outcomes, such as mortality or treatment failure. These systems can efficiently classify patients into risk categories by analyzing clinical and laboratory parameters. This enables clinicians to prioritize interventions and allocate resources effectively, especially in resource-constrained settings where optimal utilization of healthcare resources is paramount .Treatment tailoring: One of the key challenges in pleural infection management is selecting the most appropriate treatment strategies for individual patients. Rapid scoring systems play a pivotal role by providing insights into disease severity and potential response to treatment. The scoring results can guide clinicians in choosing the most suitable antimicrobial therapy and drainage procedures and tailoring treatment plans based on the predicted disease severity. This personalized approach enhances therapeutic efficacy, minimizes the risk of treatment failure, and reduces the likelihood of complications .Follow-up planning: Beyond short-term prognostication, rapid scoring systems offer valuable information for long-term follow-up planning. By predicting disease trajectory and long-term outcomes, such as recurrence rates, these systems can inform healthcare providers about the need for post-discharge care and surveillance. Timely detection of potential complications and recurrent infections allows for proactive intervention, leading to better disease management and patient outcomes .Integrating rapid scoring systems into clinical practice empowers healthcare providers with evidence-based decision-making tools, aiding in more informed and precise patient care. The ability to quickly assess disease severity, predict outcomes, and tailor treatments based on individual patient characteristics enhances the overall management of pleural infection, ultimately leading to improved patient outcomes and enhanced healthcare resource utilization. However, ongoing research and validation are essential to continually refine and optimize these scoring systems, ensuring their effectiveness and reliability in real-world clinical settings. By harnessing the full potential of rapid scoring systems, healthcare providers can revolutionize pleural infection management and elevate the standard of care for patients affected by this challenging condition .Integration of Rapid Scoring into Current Management GuidelinesValidation and adaptation: Thorough validation is essential before implementing rapid scoring systems. This involves evaluating the performance of the scoring system in diverse patient populations to ensure its generalizability across different contexts. Validation studies should encompass various demographic profiles, disease severities, and geographic locations. In some cases, adaptation of the scoring system to specific local contexts may be necessary to account for variations in patient characteristics and healthcare practices .Education and training: Effective education and training programs are crucial to familiarize healthcare providers with the rapid scoring system's use and interpretation. Clinicians need to understand the rationale behind the scoring criteria, the significance of specific variables, and how to calculate the scores accurately. Additionally, emphasizing that the scoring system is an adjunctive tool to support clinical judgment can help mitigate any concerns of over-reliance on the scoring results .Standardized documentation: Consistent and standardized documentation of scoring system parameters in patient records is vital for continuity of care. Healthcare providers should diligently record the data used to calculate the score, the actual score itself, and any updates during treatment. Standardized documentation enables clinicians to track disease progression over time and aids in assessing the effectiveness of treatment strategies based on changes in the scoring results .Interdisciplinary collaboration: Pleural infection management involves the collaboration of various medical specialists, each contributing their expertise to patient care. Effective interdisciplinary collaboration is essential for integrating rapid scoring systems seamlessly into the overall management approach. Collaborating with pulmonologists, infectious disease specialists, radiologists, and other relevant disciplines like cardiothoracic ensures that all aspects of patient care are considered, and the scoring system's results are interpreted comprehensively .Practical Considerations and Challenges in Implementing Rapid ScoringAccessibility of data: The availability of necessary clinical and laboratory data required for rapid scoring may vary across different healthcare settings. In some facilities, electronic health records (EHRs) might be well-established, allowing easy access to relevant patient data. However, in resource-limited or smaller healthcare facilities, the absence of comprehensive EHR systems could hinder data accessibility. Efforts should be made to streamline data collection and ensure the completeness of data inputs for accurate scoring. This may involve improving data capture and recording practices, and fostering interoperability between healthcare information systems .Scoring system selection: Choosing the most appropriate rapid scoring system for a specific clinical setting requires careful consideration of various factors. Different scoring systems may have been developed and validated in diverse patient populations or healthcare contexts. As such, it is essential to evaluate the applicability and accuracy of each scoring system in the specific patient cohort and resource constraints of the healthcare facility. Factors such as patient demographics, available resources, and the overall clinical context should be considered during the selection process to ensure that the chosen scoring system aligns with the needs and capabilities of the healthcare setting .Time constraints: Rapid scoring systems are designed to provide quick and efficient disease severity and prognosis assessments. However, the practicality of their implementation can be impacted by time constraints, particularly in busy clinical settings where healthcare professionals have limited time to dedicate to scoring calculations. The scoring process should be user-friendly, intuitive, and time-efficient to integrate rapid scoring into routine clinical practice seamlessly. Implementing user-friendly electronic scoring tools or incorporating scoring algorithms into electronic medical record systems can help minimize the time investment required for scoring calculation .Future Directions and Potential Improvements in Rapid Scoring SystemsComprehensive scoring models: To enhance the accuracy of prognostication, future scoring systems could be developed as more comprehensive models. By incorporating additional variables, such as molecular biomarkers or novel radiological parameters, these systems can provide a more nuanced and detailed assessment of disease severity and progression. Integrating biomarkers reflecting the host's immune response or pathogen virulence may offer valuable insights into disease outcomes and treatment responses .Artificial intelligence (AI) integration: Leveraging AI algorithms could revolutionize rapid scoring systems by analyzing vast datasets and identifying patterns that might not be apparent through conventional methods. AI integration can significantly improve the predictive power of these systems. Machine learning algorithms can adapt and refine scoring models based on real-world data, ensuring continuous improvement and adaptability to evolving clinical scenarios .External validation and standardization: It is essential to rigorously validate and standardize rapid scoring systems across diverse patient populations and healthcare settings for widespread clinical adoption. Continued research and collaboration among healthcare institutions and research centers can facilitate the validation process, ensuring the reliability and generalizability of these scoring approaches .Longitudinal predictions: Advancements in scoring methodologies may enable dynamic and longitudinal predictions, offering real-time updates on patient outcomes throughout treatment. Longitudinal assessments can provide insights into disease progression, response to therapies, and potential complications, allowing clinicians to adjust treatment strategies proactively .Top of form critique and limitations of rapid scoring systemsCriticism of Current Scoring ApproachesWhile rapid scoring systems have demonstrated their potential in aiding prognostication for pleural infection, they are not exempt from criticism. Several key criticisms of current scoring approaches warrant consideration:Oversimplification: One significant critique of some rapid scoring systems is their tendency to oversimplify the complexity of pleural infection. By focusing on a limited set of parameters, these scoring systems may not fully capture the multifaceted nature of the disease. The oversimplification could lead to inadequate prognostic accuracy, particularly in cases with diverse clinical presentations and variable disease trajectories .Generalizability: Many rapid scoring systems have been developed and validated in specific patient populations, often based on data from single institutions or regions. Consequently, there may be concerns regarding the applicability of these scoring approaches to more diverse patient cohorts. The lack of external validation across different healthcare settings and populations hinders their generalizability and raises questions about their reliability in real-world clinical scenarios .Lack of longitudinal assessment: Rapid scoring systems typically provide static assessments of disease severity at a particular time point. The absence of dynamic or longitudinal assessments limits their ability to predict changes in disease trajectory over time. In pleural infection, disease progression and response to treatment may evolve over days or weeks, and a single snapshot may not capture the full clinical picture .Limited predictive value for long-term outcomes: While rapid scoring systems have shown promise in predicting short-term outcomes, such as mortality and treatment response, their ability to predict long-term outcomes remains a challenge reliably. Variables influencing long-term recovery, recurrence rates, and functional outcomes are complex and multifactorial, making their inclusion in scoring systems more complex .Limitations of Available Studies and DataSmall sample sizes: Many studies evaluating rapid scoring systems may have limited sample sizes due to the rarity of severe pleural infections or other logistical constraints. Small sample sizes can reduce statistical power, making it challenging to draw definitive conclusions. Moreover, findings from such studies may lack generalizability to larger, more diverse patient populations, potentially limiting the external validity of the scoring systems .Heterogeneity of studies: Studies evaluating rapid scoring systems often utilize varying patient populations, methodologies, and outcome measures. This heterogeneity can complicate direct comparisons between different scoring approaches, hindering the establishment of a consensus on the most effective scoring system for pleural infection prognostication. A standardized approach to study design and outcome measurements would facilitate meaningful comparisons and enhance the reliability of the findings .Retrospective nature: Many studies assessing rapid scoring systems in pleural infection rely on retrospective data, which can introduce inherent biases and limitations. Retrospective data collection may be subject to selection bias, as patient data may not have been originally collected for scoring system evaluation. Additionally, retrospective data might lack completeness, leading to missing or incomplete information that could impact the accuracy and reliability of prognostic evaluations .Missing data and follow-up: Incomplete data and loss to follow-up present significant challenges in prognostic evaluations using rapid scoring systems. Missing data can result from various factors, including patients' refusal to participate in follow-up assessments or incomplete medical records. This missing data can lead to underestimation or overestimation of prognostic accuracy and potentially introduce bias into the results. Ensuring complete and accurate data collection, as well as adequate follow-up, is essential to minimize these potential biases and enhance the validity of prognostic assessments .Addressing Potential Sources of BiasLarge, prospective multicenter studies: Conducting well-designed prospective studies with large and diverse patient populations is essential. By collecting data from multiple healthcare centers, these studies can improve the generalizability of the findings and ensure that the results represent the broader patient population. Prospective studies also allow for standardized data collection, minimizing the risk of data inconsistencies and enhancing the statistical robustness of the scoring system .External validation: External validation of rapid scoring systems is critical to establishing their predictive accuracy in different clinical settings and patient cohorts. Validating the scoring systems in settings beyond their original development ensures that they perform consistently and reliably across diverse healthcare contexts. Such validation strengthens the evidence base supporting the use of these scoring systems and increases confidence in their clinical applicability .Longitudinal assessment: Incorporating longitudinal assessments into rapid scoring systems can provide more accurate predictions of disease trajectories over time. By tracking changes in patient status and outcomes throughout treatment and follow-up, longitudinal assessments can offer insights into the dynamic nature of pleural infection and its response to interventions. This temporal dimension can further enhance the predictive value of the scoring systems, guiding clinicians in making more informed decisions throughout the patient's healthcare journey .Transparent reporting: Transparent reporting of study methods, data collection, and potential sources of bias is crucial for ensuring the credibility and reproducibility of research findings. Comprehensive and clear reporting allows other researchers and healthcare professionals to understand and evaluate the study's methodology and potential limitations. It fosters an environment of scientific rigor and encourages transparency in research, which is vital for building trust in the validity and applicability of rapid scoring systems in pleural infection prognostication .This review article has comprehensively assessed the predictive potential of rapid scoring systems in pleural infection prognostication. Pleural infection is a significant health concern, demanding accurate prognostic tools for effective management. Our analysis of existing scoring systems such as CURB-65, A-DROP, and APACHE II revealed their strengths and limitations in predicting short-term and long-term outcomes. While rapid scoring systems show promise in aiding clinical decision-making and risk stratification, they are not without drawbacks and face challenges related to data limitations and potential biases. However, their integration into clinical practice can potentially improve patient outcomes and optimize resource allocation. Moving forward, rigorous validation, reporting transparency, and data science advances offer promising avenues for refining rapid scoring systems. Multidisciplinary collaboration and prospective studies are critical to unlocking the full potential of rapid scoring in pleural infection management, ultimately contributing to better patient care and outcomes.


Extracellular RNAs are an emerging research topic in fungal-plant interactions. Fungal plant pathogens and symbionts release small RNAs that enter host cells to manipulate plant physiology and immunity. This communication via extracellular RNAs between fungi and plants is bidirectional. On the one hand, plants release RNAs encapsulated inside extracellular vesicles as a defense response as well as for intercellular and inter-organismal communication. On the other hand, recent reports suggest that also full-length mRNAs are transported within fungal EVs into plants, and these fungal mRNAs might get translated inside host cells. In this review article, we summarize the current views and fundamental concepts of extracellular RNAs released by plant-associated fungi, and we discuss new strategies to apply extracellular RNAs in crop protection against fungal pathogens.Extracellular RNAs are an emerging topic in plant-fungal communication.• Fungi utilize RNAs to manipulate host plants for colonization.• Extracellular RNAs can be engineered to protect plants against fungal pathogens.•  Fungal-plant interactions can have beneficial, detrimental, or neutral effects on plant hosts. Pathogenic fungi pose serious threats to agronomic yield and ecosystems  that is largely conserved between fungi and plants. Key factors of RNAi, namely RNA-dependent RNA polymerase (RDR), Dicer-like (DCL), and Argonaute (AGO) proteins, are highly conserved in both plants and fungi . EVs are nanoparticles encasing cytoplasmic molecules including proteins and RNAs in a lipid bilayer, which are secreted into the extracellular space   into the root cortex of its host plant Eucalyptus grandis. Treatment of roots with synthetic Pisolithus milRNAs mimicked regulation of Eucalyptus target genes and strengthened formation of deep Hartig net during root colonization  roots in order to establish root nodule symbiosis  into the soybean AGO1b to induce cross-kingdom RNAi of nodule-repressive plant genes. Interestingly, both cases of cross-kingdom RNAi help to establish distinct forms of root symbiosis, in which microbial small RNAs seem to act as early stage interaction signals, because RNA delivery into plant cells occurs before the formation of fungal Hartig net and bacterial nodules.The fungal vascular pathogen Zymoseptoria tritici 11 and RH37, which all specifically bind to the EV small RNAs, as well as the two non-specific RBPs annexins (ANN)1 and ANN2  and mRNAs with cognate motifs for targeting them to sites of EV biogenesis. In mammalian systems, multiple RBPs have been implicated in RNA loading into EVs siRNAs into infecting B. cinerea. These A. thaliana small RNAs are suggested to be transported via plant EVs  RNAs was found in the plant extracellular fraction . Accordingly, essential field trials in the USA are proceeding to pave the way for final approval. Such development of successful SIGS application keeps high hopes that RNA spray also becomes conceivable for plant protection against fungal pathogens in the near future.Since the discovery of cross-kingdom RNAi and its technological implementation into HIGS application, RNAs have been engineered to confer resistance in plants against diverse pathogenic organisms with significant success  and DCLs (RNAi pathway)  on cotton (Jain et al. B. cinerea infection in tomato and chickpea under controlled conditions (Nino-Sanchez et al. B. cinerea infection (Qiao et al. Since extracellular RNA stability and delivery have been identified as the major challenges to bring SIGS into a success story against fungal pathogens (Hernandez-Soto and Chacon-Cerdas The SIGS approach stands for a more eco-friendly plant protection strategy that is already in transition into potential field application in first trails (Rank and Koch


Pancreatic cancer is currently the fourth most common cause of cancer-related deaths and is predicted to be the second leading cause of cancer-related mortality by the year 2030. Most of the malignant neoplasms of the pancreas are ductal adenocarcinomas (PDAC). For several reasons, the prognosis of PDAC is exceedingly poor. Surgical resection is the only curative treatment option, but only about 10–20% of patients present with resectable tumors at the time of diagnosis. Moreover, most pancreatic carcinomas are resistant to conventional (radio)chemotherapy. Current clinical standard treatment regimens comprise either mFOLFIRINOX or gemcitabine (with nab-paclitaxel). The selection of either of these regimens is currently based on the patient performance status and comorbidities, while the underlying individual tumor biology is commonly not considered. Although multiple studies aimed at establishing molecular profiles and identification of actionable molecular targets of PDACs, targeted therapies for PDAC patients have not (yet) become an integral part of clinical routine practice. In addition to treatment prediction based on molecular profiling, e.g., in the context of a molecular tumorboard, preclinical culture models that reflect the complex interactions of tumor cells with their microenvironment are promising tools to understand molecular mechanisms of PDAC and to test individual treatment responses. These patient-derived models comprise adherently growing primary cell lines, organoid cultures, organotypic slice cultures and patient-derived xenografts.Garcia et al. recently published a comprehensive review on two-dimensional (2D) cell cultures, 3D organoid cultures, and genetically engineered mouse models (GEMMs) with a special focus on patient derived xenograft (PDX) models . AdherenIn light of the limitations of PDXs as a preclinical model for individual therapy response prediction in personalized medicine, 3D organoid cultures are a highly promising tool. Frappart and Hofmann comprehensively reviewed the methodology and potential use of organoid cultures to improve clinical management of PDAC . EfficieAs mentioned above, most preclinical culture models lack the immune cell compartment. However, multiple suppressive immune cell types including macrophages, myeloid-derived suppressor cells (MDSCs) and regulatory T cells are found in early pancreatic lesions and persist through cancer progression. Holokai et al. described an elegantautologous pancreatic cancer organoid-immune cell co-culture model that may be able to predict the efficacy of targeted therapies . Based oAnother model system that should be mentioned in this context are organotypic slice cultures (OTSCs). We recently established this ex vivo model in our laboratory to explore its potential use for ex vivo response prediction . OTSCs aIn summary, we conclude that to date there is no optimal model system for preclinical response prediction of individual PDAC patients to therapy. Each model system discussed here has its benefits and weaknesses and does not fully reflect the individual patient’s tumor biology in vivo. However, since they complement each other, they may be used in combination rather than singly for reliable response prediction. All of these preclinical patient-derived culture models are certainly highly promising tools in personalized treatment of PDAC; their clinical implementation, though, requires further evaluation and fine tuning.