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Oncolytic virus (OV) is increasingly being recognized as a novel vector in cancer immunotherapy. Increasing evidence suggests that OV has the ability to change the immune status of tumor microenvironment, so called transformation of 'cold' tumors into 'hot' tumors. The improved anti-tumor immunity can be induced by OV and further enhanced through the combination of various immunomodulators. The Neo-2/15 is a newly de novo synthesized cytokine that functions as both IL-2 and IL-15. However, it specifically lacks the binding site of IL-2 receptor α subunit (CD25), therefore unable to induce the Treg proliferation. In present study, a recombinant vesicular stomatitis virus expressing the Neo-2/15 (VSVM51R-Neo-2/15) was generated. Intratumoral delivery of VSVM51R-Neo-2/15 efficiently inhibited tumor growth in mice without causing the IL-2-related toxicity previously observed in clinic. Moreover, treatment with VSVM51R-Neo-2/15 increased the number of activated CD8+ T cells but not Treg cells in tumors. More tumor-bearing mice were survival with VSVM51R-Neo-2/15 treatment, and the surviving mice displayed enhanced protection against tumor cell rechallenge due to the induced anti-tumor immunity. In addition, combination therapy of OV and anti-PD-L1 immune checkpoint inhibitors further enhanced the anti-tumor immune response. These findings suggest that our novel VSVM51R-Neo-2/15 can effectively inhibit the tumor growth and enhance the sensitivity to immune checkpoint inhibitors, providing promising attempts for further clinical trials.

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Introduction: The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. Methods: We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression. Results: A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well. Discussion: Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.

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Background: Stress hyperglycemia is now more common in intensive care unit (ICU) patients and is strongly associated with poor prognosis. Whether this association exists in critically ill patients with cardiogenic shock (CS) is unknown. This study investigated the prognostic relationship of stress hyperglycemia on critically ill patients with CS. Methods: We included 393 critically ill patients with CS from the MIMIC IV database in this study and categorized the patients into four groups based on quartiles of Stress hyperglycemia ratio (SHR). We assessed the correlation between SHR and mortality using restricted cubic spline analysis and Cox proportional hazards models. The primary outcomes observed were ICU mortality and hospitalization mortality. Results: The mean age of the entire study population was 68 years, of which 30% were male (118 cases). There was no significant difference between the four groups in terms of age, gender, BMI, and vital signs (P>0.05). There was an increasing trend in the levels of lactate (lac), white blood cell count (WBC), glutamic oxaloacetic transaminase (AST), glucose and Hemoglobin A1C (HbA1c) from group Q1 to group Q2, with the greatest change in patients in group Q4 (P<0.05) and the patients in group Q4 had the highest use of mechanical ventilation, the longest duration of mechanical ventilation, ICU stay and hospital stay. After adjusting for confounders, SHR was found to be strongly associated with patient ICU mortality, showing a U-shaped relationship. Conclusion: In critically ill patients with CS, stress hyperglycemia assessed by SHR was significantly associated with patient ICU mortality.

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The abundance of molecules on early Earth likely enabled a wide range of prebiotic chemistry, with peptides playing a key role in the development of early life forms and the evolution of metabolic pathways. Among peptides, those with enzyme-like activities occupy a unique position between peptides and enzymes, combining both structural flexibility and catalytic functionality. However, their full potential remains largely untapped. Further exploration of these enzyme-like peptides at the nanoscale could provide valuable insights into modern nanotechnology, biomedicine, and even the origins of life. Hence, this review introduces the groundbreaking concept of "peptide nanozymes (PepNzymes)", which includes single peptides exhibiting enzyme-like activities, peptide-based nanostructures with enzyme-like activities, and peptide-based nanozymes, thus enabling the investigation of biological phenomena at nanoscale dimensions. Through the rational design of enzyme-like peptides or their assembly with nanostructures and nanozymes, researchers have found or created PepNzymes capable of catalyzing a wide range of reactions. By scrutinizing the interactions between the structures and enzyme-like activities of PepNzymes, we have gained valuable insights into the underlying mechanisms governing enzyme-like activities. Generally, PepNzymes play a crucial role in biological processes by facilitating small-scale enzyme-like reactions, speeding up molecular oxidation-reduction, cleavage, and synthesis reactions, leveraging the functional properties of peptides, and creating a stable microenvironment, among other functions. These discoveries make PepNzymes useful for diagnostics, cellular imaging, antimicrobial therapy, tissue engineering, anti-tumor treatments, and more while pointing out opportunities. Overall, this research provides a significant journey of PepNzymes' potential in various biomedical applications, pushing them towards new advancements.

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Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.•We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.•Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies.

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Object: Establish a clinical prognosis model of coronary heart disease (CHD) to predict 28-day mortality in patients with sepsis. Method: The data were collected retrospectively from septic patients with a previous history of coronary heart disease (CHD) from the Medical Information Mart for Intensive Care (MIMIC)-III database. The included patients were randomly divided into the training cohorts and validation cohorts. The variables were selected using the backward stepwise selection method of Cox regression, and a nomogram was subsequently constructed. The nomogram was compared to the Sequential Organ Failure Assessment (SOFA) model using the C-index, area under the receiver operating characteristic curve (AUC) over time, Net reclassification index (NRI), Integrated discrimination improvement index (IDI), calibration map, and decision curve analysis (DCA). Result: A total of 800 patients were included in the study. We developed a nomogram based on age, diastolic blood pressure (DBP), pH, lactate, red blood cell distribution width (RDW), anion gap, valvular heart disease, peripheral vascular disease, and acute kidney injury (AKI) stage. The nomogram was evaluated using C-index, AUC, NRI, IDI, calibration plot, and DCA. Our findings revealed that this nomogram outperformed the SOFA score in predicting 28-day mortality in sepsis patients.

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Background: FNDs mimicking a stroke represent a growing challenge in the emergency department (ED). A comprehensive diagnostic approach involving clinical evaluation and neuroimaging is essential to differentiate stroke from mimics. The safety profile of thrombolysis justifies its use where FNDs cannot be ruled out. This approach highlights the need for more precise diagnostic tools and protocols to improve patient care and reduce unnecessary treatments. Distinguishing FNDs from actual cerebrovascular events is critical yet difficult, particularly under time constraints. Given the urgency and potential severity of strokes, intravenous thrombolysis is frequently administered even when FNDs cannot be definitively excluded. Methods: This retrospective study analyzed data of participants admitted to the Trieste University Hospital Stroke Unit between January 2018 and December 2022, focusing on those presenting with sudden-onset focal neurological deficits mimicking a stroke, with some presenting within the reperfusion treatment window (<4.5 h from symptoms onset). We obtained detailed clinical evaluations and neuroimaging, and administered thrombolytic therapy in selected cases. Results and discussion: We included 84 participants presenting with stroke mimics (average age of 45 yo) predominantly female (65.5%). Most common presentations: hemiparesis or hemisensory loss (75%), speech disorder (10.7%), vertigo/gait disorders (4.8%). History of psychiatric disorders was found in 32.1% of cases, and 48.8% had prior neurological disease or stroke risk factors. Advanced neuroimaging was performed in 43 cases yielding normal or non-specific results. Thrombolysis was safely administered in 31%. Patients mostly recovered within the first 24 h from admission (44.7%). We compared this FND's sample with 291 patients with mild ischemic stroke (NIHSS ≤7).

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BACKGROUND: Some patients with psychotropic drug poisoning need intensive care unit (ICU) admission, but risk prediction models for prolonged ICU stays are lacking. OBJECTIVES: Develop and evaluate a prediction model for prolonged ICU stays in patients with psychotropic drug poisoning. METHODS: The clinical data of patients with psychotropic drug poisoning were collected from the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ 2.2 database. Patients were grouped by their ICU length of stay: non-prolonged (<2 days) and prolonged (≥2 days). Variable selection methods included LASSO and logistic regression. The selected variables were used to construct the model, which was subsequently evaluated for discrimination, calibration, and clinical utility. RESULTS: The cohort included 413 patients with psychotropic drug poisoning, 49.4 % male, with a median age of 41 years. The variables stepwise selected for model construction through LASSO and logistic regression include sepsis, SAPS Ⅱ, heart rate, respiratory rate, and mechanical ventilation. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.785 (95 % CI: 0.736-0.833) and was validated well with bootstrap internal validation (AUC: 0.792, 95 % CI: 0.745-0.839). Calibration curves indicated good fit (χ2 = 4.148, P = 0.844), aligning observed and predicted rates of prolonged ICU stays. Decision curve analysis (DCA) showed positive net benefits across a threshold probability range of 0.07-0.85. CONCLUSIONS: The model developed in this study may help predict the risk of prolonged ICU stays for patients with psychotropic drug poisoning.

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OBJECTIVE: This study aims to assess the effects of antithrombotic therapy on the outcomes of lower gastrointestinal bleeding (LGIB) in ICU patients, focusing on in-hospital mortality, rebleeding, and length of hospital and ICU stays. METHOD: This retrospective observational study utilized the MIMIC-IV 2.2 database, which includes 513 ICU patients with LGIB. RESULT: The in-hospital mortality rate was 7.6%, and the rebleeding rate was 11.1%. The average Oakland risk score among the study population was 22.54. Multivariate Cox regression analysis identified the use of antiplatelet drugs as an independent protective factor for in-hospital mortality (HR = 0.37, 95% CI 0.15-0.90, p = 0.029). Patients on anticoagulants experienced significantly longer hospital stays (13.1 ± 12.2 days vs. 17.4 ± 12.6 days, p = 0.031) compared to those not using these drugs. Propensity score matching also supported these findings, indicating that antithrombotic therapy was associated with lower in-hospital mortality and longer hospital stays even after adjusting for factors like age, gender, and primary diagnosis. CONCLUSIONS: Our analysis using various statistical methods, including propensity score matching and multivariate regression, confirms that use of antithrombotic drugs in 2.3 days, particularly antiplatelets, are associated with a lower risk of in-hospital mortality. However, they may increase the risk of rebleeding and extend hospital stays in certain subgroups.

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Sepsis and hypertension pose significant health risks, yet the optimal mean arterial pressure (MAP) target for resuscitation remains uncertain. This study investigates the association between average MAP (a-MAP) within the initial 24 h of intensive care unit admission and clinical outcomes in patients with sepsis and primary hypertension using the Medical Information Mart for Intensive Care (MIMIC) IV database. Multivariable Cox regression assessed the association between a-MAP and 30-day mortality. Kaplan-Meier and log-rank analyses constructed survival curves, while restricted cubic splines (RCS) illustrated the nonlinear relationship between a-MAP and 30-day mortality. Subgroup analyses ensured robustness. The study involved 8,810 patients. Adjusted hazard ratios for 30-day mortality in the T1 group (< 73 mmHg) and T3 group (≥ 80 mmHg) compared to the T2 group (73-80 mmHg) were 1.25 (95% CI 1.09-1.43, P = 0.001) and 1.44 (95% CI 1.25-1.66, P < 0.001), respectively. RCS revealed a U-shaped relationship (non-linearity: P < 0.001). Kaplan-Meier curves demonstrated significant differences (P < 0.0001). Subgroup analysis showed no significant interactions. Maintaining an a-MAP of 73 to 80 mmHg may be associated with a reduction in 30-day mortality. Further validation through prospective randomized controlled trials is warranted.

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BACKGROUND: To develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS). METHODS: This retrospective cohort study included 2466 patients diagnosed with sepsis and ARDS within 24 h of ICU admission. Demographic, clinical, and laboratory parameters were extracted from Medical Information Mart for Intensive Care III (MIMIC-III) database. Feature selection was performed using the Boruta algorithm, followed by the construction of seven ML models: logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, decision tree, Random Forest, and extreme gradient boosting. Model performance was evaluated using the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The study identified 24 variables significantly associated with mortality. The optimal ML model, a Random Forest model, demonstrated an AUC of 0.8015 in the test set, with high accuracy and specificity. The model highlighted the importance of blood urea nitrogen, age, urine output, Simplified Acute Physiology Score II, and albumin levels in predicting mortality. CONCLUSIONS: The model's superior predictive performance underscores the potential for integrating advanced analytics into clinical decision-making processes, potentially improving patient outcomes and resource allocation in critical care settings.

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Heparin, a widely studied glycosaminoglycan, plays crucial roles in the regulation of various physiological and pathological processes. Therefore, it's important to develop highly selective and sensitive methods for convenient monitoring of heparin levels in biological systems. We report the design and synthesis of Fe3O4@PDA@MnO2 nanoparticles (FPM-NPs), which exhibit dual enzymatic activities, enabling quantitative detection of heparin. The FPM-NPs feature a unique tri-layer spherical shell structure, possessing both peroxidase-like and oxidase-like activities, and catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) in the presence or absence of H2O2. Remarkably, upon co-incubated with heparin, the oxidase activity of FPM-NPs decreases, while the peroxidase activity increases. By leveraging these dual enzymatic properties of FPM-NPs, a highly sensitive and specific colorimetric detection of heparin is achieved, with a detection limit reaching 6.51 nM and a good linear response to quantify heparin ranging 10-800 nM. Additionally, the developed FPM-NPs are successfully applied to measure heparin in fetal bovine serum samples. We also extend this detection method to a paper-based chip, enabling portable detection of heparin through grayscale analysis of mobile phone photographs. The multi-nanozyme-based heparin detection approach provides a new perspective for future research on expanding the application of nanocomposite materials in biomedical detection and analysis.

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The prognosis of septic patients with cirrhosis is worse compared to septic patients without cirrhosis. Early and accurate prognosis determination in patients with cirrhosis and sepsis is pivotal for guiding treatment decisions. The aim of this study was to investigate the association between albumin-corrected anion gap (ACAG) and clinical prognosis of patients with sepsis and cirrhosis. This study extracted data of patients with sepsis and cirrhosis from the Medical Information Mart for Intensive Care (MIMIC-IV) database. A total of 1340 patients (64.6% male) were enrolled. After confounders adjusting, elevated ACAG had a significant association with 28-day mortality (HR1.604; 95% CI 1.258-2.048; P < 0.001). Restricted cubic spline revealed that a linear relationship between ACAG and 28-day mortality (P-nonlinear = 0.089, P-overall = 0.001). According to the ROC curve analysis, the ACAG demonstrated a higher area under the curve (AUC) of 0.703 compared to AG (0.675). Kaplan-Meier analysis revealed higher 28-day mortality in high ACAG group (log-rank test, χ^2 = 175.638, P < 0.001). Furthermore, subgroup analysis showed a significant interaction between ACAG and etiology of cirrhosis (P for interaction = 0.014). Therefore, ACAG could provide clinicians with valuable insights for guiding interventions in this high-risk population.

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This study delves into the correlation between the triglyceride glucose-body mass index (TyG-BMI) index upon hospital admission and clinical outcomes among this patient population. We investigated the association between TyG-BMI at hospital admission and clinical outcomes in this patient group, and analyzed data from the Medical Information Mart for Intensive Care IV database, identifying acute pancreatitis (AP) patients admitted to ICUs and stratifying them by TyG-BMI quartiles. We assessed the relationship between TyG-BMI and mortality (both in-hospital and ICU) using Cox proportional hazards regression and restricted cubic splines. The cohort included 419 patients, average age 56.34 ± 16.62 years, with a majority being male (61.58%). Hospital and ICU mortality rates were 11.93% and 7.16%, respectively. Higher TyG-BMI was positively correlated with increased all-cause mortality. Patients in the highest TyG-BMI quartile had significantly greater risks of in-hospital and ICU mortality. An S-shaped curve in the spline analysis indicated a threshold effect at a TyG-BMI of 243 for increased in-hospital mortality risk. TyG-BMI is a reliable predictor of both in-hospital and ICU mortality in severely ill AP patients, suggesting its utility in enhancing risk assessment and guiding clinical interventions for this vulnerable population.

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BACKGROUND: The association between red blood cell distribution width (RDW) to albumin ratio (RAR) and prognosis in patients with acute respiratory failure (ARF) admitted to the Intensive Care Unit (ICU) remains unclear. This retrospective cohort study aims to investigate this association. METHODS: Clinical information of ARF patients was collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 2.0 database. The primary outcome was, in-hospital mortality and secondary outcomes included 28-day mortality, 60-day mortality, length of hospital stay, and length of ICU stay. Cox regression models and subgroup analyses were conducted to explore the relationship between RAR and mortality. RESULTS: A total of 4547 patients with acute respiratory failure were enrolled, with 2277 in the low ratio group (RAR < 4.83) and 2270 in the high ratio group (RAR > = 4.83). Kaplan-Meier survival analysis demonstrated a significant difference in survival probability between the two groups. After adjusting for confounding factors, the Cox regression analysis showed that the high RAR ratio had a higher hazard ratio (HR) for in-hospital mortality (HR 1.22, 95% CI 1.07-1.40; P = 0.003), as well as for 28-day mortality and 60-day mortality. Propensity score-matched (PSM) analysis further supported the finding that high RAR was an independent risk factor for ARF. CONCLUSION: This study reveals that RAR is an independent risk factor for poor clinical prognosis in patients with ARF admitted to the ICU. Higher RAR levels were associated with increased in-hospital, 28-day and 60-day mortality rates.

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Different morphological Cu2O nanoparticles including cube, truncated cube, and octahedron were successfully prepared by a selective surface stabilization strategy. The prepared cube Cu2O exhibited superior peroxidase-like activity over the other two morphological Cu2O nanoparticles, which can readily oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to form visually recognizable color signals. Consequently, a sensitive and simple colorimetric biosensor was proposed for deoxynivalenol (DON) detection. In this biosensor, the uniform cube Cu2O was employed as the vehicle to label the antibody for the recognition of immunoreaction. The sensing strategy showed a detection limit as low as 0.01 ng/mL, and a wide linear range from 2 to 100 ng/mL. Concurrently, the approximate DON concentration can be immediately and conveniently observed by the vivid color changes. Benefiting from the high sensitivity and selectivity of the designed biosensor, the detection of DON in wheat, corn, and tap water samples was achieved, suggesting the bright prospect of the biosensor for the convenient and intuitive detection of DON in actual samples.

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Background: Heart failure (HF) is a primary public health issue associated with a high mortality rate. However, effective treatments still need to be developed. The optimal level of glycemic control in non-diabetic critically ill patients suffering from HF is uncertain. Therefore, this study examined the relationship between initial glucose levels and in-hospital mortality in critically ill non-diabetic patients with HF. Methods: A total of 1159 critically ill patients with HF were selected from the Medical Information Mart for Intensive Care-III (MIMIC-III) data resource and included in this study. The association between initial glucose levels and hospital mortality in seriously ill non-diabetic patients with HF was analyzed using smooth curve fittings and multivariable Cox regression. Stratified analyses were performed for age, gender, hypertension, atrial fibrillation, CHD with no MI (coronary heart disease with no myocardial infarction), renal failure, chronic obstructive pulmonary disease (COPD), estimated glomerular filtration rate (eGFR), and blood glucose concentrations. Results: The hospital mortality was identified as 14.9%. A multivariate Cox regression model, along with smooth curve fitting data, showed that the initial blood glucose demonstrated a U-shape relationship with hospitalized deaths in non-diabetic critically ill patients with HF. The turning point on the left side of the inflection point was HR 0.69, 95% CI 0.47-1.02, p = 0.068, and on the right side, HR 1.24, 95% CI 1.07-1.43, p = 0.003. Significant interactions existed for blood glucose concentrations (7-11 mmol/L) (p-value for interaction: 0.009). No other significant interactions were detected. Conclusions: This study demonstrated a U-shape correlation between initial blood glucose and hospital mortality in critically ill non-diabetic patients with HF. The optimal level of initial blood glucose for non-diabetic critically ill patients with HF was around 7 mmol/L.

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This study addresses the existing gap in theoretical and empirical research concerning the impact of social media influencers (SMIs) on followers' purchasing decisions. The primary aim is to explore and elucidate followers' journey from exposure to SMIs to the manifestation of conspicuous consumption. Grounded in the stimulus-organism-response framework and self-determination theory, the research proposes a dual model focusing on mediating factors such as social comparison, desire to mimic, materialism, and fear of missing out (FOMO). To achieve this objective, a survey targeting 272 respondents was conducted on the MTurk platform. The study findings reveal that exposure to SMIs triggers social comparisons and FOMO, subsequently influencing the acquisition of conspicuous products. Additionally, the study identifies that exposure to SMIs amplifies the desire to mimic and stimulates materialistic tendencies, thereby contributing to conspicuous consumption. The proposed Intrinsic-Extrinsic Consumption Motivation Model emerges as a novel framework to enhance our understanding of how SMIs influence conspicuous consumption, providing valuable insights for developing effective advertising programs.

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The association between the red blood cell distribution width-platelet ratio (RPR) and mortality in heart failure patients remains unclear. We aimed to investigate the potential non-linear relationship between RPR and 1-year mortality risk. A retrospective cohort study was conducted involving 6982 participants from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Multivariable Cox regression and restricted cubic spline analyses were performed to evaluate the association between RPR and 1-year mortality, adjusting for potential confounders. We observed 1091 patients died in hospital and 2535 patients died during 1 year follow-up period. The prevalence or incidence of mortality did not show statistically significant differences among RPR groups in the overall study population. However, a positive association between RPR and the risk of mortality was noted after adjusting for multiple variables (HR = 1.38, 95 % CI = 1.06-1.81, P = 0.018). Analysis using restricted cubic splines indicated a U-shaped relationship between RPR levels and the risk of mortality (P nonlinearity <0.05), with the point of lowest risk at 0.104. Compared to this level, lower RPR (<0.104) was associated with increased mortality (HR = 0.046, 95 % CI: 0.004-0.546), as was higher RPR (>0.104) (HR = 2.656, 95 % CI: 1.692-4.170).This U-shaped association was consistent across subgroup analyses (all interaction P values > 0.05). RPR exhibits a U-shaped association with 1-year mortality in heart failure patients, suggesting both low and high RPR levels are linked to increased risk. RPR may serve as a relevant biomarker for risk stratification in this population. We incorporated RPR into the SOFA (AUC 0.731) and SAPS II (AUC 0.746) models, which significantly improved their predictive ability for in-hospital mortality. For 1-year mortality prediction, RPR + SAPS II (AUC 0.683) showed significantly improved accuracy, while RPR + SOFA (AUC 0.626) did not improve significantly.

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Background: Cholelithiasis-induced acute cholangitis (CIAC) is an acute inflammatory disease with poor prognosis. This study aimed to create machine-learning (ML) models to predict the outcomes of patients with CIAC. Methods: In this retrospective cohort and ML study, patients who met the both diagnosis of 'cholangitis' and 'calculus of gallbladder or bile duct' according to the International Classification of Disease (ICD) 9th revision, or met the diagnosis of 'calculus of bile duct with acute cholangitis with or without obstruction' according to the ICD 10th revision during a single hospitalization were included from the Medical Information Mart for Intensive Care database, which records patient admissions to Beth Israel Deaconess Medical Center, MA, USA, spanning June 1, 2001 to November 16, 2022. Patients who were neither admitted in an emergency department nor underwent biliary drainage within 24 h after admission, had an age of less than 18, or lost over 20% of the information were excluded. Nine ML methods, including the Logistic Regression, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Adaptive Boosting, Decision Tree, Gradient Boosting Decision Tree, Gaussian Naive Bayes, Multi-Layer Perceptron, and Support Vector Machine were applied for prediction of in-hospital mortality, re-admission within 30 days after discharge, and mortality within 180 days after discharge. Patients from Zhongda Hospital affiliated to Southeast University in China between January 1, 2019 and July 30, 2023 were enrolled as an external validation set. The area under the receiver operating characteristic curve (AUROC) was the main index for model performance assessment. Findings: A total of 1156 patients were included to construct models. We performed stratified analyses on all patients, patients admitted to the intensive care unit (ICU) and those who underwent biliary drainage during ICU treatment. 13-16 features were selected from 186 variables for model training. The XGBoost method demonstrated the most optimal predictive efficacy, as evidenced by training set AUROC of 0.996 (95% CI NaN-NaN) for in-hospital mortality, 0.886 (0.862-0.910) for re-admission within 30 days after discharge, and 0.988 (0.982-0.995) for mortality within 180 days after discharge in all patients, 0.998 (NaN-NaN), 0.933 (0.909-0.957), and 0.988 (0.983-0.993) in patients admitted to the ICU, 0.987 (0.970-0.999), 0.908 (0.873-0.942), and 0.982 (0.971-0.993) in patients underwent biliary drainage during ICU treatment, respectively. Meanwhile, in the internal validation set, the AUROC reached 0.967 (0.933-0.998) for in-hospital mortality, 0.589 (0.502-0.677) for re-admission within 30 days after discharge, and 0.857 (0.782-0.933) for mortality within 180 days after discharge in all patients, 0.963 (NaN-NaN), 0.668 (0.486-0.851), and 0.864 (0.757-0.970) in patients admitted to the ICU, 0.961 (0.922-0.997), 0.669 (0.540-0.799), and 0.828 (0.730-0.925) in patients underwent biliary drainage during ICU treatment, respectively. The AUROC values of external validation set consisting of 61 patients were 0.741 (0.725-0.763), 0.812 (0.798-0.824), and 0.848 (0.841-0.859), respectively. Interpretation: The XGBoost models could be promising tools to predict outcomes in patients with CIAC, and had good clinical applicability. Multi-center validation with a larger sample size is warranted. Funding: The Technological Development Program of Nanjing Healthy Commission, and Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds.