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Colon adenocarcinoma (COAD) represents a significant health concern within the population. Advancing our understanding of COAD is imperative for early detection, enabling personalized treatment interventions, and facilitating the development of effective preventive measures. The coagulation system plays a role in tumor-related pathological processes; however, its specific involvement in COAD and potential contributors remain unclear. This study aimed to establish a novel risk stratification approach by analyzing coagulation related genes (CRGs) associated with COAD. Through a comprehensive bioinformatics analysis of data from public databases, we screened COAD associated CRGs and characterized the associated molecular subtypes. After a comprehensive analysis of the characteristics of each subtype, we applied differentially expressed genes in CRG subtypes to establish a new risk stratification method. Clinical subgroup analysis, immunoinfiltration analysis, therapeutic reactivity prediction and other analytical methods suggest the potential clinical value of the established risk stratification method. As one of the selected targets, the effect of MS4A4A on the proliferation and invasion of COAD was confirmed by in vitro experiments, which partially verified the reliability of bioinformatics results. Our findings delineate CRGs potentially implicated in COAD pathogenesis and offer fresh insights into the influence of the coagulation process on tumorigenesis and progression. [ABSTRACT FROM AUTHOR]

Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure. [ABSTRACT FROM AUTHOR]

Background: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. Methods: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. Results: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. Conclusions: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking. Highlights: • This article explored decision-making processes of clinicians using a clinical prediction model for deteriorating patients, also known as an early warning score. • Our study identified that the clinical utility of deterioration models may lie in their assistance in generating, evaluating, and selecting diagnostic hypotheses, an important part of clinical decision making that is underrepresented in the prediction modelling literature. • Nurses in particular stressed the need for models that encourage critical thinking and further investigation rather than prescribe strict care protocols. [ABSTRACT FROM AUTHOR]

Background: Antenatal steroid therapy for fetal lung maturation is routinely administered to women at risk of preterm delivery. There is strong evidence to demonstrate benefit from antenatal steroids in terms of survival and respiratory disease, notably in infants delivered at or below 32 weeks' gestation. However, dosing remains unoptimized and lung benefits are highly variable. Current treatment regimens generate high-concentration, pulsatile fetal steroid exposures now associated with increased risk of childhood neurodevelopmental diseases. We hypothesized that damage-associated changes in the fetal hippocampal transcriptome would be independent of preterm lung function. Methods: Date-mated ewes carrying a single fetus at 122 ± 2dGA (term = 150dGA) were randomized into 4 groups: (i) Saline Control Group, 4×2ml maternal saline intramuscular(IM) injections at 12hr intervals (n = 11); or (ii) Dex High Group, 2×12mg maternal IM dexamethasone phosphate injections at 12hr intervals followed by 2×2ml IM saline injections at 12hr intervals (n = 12; representing a clinical regimen used in Singapore); or (iii) Dex Low Group, 4×1.5mg maternal IM dexamethasone phosphate injections 12hr intervals (n = 12); or (iv) Beta-Acetate Group, 1×0.125mg/kg maternal IM betamethasone acetate injection followed by 3×2ml IM sterile normal saline injections 12hr intervals (n = 8). Lambs were surgically delivered 48hr after first maternal injection at 122–125dGA, ventilated for 30min to establish lung function, and euthanised for necropsy and tissue collection. Results: Preterm lambs from the Dex Low and Beta-Acetate Groups had statistically and biologically significant lung function improvements (measured by gas exchange, lung compliance). Compared to the Saline Control Group, hippocampal transcriptomic data identified 879 differentially significant expressed genes (at least 1.5-fold change and FDR < 5%) in the steroid-treated groups. Pulsatile dexamethasone-only exposed groups (Dex High and Dex Low) had three common positively enriched differentially expressed pathways related in part to neurodegeneration ("Prion Disease", "Alzheimer's Disease", "Arachidonic Acid metabolism"). Adverse changes were independent of respiratory function during ventilation. Conclusions: Our data suggests that exposure to antenatal steroid therapy is an independent cause of damage- associated transcriptomic changes in the brain of preterm, fetal sheep. These data highlight an urgent need for careful reconsideration and balancing of how antenatal steroids are used, both for patient selection and dosing regimens. [ABSTRACT FROM AUTHOR]

Background: According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment. Methods: We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve. Results: The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period. Conclusions: We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data. [ABSTRACT FROM AUTHOR]

Background and aim: Health anxiety is a mental disorder that characterized by an excessive fear about health and physical symptoms. High anxiety in pregnancy is associated with adverse outcomes. The aim of this study was to investigate the effect of prenatal education on health anxiety of primigravid women. Methods: The present study was quasi-experimental study. 122 primiparous pregnant women referred to comprehensive health services Shahrekord (A city in the southwest of Iran) clinics in 2019, after receiving consent to participate in the study, randomly divided into two intervention and control groups. The intervention group participated in 8 sessions (1.5-h), once every 2 weeks, from 20 to 37th weeks of gestation. The health anxiety questionnaire was completed on 20th (before the beginning of the courses), 28th and 37th weeks by two groups. Consequences of pregnancy included weight, Apgar score, delivery type, labor time and first breastfeeding time. SPSS version 16 software was used for data analysis. Results: No significant difference was found type of delivery, gestational age, height, weight, head length, Apgar score, duration of hospitalization and first breastfeeding time. The duration of the active and latent phase of labor was significantly lower and the weight of newborn was significantly higher in the intervention group than the control group (P < 0.05). At 37th week, the scores of illness concern, negative consequence and total health anxiety in the intervention group decreased by 3.42, 0.93 and 4.36 respectively and in control group increased by 2.82, 0.03 and 2.86. Conclusion: Pregnancy educational courses has positive effects on health anxiety, decrease duration of labor time and increased newborn weight. In order to improve the outcome of pregnancy, educational classes during pregnancy should be considered. [ABSTRACT FROM AUTHOR]

Background: Bone metastases can compromise the integrity of the spinal canal and cause epidural spinal cord compression (ESCC). The Spinal Instability Neoplastic Score (SINS) was developed in order to evaluate spinal instability due to a neoplastic process. The SINS has reached wide acceptance among clinicans but its prognostic value is still controversial. The aim was to investigate the correlation between the SINS and ESCC and the association between SINS and ambulation before and survival after surgery. Methods: Correlations were assessed between SINS and grades of ESCC in patients who underwent spine surgery for spinal metastases. CT and MRI were used to calculate SINS and the grades of ESCC respectively. Correlations were analyzed with the Spearman's correlation test. Postoperative survival was estimated with Kaplan-Meier analysis and survival curves were compared with the log-rank test. The Cox proportional hazard model was used to assess the effect of prognostic variables including age, ambulation before surgery, SINS, and the Karnofsky Performance Status (KPS) as covariates. Results: The study included 256 patients (196 men and 60 women) with a median age of 70 (24–88) years. The mean SINS was 10. One hundred fifty-two patients (59%) had lost ambulation before surgery. One hundred and one patients had grades 0–2 and 155 patients had grade 3 according to the ESCC-scale. SINS correlated with the grades of ESCC (p = 0.001). The SINS score was not associated with ambulation before surgery (p = 0.63). The median postoperative survival was 10 months, and there was no difference in postoperative survival between the SINS categories (p = 0.25). The ability to walk before surgery and a high KPS were associated with longer postoperative survival. Conclusion: SINS correlated with grades of ESCC, which implies that higher SINS may be considered as an indicator of risk for developing ESCC. The SINS was not associated with ambulation before or survival after surgery. [ABSTRACT FROM AUTHOR]

The tumor microenvironment demonstrates great immunophenotypic heterogeneity, which has been leveraged in traditional immune-hot/cold tumor categorization based on the abundance of intra-tumoral immune cells. By incorporating the spatial immune contexture, the tumor immunophenotype was further elaborated into immune-inflamed, immune-excluded, and immune-desert. However, the mechanisms underlying these different immune phenotypes are yet to be comprehensively elucidated. In this review, we discuss how tumor cells and the tumor microenvironment interact collectively to shape the immune landscape from the perspectives of tumor cells, immune cells, the extracellular matrix, and cancer metabolism, and we summarize potential therapeutic options according to distinct immunophenotypes for personalized precision medicine. [ABSTRACT FROM AUTHOR]

Background: Anemia represents a well-established risk factor for patients diagnosed with gastric cancer, and is often associated with an unfavorable prognosis. In this context, the timely prediction of distant metastasis risk in patients with anemic gastric cancer assumes paramount importance. Methods: Information of gastric cancer patients complicated with preoperative anemia in the First Affiliated Hospital of Sun Yat-sen University was collected. The cohort from the First Affiliated Hospital of Guangxi Medical University was used as an external validation set. A Nomogram was established based on the risk factors screened by univariate and multivariate logistic regression analyses. Results: A total of 848 gastric cancer patients with preoperative anemia were enrolled. Pyloric obstruction, carcinoma antigen 125, T stage, N stage, tumor size, and preoperative weight loss were independent predictors of distant metastasis in gastric cancer patients with anemia (p < 0.05), based on which a nomogram was constructed. The accuracy, reliability and clinical value of the nomogram were evaluated by concordance index, receiver operating characteristic curve, decision curve analysis, calibration curve and showed good stability and clinical predictive value. Conclusions: Preoperative anemic gastric cancer patients, complicated with pyloric obstruction, elevated CA125, advanced T and N stage, larger tumor size, and preoperative weight loss, should be paid more attention to distant metastasis. [ABSTRACT FROM AUTHOR]

Background: Pocock-Simon's minimisation method has been widely used to balance treatment assignments across prognostic factors in randomised controlled trials (RCTs). Previous studies focusing on the survival outcomes have demonstrated that the conservativeness of asymptotic tests without adjusting for stratification factors, as well as the inflated type I error rate of adjusted asymptotic tests conducted in a small sample of patients, can be relaxed using re-randomisation tests. Although several RCTs using minimisation have suggested the presence of non-proportional hazards (non-PH) effects, the application of re-randomisation tests has been limited to the log-rank test and Cox PH models, which may result in diminished statistical power when confronted with non-PH scenarios. To address this issue, we proposed two re-randomisation tests based on a maximum combination of weighted log-rank tests (MaxCombo test) and the difference in restricted mean survival time (dRMST) up to a fixed time point τ , both of which can be extended to adjust for randomisation stratification factors. Methods: We compared the performance of asymptotic and re-randomisation tests using the MaxCombo test, dRMST, log-rank test, and Cox PH models, assuming various non-PH situations for RCTs using minimisation, with total sample sizes of 50, 100, and 500 at a 1:1 allocation ratio. We mainly considered null, and alternative scenarios featuring delayed, crossing, and diminishing treatment effects. Results: Across all examined null scenarios, re-randomisation tests maintained the type I error rates at the nominal level. Conversely, unadjusted asymptotic tests indicated excessive conservatism, while adjusted asymptotic tests in both the Cox PH models and dRMST indicated inflated type I error rates for total sample sizes of 50. The stratified MaxCombo-based re-randomisation test consistently exhibited robust power across all examined scenarios. Conclusions: The re-randomisation test is a useful alternative in non-PH situations for RCTs with minimisation using the stratified MaxCombo test, suggesting its robust power in various scenarios. [ABSTRACT FROM AUTHOR]

Background: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. Methods: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal–external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. Results: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. Conclusion: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable. [ABSTRACT FROM AUTHOR]

Background: Therapeutic ceiling of care is the maximum level of care deemed appropiate to offer to a patient based on their clinical profile and therefore their potential to derive benefit, within the context of the availability of resources. To our knowledge, there are no models to predict ceiling of care decisions in COVID-19 patients or other acute illnesses. We aimed to develop and validate a clinical prediction model to predict ceiling of care decisions using information readily available at the point of hospital admission. Methods: We studied a cohort of adult COVID-19 patients who were hospitalized in 5 centres of Catalonia between 2020 and 2021. All patients had microbiologically proven SARS-CoV-2 infection at the time of hospitalization. Their therapeutic ceiling of care was assessed at hospital admission. Comorbidities collected at hospital admission, age and sex were considered as potential factors for predicting ceiling of care. A logistic regression model was used to predict the ceiling of care. The final model was validated internally and externally using a cohort obtained from the Leeds Teaching Hospitals NHS Trust. The TRIPOD Checklist for Prediction Model Development and Validation from the EQUATOR Network has been followed to report the model. Results: A total of 5813 patients were included in the development cohort, of whom 31.5% were assigned a ceiling of care at the point of hospital admission. A model including age, COVID-19 wave, chronic kidney disease, dementia, dyslipidaemia, heart failure, metastasis, peripheral vascular disease, chronic obstructive pulmonary disease, and stroke or transient ischaemic attack had excellent discrimination and calibration. Subgroup analysis by sex, age group, and relevant comorbidities showed excellent figures for calibration and discrimination. External validation on the Leeds Teaching Hospitals cohort also showed good performance. Conclusions: Ceiling of care can be predicted with great accuracy from a patient's clinical information available at the point of hospital admission. Cohorts without information on ceiling of care could use our model to estimate the probability of ceiling of care. In future pandemics, during emergency situations or when dealing with frail patients, where time-sensitive decisions about the use of life-prolonging treatments are required, this model, combined with clinical expertise, could be valuable. However, future work is needed to evaluate the use of this prediction tool outside COVID-19. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the relationship between the severity of autism, emotional and behavioral problems of autistic children, internalized stigma, depressive symptoms, and primary caregiver parental stress. Specifically, we explored the mediating role of internal stigmatization and total difficulties of individuals with autism on parenting stress and depressive symptoms of the primary caregiver. Method: Mothers of 93 children with autism were included in the study. The mothers were given the Beck Depression Inventory (BDI), The Internalized Stigma of Mental Illness Scale (ISMI), the Autism Behavior Checklist, the Parenting Stress Index-Short Form, Strength, and Difficulties Questionnaire -Parent Form (SDQ-P). Results: As a result of our study, the emotional and behavioral problems of the child and the internalized stigmatization felt by the parent played a mediator role in the relationship between the child's autism severity and the parent's stress and depressive symptoms. Conclusions: Our findings highlight that internalized stigmatization and behavioral characteristics of individuals with autism are among the most critical problems for their primary caregivers. These results have important implications for the development of interventions aimed at reducing the internalized stigma experienced by primary caregivers of individuals with autism and for improving their mental health outcomes. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission. Methods: Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension. Results: A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients. Conclusions: The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application. [ABSTRACT FROM AUTHOR]

Objectives: Our study aimed to assess the safety and efficacy of cardiac contractility modulation (CCM) therapy in patients with heart failure with reduced ejection fraction (HFrEF) depending on HF etiology. Methods: We enrolled 166 patients with optimal medical therapy-resistant HFrEF (median age 59 years, 83.7% males, median NYHA class − 2, median left ventricular ejection fraction (LVEF) − 29.0%) who underwent CCM therapy device implantation from 2013 to 2019 in four medical centers in Russia. The HF etiology was determined based on invasive coronary angiography or cardiac MRI data. Transthoracic echocardiography (TTE), 6-minute walking test (6MWT), and NTproBNP-tests were performed at a baseline and 12 months after the implantation. Results: The ischemic etiology of HF was revealed in 100 patients (61.5%) (ICM group); the non-ischemic group (NICM) evolved 66 patients (38.5%). Patients in the ICM group were significantly older (61[57–69] vs. 55 [42.8–61], p < 0.001), more frequently had hypertension (79% vs. 42.4%, p < 0.001) and chronic kidney disease (43% vs. 22.7%, p = 0.012). Patients in the NICM group had significantly more often atrial fibrillation (AF) (58% vs. 74%, p = 0.048), larger end-diastolic volume (EDV) (249 [208–309] vs. 220 [192–271], p = 0.019) and end-systolic volume (ESV) (183 [147–230] vs. 154 [128–199], p = 0.003). There were no significant differences in mortality between ICM and NICM groups (14.4 vs. 10.8%, p = 0.51). In 12 months, there was a significant increase in LVEF in the NICM group (+ 2.0 [2–6] vs. +7.7 [2–12], p < 0.001), while the improvement in the 6MWT (+ 75 [22–108] vs. +80 [10–160], p = 0.851) and NYHA class did not reach the level of significance. The subanalysis between patients with improved NYHA class and those without improvement revealed that patients without improvement more frequently had AF (56% vs. 89%; p < 0.01), chronic obstructive lung disease (18% vs. 35% p = 0.047), higher blood pressure (110 [105–120] vs. 120[110–129]; p = 0.032). Conclusion: In this multicenter retrospective study, patients with non-ischemic HFrEF showed a significantly higher improvement in LVEF and LV reverse remodeling following CCM therapy device implantation. There was no significant association between HF etiology and survival in drug-resistant HFrEF patients following CCM therapy. [ABSTRACT FROM AUTHOR]

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models—specifically CoxPH, RSF, and DeepHit NN—were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy. Methods: Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments. Results: The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy. Conclusions: We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy. [ABSTRACT FROM AUTHOR]

Background & aim: Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality. Method: In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods. Results: Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20–91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality. Conclusion: According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran. [ABSTRACT FROM AUTHOR]

Background: Sarcopenic obesity, a clinical and functional condition characterized by the coexistence of obesity and sarcopenia, has not been investigated in relation to dementia risk and its onset. Methods: We included 208,867 participants from UK biobank, who aged 60 to 69 years at baseline. Dementia diagnoses were identified using hospital records and death register data. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models to evaluate the associations of obesity, sarcopenia, and sarcopenic obesity with dementia risk, stratified by sex. Stratified analyses were performed across dementia-related polygenic risk score (PRS). Restricted mean survival time models were established to estimate the difference and 95%CIs of dementia onset across different status. Additionally, linear regression models were employed to estimate associations of different status with brain imaging parameters. The mediation effects of chronic diseases were also examined. Results: Obese women with high PRS had a decreased risk (HR = 0.855 [0.761–0.961]), but obese men with low PRS had an increased risk (HR = 1.223 [1.045–1.431]). Additionally, sarcopenia was associated with elevated dementia risk (HRwomen = 1.323 [1.064–1.644]; HRmen = 2.144 [1.753–2.621]) in those with low PRS. Among those with high PRS, however, the association was only significant in early-life (HRwomen = 1.679 [1.355–2.081]; HRmen = 2.069 [1.656–2.585]). Of note, sarcopenic obesity was associated with higher dementia risk (HRwomen = 1.424 [1.227–1.653]; HRmen = 1.989 [1.702–2.323]), and results remained similar stratified by PRS. Considering dementia onset, obesity was associated with dementia by 1.114 years delayed in women, however, 0.170 years advanced in men. Sarcopenia (women: 0.080 years; men: 0.192 years) and sarcopenic obesity (women: 0.109 years; men: 0.511 years) respectively advanced dementia onset. Obesity, sarcopenia, and sarcopenic obesity were respectively related to alterations in different brain regions. Association between sarcopenic obesity and dementia was mediated by chronic diseases. Conclusions: Sarcopenic obesity and sarcopenia were respectively associated with increased dementia risk and advanced dementia onset to vary degree. The role of obesity in dementia may differ by sex and genetic background. [ABSTRACT FROM AUTHOR]

Background: Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). Methods: Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). Results: This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. Conclusions: This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients. [ABSTRACT FROM AUTHOR]

Background: Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival. Methods: We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender. Results: We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality. Discussion: This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times. [ABSTRACT FROM AUTHOR]

Background: Urosepsis is a life-threatening organ disease in which pathogenic microorganisms in the urine enter the blood through the vessels, causing an imbalance in the immune response to infection. The aim of this study was to elucidate the role of testicular orphan receptor 4 (TR4) in urosepsis. Methods: The role of TR4 in the progression and prognosis of urosepsis was confirmed by analyzing data from online databases and clinical human samples. To mimic urosepsis, we injected E. coli bacteria into the renal pelvis of mice to create a urosepsis model. Hematoxylin and eosin staining was used to observe histopathological changes in urosepsis. The effects of the upregulation or downregulation of TR4 on macrophage pyroptosis were verified in vitro. Chromatin immunoprecipitation assay was used to verify the effect of TR4 on Gasdermin D (GSDMD) transcription. Results: TR4 was more highly expressed in the nonsurviving group than in the surviving group. Furthermore, overexpressing TR4 promoted inflammatory cytokine expression, and knocking down TR4 attenuated inflammatory cytokine expression. Mechanistically, TR4 promoted pyroptosis by regulating the expression of GSDMD in urosepsis. Furthermore, we also found that TR4 knockdown protected mice from urosepsis induced by the E. coli. Conclusions: TR4 functions as a key regulator of urosepsis by mediating pyroptosis, which regulates GSDMD expression. Targeting TR4 may be a potential strategy for urosepsis treatment. [ABSTRACT FROM AUTHOR]

Background: Direct oral anticoagulants (DOACs) used as an alternative to low-molecular-weight heparin (LMWH) for thromboprophylaxis after cancer surgery for venous thromboembolic events (VTE) remains unclear. This study aimed to investigate the efficacy and safety of DOACs versus LMWH in these patients. Materials and methods: A search of EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science was carried out and included all randomized controlled trials (RCTs) and observational studies that directly compared DOACs with LMWH for thromboprophylaxis in patients after cancer surgery through July 25, 2023. The primary efficacy and safety outcomes were VTE, major bleeding, and clinically relevant non-major bleeding (CRNMB) within 30 days of surgery. The risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB2) tool for RCTs and ROBINS-I tool for non-randomized studies. This study was registered in PROSPERO (CRD42023445386). Results: We retrieved 5149articles, selected 27 for eligibility, and included 10 studies (three RCTs and seven observational studies) encompassing 3054 patients who underwent postoperative thromboprophylaxis with DOACs (41%) or LMWH (59%). Compared to LMWH thromboprophylaxis, DOACs had a comparable risk of VTE (RR:0.69[95% CI:0.46–1.02], I2 = 0%), major bleeding (RR:1.55 [95% CI:0.82–2.93], I2 = 2%), and CRNMB (RR, 0.89 [95% CI, 0.4–1.98], I2 = 31%) during the 30-day postoperative period. Subgroup analysis of VTE and major bleeding suggested no differences according to study type, extended thromboprophylaxis, tumor types, or different types of DOAC. Conclusion: DOACs are potentially effective alternatives to LMWH for thromboprophylaxis in patients undergoing cancer surgery, without increasing the risk of major bleeding events. [ABSTRACT FROM AUTHOR]

Background: To evaluate structural changes in retina and choroid in patients with type 2 diabetes (T2D) and their association with diabetic kidney disease (DKD). Methods: T2D patients with mild or no diabetic retinopathy (DR) were followed for 3 years using structural SS-OCT and OCT angiography (OCT-A) taken every 6 months. Parameters were compared longitudinally and according to the DKD status on baseline. Results: One hundred and sixty eyes from 80 patients were followed for 3 years, 72 with no DKD (nDKD) at baseline and 88 with DKD. Trend analysis of T2D showed significant thinning in GCL + and circumpapillary retinal fiber neural layer (cRFNL), choroid, and decreased vascular density (VD) in superficial plexus and central choriocapillaris with foveal avascular zone (FAZ) enlargement. Patients with no DKD on baseline presented more significant declines in retinal center and choroidal thickness, increased FAZ and loss of nasal and temporal choriocapillaris volume. In addition, the nDKD group had worse glycemic control and renal parameters at the end of the study. Conclusion: Our data suggests the potential existence of early and progressive neurovascular damage in the retina and choroid of patients with Type 2 Diabetes (T2D) who have either no or mild Diabetic Retinopathy (DR). The progression of neurovascular damage appears to be correlated with parameters related to glycemic control and renal damage. [ABSTRACT FROM AUTHOR]

Background: People living with HIV (PLWH), even when viral replication is controlled through antiretroviral therapy (ART), experience persistent inflammation. This inflammation is partly attributed to intestinal microbial dysbiosis and translocation, which may lead to non-AIDS-related aging-associated comorbidities. The extent to which living with HIV — influenced by the infection itself, ART usage, sexual orientation, or other associated factors — affects the biological age of the intestines is unclear. Furthermore, the role of microbial dysbiosis and translocation in the biological aging of PLWH remains to be elucidated. To investigate these uncertainties, we used a systems biology approach, analyzing colon and ileal biopsies, blood samples, and stool specimens from PLWH on ART and people living without HIV (PLWoH) as controls. Results: PLWH exhibit accelerated biological aging in the colon, ileum, and blood, as measured by various epigenetic aging clocks, compared to PLWoH. Investigating the relationship between microbial translocation and biological aging, PLWH had decreased levels of tight junction proteins in the intestines, along with increased microbial translocation. This intestinal permeability correlated with faster biological aging and increased inflammation. When investigating the relationship between microbial dysbiosis and biological aging, the intestines of PLWH had higher abundance of specific pro-inflammatory bacteria, such as Catenibacterium and Prevotella. These bacteria correlated with accelerated biological aging. Conversely, the intestines of PLWH had lower abundance of bacteria known for producing the anti-inflammatory short-chain fatty acids, such as Subdoligranulum and Erysipelotrichaceae, and these bacteria were associated with slower biological aging. Correlation networks revealed significant links between specific microbial genera in the colon and ileum (but not in feces), increased aging, a rise in pro-inflammatory microbe-related metabolites (e.g., those in the tryptophan metabolism pathway), and a decrease in anti-inflammatory metabolites like hippuric acid. Conclusions: We identified specific microbial compositions and microbiota-related metabolic pathways that are intertwined with intestinal and systemic biological aging. This microbial signature of biological aging is likely reflecting various factors including the HIV infection itself, ART usage, sexual orientation, and other aspects associated with living with HIV. A deeper understanding of the mechanisms underlying these connections could offer potential strategies to mitigate accelerated aging and its associated health complications. 1fCw832AZENtKENNTh8vkG Video Abstract [ABSTRACT FROM AUTHOR]

Background: The residual life of a patient with human immunodeficiency virus (HIV) is of major interest to patients and their physicians. While existing analyses of HIV patient survival focus mostly on data collected at baseline, residual life analysis allows for dynamic analysis based on additional data collected over a period of time. As survival times typically exhibit a right-skewed distribution, the median provides a more useful summary of the underlying distribution than the mean. In this paper, we propose an efficient inference procedure that fits a semiparametric quantile regression model assessing the effect of longitudinal biomarkers on the residual life of HIV patients until the development of dyslipidemia, a disease becoming more prevalent among those with HIV. Methods: For estimation of model parameters, we propose an induced smoothing method that smooths nonsmooth estimating functions based on check functions. For variance estimation, we propose an efficient resampling-based estimator. The proposed estimators are theoretically justified. Simulation studies are used to evaluate their finite sample performances, including their prediction accuracy. We analyze the Korea HIV/AIDS cohort study data to examine the effects of CD4 (cluster of differentiation 4) cell count on the residual life of HIV patients to the onset of dyslipidemia. Results: The proposed estimator is shown to be consistent and normally distributed asymptotically. Under various simulation settings, our estimates are approximately unbiased. Their variances estimates are close to the empirical variances and their computational efficiency is superior to that of the nonsmooth counterparts. Two measures of prediction performance indicate that our method adequately reflects the dynamic character of longitudinal biomarkers and residual life. The analysis of the Korea HIV/AIDS cohort study data shows that CD4 cell count is positively associated with residual life to the onset of dyslipidemia but the effect is not statistically significant. Conclusions: Our method enables direct prediction of residual lifetimes with a dynamic feature that accommodates data accumulated at different times. Our estimator significantly improves computational efficiency in variance estimation compared to the existing nonsmooth estimator. Analysis of the HIV/AIDS cohort study data reveals dynamic effects of CD4 cell count on the residual life to the onset of dyslipidemia. [ABSTRACT FROM AUTHOR]

Objectives: To compare the efficacy, safety and effects on quality of life of different ALK-inhibitors for global and Asian patients with advanced ALK-positive non-small-cell lung cancer (NSCLC). Methods: The included RCTs were identified through a systematic search of PubMed, EMBASE, Cochrane Library, Clinical Trials.gov, and major cancer conferences. The assessment of progression-free survival (PFS), intracranial PFS, overall survival (OS), and patient-reported outcomes (PROs) was carried out using restricted mean survival time (RMST) model, fractional polynomial model and Royston-Parmar model. Time-invariant hazard ratio (HR) models were also used to validate and supplement the primary analysis. Objective response rate (ORR) and adverse events with any grade, grade 3–5 were assessed through a Bayesian network meta-analysis. The primary measures for OS, PFS, and PROs were HR and RMST. The odds ratio was the metric for evaluating safety, ORR, 12-month PFS rate, 24-month OS rate, and the 12-month non-deterioration rate of PROs. Subgroup analyses based on patient characteristics were performed. Results: A total of fourteen studies (ten for first-line, four for second-line) consisting of nine treatments (chemotherapy, crizotinib, alectinib [600mg BID], low-dose alectinib [300mg BID], brigatinib, ceritinib, ensartinib, envonalkib, and lorlatinib) were included. In the first-line setting, alectinib showed a significant advantage over crizotinib and had the longest OS among all ALK-inhibitors. Compared to crizotinib, lorlatinib had the best efficacy regarding PFS for global patients, followed closely by alectinib and brigatinib. For Asian patients, alectinib significantly improved PFS compared to other treatments. In second-line, alectinib had the highest PFS for patients pretreated with crizotinib, followed by brigatinib, ceritinib and chemotherapy. Alectinib, irrespective of the dose, was the safest first-line option, whereas lorlatinib, brigatinib, and ceritinib showed poorer safety profiles. Alectinib was also the safest ALK-inhibitor for crizotinib-resistant patients. Brigatinib had the best performance in terms of PROs. Conclusions: Considering both efficacy and safety, alectinib appears to be the preferable treatment in first-line and second-line, particularly for Asian patients. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the most lethal gynecologic neoplasm, and most patients experience recurrence and chemoresistance. Even the promising immunotherapy showed limited efficacy in ovarian cancer, probably due to the immunosuppressive microenvironment. However, the behind mechanisms of the immune exclusion or cold phenotype in ovarian cancer still remain to be explored. As a cancer dominated by copy number variations instead of mutations, ovarian cancer contains a high fraction of aneuploid, which might correlate with immune inhibition. Nevertheless, whether or how aneuploid affects ovarian cancer is still unclear. For exploring the role of aneuploid cancer cells and the potential ploidy-immune relationship, herein, the ploidy information was first comprehensively analyzed combining the karyotype data and copy number variation data obtained from Mitelman and cBioPortal databases, respectively. Ovarian cancer showed strong ploidy heterogeneity, with high fraction of aneuploid and recurrent arm-level and whole chromosome changes. Furthermore, clinical parameters were compared between the highly-aneuploid and the near-diploid ovarian cancers. Aneuploid indicated high grade, poor overall survival and poor disease-free survival in ovarian cancer. To understand the biofunction affected by aneuploid, the differentially expressed genes between the highly-aneuploid and the near-diploid groups were analyzed. Transcription data suggested that aneuploid cancer correlated with deregulated MHC expression, abnormal antigen presentation, and less infiltration of macrophages and activated T cells and higher level of T cell exclusion. Furthermore, the ploidy-MHC association was verified using the Human Protein Atlas database. All these data supported that aneuploid might be promising for cancer management and immune surveillance in ovarian cancer. [ABSTRACT FROM AUTHOR]

Background: The association between dietary iron intake and the risk of type 2 diabetes mellitus (T2DM) remains inconsistent. In this study, we aimed to investigate the relationship between trajectories of dietary iron intake and risk of T2DM. Methods: This study comprised a total of 61,115 participants without a prior T2DM from the UK Biobank database. We used the group-based trajectory model (GBTM) to identify different dietary iron intake trajectories. Cox proportional hazards models were used to evaluate the relationship between trajectories of dietary iron intake and risk of T2DM. Results: During a mean follow-up of 4.8 years, a total of 677 T2DM events were observed. Four trajectory groups of dietary iron intake were characterized by the GBTM: trajectory group 1 (with a mean dietary iron intake of 10.9 mg/day), 2 (12.3 mg/day), 3 (14.1 mg/day) and 4 (17.6 mg/day). Trajectory group 3 was significantly associated with a 38% decreased risk of T2DM when compared with trajectory group 1 (hazard ratio [HR] = 0.62, 95% confidence interval [CI]: 0.49–0.79), while group 4 was significantly related with a 30% risk reduction (HR = 0.70, 95% CI: 0.54–0.91). Significant effect modifications by obesity (p = 0.04) and history of cardiovascular disease (p < 0.01) were found to the relationship between trajectories of dietary iron intake and the risk of T2DM. Conclusions: We found that trajectories of dietary iron intake were significantly associated with the risk of T2DM, where the lowest T2DM risk was observed in trajectory group 3 with a mean iron intake of 14.1 mg/day. These findings may highlight the importance of adequate dietary iron intake to the T2DM prevention from a public health perspective. Further studies to assess the relationship between dietary iron intake and risk of T2DM are needed, as well as intervention studies to mitigate the risks of T2DM associated with dietary iron changes. [ABSTRACT FROM AUTHOR]

Background: Recent climate changes have produced extreme climate events. This study focused on extreme snowfall and intended to discuss the vulnerability of temperate mammals against it through interspecies comparisons of spatial niches in northern Japan. We constructed niche models for seven non-hibernating species through wide-scaled snow tracking on skis, whose total survey length was 1144 km. Results: We detected a low correlation (rs < 0.4) between most pairs of species niches, indicating that most species possessed different overwintering tactics. A morphological advantage in locomotion cost on snow did not always expand niche breadth. In contrast, a spatial niche could respond to (1) drastic landscape change by a diminishing understory due to snow, possibly leading to changes in predator-prey interactions, and (2) the mass of cold air, affecting thermoregulatory cost and food accessibility. When extraordinary snowfall occurred, the nonarboreal species with larger body sizes could niche shift, whereas the smaller-sized or semi-arboreal mammals did not. In addition, compared to omnivores, herbivores were prone to severe restriction of niche breadth due to a reduction in food accessibility under extreme climates. Conclusions: Dietary habits and body size could determine the redundancy of niche width, which may govern robustness/vulnerability to extreme snowfall events. [ABSTRACT FROM AUTHOR]

Purpose: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. Method: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. Results: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774–0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700–0.888) and 0.883 (95% CI: 0.807–0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. Conclusion: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability. [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates. Methods: We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients. Results: We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation. Conclusion: Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy. [ABSTRACT FROM AUTHOR]

Background: Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood. Methods: Based on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator. Results: Heterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators. Conclusions: Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator. [ABSTRACT FROM AUTHOR]

Objective: The study aimed to explore the effectiveness of bedside lung ultrasound (LUS) combined with the PaO2/FiO2 (P/F) ratio in evaluating the outcomes of high-flow nasal cannula (HFNC) therapy in infants with severe pneumonia. Methods: This retrospective study analyzed the clinical data of 150 infants diagnosed with severe pneumonia and treated with HFNC therapy at our hospital from January 2021 to December 2021. These patients were divided into two groups based on their treatment outcomes: the HFNC success group (n = 112) and the HFNC failure group (n = 38). LUS was utilized to evaluate the patients’ lung conditions, and blood gas results were recorded for both groups upon admission and after 12 h of HFNC therapy. Results: At admission, no significant differences were observed between the two groups in terms of age, gender, respiratory rate, partial pressure of oxygen, and partial pressure of carbon dioxide. However, the P/F ratios at admission and after 12 h of HFNC therapy were significantly lower in the HFNC failure group (193.08 ± 49.14, 228.63 ± 80.17, respectively) compared to the HFNC success group (248.51 ± 64.44, 288.93 ± 57.17, respectively) (p < 0.05). Likewise, LUS scores at admission and after 12 h were significantly higher in the failure group (18.42 ± 5.3, 18.03 ± 5.36, respectively) than in the success group (15.09 ± 4.66, 10.71 ± 3.78, respectively) (p < 0.05). Notably, in the success group, both P/F ratios and LUS scores showed significant improvement after 12 h of HFNC therapy, a trend not observed in the failure group. Multivariate regression analysis indicated that lower P/F ratios and higher LUS scores at admission and after 12 h were predictive of a greater risk of HFNC failure. ROC analysis demonstrated that an LUS score > 20.5 at admission predicted HFNC therapy failure with an AUC of 0.695, a sensitivity of 44.7%, and a specificity of 91.1%. A LUS score > 15.5 after 12 h of HFNC therapy had an AUC of 0.874, with 65.8% sensitivity and 89.3% specificity. An admission P/F ratio < 225.5 predicted HFNC therapy failure with an AUC of 0.739, 60.7% sensitivity, and 71.1% specificity, while a P/F ratio < 256.5 after 12 h of HFNC therapy had an AUC of 0.811, 74.1% sensitivity, and 73.7% specificity. Conclusion: Decreased LUS scores and increased P/F ratio demonstrate a strong correlation with successful HFNC treatment outcomes in infants with severe pneumonia. These findings may provide valuable support for clinicians in managing such cases. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to develop a Nomogram model to identify the risk of all-cause mortality during hospitalization in patients with heart failure (HF). Methods: HF patients who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV databases were included. The primary outcome was the occurrence of all-cause mortality during hospitalization. Two Logistic Regression models (LR1 and LR2) were developed to predict in-hospital death for HF patients from the MIMIC-IV database. The MIMIC-III database were used for model validation. The area under the receiver operating characteristic curve (AUC) was used to compare the discrimination of each model. Calibration curve was used to assess the fit of each developed models. Decision curve analysis (DCA) was used to estimate the net benefit of the predictive model. Results: A total of 16,908 HF patients were finally enrolled through screening, of whom 2,283 (13.5%) presented with in-hospital death. Totally, 48 variables were included and analyzed in the univariate and multifactorial regression analysis. The AUCs for the LR1 and LR2 models in the test cohort were 0.751 (95% CI: 0.735∼0.767) and 0.766 (95% CI: 0.751–0.781), respectively. Both LR models performed well in the calibration curve and DCA process. Nomogram and online risk assessment system were used as visualization of predictive models. Conclusion: A new risk prediction tool and an online risk assessment system were developed to predict mortality in HF patients, which performed well and might be used to guide clinical practice. [ABSTRACT FROM AUTHOR]