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The Conditional Colour-Magnitude Distribution (CCMD) is a comprehensive formalism of the colour–magnitude–halo mass relation of galaxies. With joint modelling of a large sample of SDSS galaxies in fine bins of galaxy colour and luminosity, Xu et al. inferred parameters of a CCMD model that well reproduces colour- and luminosity-dependent abundance and clustering of present-day galaxies. In this work, we provide a test and investigation of the CCMD model by studying the colour and luminosity distribution of galaxies in galaxy groups. An apples-to-apples comparison of group galaxies is achieved by applying the same galaxy group finder to identify groups from the CCMD galaxy mocks and from the SDSS data, avoiding any systematic effect of group finding and mass assignment on the comparison. We find an overall nice agreement in the conditional luminosity function (CLF), the conditional colour function (CCF), and the CCMD of galaxies in galaxy groups inferred from CCMD mock and SDSS data. We also discuss the subtle differences revealed by the comparison. In addition, using two external catalogues constructed to only include central galaxies with halo mass measured through weak lensing, we find that their colour-magnitude distribution shows two distinct and orthogonal components, in line with the prediction of the CCMD model. Our results suggest that the CCMD model provides a good description of halo mass-dependent galaxy colour and luminosity distribution. The halo and CCMD mock catalogues are made publicly available to facilitate other investigations. [ABSTRACT FROM AUTHOR]

The far-ultraviolet (FUV) flare activity of low-mass stars has become a focus in our understanding of the exoplanet atmospheres and how they evolve. However, direct detection of FUV flares and measurements of their energies and rates are limited by the need for space-based observations. The difficulty of obtaining such observations may push some works to use widely available optical data to calibrate multiwavelength spectral models that describe UV and optical flare emission. These models either use single temperature blackbody curves to describe this emission, or combine a blackbody curve with archival spectra. These calibrated models would then be used to predict the FUV flare rates of low-mass stars of interest. To aid these works, we used TESS optical photometry and archival HST FUV spectroscopy to test the FUV predictions of literature flare models. We tested models for partially (M0–M2) and fully convective (M4–M5) stars, 40 Myr and field age stars, and optically quiet stars. We calculated FUV energy correction factors that can be used to bring the FUV predictions of tested models in line with observations. A flare model combining optical and NUV blackbody emission with FUV emission based on HST observations provided the best estimate of FUV flare activity, where others underestimated the emission at all ages, masses, and activity levels, by up to a factor of 104 for combined FUV continuum and line emission and greater for individual emission lines. We also confirmed previous findings that showed optically quiet low-mass stars exhibit regular FUV flares. [ABSTRACT FROM AUTHOR]

Objectives To assess whether there are identifiable subgroups of disease activity trajectory in a population of JDM patients—followed throughout childhood and into adulthood—and determine factors that predict those trajectory groupings. Methods This is a retrospective, longitudinal inception cohort of patients with idiopathic inflammatory myopathies, largely JDM. We sought to identify baseline factors that predict membership into different groups (latent classes) of disease activity trajectory. Results A total of 172 patients (64% females), with median age at diagnosis of 7.7 years, were analysed. We studied 4725 visits (1471 patient-years). We identified three latent classes of longitudinal disease activity, as measured by the modified DAS (DASm), with distinct class trajectories predicted by DASm at baseline, and by the changes of DASm from either baseline to 3 months or baseline to 6 months (early response to therapy). In the analysis in which DASm at baseline and the changes of DASm from baseline to 6 months are included as predictors, Class 1 (10%) has persistently high disease activity, Class 2 (34%) is characterized by moderate disease activity and Class 3 (56%) is characterized by individuals with a high early disease activity but an apparently good response to treatment and long-term low disease activity. Conclusion High early disease activity, and treatment resistance in the first few months, predict a more chronic longitudinal course of JDM. [ABSTRACT FROM AUTHOR]

Small area estimates are usually constructed from complex survey data. If the design is informative for the model, then procedures that ignore the sample design can suffer from important biases. Past work on small area estimation under informative sampling has focused heavily on linear models or on the prediction of means. We propose to generalize existing small area procedures for an informative sample design. We develop procedures in the context of a broad class of exponential dispersion families with random small area effects. We consider two models for the survey weights. We construct predictions of means as well as more general parameters that are nonlinear functions of the model response variable. We evaluate the procedures through simulation using a logistic mixed model. We then apply the methods to construct small area estimates of several functions of a wetlands indicator using data from the National Resources Inventory, a large scale agricultural survey. [ABSTRACT FROM AUTHOR]

Objective To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. Methods A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. Results Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78–0.784) and precision-recall AUC of 0.208 (95% CI 0.195–0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706–0.723) and a negative predictive value (NPV) of 95%. Conclusions The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout. [ABSTRACT FROM AUTHOR]

Polysocial risk scores were recently proposed as a strategy for improving the clinical relevance of knowledge about social determinants of health. Our objective in this study was to assess whether the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13 773 individuals aged ≥50 years at baseline from the 2006-2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort study. Four linear mixed models were compared: 2 simple models including a priori–selected covariates and 2 polysocial risk score models which used least absolute shrinkage and selection operator (LASSO) regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R 2 and root mean-squared prediction error (RMSPE) using training/test split validation and cross-validation. For predicting cognition, the simple model including age, race, sex, and education had an R 2 value of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R 2 = 0.35 and RMSPE = 0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, sex, and education had an area under the receiver operating characteristic curve (AUROC) of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors." [ABSTRACT FROM AUTHOR]

Social determinants of health encompass the social environmental factors and lived experiences that collectively shape an individual's health. Recently, the polysocial score approach has been introduced as an innovative method for capturing the cumulative impact of a broad spectrum of social factors. This approach offers a promising opportunity to complement and enhance conventional methodologies in the advancement of research on social determinants of health. In this issue of the Journal , Jawadekar et al (Am J Epidemiol. 2024;193(9):1296‑1300) evaluate the value of the polysocial score for predicting cognitive performance and mortality among middle-aged and older adults. Models built on a smaller set of social determinants, including race/ethnicity, sex, and education, performed comparably to the polysocial score models in which a more complex set of social factors was included. In this invited commentary, I evaluate the predictive ability of the polysocial score and discuss its merits and limitations. I also summarize the practical utility of the polysocial score in predicting health outcomes and its mechanistic significance in unveiling the relationship between genetics, social environment, and lifestyles in shaping an individual's health and elucidate health disparities. Lastly, I propose several avenues for future research. [ABSTRACT FROM AUTHOR]

Background Following the Taliban's resurgence, Afghan girls were probited from attending school and university, likely causing adverse psychological effects. Therefore, the present study investigated depression, suicidal ideation, and quality of life (QoL) among Afghan girls banned from education by the Taliban. Methods A survey study assessed depression, suicidal ideation, and QoL among 426 females prohibited from attending secondary schools, high schools, and universities by the Taliban. The study used the CES-D 20 Scale to assess depression, and the WHOQOL-Bref Questionnaire to assess QoL. Associations between variables were tested with chi-square tests, and predictors of QoL were analyzed through multivariate regression analysis. Results Among the 426 participants, 87.6% exhibited depression symptoms and 49.8% reported suicidal thoughts. Significant predictors of overall QoL included monthly family income (B  = 0.147, P  = 0.001), recent others' behavior toward participants (B  = −0.247, P  < 0.001), physical illness (B  = 0.110, P  = 0.011), experiencing a recent traumatic event (B  = 0.108, P  = 0.015), depression (B  = −0.159, P  < 0.001) and suicidal ideation (B  = −0.187, P  < 0.001). Conclusions In Taliban-ruled Afghanistan, the results indicated that female students who have been prohibited from studying in classes have a high prevalence of depression and suicidal ideation. Having depression and suicidal ideation significantly predicted decreased quality of life among female students. [ABSTRACT FROM AUTHOR]

Background In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility. Methods Eligible females aged ≥12 years with Escherichia coli– caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015–29 February 2020). Four predictive models were developed to predict nonsusceptibility to each antibiotic class and a risk categorization framework was developed to classify patients' isolates as low, moderate, and high risk of nonsusceptibility to each antibiotic class. Results Predictive models were developed among 87 487 patients. Key predictors of having a nonsusceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior β-lactam nonsusceptibility, prior fluoroquinolone treatment, Census Bureau region, and race. The risk categorization framework classified 8.1%, 14.4%, 17.4%, and 6.3% of patients as having isolates at high risk of nonsusceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, β-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3- to 12-fold higher among patients with nonsusceptible isolates versus susceptible isolates. Conclusions Our predictive models highlight factors that increase risk of nonsusceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of nonsusceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population. [ABSTRACT FROM AUTHOR]

Background Virtually all cases of hepatitis C virus (HCV) infection in children in the United States occur through vertical transmission, but it is unknown how many children are infected. Cases of maternal HCV infection have increased in the United States, which may increase the number of children vertically infected with HCV. Infection has long-term consequences for a child's health, but treatment options are now available for children ≥3 years old. Reducing HCV infections in adults could decrease HCV infections in children. Methods Using a stochastic compartmental model, we forecasted incidence of HCV infections in children in the United States from 2022 through 2027. The model considered vertical transmission to children <13 years old and horizontal transmission among individuals 13–49 years old. We obtained model parameters and initial conditions from the literature and the Centers for Disease Control and Prevention's 2021 Viral Hepatitis Surveillance Report. Results Model simulations assuming direct-acting antiviral treatment for children forecasted that the number of acutely infected children would decrease slightly and the number of chronically infected children would decrease even more. Alone, treatment and early screening in individuals 13–49 years old reduced the number of forecasted cases in children and, together, these policy interventions were even more effective. Conclusions Based on our simulations, acute and chronic cases of HCV infection are remaining constant or slightly decreasing in the United States. Improving early screening and increasing access to treatment in adults may be an effective strategy for reducing the number of HCV infected children in the United States. [ABSTRACT FROM AUTHOR]

Aims Although many studies have examined the predictors of medication adherence (MA), further empirical research is required to clarify the best model for predicting MA for older adults with heart failure (HF). Thus, we hypothesized a model in which information (knowledge), motivation (social support and depressive symptoms), and behavioural skills (barriers to self-efficacy) would be associated with MA in patients with HF. Methods and results Using a cross-sectional survey, 153 adults aged ≥ 65 years taking medication for HF were recruited from a university hospital in Korea. Data were collected based on the information–motivation–behavioural skills (IMB) model constructs and MA. In the hypothesized path model, self-efficacy was directly related to MA (β = −0.335, P = 0.006), whereas social support was indirectly related to MA through self-efficacy (β = −0.078, P = 0.027). Depressive symptoms were directly related to MA (β = 0.359, P = 0.004) and indirectly related to MA through self-efficacy (β = 0.141, P = 0.004). The hypothesized MA model showed a good fit for the data. Knowledge, social support, and depressive symptoms accounted for 44.3% of the variance in self-efficacy (P = 0.004). Left ventricular ejection fraction, knowledge, social support, depressive symptoms, and self-efficacy explained 64.4% of the variance in MA (P = 0.004). Conclusion These results confirmed the IMB model's suitability for predicting MA in older adults with HF. These findings may guide and inform intervention programmes designed to alleviate depressive symptoms in older adults with HF and enhance their HF knowledge, social support, and self-efficacy, with the ultimate goal of improving their MA. [ABSTRACT FROM AUTHOR]

Aims SARC-F ≥ 4 points are used for detecting sarcopenia; however, finding a lower SARC-F cut-off value may lead to early detection of sarcopenia. We investigated the SARC-F score with the highest sensitivity and specificity values to identify sarcopenia in older patients with cardiovascular disease (CVD). Motor performances were also examined for each SARC-F score. Methods and results This retrospective cross-sectional study examined the sensitivity and specificity of every 1-point increase in the SARC-F score to predict sarcopenia. Eligible participants included patients with CVD (≥65 years old) who were admitted for acute CVD treatment and participated in cardiac rehabilitation. Patients completed the SARC-F questionnaire and the sarcopenia assessment. Area under the curves (AUCs) were investigated for the ability to predict sarcopenia. Multivariable linear regression was used to compare the mean value of physical functions (e.g. walking speed, leg strength, and 6 min walking distance) of each SARC-F score. A total of 1066 participants (63.8% male; median age: 76 years) were included. Sarcopenia was present in 401 patients. A SARC-F cut-off ≥2 presented the optimal balance between sensitivity (68.3%) and specificity (55.6%) to detect sarcopenia (AUCs = 0.658; 95% confidence interval: 0.625–0.691). When the patients had low scores (1–3), every 1 point increase in the SARC-F score was associated with lower physical functions such as lower muscle strength and shorter walking distance (all P < 0.001). Conclusion A SARC-F cut-off ≥2 was optimal for screening sarcopenia, and even a low SARC-F score is useful in detecting sarcopenia and low physical function at an early stage in patients with CVD. [ABSTRACT FROM AUTHOR]

Motivation Despite an increase in protein modelling accuracy following the development of AlphaFold2, there remains an accuracy gap between predicted and observed model quality assessment (MQA) scores. In CASP15, variations in AlphaFold2 model accuracy prediction were noticed for quaternary models of very similar observed quality. In this study, we compare plDDT and pTM to their observed counterparts the local distance difference test (lDDT) and TM-score for both tertiary and quaternary models to examine whether reliability is retained across the scoring range under normal modelling conditions and in situations where AlphaFold2 functionality is customized. We also explore plDDT and pTM ranking accuracy in comparison with the published independent MQA programmes ModFOLD9 and ModFOLDdock. Results plDDT was found to be an accurate descriptor of tertiary model quality compared to observed lDDT-Cα scores (Pearson r  = 0.97), and achieved a ranking agreement true positive rate (TPR) of 0.34 with observed scores, which ModFOLD9 could not improve. However, quaternary structure accuracy was reduced (plDDT r  = 0.67, pTM r  = 0.70) and significant overprediction was seen with both scores for some lower quality models. Additionally, ModFOLDdock was able to improve upon AF2-Multimer model ranking compared to TM-score (TPR 0.34) and oligo-lDDT score (TPR 0.43). Finally, evidence is presented for increased variability in plDDT and pTM when using custom template recycling, which is more pronounced for quaternary structures. Availability and implementation The ModFOLD9 and ModFOLDdock quality assessment servers are available at https://www.reading.ac.uk/bioinf/ModFOLD/ and https://www.reading.ac.uk/bioinf/ModFOLDdock/ , respectively. A docker image is available at https://hub.docker.com/r/mcguffin/multifold. [ABSTRACT FROM AUTHOR]

Motivation Biomedical entity linking (BEL) is the task of grounding entity mentions to a given knowledge base (KB). Recently, neural name-based methods, system identifying the most appropriate name in the KB for a given mention using neural network (either via dense retrieval or autoregressive modeling), achieved remarkable results for the task, without requiring manual tuning or definition of domain/entity-specific rules. However, as name-based methods directly return KB names, they cannot cope with homonyms, i.e. different KB entities sharing the exact same name. This significantly affects their performance for KBs where homonyms account for a large amount of entity mentions (e.g. UMLS and NCBI Gene). Results We present BELHD (Biomedical Entity Linking with Homonym Disambiguation), a new name-based method that copes with this challenge. BELHD builds upon the BioSyn model with two crucial extensions. First, it performs pre-processing of the KB, during which it expands homonyms with a specifically constructed disambiguating string, thus enforcing unique linking decisions. Second, it introduces candidate sharing, a novel strategy that strengthens the overall training signal by including similar mentions from the same document as positive or negative examples, according to their corresponding KB identifier. Experiments with 10 corpora and 5 entity types show that BELHD improves upon current neural state-of-the-art approaches, achieving the best results in 6 out of 10 corpora with an average improvement of 4.55pp recall@1. Furthermore, the KB preprocessing is orthogonal to the prediction model and thus can also improve other neural methods, which we exemplify for GenBioEL, a generative name-based BEL approach. Availability and implementation The code to reproduce our experiments can be found at: https://github.com/sg-wbi/belhd. [ABSTRACT FROM AUTHOR]

Objectives SLE is a systemic autoimmune disease characterized by heterogeneous manifestations and severity, with frequent lung involvement. Among pulmonary function tests, the measure of the diffusing capacity of the lungs for carbon monoxide (DLCO) is a noninvasive and sensitive tool assessing pulmonary microcirculation. Asymptomatic and isolated DLCO alteration has frequently been reported in SLE, but its clinical relevance has not been established. Methods This retrospective study focused on 232 SLE patients fulfilling the 2019 EULAR/ACR classification criteria for SLE. Data were collected from the patient's medical record, including demographic, clinical and immunological characteristics, while DLCO was measured when performing pulmonary function tests as part of routine patient follow-up. Results At the end of follow-up, DLCO alteration (<70% of predicted value) was measured at least once in 154 patients (66.4%), and was associated with a history of smoking as well as interstitial lung disease, but was also associated with renal and neurological involvement. History of smoking, detection of anti-nucleosome autoantibodies and clinical lymphadenopathy at diagnosis were independent predictors of DLCO alteration, while early cutaneous involvement with photosensitivity was a protective factor. DLCO alteration, at baseline or any time during follow-up, was predictive of admission in intensive care unit and/or of all-cause death, both mainly due to severe disease flares and premature cardiovascular complications. Conclusion This study suggests a link between DLCO alteration and disease damage, potentially related to SLE vasculopathy, and a prognostic value of DLCO on death or intensive care unit admission in SLE. [ABSTRACT FROM AUTHOR]

Objectives Shoulder pain is common but current clinical classification has limited utility. We aimed to determine whether groups of ultrasound-based shoulder pathologies exist and to evaluate outcomes according to identified groups and individual pathologies. Methods This was a prospective study of a community-based cohort with shoulder pain referred for their first ultrasound scan at a single radiology unit, with subsequent routine clinical care. Patient-reported outcomes were collected at baseline, 2 weeks and 6 months; standardized ultrasound reporting was employed. Latent class analysis (LCA) identified ultrasound pathology–based groups. Multiple linear regression analysis explored associations between baseline pathologies, subsequent treatment and Shoulder Pain and Disability Index (SPADI). Short-term response to corticosteroid injections was investigated. Results Of 500 participants (mean age 53.6 years; 52% female), 330 completed follow-up. LCA identified four groups: bursitis with (33%) or without (27%) acromioclavicular joint degeneration, rotator cuff tear (21%) and no bursitis/tear (19%). Total SPADI was higher at baseline for cuff tears (mean 55.1 vs 49.7–51.3; overall P  = 0.005), but accounting for this, groups did not differ at 6 months (43.5 vs 38.5–40.5; P  = 0.379). Baseline SPADI was the only predictor of 6-month SPADI retained by penalized modelling; neither LCA-derived ultrasound groups nor individual pathologies were selected. Response to baseline injection at week 2 did not differ between groups (mean SPADI 40.1–43.8; P  = 0.423). Conclusion Ultrasound-based classification (groups or individual pathologies) of shoulder pain did not predict medium-term outcomes using current treatments. The role of routine diagnostic ultrasound for shoulder pain needs consideration; it may be useful to establish evidence-based therapies for specific pathologies. [ABSTRACT FROM AUTHOR]

Objectives Polymyxin-induced nephrotoxicity (PIN) is a major safety concern and challenge in clinical practice, which limits the clinical use of polymyxins. This study aims to investigate the risk factors and to develop a scoring tool for the early prediction of PIN. Methods Data on critically ill patients who received intravenous polymyxin B or colistin sulfate for over 24 h were collected. Logistic regression with the least absolute shrinkage and selection operator (LASSO) was used to identify variables that are associated with outcomes. The eXtreme Gradient Boosting (XGB) classifier algorithm was used to further visualize factors with significant differences. A prediction model for PIN was developed through binary logistic regression analysis and the model was assessed by temporal validation and external validation. Finally, a risk-scoring system was developed based on the prediction model. Results Of 508 patients, 161 (31.6%) patients developed PIN. Polymyxin type, loading dose, septic shock, concomitant vasopressors and baseline blood urea nitrogen (BUN) level were identified as significant predictors of PIN. All validation exhibited great discrimination, with the AUC of 0.742 (95% CI: 0.696–0.787) for internal validation, of 0.708 (95% CI: 0.605–0.810) for temporal validation and of 0.874 (95% CI: 0.759–0.989) for external validation, respectively. A simple risk-scoring tool was developed with a total risk score ranging from −3 to 4, corresponding to a risk of PIN from 0.79% to 81.24%. Conclusions This study established a prediction model for PIN. Before using polymyxins, the simple risk-scoring tool can effectively identify patients at risk of developing PIN within a range of 7% to 65%. [ABSTRACT FROM AUTHOR]

The rapid expansion of Uber Technologies, Inc.'s ride-sharing, courier service, and food delivery system and e-hailing applications has been transforming the logistics network and urban mobility around the world. We aimed to evaluate the impact of the Uber system on traffic injury (TI) mortality during its implementation in Brazilian capital cities. A quasiexperimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analyzed. The date of availability of the Uber app, specific to each capital, was considered the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis, from an interrupted time-series design, autoregressive integrated moving average (ARIMA) models with a transfer function were fitted. In 92.6% (n  = 25) of Brazilian capitals, there was no impact of Uber system implementation, 12 months after the start of its activities, on TI mortality. A reduction in mortality from this cause was observed after the system was implemented in Belo Horizonte and Rio de Janeiro. The impact on TI mortality was progressive and continuous in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of Uber system implementation between different locations and the heterogeneity of effects. [ABSTRACT FROM AUTHOR]

Purpose To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance. Methods All 254 patients with primary cerebral hemorrhage from January 2017 to December 2022 in the General Hospital of the Western Theater Command were included. According to the criteria of hematoma enlargement exceeding 33% or the volume exceeding 6 ml, the patients were divided into the HE group and the hematoma non-enlargement (NHE) group. Multiple models and the 10-fold cross-validation method were used to screen the most valuable features and model the probability of predicting HE. The area under the curve (AUC) was used to analyze the prediction efficiency of each model for HE. Results They were randomly divided into a training set of 204 cases in an 8:2 ratio and 50 cases of the test set. The clinical imaging deep feature joint model (22 features) predicted the area under the curve of HE as follows: clinical Navie Bayes model AUC 0.779, traditional radiology logistic regression (LR) model AUC 0.818, deep learning LR model AUC 0.873, and clinical NCCT deep learning multilayer perceptron model AUC 0.921. Conclusion The combined clinical imaging deep learning model has a high predictive effect for early HE in sICH patients, which is helpful for clinical individualized assessment of the risk of early HE in sICH patients. Key message What is already known on this topic: Prior to this study, existing research has highlighted various factors associated with hematoma expansion (HE) in intracerebral hemorrhage. These factors include age, Glasgow Coma Scale (GCS) score, hematoma characteristics, and time-related parameters. However, the predictive accuracy of individual factors has been limited, necessitating the development of more comprehensive prediction models. Deep learning features methods have shown promise in improving risk prediction but have not been applied to HE prediction. What this study adds: This study introduces the clinical noncontrastive computed tomography (NCCT) deep learning combined model, a novel approach that effectively predicts HE and enhances predictive accuracy. By incorporating a combination of clinical, imaging, and deep learning features, this model offers improved predictive capabilities compared to existing methods. Independent risk factors identified in this study for early hematoma enlargement include age, age group, GCS score, hematoma shape and location, midline shift, blend sign, pre-H-slicer, and time from onset to examination (H). How this study might affect research, practice or policy: The development of the clinical NCCT deep learning combined model presents a significant advancement in predicting HE, potentially aiding in the identification of high-risk patients for targeted interventions and enrollment in clinical trials. By demonstrating the value of combining clinical, imaging, and deep learning features in predicting hematoma enlargement, this study paves the way for future research to explore innovative approaches in risk assessment and management strategies for patients with intracerebral hemorrhage. [ABSTRACT FROM AUTHOR]

Background PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2. Methods Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant. Results A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by −2.0% for the entire cohort, −2.9% for ER-positive and −0.0% for ER-negative patients, compared to −7.3%, −4.7% and −13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%. Conclusions PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis. [ABSTRACT FROM AUTHOR]

The article offers a perspective on variants of unknown significance (VUS) in precision oncology and their impact on treatment choice. It presents clinical cases of patients with pancreatic cancer, a family history of cancer, and germline or somatic VUS, to illustrate the challenges of VUS in clinical practice. It considers pathogenic, germline mutations as loss-of-function mutations in tumor suppressors and presents data regarding pathogenic TET3 mutations and VUS.

Background Few studies have assessed the comprehensive associations among comorbid diseases in elderly patients with nasopharyngeal carcinoma (NPC). This study sought to identify potential comorbidity patterns and explore the relationship of comorbidity patterns with the mortality risk in elderly patients with NPC. Methods A total of 452 elderly patients with NPC were enrolled in the study. The network analysis and latent class analysis were applied to mine comorbidity patterns. Propensity score matching was used for adjusting confounders. A restricted cubic spline model was used to analyze the nonlinear association between age and the risk of all-cause mortality. Results We identified 2 comorbidity patterns, metabolic disease-related comorbidity (MDRC) and organ disease-related comorbidity (ODRC) in elderly patients with NPC. Patients in MDRC showed a significantly higher risk of all-cause mortality (71.41% vs 87.97%, HR 1.819 [95% CI, 1.106-2.994], P  = .031) and locoregional relapse (68.73% vs 80.88%, HR 1.689 [95% CI, 1.055-2.704], P  = .042). Moreover, in patients with MDRC pattern, we observed an intriguing inverted S-shaped relationship between age and all-cause mortality among patients aged 68 years and older. The risk of mortality up perpetually with age increasing in ODRC group, specifically within the age range of 68-77 years (HR 4.371, 1.958-9.757). Conclusion Our study shed light on the potential comorbidity patterns in elderly patients with NPC, thereby providing valuable insights into the development of comprehensive health management strategies for this specific population. [ABSTRACT FROM AUTHOR]

Background People with human immunodeficiency virus (PWH) with recurrent visceral leishmaniasis (VL) could potentially drive Leishmania transmission in areas with anthroponotic transmission such as East Africa, but studies are lacking. Leishmania parasitemia has been used as proxy for infectiousness. Methods This study is nested within the Predicting Visceral Leishmaniasis in HIV-InfectedPatients (PreLeisH) prospective cohort study, following 490 PWH free of VL at enrollment for up to 24–37 months in northwest Ethiopia. Blood Leishmania polymerase chain reaction (PCR) was done systematically. This case series reports on 10 PWH with chronic VL (≥3 VL episodes during follow-up) for up to 37 months, and 3 individuals with asymptomatic Leishmania infection for up to 24 months. Results All 10 chronic VL cases were male, on antiretroviral treatment, with 0–11 relapses before enrollment. Median baseline CD4 count was 82 cells/µL. They displayed 3–6 VL treatment episodes over a period up to 37 months. Leishmania blood PCR levels were strongly positive for almost the entire follow-up (median cycle threshold value, 26 [interquartile range, 23–30]), including during periods between VL treatment. Additionally, we describe 3 PWH with asymptomatic Leishmania infection and without VL history, with equally strong Leishmania parasitemia over a period of up to 24 months without developing VL. All were on antiretroviral treatment at enrollment, with baseline CD4 counts ranging from 78 to 350 cells/µL. Conclusions These are the first data on chronic parasitemia in PWH from Leishmania donovani –endemic areas. PWH with asymptomatic and symptomatic Leishmania infection could potentially be highly infectious and constitute Leishmania superspreaders. Xenodiagnosis studies are required to confirm infectiousness. [ABSTRACT FROM AUTHOR]

Motivation Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent. Results Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding. Availability and implementation Code and example datasets are available at https://github.com/pgwall/qms. [ABSTRACT FROM AUTHOR]

Motivation Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses. Results We develop Forseti , a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads. Availability and implementation Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license. [ABSTRACT FROM AUTHOR]

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications. [ABSTRACT FROM AUTHOR]

Objectives The objectives of this study were to investigate the incidence of demyelinating disease (DD) among SpA patients and to identify risk factors that predict DD in this patient population. Methods Axial SpA (axSpA) and PsA patients were identified from a longitudinal cohort database. Each group was analysed according to the presence or absence of DD. Incidence rates (IRs) of DD were obtained, with competing risk analysis. Cox regression analysis (with Fine and Gray's method) was used to evaluate predictors of DD development. Results Among 2260 patients with follow-up data, we identified 18 DD events, corresponding to an average IR of 31 per 100 000 persons per year for SpA. The IR of DD at 20 years was higher in axSpA than in PsA (1.30% vs 0.13%, P  = 0.01). The risk factors retained in the best predictive model for DD development included ever- (vs never-) smoking [hazard ratio (HR) 2.918, 95% CI 1.037–8.214, P  = 0.0426], axSpA (vs PsA) (HR 8.790, 95% CI 1.242–62.182, P  = 0.0294) and presence (vs absence) of IBD (HR 5.698, 95% CI 2.083–15.589, P  = 0.0007). History of TNF-α inhibitor therapy was not a predictor of DD. Conclusion The overall incidence of DD in this SpA cohort was low. Incident DD was higher in axSpA than in PsA. A diagnosis of axSpA, the presence of IBD, and ever-smoking predicted the development of DD. History of TNF-α inhibitor use was not found to be a predictor of DD in this cohort. [ABSTRACT FROM AUTHOR]

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018–2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings. [ABSTRACT FROM AUTHOR]

Physical inactivity and loneliness are both associated with health risks and can affect each other through various social and behavioral mechanisms. However, current evidence on this relationship is equivocal and mostly based on cross-sectional data. This longitudinal study aimed to determine whether current levels of physical activity (moderate and vigorous intensity) and loneliness are associated with future respective states of themselves and each other. We used data from waves 6-14 (2002-2018) of the Health and Retirement Study (n  = 20 134) in a mixed-effects and random-intercept cross-lagged panel model. Analysis showed that current loneliness and physical activity were associated with each future respective state. Additionally, weekly participation in moderate-intensity, but not vigorous-intensity, physical activity was associated with a lower likelihood of becoming lonely in the future (relative risk [RR] = 0.94; 95% CI, 0.90-0.99). However, changes in physical activity were not associated with deviation from a person's typical level of loneliness (for vigorous intensity, mean deviation [MD] = 0.00; 95% CI: −0.04 to 0.03; for moderate-intensity, MD = 0.01; 95% CI: −0.03 to 0.04). Loneliness was not associated with moderate- or vigorous-intensity physical activity in subsequent waves. This suggests that while lower physical activity levels can be associated with future loneliness, changing levels of physical activity has little impact on loneliness at the individual level. [ABSTRACT FROM AUTHOR]

Background Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. Methods Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. Results We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426–2766] versus 90 441 (IQR 56 442–128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5–11); for RCD-based models, it was 16 (IQR 11–26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. Conclusions Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality. [ABSTRACT FROM AUTHOR]

Purpose This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes. Methods Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference. One hundred four MCI subjects with 48 months of follow-up were divided into low and high heterogeneity groups. Kaplan–Meier survival curve analysis was used to observe the importance of heterogeneity results for predicting MCI progression. Results The area under the receiver operating characteristic curve of the model in the training, internal test and external test sets was 0.7503, 0.7512 and 0.7514, respectively. There was a significantly positive correlation between age and radscore of CN subjects (r  = 0.501; P  < .001). The radscore of MCI subjects was significantly higher than that of matched CN subjects (P  < .001). The median radscore ratios of MCI to CN from four age groups (66–70y, 71–75y, 76–80y and 81–85y) were 1.611, 1.760, 1.340 and 1.266, respectively. The probability to progression of low and high heterogeneity groups had a significant difference (P  = .002). Conclusion When radscore is significantly higher than that of normal ageing, it is necessary to alert the possibility of cognitive impairment and deterioration. [ABSTRACT FROM AUTHOR]

Objective Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods. Methods The study involved predicting directly measured LDL-C, using individual characteristics, lipid profiles, and other laboratory results as predictors. The models applied to predict LDL-C values were multiple regression, penalized regression, random forest, and XGBoost. Additionally, a novel 2-step prediction model was developed and introduced. The machine learning methods were evaluated against the Friedewald, Martin, and Sampson equations. Results The Friedewald, Martin, and Sampson equations had root mean squared error (RMSE) values of 12.112, 8.084, and 8.492, respectively, whereas the 2-step prediction model showed the highest accuracy, with an RMSE of 7.015. The LDL-C levels were also classified as a categorical variable according to the diagnostic criteria of the dyslipidemia treatment guideline, and concordance rates were calculated between the predictive values obtained from each method and the directly measured ones. The 2-step prediction model had the highest concordance rate (85.1%). Conclusion The machine learning method can calculate LDL-C more accurately than existing equations. The proposed 2-step prediction model, in particular, outperformed the other machine learning methods. [ABSTRACT FROM AUTHOR]

Aims The aim of the present study was to assess the relationship between adolescent IQ and midlife alcohol use and to explore possible mediators of this relationship. Methods Study data were from 6300 men and women who participated in the Wisconsin Longitudinal Study of high-school students graduating in 1957. IQ scores were collected during the participants' junior year of high school. In 2004, participants reported the number of alcoholic beverages consumed (past 30 days) and the number of binge-drinking episodes. A multinomial logistic regression was conducted to determine the relationship between adolescent IQ and future drinking pattern (abstainer, moderate drinker, or heavy drinker), and Poisson regression was used to examine the number of binge-drinking episodes. Two mediators—income and education—were also explored. Results Every one-point increase in IQ score was associated with a 1.6% increase in the likelihood of reporting moderate or heavy drinking as compared to abstinence. Those with higher IQ scores also had significantly fewer binge-drinking episodes. Household income, but not education, partially mediated the relationship between IQ and drinking pattern. Conclusions The present study suggests that higher adolescent IQ may predict a higher likelihood of moderate or heavy drinking in midlife, but fewer binge-drinking episodes. The study also suggests that this relationship is mediated by other psychosocial factors, specifically income, prompting future exploration of mediators in subsequent studies. [ABSTRACT FROM AUTHOR]

Background Resilience of national health systems in Europe remains a major concern in times of multiple crises and as more evidence is emerging relating to the indirect effects of the COVID-19 pandemic on health care utilization (HCU), resulting from de-prioritization of regular, non-pandemic healthcare services. Most extant studies focus on regional, disease specific or early pandemic HCU creating difficulties in comparing across multiple countries. We provide a comparatively broad definition of HCU across multiple countries, with potential to expand across regions and timeframes. Methods Using a cross-country federated research infrastructure (FRI), we examined HCU for acute cardiovascular events, elective surgeries and serious trauma. Aggregated data were used in forecast modelling to identify changes from predicted European age-standardized counts via fitted regressions (2017–19), compared against post-pandemic data. Results We found that elective surgeries were most affected, universally falling below predicted levels in 2020. For cardiovascular HCU, we found lower-than-expected cases in every region for heart attacks and displayed large sex differences. Serious trauma was the least impacted by the COVID-19 pandemic. Conclusion The strength of this study comes from the use of the European Population Health Information Research Infrastructure's (PHIRI) FRI, allowing for rapid analysis of regional differences to assess indirect impacts of events such as pandemics. There are marked differences in the capacity of services to return to normal in terms of elective surgery; additionally, we found considerable differences between men and women which requires further research on potential sex or gender patterns of HCU during crises. [ABSTRACT FROM AUTHOR]

Experimental studies of the observables associated with |$b \rightarrow c$| transitions in semileptonic B -meson decays at BaBar, Belle, and LHCb have shown some deviations from the Standard Model predictions, consequently providing a handy tool to probe the possible new physics (NP). In this context, we first revisit the impact of recent measurements of |$R({D^{(*)}})$| and |$R(\Lambda _c)$| on the parametric space of the NP scenarios. In addition, we include the |$R(J/\psi)$| data in the analysis and find that their influence on the best-fit point and the parametric space is mild. Using the recent HFLAV data, after validating the well established sum rule of |$R(\Lambda _c)$|⁠ , we derive a similar sum rule for |$R(J/\psi)$|⁠. Furthermore, according to the updated data, we modify the correlation among the different observables, giving us their interesting interdependence. Finally, to discriminate the various NP scenarios, we plot the different angular observables and their ratios for |$B \rightarrow D^{*} \tau \nu _\tau$| against the transfer momentum square |$\left(q^2\right)$|⁠ , using the |$1\sigma$| and |$2\sigma$| parametric space of considered NP scenarios. By implementing the collider bounds on NP Wilson coefficients (WCs), we find that the parametric space of some NP WCs is significantly restrained. To see the clear influence of NP on the amplitude of the angular observables, we also calculate their numerical values in different |$q^2$| bins and show them through bar plots. We hope that their precise measurements will help to discriminate various NP scenarios. [ABSTRACT FROM AUTHOR]

Background Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models. Methods This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit. Results The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes. Conclusion By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field. [ABSTRACT FROM AUTHOR]

Social workers have a greater than average risk of suffering burnout syndrome. In the same way as the professionals, students too can suffer academic burnout. However, few empirical studies have studied burnout and protective factors in social work students exclusively. Emotional intelligence (EI) has been described as a protective factor for burnout both in the professional and the academic context. The present study aims to explore which components of EI predict the burnout domains in students studying a degree in social work, in Spain. To this end, 87 social work students completed a questionnaire in which information was gathered on EI, burnout, average grade in the degree, and sociodemographic variables. The regression analysis results indicate that the students with poorer academic success and from higher years reported greater symptoms of burnout. Regarding the components of EI, emotional clarity and emotional repair predict the academic efficacy perceived by the students. These results highlight the importance of including EI in academic policies to promote the design of EI training programs aimed at developing EI skills, particularly emotional clarity and emotional repair, as a way of improving the perception of efficacy in social work students. [ABSTRACT FROM AUTHOR]

With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an 'Uncertain Neural Network' model trained on APOGEE data to provide high-quality predictions with robust estimates for per-prediction uncertainty. We report median formal uncertainties of 0.068 dex, 69.1 K, 0.14 dex, 0.031 dex, 0.040 dex, and 0.029 dex for [Fe/H], T eff, log  g , [C/Fe], [N/Fe], and [α/M], respectively. We validate these predictions against our APOGEE training data, LAMOST, and Gaia GSP- phot stellar parameters, and see a strong correlation between our predicted parameters and those derived from these surveys. We investigate the information content of the spectra by considering the 'attention' our model pays to different spectral features compared to expectations from synthetic spectra calculations. Our model's predictions are applied to the Gaia data set, and we produce a publicly available catalogue of our model's predictions. [ABSTRACT FROM AUTHOR]

Motivation Accurately identifying the drug–target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. Results In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug–Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. Availability and implementation https://github.com/XuLew/MIDTI. [ABSTRACT FROM AUTHOR]

Summary The increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present S equence M u tator for Pre dictive Mo dels (SuPreMo), a scalable and comprehensive tool for performing and supporting in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences. Availability and implementation SuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the GitHub page: https://github.com/ketringjoni/SuPreMo. [ABSTRACT FROM AUTHOR]

Objectives Prediction models based on traditional risk factors underestimate cardiovascular (CV) risk in systemic lupus erythematosus (SLE). In a large sample of unselected SLE patients, we investigated cross-sectional associations of NT-proBNP with cardiovascular damage (CVD). Methods Serum NT-proBNP was measured in SLE patients enrolled in the MUHC Lupus Clinic registry. Serum was collected between March 2022 and April 2023 at annual research visits. The primary outcome was CVD identified on the SLICC Damage Index. Factors associated with CVD and NT-proBNP levels were determined. Results Overall, 270 SLE patients [female 91%, median age 50.7 (first quartile to third quartile: 39.6–62.1) years] were analysed for the primary outcome. Among them, 33 (12%) had CVD. The ROC curve for NT - proBNP demonstrated strong associations with CVD (AUC 0.78, 95% CI 0.69–0.87) with a threshold of 133 pg/ml providing the best discrimination for those with/without CVD. Hypertension (OR 3.3, 95% CI 1.2–9.0), dyslipidaemia (OR 3.6, 95% CI 1.3–9.6) and NT-proBNP >133 pg/ml (OR 7.0, 95% CI, 2.6–19.1) were associated with CVD in the multivariable logistic regression model. Increased NT-proBNP levels were associated with age (OR 4.2, 95% CI 2.2–8.3), ever smoking (OR 1.9, 95% CI 1.0–3.5), reduced eGFR (4.1, 95% CI 1.3–13.1), prior pericarditis/pleuritis (OR 2.5, 95% CI 1.4–4.5) and aPL antibodies (OR 2.6, 95% CI 1.4–4.9). Conclusion NT-proBNP is a biomarker for CV damage in SLE. The novel associations of NT-proBNP levels with prior pericarditis/pleuritis and aPL antibodies suggest new avenues for research to better understand what drives CV risk in SLE. [ABSTRACT FROM AUTHOR]

Objective This study aimed to establish prediction models for respiratory-related mortality in microscopic polyangiitis (MPA) complicated by interstitial lung disease (ILD) using clinical characteristics. Methods We enrolled patients with MPA with ILD between May 2005 and June 2021 in a multicentre cohort of Japanese patients with MPA (REVEAL cohort). We evaluated the demographic, clinical, laboratory, radiological findings, treatments and the presence of honeycombing 1 cm above the diaphragm using chest high-resolution CT (HRCT) on admission. We explored the risk factors predictive of respiratory-related mortality. Results Of 115 patients, 26 cases died of respiratory-related diseases during a median follow-up of 3.8 years. Eighteen patients (69%) died due to respiratory infection, three (12%) had diffuse alveolar haemorrhage, and five (19%) had exacerbation of ILD. In univariate analysis, older age, lower percent forced vital capacity (%FVC), lower percent diffusing capacity of carbon monoxide (%DLCO), and the presence of honeycombing in the right lower lobe were identified as risk factors. Additionally, in multivariate analysis adjusted for age and treatment, %FVC, %DLCO and the presence of honeycombing in the right lower lobe were independently associated with respiratory-related mortality. We created prediction models based on the values of %FVC, %DLCO and presence of honeycombing on chest HRCT (termed "MPF model"). The 5-year respiratory-related death-free rate was significantly different between patients with MPA with ILD stratified by the number of risk factors based on the MPF model. Conclusions Our study indicates that the MPF model may help predict respiratory-related death in patients with MPA with ILD. [ABSTRACT FROM AUTHOR]

Objectives To assess the ability of two risk prediction models in interstitial lung disease (ILD) to predict death or lung transplantation in a cohort of patients with interstitial pneumonia with autoimmune features (IPAF). Methods We performed a retrospective cohort study of adults with IPAF at an academic medical centre. The primary outcome was a composite of lung transplantation or death. We applied the patient data to the previously described Gender–Age–Physiology (GAP) and ILD-GAP models to determine the ability of these models to predict the composite outcome. Model discrimination was assessed using the c-index, and model calibration was determined by comparing the incidence ratios of observed vs expected deaths. Results Ninety-four patients with IPAF were included. Mean (s. d.) age was 58 (13.5) years and the majority were female (62%). The majority met serologic and morphologic criteria for IPAF (94% and 91%, respectively). The GAP model had a c-index of 0.664 (95% CI 0.547–0.781), while the ILD-GAP model had a c-index of 0.569 (95% CI 0.440–0.697). In those with GAP stage 1 or GAP stage 2 disease, calibration of the GAP model was satisfactory at 2 and 3 years for the cumulative end point of lung transplantation or death. Conclusion In patients with IPAF, the GAP model performed well as a predictor of lung transplantation or death at 2 years and 3 years from ILD diagnosis in patients with GAP stage 1 and GAP stage 2 disease. [ABSTRACT FROM AUTHOR]

Objectives To analyse genetic mechanisms triggering familial sarcoidosis, whole exome screening of a family of six persons with four cases of sarcoidosis and two healthy controls was performed integrating progressive and spontaneous remission cases and evaluating involved genetic alterations that could potentially determine the individual course of the disease. Methods Clinical diagnostic criteria in patients of the selected sarcoidosis family were according to American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and other Granulomatous Disorders guidelines. Exome screening of four patients and the two intrafamilial healthy relatives was performed by paired-end (2 × 100 bp) sequencing. We then selected the gene variants considered pathogenic on the basis of a series of prediction software applications and presence only in members of the family affected by sarcoidosis, after subtracting the common variations observed in healthy subjects. Results Four persons out of six family members were affected by sarcoidosis. Fifty genes with uncommon in silico pathogenic variants could be identified that differentiated affected and healthy family members. One patient with sarcoidosis showed spontaneous remission whereas the remaining three patients required immunosuppressive treatment. Subtraction analysis revealed 18 genes that distinguished the three progressive cases from the patient with spontaneous remission. Conclusion The genetic analysis of these cases with familial sarcoidosis identified several involved genes and functional pathways that could help in understanding the basic mechanisms that determine the development of the disease and that discriminate spontaneously regressive and progressive forms. [ABSTRACT FROM AUTHOR]

Background: Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods: This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July–November 2014) and GlobalSurg-2 (January–July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator ('LASSO')) and machine learning (extreme gradient boosting ('XGBoost')). Final model selection was based on prognostic accuracy and clinical utility. Results: There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept −0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion: The 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm. There are significant concerns regarding the methodological quality and transportability of models previously developed to predict patients at risk of surgical-site infections to allow surgeons to minimize associated morbidity. The novel 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm. Lay Summary: This study is about finding ways to predict if someone will get an infection after having surgery on their stomach and intestines. If doctors know who is at high risk of getting an infection, they can take steps to prevent it and help the patient recover faster. The researchers used information from patients who had surgery all over the world to try to make a way to score how likely someone is to get infection in their wound from surgery. They did this in two different ways, then picked the one that was best at picking up infections, while still being easy to use. They then compared the new score against older scores to check if it really was better or not. The researchers found that their new score was good at predicting who might get an infection using just six bits of information on the patient. This new score worked even better than older scores when these were compared all together. This means that doctors can use this new score to check who is most likely to get an infection after surgery and so can take extra steps during and after the surgery to try and stop these from happening. Because the score is so simple, this is easy to use all around the world. [ABSTRACT FROM AUTHOR]

Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics. [ABSTRACT FROM AUTHOR]

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not (least absolute shrinkage and selection operator regression [LASSO]) model interactions. Cases made a nonfatal suicide attempt (n  = 6032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n  = 11 963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver-operating characteristic curve (AUC) values were higher in LASSO models (in men, 0.85, 95% CI, 0.84-0.86; in women, 0.89, 95% C, 0.88-0.90) than random forests (in men, 0.76, 95% CI, 0.74-0.78; in women, 0.79, 95% CI = 0.78-0.81). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

This article evaluates the use of dynamic adaptive design methods to target outbound computer-assisted telephone interviewing (CATI) in the California Health Interview Survey (CHIS). CHIS is a large-scale, annual study that uses an address-based sample (ABS) with push-to-Web mailings, followed by outbound CATI follow-up for addresses with appended phone numbers. CHIS 2022 implemented a dynamic adaptive design in which predictive models were used to end dialing early for some cases. For addresses that received outbound CATI follow-up, dialing was paused after three calls. A response propensity (RP) model was applied to predict the probability that the address would respond to continued dialing, based on the outcomes of the first three calls. Low-RP addresses were permanently retired with no additional dialing, while the rest continued through six or more attempts. We use a difference-in-difference design to evaluate the effect of the adaptive design on calling effort, completion rates, and the demographic composition of respondents. We find that the adaptive design reduced the mean number of calls per sampled unit by about 14 percent (relative to a modeled no-adaptive-design counterfactual) with a minimal reduction in the completion rate and no strong evidence of changes in the prevalence of target demographics. This suggests that RP modeling can meaningfully distinguish between ABS sample units for which additional dialing is and is not productive, helping to control outbound dialing costs without compromising sample representativeness. [ABSTRACT FROM AUTHOR]

Background In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers. Results In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity. Conclusion The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients. [ABSTRACT FROM AUTHOR]

The immediate result of percutaneous balloon mitral commissurotomy is largely determined by the anatomy of the mitral valve complex. Several scores and models have been developed to assess anatomical suitability for percutaneous balloon mitral commissurotomy. Although none has an optimal predictive power, these models look at the valvular apparatus from different perspectives bearing the potential for a complementary role. [ABSTRACT FROM AUTHOR]