Science Bulletin

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional Variational Autoencoder (cVAE), a class-conditional latent Denoising Diffusion Probabilistic Model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits to augment minority arrhythmia classes in the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over five independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification and motivate further investigation of quantum-classical hybrid methods in cardiac diagnostics.

Mainland China experienced multiple waves of COVID19 pandemic during 2020 2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible Vaccinated Exposed Infectious Recovered Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized predelta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

Mechanistic understanding and biomarkers of gonadotropin-releasing hormone antagonist treatment effect in paedophilic disorder are absent but may enhance outcomes and reduce sexual-offending risk. 52 help-seeking self-referred Swedish men with paedophilic disorder enrolled in a double-blinded, placebo-controlled, randomized clinical trial. Participants underwent task-based fMRI before, and two weeks after, subcutaneous injection of 120mg of degarelix or equal volume of placebo. fMRI blood-oxygen-level-dependent activation was compared between child and adult (child>adult) stimuli in task-derived regions of interest. Primary outcome was within region-of-interest child>adult activation change, whereas secondary outcomes correlated region-of-interest child>adult activation change to change in clinical measurements of risk, paedophilic interest, sexual preoccupation, hyper- and hyposexuality. 19 degarelix and 22 placebo participants had sufficient fMRI data quality. Reductions in paedophilic interest were strongly correlated with increased child>adult cerebellar (vermis) region-of-interest activation following degarelix (r=-0.740, p<0.001) but not placebo (r=0.183, p=0.41; between-group correlation coefficient z=3.347, p<0.001). Treatment did not significantly change child>adult region-of-interest activity. Post hoc analysis indicated that baseline autism symptoms correlated with degarelix-induced changes in paedophilic interest (r=0.717, p<0.001; between-group correlation coefficient z=2.958, p=0.003) and cerebellar activation (r=-0.581, p=0.01; between-group correlation coefficient z=-1.930, p=0.05). Increased child>adult cerebellar activation was associated with degarelix-induced reductions of paedophilic interest, suggesting cerebellar activity as mechanistically important to, and a prospective biomarker of, degarelix treatment effect. Additionally, autism symptoms may inform treatment prediction. Together, these findings have mechanistic and clinical implications for degarelix treatment of paedophilic disorder. EU clinical trials register identifier: 2014-000647-32 https://www.clinicaltrialsregister.eu/ctr-search/trial/2014-000647-32/SE, registered on 05/06/2014.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.

Although several ancillary tests are available in limited laboratories, diagnosis of microphthalmia (MiT)/TFE family translocation renal cell carcinoma (tRCC) could be challenging due to diverse and overlapping tumor morphology and the lack of reliable biomarkers. GPNMB has been recently identified as a diagnostic marker for various renal neoplasms with FLCN/TSC/mTOR-TFE alterations. However, the sensitivity and specificity of GPNMB immunostain are suboptimal and the result interpretation in ambiguous cases could be difficult. To search additional biomarkers that could improve the screening sensitivity and predict genetic aberrations in FLCN/TSC/mTOR-TFE pathway in renal tumors, we performed bioinformatic analysis of publicly available cancer databases and found GPR143, a transmembrane protein regulated by MiT transcription factors, was highly expressed in a subset of renal cell carcinomas (RCCs). In two the Cancer Genome Atlas (TCGA) kidney cancer cohorts, RCCs with high levels of GPR143 expression were enriched for renal neoplasms with FLCN/TSC/mTOR-TFE alterations. Similar to GPNMB labeling, GPR143 immunostain was positive in the majority of tRCC cases and renal tumors with FLCN/TSC/mTOR alterations, suggesting that GPR143 could function as another surrogate marker for FLCN/TSC/mTOR-TFE alterations in certain renal tumors. Interestingly, despite the concordant GPR143 and GPNMB immunoreactivity in most renal neoplasms with FLCN/TSC/mTOR-TFE alterations, diffuse GPR143 immunostain was observed in some cases with negative or focal GPNMB labeling. Taken together, our results indicate GPR143 could serve as a useful adjunct marker to improve the sensitivity for screening renal tumors with FLCN/TSC/mTOR-TFE alterations.

PurposeIdiopathic hypogonadotropic hypogonadism (IHH) is characterized by impaired reproductive maturation, and approximately half of all cases lack an identified genetic cause. We investigated the genetic basis of IHH in two large cohorts to identify novel disease-causing genes. MethodsWe analyzed exome and genome sequencing data from 1,822 patients with IHH from two independent cohorts. Rare variants were filtered using pedigree-informed inheritance models. PLEKHA6 expression in the postmortem human hypothalamus were tested at the mRNA and protein level. Functional studies assessed kisspeptin secretion in cell-based assays. ResultsWe identified 18 distinct PLEKHA6 variants in 24 patients from 20 unrelated families (1.3% of cohort). Variants segregated with disease under autosomal recessive and autosomal dominant (with variable penetrance) inheritance patterns. PLEKHA6 was robustly expressed in the hypothalamus and showed clear colocalization with neurokinin B, which served as the marker for the GnRH pulse generator. Functional studies demonstrated that patient variants significantly impaired kisspeptin secretion. ConclusionPLEKHA6 is a novel IHH gene and the first reported regulator of kisspeptin secretion from the kisspeptin-neurokinin B-dynorphin (KNDy) neurons, which have recently been established as the GnRH pulse generator. These findings establish impaired kisspeptin release as a new disease mechanism in IHH and highlight the critical role of neuropeptide trafficking in reproductive function.

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [&ge;] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

Background: Preterm births contribute to approximately 35% of neonatal deaths globally, with an estimated 13.4 million infants born prematurely each year. Despite this substantial burden, limited evidence exists on time to discharge and its determinants among preterm neonates admitted to Neonatal Intensive Care Units (NICUs), particularly in rural Ugandan settings. This study aimed to investigate time to discharge and associated factors among preterm neonates admitted to Kiwoko Hospital in Nakaseke District, Uganda. Methods: A retrospective cohort study was conducted using secondary data from Kiwoko Hospital on preterm neonates admitted to the Neonatal Intensive Care Unit (NICU) between 2020 and 2021 (n = 847). The cumulative incidence function was used to estimate the probability of discharge within 28 days of admission, accounting for competing events. A Fine and Gray sub-distribution hazard regression model was fitted to identify factors associated with time to discharge. Results: Of the 847 preterm admissions, 70.1% were discharged alive within 28 days. The median time to discharge was 14 days. The cumulative incidence of discharge by 28 days was 68%, accounting for competing events. During follow-up, 165 neonates did not complete the 28-day period, including 88 deaths. Factors significantly associated with time to discharge included place of delivery (SHR: 0.62; 95% CI: 0.53-0.73; p<0.001), maternal residence in other districts (SHR: 0.69; 95% CI: 0.48-0.99; p=0.044), extreme preterm (SHR: 0.05; 95% CI: 0.03-0.09; p<0.001), very preterm (SHR: 0.18; 95% CI: 0.14-0.25; p<0.001), moderate preterm (SHR: 0.59; 95% CI: 0.46-0.76; p<0.001), triplet births (SHR: 0.40; 95% CI: 0.23-0.68; p=0.001), 2-4 ANC visits (SHR: 0.70; 95% CI: 0.56-0.87; p=0.002), <=1 ANC visit (SHR: 0.64; 95% CI: 0.49-0.85; p=0.002), respiratory distress syndrome (SHR: 0.64; 95% CI: 0.48-0.74; p<0.001), and birth trauma (SHR: 2.62; 95% CI: 1.60-4.29; p<0.001). Conclusions: Respiratory distress syndrome, fewer antenatal care visits, out-of-district residence, and higher degrees of prematurity were associated with prolonged time to discharge among preterm neonates. Strengthening antenatal care utilization and improving access to quality neonatal care in underserved areas may enhance discharge outcomes.

Purpose: Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries. Methods: From hybrid MPI/CT imaging (N=43,099) across 15 sites, we included 4,552 most relevant patients with 1) no prior coronary artery disease; 2) AI-derived mild CAC scores (1-99); and 3) normal perfusion (stress total perfusion deficit <5%). The independent associations between AI-identified proximal CAC and major adverse cardiovascular events (MACE) and all-cause mortality (ACM) were evaluated using multivariable Cox regression, likelihood ratio test (LRT), and continuous net reclassification index (NRI). Results: Among the patients with mild CAC and normal perfusion (mean age 65{+/-}12 years, 51% male), 1,730 (38%) had proximal CAC. Over 3.6 (inter-quartile interval 2.1, 5.2) years follow up, 599 (13%) and 444 (10%) patients had MACE or ACM, respectively. Proximal CAC was associated with an increased risk of MACE (adjusted hazard ratio [HR] 1.24, 95% CI 1.03-1.48, P=0.02) and ACM (adjusted HR 1.25, 95% CI 1.01-1.53, P=0.04) after the adjustment of CAC score and density, clinical risk factors, and perfusion deficit. Proximal CAC improved the risk stratification of MACE (LRT P=0.02; NRI 12%) and ACM (LRT P=0.04; NRI 12%). Conclusion: In patients with mild CAC and normal perfusion, AI detection of proximal CAC identified a higher-risk group for adverse outcomes, highlighting its prognostic utility.

STRUCTERED ABSTRACTO_ST_ABSBACKGROUNDC_ST_ABSCoronary artery bypass graft (CABG) is a widely performed procedure for coronary artery disease (CAD), yet its association with Impaired Cognition (IC), i.e., mild-cognitive impairment or all-cause dementia, while accounting for APO ({varepsilon}) genotype, remains unclear. METHODSWe analyzed AllofUS participants with CAD (Age[&ge;]60 yrs) from 2017-2023. We defined CAD as a history of angina/myocardial infarction/chronic ischemic heart disease or having percutaneous coronary intervention/CABG, and IC as mild cognitive impairment or all-cause dementia using ICD/SNOMED codes. We performed logistic regression analyses to assess the association between CABG and IC, adjusting for clinical factors (age, sex, hypertension, diabetes, hyperlipidemia, depression, stroke, smoking, alcohol use, statin/antihypertensive/antidiabetic use), social determinants (self-reported race/ethnicity, income, employment), and APO ({varepsilon}) genotypes. We further performed stratified analyses across APO ({varepsilon}) genotypes ({varepsilon}2/{varepsilon}2, {varepsilon}2/{varepsilon}3 {varepsilon}3/{varepsilon}3, {varepsilon}2/{varepsilon}4, {varepsilon}3/{varepsilon}4, {varepsilon}4/{varepsilon}4). We defined significance at p [&le;] 0.05. RESULTSWe included 22,349 with CAD and identified 908 with IC after CAD till 2023. 40% were females, 70% were White, 12% were Black, and 9% were Hispanic. The proportion of IC was higher (5.1% vs 3.5%, p=1e-08) in CABG (n=8,135) vs non-CABG (n=14,214). After adjusting for clinical factors, social determinants, and APO ({varepsilon}) genotypes, CABG (1.23;1.06-1.41, p = 0.005) was associated with IC. In APO ({varepsilon}) stratified analysis, the association of CABG with IC was strongest in the APO {varepsilon}2/{varepsilon}3 group (1.91;1.21-3.02, p = 0.005). CONCLUSIONIn the AllofUS cohort, we observed an association between CABG and IC in CAD participants, with the strongest association in the APO {varepsilon}2/{varepsilon}3 group. Key MessageO_ST_ABSWhat is already known on this topicC_ST_ABSCoronary artery disease (CAD) and Impaired Cognitive (IC) disease, i.e., mild cognitive impairment and all-cause dementia, share genetic, sociodemographic, and clinical factors, including cardiovascular conditions like coronary artery bypass grafting (CABG) procedure. What this study addsWe observed an association between CABG and IC in CAD participants after adjusting for sociodemographic, clinical factors, and APO ({varepsilon}) effects. Further, when CAD participants were stratified across APO ({varepsilon}) groups, CABG was significantly associated with IC in the APO {varepsilon}2/{varepsilon}3 group. How this study might affect research, practice or policyOur observations highlight the role of APO ({varepsilon}) genotype evaluation in CAD patients for IC risk assessment.

Objective Cerebral palsy (CP) affects approximately 1 million Americans and 18 million individuals worldwide, yet contemporary US epidemiologic data remains limited. We aimed to use Cerebral Palsy Research Network (CPRN) clinical registry to describe demographics and clinical characteristics of individuals with CP across the US and determine associations with gross motor function and genetic etiology. Methods Registry subjects were included if they had clinician-confirmed CP and prospectively entered data for Gross Motor Function Classification System (GMFCS) Level, gestational age, genetic etiology, CP distribution, and tone/movement types. Logistic regression was used to determine which of these variables plus race, sex, ethnicity, and age were associated with GMFCS level and genetic etiology. Results A total of 9,756 children and adults with CP from 22 CPRN sites met inclusion criteria. Participants were predominantly White (73.0%), male (57.3%), non-Hispanic (87.8%), and younger than 18 years (73.7%). Most were classified as GMFCS levels I-III (55.6%), born preterm (52.8%), had spasticity (83.8%), and had quadriplegia (41.9%); 12.2% were identified as having a genetic etiology. Tone/movement types, CP distribution, and gestational age were significantly associated with both GMFCS level and genetic etiology (p<0.001). Compared to White individuals, Black individuals were more likely to have greater gross motor impairment (p<0.001). Conclusion In this large US cohort, clinical and demographic factors, including race, were associated with gross motor function and genetic etiology in CP. These findings highlight persistent disparities and demonstrate the value of a national clinical registry for informing prognostication, quality improvement efforts, and targeted genetic testing strategies.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

BackgroundPatent ductus arteriosus (PDA) is a common and potentially serious cardiovascular condition in preterm infants, particularly those with low gestational age and birth weight. Its management remains controversial due to variability in screening, diagnostic criteria, and treatment strategies. This study aimed to evaluate risk factors, outcomes, and management strategies for PDA in preterm infants, and to identify predictors of clinical and echocardiographic response to therapy. MethodsWe conducted a retrospective cohort study over a 4-year period (2016-2019) in the neonatal intensive care unit (NICU) of a tertiary care center. All consecutive preterm infants admitted during the study period were eligible. Infants with echocardiographically confirmed PDA who received pharmacological treatment with intravenous paracetamol or ibuprofen were included in the analysis. Missing data were minimal and handled using available-case analysis. Statistical analyses included descriptive statistics, Pearsons chi-square test, and multivariable logistic regression. ResultsAmong 2154 preterm infants admitted to the NICU, 60 were diagnosed with PDA (incidence : 2.8%). The mean gestational age was 29 {+/-} 2.6 weeks, and the median birth weight was 1200 g. Respiratory distress occurred in 95% of cases, mainly due to hyaline membrane disease (86.7%). PDA was symptomatic in 80% of infants. First-line treatment resulted in clinical improvement in 77% and ductal closure in 83.3% of cases, most within 3 days. Predictors of successful closure included gestational age [&ge;] 28 weeks (OR = 5.9; 95% CI : 1.7-20.2) and antenatal corticosteroid exposure (OR = 1.2; 95% CI : 1.0-1.6). Overall mortality was 35% and was significantly higher in infants < 28 weeks (OR = 5.0; 95% CI : 2.4-10.3). Clinical improvement (OR = 3.7) and echocardiographic closure (OR = 4.5) after first-line treatment were associated with reduced mortality. ConclusionsPDA in preterm infants is associated with substantial morbidity and mortality, particularly in those born before 28 weeks of gestation. Early diagnosis, antenatal corticosteroid exposure, and timely pharmacological treatment may improve outcomes. Systematic echocardiographic screening in high-risk neonates should be considered.

COVID-19 has spread rapidly and caused a global pandemic making it one of the deadliest in history. Early identification of patients with coronavirus disease 2019 who may develop critical illness is of immense importance. Therefore, novel biomarkers were needed to identify patients who will suffer rapid disease progression to severe complications and death. Many treatments were adopted including the antiviral Remdesivir, the antiretroviral Lopinavir /Ritonavir and Tocilizumab. Our study aimed not only to specify high-risk factors and biomarkers of fatal outcome in hospitalized subjects with coronavirus but also to compare the efficacy of the three considered treatments to help clinicians better choose a therapeutic strategy and reduce mortality. We divided the population (n=711) into four main groups based according to the WHO ordinal severity scale. The percentage of mortality, in and out the hospital, the length of stay in the hospital, the pulmonary inflammatory lesion and its distribution, the SARS-CoV-2 IgM and IgG variations at admission, the inflammatory markers, the complete blood count, the coagulation factors and enzymes, proteins and electrolytes profile, glucose and lipid profile, and other relevant markers were measured. The significance of the observed variation was assessed by multivariate and ANOVA analyses. We succeeded to establish a novel predictive scoring model of disease progression based on a cohort of Lebanese hospitalized patients relying on the pulmonary inflammatory lesions, inflammation biomarkers such as LDH, D-Dimer, CRP, IL-6 and the lymphocyte count, the number of comorbidities and the age of the patient which all were significantly correlated with the illness severity showing best outcomes with immunomodulatory and anticoagulant treatments by the results. As top tier, Tocilizumab was more efficient than the two other treatments in non-severe cases but none of the used treatments was insanely effective alone to reduce mortality in severe cases.

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

Automating Hierarchical Condition Category (HCC) assignment directly from unstructured electronic health record (EHR) notes remains an important but understudied problem in clinical informatics. We present HCC-Coder, an end to end NLP system that maps narrative documentation to 115 Centers for Medicare & Medicaid Services(CMS) HCC codes in a multi-label setting. On the test dataset, HCC-Coder achieves a macro-F1 of 0.779 and a micro-F1 of 0.756, with a macro-sensitivity of 0.819 and macro-specificity of 0.998. By contrast, Generative Pre-trained Transformer (GPT)-4o achieves highest score of a macro-F1 of 0.735 and a micro-F1 of 0.708 under five-shot prompting. The fine-tuned model demonstrates consistent absolute improvements of 4%-5% in F1-scores over GPT-4o. To address severe label imbalance, we incorporate inverse-frequency weighting and per-label threshold calibration. These findings suggest that domain-adapted transformers provide more balanced and reliable performance than prompt-based large language models for hierarchical clinical coding and risk adjustment.

Abstract Background: Despite widespread adoption of contemporary guideline-directed medical therapy (GDMT), patients with heart failure with reduced ejection fraction (HFrEF) continue to experience substantial residual morbidity and mortality. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated cardiometabolic benefits in diabetes and obesity, but their role in HFrEF remains uncertain. Objectives: To evaluate whether the addition of GLP-1RAs to optimized GDMT is associated with improved clinical outcomes in patients with HFrEF (NYHA class II-IV). Methods: We conducted a retrospective, multicenter cohort study using the TriNetX Research Network. Adults ([&ge;]18 years) with HFrEF (LVEF [&le;]40%) receiving GDMT between January 2020 and October 2024 were included. Patients treated with GLP-1RAs were compared with those on GDMT alone. After 1:1 propensity score matching, 1,518 patients were included in each cohort. Outcomes over 2 years included all-cause mortality, major adverse cardiovascular events (MACE), critical care utilization, and acute kidney failure. Time-to-event analyses were performed using Kaplan-Meier methods and Cox proportional hazards models. Results: In the matched cohort (mean age [~]63 years, [~]33% female), GLP-1RA use was associated with significantly lower all-cause mortality compared with GDMT alone (12.8% vs 23.8%; hazard ratio [HR] 0.48; 95% CI 0.40-0.57; p<0.001), corresponding to an absolute risk reduction of 11.0%. MACE was also reduced (35.8% vs 47.4%; HR 0.64; 95% CI 0.58-0.72; p<0.001). Additionally, GLP-1RA therapy was associated with lower critical care utilization (18.4% vs 28.9%; HR 0.55; 95% CI 0.47-0.64; p<0.001) and reduced acute kidney failure (29.2% vs 37.3%; HR 0.67; 95% CI 0.59-0.76; p<0.001). Rates of pancreatitis and substance-related disorders were low and not significantly different between groups. Conclusions: Among patients with HFrEF receiving contemporary GDMT, adjunctive GLP-1RA therapy was associated with significant reductions in mortality, cardiovascular events, and healthcare utilization. These findings support the potential role of GLP-1RAs as a novel, mechanism-complementary therapy in HFrEF. Prospective randomized trials are needed to confirm these observations and determine whether GLP-1RAs should be incorporated as a fifth pillar of GDMT.

Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of early IC response and OS. MoEMIL employed multi-instance learning to process WSIs, PyRadiomics and a convolutional neural network (ResNet50) to extract MRI features, and fused multimodal features through a multi-gate mixture-of-experts architecture. Clustering-constrained attention multiple instance learning and gradient-weighted class activation mapping were applied for visualization and interpretation. MoEMIL effectively stratified patients into good and poor IC response groups, achieving areas under the curve of 0.917, 0.869, and 0.801 in the train, validation, and test sets, respectively, and outperformed the deep learning radiomics model, the pathomics model and TNM staging. The model also stratified patients into high- and low-risk OS groups (P < 0.05). MoEMIL shows promise as a decision-support tool for early IC response prediction and prognostication in LANPC. Author SummaryWe have developed a deep learning model that integrates two types of medical images, including magnetic resonance imaging (MRI) and digital pathological slices, to simultaneously predict response to induction chemotherapy and prognosis in patients with locally advanced nasopharyngeal carcinoma. Current treatment decisions primarily rely on traditional tumor staging (TNM), which often fails to comprehensively reflect the complexity of the disease. Our model, named MoEMIL, was trained and tested on data from 404 patients across two hospitals and consistently outperformed both single-model approaches and TNM staging methods. By identifying patients who exhibit poor response to induction chemotherapy or higher prognostic risk, our tool can assist clinicians in achieving personalized treatment, enabling intensified management for high-risk patients and avoiding unnecessary side effects for low-risk patients. Additionally, we visualize the models reasoning process through heat map generation, which highlights the image regions exerting the greatest influence on prediction outcomes. This work represents a step toward more precise treatment for nasopharyngeal carcinoma; however, larger-scale prospective studies are required before the model can be integrated into routine clinical practice.

Orphan genes, lacking homologs in other species, are systematically found across genomes. Their presence may result from extensive divergence from pre-existing genes or from de novo gene birth, which occurs when a gene emerges from a previously non-genic region. In this study, we identified orphan genes in the genomes of globally distributed plant-parasitic nematodes of the genus Meloidogyne and investigated their origins, evolution, and characteristics. Using a comparative genomics framework across 85 nematode species, we found that 18% of Meloidogyne genes are genus-specific, transcriptionally supported orphans. By combining ancestral sequence reconstruction and synteny-based approaches, we inferred that 20% of these orphan genes originated through high divergence, while 18% likely emerged de novo. Proteomic and translatomic evidence confirmed the translation of a subset of these genes, and feature analyses revealed distinctive molecular signatures, including shorter length, signal peptide enrichment, and a tendency for extracellular localization. These findings highlight orphan genes as a substantial and previously underexplored component of the Meloidogyne genome, with potential roles in their worldwide parasitism.

The apolipoprotein E {varepsilon}4 (APOE4) allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), the most common form of dementia. APOE4 carriers exhibit cerebrovascular and metabolic dysfunction, structural brain alterations, and gut microbiome changes decades before the onset of clinical symptoms. A better understanding of the early manifestation of these physiological changes is critical for the development of timely AD interventions and risk reduction protocols. Multimodal datasets encompassing a wide range of APOE4- and AD-associated biomarkers provide a valuable opportunity to gain insight into the APOE4 phenotype; however, these datasets often present analytical challenges due to small sample sizes and high heterogeneity. Here, we propose a two-stage multimodal AI model (APOEFormer) that integrates blood metabolites, brain vascular and structural MRI, microbiome profiles, and other clinical and demographic data to predict APOE4 allele status. In the first stage, modality-specific encoders are used to generate initial representations of input data modalities, which are aligned in a shared latent space via self-supervised contrastive learning during pretraining. This objective encourages the learning of informative and consistent representations across modalities by leveraging cross-modality relationships. In the second stage, the pretrained representations are used as inputs to a multimodal transformer that integrates information across modalities to predict a key AD risk genetic variant (APOE4). Across 10 independent experimental runs with different train-validation-test splits, APOEFormer predicts whether an individual carries an APOE4 allele with an average accuracy of 75%, demonstrating robust performance under limited sample sizes. Post hoc perturbation analysis of the predictive model revealed valuable insights into the driving components of the APOE4 phenotype, including key blood biomarkers and brain regions strongly associated with APOE4.