Huashan Hospital

Infectious disease spread is a multiscale process composed of within-host (biological) and between-host (social) drivers and disentangling them from each other is a central challenge in epidemiology. Here, we introduce VIBES, a multiscale modeling framework that explicitly integrates viral dynamics based on patient-level data with population-level transmission on a data-driven network of social contacts. Using SARS-CoV-2 as a case study, we analyze three emergent epidemic properties, namely the generation time, serial interval, and presymptomatic transmission. First, we established a purely biological baseline, thus independent of the reproduction number (R), from the within-host model, estimating a generation time of 6.3 d for symptomatic individuals and 43.1% presymptomatic transmission. Then, using the full model incorporating social contacts, we found a shorter generation time (5.4 d at R = 3.0) and an increase in presymptomatic transmission (52.8% at R = 3.0), disentangling the impact of social drivers from a purely biological baseline. We further show that as pathogen transmissibility increases (R from 1.3 to 6), competition among infectious individuals shortens the generation time and serial interval by up to 21% and 13%, respectively. Conversely, a social intervention, like isolation, increases the proportion of presymptomatic transmission by about 30%. Our framework also estimates metrics that are challenging to obtain empirically, such as the generation time for asymptomatic individuals (5.6 d; 95%CI: 5.1 to 6.0 at R = 1.3). Our findings establish multiscale modeling as a powerful tool for mechanistically quantifying how pathogen biology and human social behavior shape epidemic dynamics as well as for assessing public health interventions.

Despite treatment, many hypertensive patients with coronary heart disease (CHD) have blood pressure (BP) that remains above guideline targets. However, less is known about patients in whom on-treatment BP is below target and major European guidelines differ substantially in their recommendations for such patients. Specifically, 2023 European Society of Hypertension (ESH) guidelines provide a Class 3 recommendation to proactively avoid BP below target whereas 2024 European Society of Cardiology (ESC) guidelines do not. Between 2020 and 2023, adults hospitalised in the previous 6-24 months with incident or recurrent CHD were sampled in 14 countries from all six WHO regions and invited for a standardised interview and examination as part of the International Action on Secondary and Primary Prevention by Intervention to Reduce Events (INTERASPIRE) study. We measured seated BP twice using an automated oscillometric device after 5 min of rest. We defined participants as having BP above target (BP ≥130/80 mm Hg), at target (BP 120-129/70-79 mm Hg) or below target (<120/70 mm Hg); according to both 2023 ESH and 2024 ESC guidelines. Among 4548 participants (21.1% female), the mean age (±SD) was 60.0 (±10.3) years. At a median (IQR) of 1.05 (0.76-1.45) years after index CHD hospitalisation, 10.3% of patients had BP readings within the target range of 120-129/70-79 mm Hg, with 53.9% above target and 35.7% below target. Patients with below-target BP were more likely to have heart failure (13.6% vs 10.0%, p=0.041) and estimated glomerular filtration rate <60 mL/min/1.73 m² (18.9% vs 10.7%, p<0.001), when compared with those at target. They were also statistically more likely to be severely frail (p=0.030). The median number of BP-lowering medications did not differ significantly between the 3 BP target groups. Cumulative dosing of BP-lowering medications in the below-target group was also not more intensive than that in the at-target group. More than one-third of INTERASPIRE patients with CHD presented with BP <120/70 mm Hg, which is below target per current European hypertension guidelines. Further research is needed in this population to resolve discordant guideline recommendations and determine whether de-escalation of BP-lowering therapies is beneficial or even harmful in this setting.

BackgroundArtificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and robustness when deployed across multiple medical centers due to variability in imaging protocols and data characteristics, potentially compromising clinical diagnosis and treatment decisions.ObjectiveThis study aimed to develop a real-time, plug-and-play data transformation method to enhance the robustness of deep learning models against data heterogeneity in radiographs, thereby improving their performance in assessing AIS across multiple medical centers.MethodsIn this retrospective multicenter study, 3899 full-spine radiographs from 7 hospitals (2 from Hong Kong and 5 from Mainland China), collected between January 2012 and August 2024, were included. Data from 2 hospitals in Hong Kong (n=3034) were used for model training and internal validation, while radiographs from the 5 mainland hospitals (n=865) formed 5 independent external validation datasets. A novel pixel intensity–based data transformation method was developed to standardize image contrast and brightness across datasets and integrated into the model training process to enhance our previously developed AI model, SpineHRNet+. The enhanced model's accuracy and robustness for cobb angle (CA) prediction and severity classification were evaluated using both internal and external datasets. Data heterogeneity across centers was quantified by brightness and contrast differences. CA prediction accuracy was evaluated using residual analysis, linear regression (coefficient of determination [R²]), and Bland-Altman analyses. Model performance for disease severity classification was assessed using sensitivity, specificity, precision, negative predictive value, accuracy, and confusion matrix analysis. The transformation method aligns pixel intensity distributions across datasets using statistical profiling and optimization, ensuring consistent image characteristics while preserving anatomical integrity.ResultsThe developed data transformation method significantly reduced contrast variability between datasets, improving consistency in image characteristics and enabling more reliable AI analysis. The enhanced SpineHRNet+ achieved consistent and accurate CA predictions across external validation datasets, with mean prediction errors within 4° (SD 3.12°), and maintained an R² greater than 0.90 for all centers. The sensitivity and negative predictive value for disease severity grading improved to 90.18% and 93.16%, respectively. Bland-Altman analyses demonstrated robust agreement, with 95% limits of agreement within 7.51° across all datasets.ConclusionsThe proposed data transformation approach effectively addressed data heterogeneity, significantly improving the accuracy and robustness of SpineHRNet+ in multicenter AIS assessments. The real-time processing capability and preservation of anatomical integrity underscore the method’s clinical practicality, enabling scalable and reliable AI applications in diverse health care environments.

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.