Browse Articles

Bone mineral density abnormalities in end stage chronic kidney disease patients in Pakistan

Application of vision transformers to protein-ligand affinity prediction

Abstract Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the application of vision transformers (ViTs) to the problem of affinity prediction. Unlike other neural networks used in this problem, the ViT framework can capture global, long-range interactions across the entire protein-ligand complex via self-attention, without relying on local receptive fields or predefined interaction cutoffs. We evaluate this advantage in representation of spatial information across two benchmark datasets, demonstrating competitive performance and, in some cases, surpassing state-of-the-art models. We study the model’s behavior using explainable AI (XAI) techniques, revealing that spatially proximal patches with similar attention scores cluster around biologically relevant regions, confirming the model’s ability to capture key interaction features. Furthermore, we show that data augmentation strategies can yield performance improvements, highlighting the potential for further enhancement. Despite challenges related to data sparsity and conformational variability, ViTs show strong performance and high robustness in structure-based affinity prediction tasks. Our findings underscore their effectiveness in learning spatial patterns and suggest broader applicability to related tasks, such as protein-protein or protein-nucleic acid interaction modeling.

The mediating role of moral identity in the relationship between organizational culture and emotional commitment among healthcare workers

Generation of high-producing CHO cell lines using cell picking system combined with fluorescent labeling of secreted products

Structural, electronic, magnetic, optical, and thermoelectric properties of FeCrVAl and MnCrVAl quaternary Heusler alloys for spintronics and energy harvesting applications

Computational and regression analysis of neuroprotective agents using fuzzy neural networks and topological descriptors

Physiological and biochemical responses of well-trained military personnel in a multi-day high-intensity training course

Scientific discovery in the age of AI and supercomputing

Abstract Artificial intelligence (AI) and high-performance computing (HPC) are transforming scientific capabilities and the way science is conducted. Yet their combined impact on scientific discovery remains poorly understood, as do inequalities in access to these capabilities across countries and institutions. Drawing on metadata from more than five million scientific publications (2000–2024) across 27 fields, we examine how the convergence of AI and HPC correlates with scientific breakthroughs. Our results show that this computational synergy is most pronounced at the scientific frontier: research combining AI and HPC is more likely to introduce novel ideas and achieve top-cited status than either conventional work or research using AI or HPC in isolation. We also document growing disparities in access to supercomputing resources and AI expertise, which are increasingly concentrated in a small number of regions (dominated by the United States and China, though the EU27 aggregate maintains high competitiveness in combined AI+HPC output). The future of discovery will depend not only on advances in algorithms and computing power, but also on enacting policies that democratise these capabilities across the global scientific ecosystem.

Doxorubicin-loaded gold nanoparticles for enhanced anticancer efficacy: in vitro, in vivo, and FTIR-based tissue analysis

Abstract The most challenging aspects of cancer treatment are multi-drug resistance (MDR) and damage to normal, non-malignant cells. Doxorubicin (Dox)-loaded gold nanoparticles (Dox@AuNPs) were developed and evaluated for enhanced anticancer activity. The nanocomposites exhibited spherical morphology (13 ± 3 nm) with a loading efficiency of 52%. In vitro, Dox@AuNPs reduced the IC₅₀ by approximately 50% compared to free Dox in MCF-7 cells. In vivo, treatment significantly suppressed tumor growth and increased median survival (62 days) relative to free Dox (52 days). In addition to conventional efficacy evaluation, Fourier transform infrared (FTIR) spectroscopy was applied to analyze biochemical changes in tumor tissues. Distinct alterations in protein, lipid, and nucleic acid-associated bands were observed, with treated tissues showing partial spectral shifts toward normal profiles. These findings suggest that FTIR may provide additional insight into tissue-level biochemical responses following treatment. While further validation and toxicity assessment are required, the results demonstrate that Dox@AuNPs enhance anticancer efficacy and highlight the potential utility of FTIR as a complementary tool for evaluating therapeutic response.

Effectiveness of pH on formation and characterization of chitosan-alginate nanoparticles and nanohydrogels based on the formed nanoparticles

Congenital heart defects – diagnosis and treatment

Abstract More than six decades ago, the first successful repair of a congenital heart defect opened a new frontier in pediatric medicine. Driven by advances in fetal imaging, computational fluid dynamics, machine learning, and surgical perfusion, our ability to diagnose and treat these conditions has grown remarkably. Many of today’s greatest challenges in congenital cardiology - early and accurate diagnosis, precise risk stratification, and lifelong management - are increasingly being addressed through methods borrowed from engineering and data science. The Congenital heart defects: Diagnosis and treatment Collection at Scientific Reports is dedicated to this research.

Integrated network pharmacology, molecular docking, and experimental validation to reveal the potential mechanism of Ginsenoside Rg1 on chronic obstructive pulmonary disease

Association between perioperative renal function trajectory and post-discharge prognostic outcomes in STEMI patients undergoing PCI

Machine learning improves classification of serious adverse events compared to threshold-based continuous vital sign monitoring alerts

Abstract Continuous Vital Sign Monitoring (CVSM) enables early detection of patient deteriorations, yet current alert algorithms are prone to false positives from missing data and artefacts, causing alert fatigue, particularly in low-staffed hospital settings such as general wards. Machine learning (ML) may solve this by capturing vital sign trajectories. This study compared four ML analyses of seven vital signs against a National Early Warning Score threshold-severity algorithm. Data came from 2325 patients monitored during major surgery or acute medical admission, including continuous and semi-continuous vital signs, physician-curated Serious Adverse Events (SAE), and patient metadata. At a fixed false positive rate matching the threshold system, the 24-hour ML model achieved a true positive rate of 0.96 versus 0.50. At a matched true positive rate, the false positive rate was 0.06 versus 0.84. The ML approach out-performed threshold-based alerts for classifying SAE intervals at 24 h (AUROC = 0.81) and 8 h (AUROC = 0.71). Precision-recall and calibration analyses showed moderate precision for the 24-hour model and low precision at 8 h under class imbalance. Performance differed across two same-region cohorts, thus geographically independent validation remains necessary. These findings indicate ML-based vital sign monitoring could improve SAE detection over current threshold-based systems.

Assessing potential impacts of offshore wind development on U.S. marine ecosystems using food web modeling

Pre-existing venous structural remodeling and arteriovenous fistula maturation assessed by conventional histology and nonlinear optical microscopy

Ultrasound-promoted Cu/ZnO-graphene oxide catalyzed synthesis of spirooxindole-fused quinazolines and evaluation of their antioxidant and antibacterial activities

CNN-transformer-based model explained by SHAP and multi-head attention weights for time series forecasting

Abstract Convolutional Neural Networks (CNNs) and transformer architectures offer strengths for modeling temporal data: CNNs excel at capturing local patterns and translational invariances, while transformers effectively model long-range dependencies via self-attention. This paper proposes a hybrid architecture integrating convolutional feature extraction with a multi-head attention backbone to enhance multivariate time series forecasting. The CNN module first applies a hierarchy of one-dimensional convolutional layers to distill salient local patterns from raw input sequences, reducing noise and dimensionality. The resulting feature maps are then fed into the prediction model, which applies multi-head attention to capture both short- and long-term dependencies and to weigh relevant covariates adaptively. We evaluate the proposed CNN-transformer-based model on a hydroelectric natural flow time series dataset. Experimental results demonstrate that the proposed model outperforms well-established deep learning models, with a mean absolute percentage error of up to 2.2%. The explainability of the model is obtained by a proposed SHapley Additive exPlanations (SHAP) and Multi-Head Attention Weights (MHAW). Our novel architecture, named CNN-Transformer-SHAP-MHAW, is promising for applications requiring high-fidelity and multivariate time series forecasts.

Investigation of the marginal impact of electricity generation sources on CO2 emissions in highly emitting countries by daily data and KRLS approach

Abstract Considering countries’ efforts to combat climate change and the critical role of energy use in carbon dioxide (CO 2 ) emissions, this study empirically analyzes the impact of electricity generation (EG) sub-types on CO 2 emissions. In this vein, the study focuses on the five highly emitting countries (namely, China, the United States, India, Russia, & Japan), uses daily data from 1st January 2019 to 30th June 2024, and implements a kernel regularized least squares (KRLS) approach to consider average and marginal impacts in empirical analyses. The empirical outcomes demonstrate that (i) gas and nuclear EG decreases CO 2 emissions for India; hydro EG is curbs CO 2 emissions in India and Japan; oil EG decreases CO 2 emissions at lower percentiles in the USA; (ii) coal EG cause an increase in CO 2 emissions in the all countries; (iii) wind EG provides a decrease in CO 2 emissions at lower percentiles in the USA and Japan, whereas its effect becomes ineffective across remaining percentiles and countries; (iv) solar EG is the unique source that has a declining impact across countries; (v) the KRLS approach has a high estimation capacity (R 2 : 93.53%). Overall, the study reveals average and marginal impacts of EG subtypes on CO 2 emissions across percentiles and countries. Accordingly, through consideration of empirical outcomes (e.g., solar EG is the most beneficial EG source for all countries) while some other EG sub-types are partially beneficial (e.g., gas EG in China and India; hydro EG in all countries; nuclear EG in all countries except Russia; wind EG in United States, India, and Japan), the study argues various policy implications (e.g., prioritizing the most helpful EG sources in providing further governmental support, focusing on first solar EG and then followed by hydro, nuclear, and wind EG to stimulate new capacity installation).

Meteorological modulation of ambient air quality in Delhi and Lahore during the November 2024 haze event

Abstract The Indo-Gangetic Plain in South Asia frequently experiences severe haze during the post-monsoon season. The November 2024 haze event recorded the highest daily PM 2.5 concentrations in Delhi (696 µg/m³) and Lahore (621 µg/m³) over the past 5 years. These elevated PM 2.5 concentrations coincided with a prolonged period of regional-scale atmospheric stagnation and a shift in peak fire activity from the afternoon to the evening. Here, we show that both cities had elevated pollutant concentrations with correlations between PM 2.5 and planetary boundary layer height, lower-tropospheric stability, atmospheric heat deficit, relative humidity, and fire radiative power, consistent with accumulation of emissions from multiple sources, including regional biomass-burning emissions, under stagnant atmospheric conditions. We also highlight that positive geopotential height anomalies and the mechanism of anticyclonic stagnation and weak winds favored a high-pressure system, suppressing turbulent ventilation and accumulating pollution in the lower troposphere. Furthermore, air mass back trajectories overlapped the stubble-burning region ,and higher evening fire counts observed by a geostationary satellite emphasize the likely role of increased evening fire activity alongside other local and regional emissions in elevated air pollution concentrations. These results demonstrate that local and synoptic meteorology strongly aggravates haze events, suggesting emission control strategies should be guided by meteorological forecasts across regional airsheds.