Browse Articles

The use of immersive technologies in learning about postpartum hemorrhage: Protocol for a systematic review and meta-analysis

Postpartum hemorrhage is one of the leading causes of maternal morbidity and mortality worldwide, and its proper management requires both technical and non-technical skills. This study describes the protocol for a systematic review evaluating the effectiveness of immersive technologies in training healthcare professionals in the management of this obstetric emergency. This protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD420250614446). The search will be performed in the following databases: PubMed, Embase, Scopus, ScienceDirect, Web of Science, Cochrane Library, LILACS, Enfispo and CUIDEN. Intervention studies (clinical trials ‐ randomized or non-randomized) and quasi-experimental studies will be included. The risk of bias will be assessed using appropriate tools according to study design: the Risk of Bias 2 (ROB 2) tool for randomized controlled trials and the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool for non-randomized and quasi-experimental studies. Two independent researchers will conduct all assessments, and any disagreements will be consulted with a third reviewer. The data analysis and synthesis will be performed using Review Manager software version 5.4. We will conduct the study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines. The review will summarize the current evidence on the use of virtual reality, augmented reality, and mixed reality in education and training for postpartum hemorrhage management. The planned systematic review will identify and synthesize the available evidence on how immersive technologies may improve learning about postpartum hemorrhage.

Immunosuppression-Associated Peripheral T-Cell Lymphoma

LINC00222 regulates FOXO3 to interfere with β-catenin signaling pathway and suppresses prostate cancer progression

Quantifying and modeling fabric surface roughness discrepancy: Consistency between physical and digital textures in online shopping

In digital textile commerce, the absence of tactile interaction limits consumers’ ability to perceive fabric properties, often leading to mismatched expectations and product dissatisfaction. This study aimed to quantify the perceptual discrepancy in surface roughness (RDP) between physical fabrics and their digital representations and to identify structural parameters that influence this discrepancy. Plain- and twill-woven fabric specimens were prepared with varying densities, weights, and thicknesses. Surface roughness of physical fabrics was measured using atomic force microscopy (AFM), while digital roughness values were extracted from scanned images using ImageJ. Statistical analyses, including correlation and regression modeling, were applied to identify key predictors of RDP. Results showed that in plain-woven fabrics, lower weft density and higher warp density under fixed fabric weight conditions yielded the lowest RDP values, whereas in twill-woven fabrics, the perceptual gap was minimized at higher fabric weights and lower weft densities. These findings provide practical insights for improving visual–tactile alignment in virtual textile presentation and can inform fabric structural design for enhanced accuracy in online representation. Future research may explore nonlinear modeling and multisensory feedback systems to further reduce perceptual discrepancies.

Perioperative Enfortumab Vedotin and Pembrolizumab in Bladder Cancer

A joint pilot optimization and channel estimation algorithm based on CBAM-CNN for multipath fading environments in underground coal mines

Tree ring segmentation performance in highly disturbed trees using deep learning

Dendrogeomorphology provides valuable insights into the dating of geomorphic events but requires complex analyses of tree-ring records from highly disturbed trees. While deep learning algorithms have been successfully applied to detect boundaries in normally developed growth rings, their performance under severely disturbed growth conditions remains largely unexplored. This study evaluates whether deep learning can effectively segment tree rings exhibiting abnormal growth patterns commonly observed in dendrogeomorphological contexts. Increment cores were collected from a debris-flow-affected area. High-resolution images were subsequently acquired and manually annotated to identify tree rings boundaries and growth disturbances. A series of experiments was conducted using different neural network architectures, image resolutions, and filtering techniques to examine the relationship between convolutional neural network (CNN)–based models and the level of cellular detail represented in the images. Our results indicate that segmentation performance declines in growth disturbances characterised by pronounced changes in colour and texture relative to normal growth patterns. Nevertheless, the proposed framework successfully identified sets of narrow ring boundaries spaced more than 200 μm apart when colour remained consistent, correctly segmenting most rings associated with the most severe growth suppressions in our dataset. Notably, models relying primarily on simple features such as colour variation performed comparably to those incorporating finer cellular details. We also found that, within a patch-based processing framework, performance decreased when growth direction was not specified in advance. Overall, this study provides a systematic evaluation of CNN-based methods under highly disturbed growth conditions, highlighting both their potential and current limitations in dendrogeomorphological applications.

Prehospital Whole Blood in Traumatic Hemorrhage — A Randomized Controlled Trial

Naturalistic driving data extraction and processing for studying driver head scanning behavior at intersections

A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory

Predicting the epidemic threshold τ in contact networks is a central challenge in computational epidemiology. Classical structural approaches based on spectral graph theory—most notably Quenched Mean-Field (QMF) and the recently proposed KSEL (K Spectral Energy of Laplacian) method—deliver fast but approximate predictions. We propose a hybrid AI-mathematical framework that integrates spectral graph features, epidemiological parameters, and epidemiologically motivated soft constraints derived from compartmental theory into a physics-guided neural network (PGNN). Rather than claiming state-of-the-art predictive performance, this work makes three complementary contributions: (i) a rigorous stochastic ground-truth estimation procedure with Monte Carlo uncertainty quantification (median σ τ * = 0.0072 , n  = 775 networks); (ii) a systematic comparative evaluation of seven methods—including tree-based models (Random Forest, Gradient Boosting) trained on the same feature set—revealing the conditions under which deep learning surpasses and falls short of simpler baselines; and (iii) a full ablation study and SHAP interpretability analysis identifying the role of individual spectral features and physical constraints as structured regularisers. Evaluated on 775 synthetic networks spanning Erdős–Rényi, Barabási–Albert, Watts–Strogatz, and regular topologies, Gradient Boosting achieves the best predictive accuracy ( R 2  = 0.908, RMSE = 0.0731), while the PGNN ( R 2  = 0.093) offers complementary value through physical consistency and interpretability. These results are established on synthetic benchmarks; application to empirical contact networks (hospital, school, workplace settings) is a natural next step but requires dedicated validation beyond the scope of the present study. Ablation results show that the boundedness constraint is a beneficial regulariser while the stability constraint over-regularises in the low-data regime. Automatic gradient-based calibration of the KSEL coefficient yields topology-dependent optimal values ( k * ∈ [ 0.803 , 1.458 ] ), substantially departing from the universal constant k  = 0.3 of prior work.

Stemness as the Engine of Checkpoint Durability

Exploring the panel and country wise effect of circular economy on environmental sustainability across six leading circular economies

Abstract Investigating the circular economy’s impact on addressing escalating climate change concerns is crucial. The objective of this research is to examine the possible implications of a circular economy on production-based carbon emission, this domain has yet to be thoroughly investigated. This investigation fills the research gap through presenting an under examined proxy for assessing the circular economy through municipal waste generation and treatment. This research investigated data spanning 1991 to 2020 from six advanced circular economies namely, Austria, Belgium, South Korea, Spain, Sweden, and USA, applying numerous estimation techniques to ensure robust and reliable results namely Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG), Autoregressive Distributed Lag-Mean Group (ARDL-MG), Autoregressive Distributed Lag-Dynamic Fixed Effects (ARDL-DFE), Fully Modified Ordinary Least Squares (FMOLS), Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM), stability analysis and Pairwise Dumitrescu-Hurlin (D-H) causality. The study determined the influence of the circular economy on production-based emissions using both country-specific and panel data approaches. The findings demonstrate that implementing a circular economy leads to a long-term reduction in production-based carbon emissions in the panel and country wise estimation except Spain. These findings develop understanding of sustainable development difficulties and encourage practitioners to build successful strategies.

A study protocol for mixed-methods evaluation of the structure, design, and availability of medical student wellbeing programs

Introduction In recent years, there has been growing concern over the wellbeing and mental health of medical students in the United States, driven by the academic, personal, and professional challenges inherent in medical school. Recent data indicates that medical students experience higher rates of psychological stress, anxiety, and depression compared to the general population, with the COVID-19 pandemic exacerbating these challenges. Medical student suicide, linked to burnout and depression, highlights the urgent need for effective wellbeing support. Despite the documented barriers to mental wellbeing, such as self-imposed pressures, imposter syndrome, stigma around help-seeking, and financial difficulties, medical student wellbeing programs remain understudied at the structural and design level. Methods This is a multi‑phase qualitative study (sequential-exploratory) that combines a web-based environmental scan and content analysis with key informant interviews and focus groups, using methodological triangulation to develop a framework for evaluating wellbeing programs. First, we will conduct a web-based content analysis of publicly available resources across medical school websites. We will identify key characteristics of wellbeing programs, such as mental health resources, structural well-being components, and culturally integrated approaches. Then, we will conduct key informant interviews with medical school administrative staff to discuss wellbeing programs in detail and hold focus group interviews with medical students to gather their perspectives on how to improve their health and wellbeing. Based on the findings from these three components, we will develop a comprehensive and standardized framework for evaluating medical school wellbeing programs that can be used across institutions. Ethics and dissemination Human Research Ethics Approval was obtained from the NYU Langone Health Institutional Review Board (IRB ID: i25-00965). The content analysis results and qualitative themes extracted from key informant and focus group interviews will be made available to all study participants. They will also be disseminated in a peer-reviewed journal.

Mavacamten in Nonobstructive Hypertrophic Cardiomyopathy

Short-term load forecasting using a two-stage CPO-PSO hyperparameter optimization of LSSVM

Earth’s east–west albedo symmetry

Abstract Earth’s albedo is fundamental to the planetary energy budget 1 . The Northern Hemisphere (NH) and Southern Hemisphere (SH) contribute essentially equally to the planetary albedo—a remarkable yet puzzling phenomenon known as hemispheric albedo symmetry 1–6 . Although such symmetry is rare, it is not unique 7 . Nevertheless, other symmetry pairs have remained unexplored, despite their potential to illuminate possible causes of albedo symmetries and implications for the planetary energy budget. Using a 25-year satellite record, here we show that Earth also exhibits a unique and persistent east–west (E–W) albedo symmetry: the 27° E meridian divides the planet into an Eastern Hemisphere (EH) and a Western Hemisphere (WH) that reflect nearly identical amounts of sunlight. In contrast to the NH–SH symmetry, the EH–WH symmetry encapsulates a distinctive ‘triple symmetry’ in which clear-sky albedo, cloud radiative effect and open-ocean fraction all exhibit hemispheric symmetry around this meridian. This EH–WH symmetry arises from greater high-cloud reflection in the EH balancing greater low-cloud reflection in the WH. Furthermore, interannual variability in the EH–WH symmetry tracks the phase of the El Niño–Southern Oscillation (ENSO), indicating a potential connection to general circulation. This discovery of the EH–WH albedo symmetry and its emergence as a triple symmetry provides a reduced degree-of-freedom constraint for Earth system models (ESMs) and stresses the critical nature of continued Earth radiation budget observations under a rapidly changing climate.

Biodiversity drives the choice; linguistic diversity fine-tunes the direction: Ethnofloral megadiversity in the Mexican ethnobotany

People around the world have developed distinctive sets of useful plants, known as ethnofloras, which comprise a significant portion of Earth’s biodiversity. However, little is known about the factors that determine the composition of these collections and how different groups use plants. These differences may increase with geographic distance and linguistic separation due to barriers to communication and a lack of similarity in locally-available species. Using published ethnobotanical information, we analyzed the divergence in the composition of wild-species and the ways plants are used among 22 Mexican ethnic groups that use 2,855 species. We standardized plant use into ten categories and recorded them for each plant species and ethnic group. Each ethnic group uses a very large number of species (α-diversity), but few species are shared with other groups. Consequently, species turnover (β-diversity) between ethnic groups is very high. As expected, geographic distance fostered high differentiation in species composition of ethnofloras, probably reflecting differences in wild floras. Linguistic proximity promoted ethnoflora composition similarity, suggesting that communication plays a role in shaping the set of plants that are used. Geographic proximity also promotes similarity in how plants are used, though it is unclear whether language also plays a role. This suggests that language barriers are quite permeable. Social interactions and the use of Spanish as a lingua franca may favor the convergence of uses. This study provides a novel analysis of ethnofloras. It emphasizes the value of cultural and biological diversity and their importance in shaping ethnobotanical heritage.

Is Change Possible?

Versatile, marker-free platform for life cycle-wide imaging of Plasmodium falciparum by integrating an exogenous gene cassette into a conserved intergenic locus

Abstract The creation of transgenic Plasmodium falciparum lines with robust fluorescence across the entire life cycle is essential for advancing our understanding of parasite biology, which in turn informs the development of new drugs and vaccines. In this study, we utilized Plasmodium -optimized genome editing to integrate an mCherry expression cassette into a selected intergenic locus without gene disruption. The resulting marker-free line, NF54-mCh, exhibited intense fluorescence throughout all developmental stages, including asexual and sexual blood stages, as well as mosquito (ookinete, oocyst, and sporozoite) and liver stages. NF54-mCh showed normal proliferation, gametocytogenesis, and efficient transmission to mosquitoes. The ultra-high brightness in salivary gland sporozoites allowed for the non-invasive identification of infected mosquitoes. Sporozoites remained highly infectious to humanized mouse livers, thus enabling the completion of the full life cycle. NF54-mCh serves as a parental line for performing additional genetic modifications, because the CRISPR/Cas9-based genome editing method is free of introduced drug resistance markers. The broader applicability of this strategy was validated by generating similar reporter lines in Plasmodium species utilized in rodent malaria models. In summary, NF54-mCh represents a unique, versatile platform that will accelerate fundamental research and support the future development of malaria control strategies, including new vaccines and drugs.

Comparative evaluation of deep learning models for plant disease classification with edge-aware performance analysis

Agricultural disease monitoring remains a critical challenge in precision farming, particularly when deploying computer vision systems on resource-constrained platforms. This study presents a rigorous comparative evaluation of four deep learning architectures—ResNet50, DenseNet121, a Binarized Neural Network (BNN), and YOLOv8-cls—for multi-class plant disease classification using the PlantVillage dataset (15 classes). Unlike prior benchmarking studies, we incorporate statistical validation through repeated stratified experiments (5 runs) and report mean ± standard deviation for accuracy, precision, recall, and F1-score. Results show that while DenseNet121 achieves high classification accuracy (99.48)% ± 0.12), it exhibits significantly higher inference latency. The BNN achieves minimal latency but suffers substantial performance degradation (88.31% ± 0.45). YOLOv8-cls provides the best trade-off, achieving 99.64% ± 0.09 accuracy with low latency (3.3 ms ± 0.2). Statistical comparison using paired t-tests confirms that YOLOv8 significantly outperforms ResNet50 p  < 0.05 while maintaining substantially lower inference time.We further discuss generalization limitations due to the controlled nature of PlantVillage and moderate claims regarding edge deployment feasibility based on model size and computational profiling. The study provides a statistically grounded and edge-aware benchmarking framework for plant disease classification models.