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Updated strategies for studying antivirals and antibodies will face logistical challenges on the ground.
The Bundibugyo virus only emerged twice before. Now, scientists see a chance to get to know it better.
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Higher education policies commonly engender certain spillover effects, which frequently remain unaccounted for within the formal policy evaluation framework. Focusing on China's Double World-Class (DWC) strategy, this paper investigates, from both theoretical and empirical perspectives, whether and how the policy is associated with the research output of non-selected disciplines. Theoretically, this paper puts forward two possible causes of spillovers: reputation enhancement and resource dilution. Based on discipline-level empirical data in Chinese universities from 2014 to 2023 and combined with a panel-regression model, this paper examines whether this policy is associated with changes in the quantity and quality of research outputs among Chinese- and English-language publications across different areas. The results show that the World-Class Discipline (WCD) policy is associated with considerable asymmetric spillover effects on non-selected disciplines. This policy variable is associated with a reduction of 0.62% in the quantity and 1.77% in the quality of research outputs published in Chinese core journals in non-selected disciplines. Meanwhile, it is positively associated with an increase of 5.57% in quantity and 5.06% in quality of research outputs published in English journals. The results of mechanism tests indicate that the enhancement of university reputation and the dilution of educational resources play important roles in the asymmetric spillover effects of WCD policy. The heterogeneity analysis results show that the spillover effects of this policy change across different regions and disciplinary areas. Additionally, this paper integrates the Socialformation Paradigm theory and the Sustainable Social Development theory to further discuss the "delocalization" findings of WCD policy.
Tourette syndrome (TS) involves motor and vocal tics, often with obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBMs) are a potential therapy due to their interaction with the endocannabinoid system, potentially reducing tics and associated symptoms. Compared to antipsychotics, CBMs may offer improved tolerability and fewer side effects. Although evidence is limited, emerging studies suggest their potential to improve quality of life in TS. This review was registered with PROSPERO (CRD420251088633). To evaluate the effectiveness of CBMs in treating TS. We systematically searched PubMed, Google Scholar, ScienceDirect, and the Cochrane Collaboration Database for cohort studies and randomized controlled trials (RCTs) up to July 2, 2025. Data extraction included study characteristics and efficacy outcomes measured by the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). Meta-analysis using Review Manager 5.4 compared pre- and post-treatment scores using mean difference (MD) and 95% confidence intervals (CI). From 1,105 screened articles, eight studies met inclusion criteria for the review, and seven were included in the meta-analysis, involving 306 adult TS patients. CBMs significantly reduced YGTSS scores (MD =  - 13.29, 95% CI [-21.67 to - 4.91], P = 0.002) and PUTS scores (MD =  - 4.09, 95% CI [-7.24 to - 0.93], P = 0.01). CBMs show promising potential in reducing tics and premonitory urges in TS. Larger, placebo-controlled trials are needed to confirm efficacy, ensure safety, and optimize dosing.
In South Africa, adolescent girls and young women (AGYW) are engaged in early, condomless, and unprotected (from pregnancy) sex, which puts them at risk of unintended pregnancies and sexually transmitted infections. At the individual, interpersonal, and structural levels, AGYW experience barriers to their access to and use of contraception and condoms. This study employed a cluster-randomised factorial design to examine the impact of a multi-level intervention on contraceptive use behaviours among 802 women ages 16-24 in Tshwane, South Africa. The two intervention components were the Young Women's Health CoOp (YWHC), an intervention to increase participants' knowledge and skills, and a stigma and discrimination (S&D) reduction training at the facility level. Study facilities were randomised into the four study arms: YWHC only; S&D only; both YWHC and S&D; and control (standard of care). The study demonstrated that AGYW in the YWHC arm (RRR: 2.45; 95% CI: 1.05-5.70, p = 0.038) and those in the YWHC and S&D arm (RRR: 3.65; 95%CI: 1.56-8.50, p = 0.003) were more likely to have started a contraceptive method than to remain a non-user compared to those in the control arm over the 9-month follow-up period. However, those in the S&D training-only arm had lower odds of adopting a method over the follow-up period (OR: 0.63; 95% CI: 0.41-0.97, p = 0.037). These results demonstrate the importance of supporting AGYW with tailored messaging in a safe environment as part of sexual and reproductive health services. In South Africa, many teenage girls and young women start having sex at a young age, often without using condoms or birth control. This puts them at risk of unintended pregnancy and sexually transmitted infections. This study looked at young women ages 16 to 24 in Tshwane, South Africa, and tested a program to help them use birth control and HIV prevention more often. The program had two parts: the Young Women’s Health CoOp (YWHC), which worked directly with young women to teach and support them, and training for clinic staff to reduce stigma and discrimination and improve how they treat young women. The study included four groups: YWHC only, staff training only, both YWHC and staff training, and a control group that received usual care. The results showed that young women who took part in the YWHC program, and those who received both parts, were more likely to start using birth control and to keep using it regularly over the 9-month follow-up period. In contrast, young women in the group with only staff training were less likely to start or use birth control during this time. These findings show that it is important to provide programs that directly support young women in a safe and respectful environment and address their needs for sexual health, pregnancy prevention, and HIV prevention.
Marine fishery ecosystems face unprecedented pressure from overfishing and climate variability, and these mounting threats call for management tools that go beyond traditional static quota systems. This paper puts forward an integrated framework that couples digital twin (DT) technology with deep reinforcement learning (DRL) to tackle sustainable fishery resource management. We build a five-layer hierarchical architecture whose centerpiece is a high-fidelity digital twin that mirrors fishery dynamics through explicit state-transition and observation equations rather than abstract placeholders. A Proximal Policy Optimization (PPO) agent operates within this simulated environment, receiving multidimensional state inputs-resource stocks, oceanographic conditions, fleet operations-and optimizing a composite reward function whose weights we set to [Formula: see text] (economic), [Formula: see text] (ecological), and [Formula: see text] (sustainability). We conduct both comparative experiments and ablation studies using East China Sea fishery data spanning 2010-2023. The ablation study, which isolates the digital twin contribution by comparing PPO with and without DT integration, confirms that the DT alone accounts for a 31.9% reward improvement. Overall, our method achieves a resource recovery index (RRI) of 0.83, outperforming traditional maximum sustainable yield management by 97.6% and standard deep Q-networks by 36.1%. Spatial heatmaps and temporal effort-control time series generated from the learned policy reveal ecologically sensible seasonal and spatial harvest patterns. This research establishes a virtual-real fusion paradigm for intelligent fishery governance and provides decision-support tools for sustainability challenges in an era of accelerating environmental change.
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a global health challenge for which there are currently no approved drugs. Indole-3-propionic acid (IPA), as one of the primary metabolites of gut microbiota, can influence the development of liver diseases through the gut-liver axis, but how it contributes to liver diseases remains unclear. The present study was conducted to explore the possible molecular mechanisms of IPA in the development of MASLD. Metabolomic analysis compared serum and fecal metabolite profiles between MASLD and normal mice. The effects of microbial metabolite IPA on MASLD were evaluated through the utilization of a mouse model and cell models. Transcriptome data analysis was used, and further validation was conducted through flow cytometry, western blotting, RNA interference, and immunoprecipitation. Serum and fecal IPA levels in high-fat diet-fed mice were significantly decreased compared with those of normal chow diet-fed mice. IPA supplementation reduced hepatic lipid accumulation and alleviated insulin resistance, liver damage, and steatosis development in high-fat diet-fed mice, while gut microbiota dysbiosis was restored. Mechanistic analysis suggested that IPA promoted FMO2 expression, enhancing the interaction between FMO2 and protein kinase R-like endoplasmic reticulum kinase (PERK) and inhibiting the PERK/eIF2α/ATF4/CHOP signaling cascade, then mitigating endoplasmic reticulum (ER) stress, such as reducing hepatocyte apoptosis and reactive oxygen species levels, ultimately improving MASLD. IPA can promote the expression of FMO2, which binds to PERK within the ER of hepatocytes. This binding process inhibits the phosphorylation of PERK, thereby affecting PERK-mediated ER stress, and subsequently leading to a reduction in hepatocyte apoptosis and oxidation. This study puts forward the IPA/FMO2/PERK axis as a potential therapeutic target in ER stress for MASLD.
Transfusion-transmitted malaria (TTM) remains a major concern in malaria-endemic regions of Sub-Saharan Africa (SSA), where routine screening of blood donors for Plasmodium species is rarely implemented. Asymptomatic carriers among donors serve as silent reservoirs of infection, which puts transfusion recipients at substantial risk. This systematic review and meta-analysis aimed to assess the prevalence of Plasmodium spp. carriage among blood donors and evaluate the associated risk of TTM across SSA. A comprehensive literature search was conducted in PubMed (MEDLINE), Google Scholar, ScienceOpen, MedRxiv and BioRxiv for studies published up to July 2025. Eligible studies were those reporting the prevalence of Plasmodium spp. among blood donors or documenting cases of post-transfusion malaria in recipients. Data extraction was performed in accordance with PRISMA guidelines. The methodological quality of the included studies was independently assessed by two reviewers using the Joanna Briggs Institute (JBI) checklist and the AXIS tool for cross-sectional studies. A random-effects meta-analysis was performed using MetaAnalysis Online to estimate pooled prevalence, and publication bias was explored using Egger's test. A total of 1,364 records were identified, of which 12 studies met the inclusion criteria, representing 11,818 participants across West, Central, and East/Southern Africa. The pooled prevalence of asymptomatic Plasmodium spp. carriage among blood donors was 12% (95% CI 6-20%), based on a random-effects model, heterogeneity was substantial (I2 = 99%, p < 0.01), while Egger's test (p = 0.581) indicated no significant publication bias. Three studies reported post-transfusion follow-up data. Comparative risk analysis revealed pronounced discrepancies between parasite prevalence in transfused blood and the number of documented TTM cases, with transmission rates among infected blood units ranging from 63% to 100%. Asymptomatic Plasmodium spp. carriage among blood donors poses a potential threat to transfusion safety in sub-Saharan Africa, underscoring the need for improved screening, strengthened haemovigilance, and further research.
The automated inspection of aluminum profile surface defects, which heavily relies on data acquired by machine vision sensors, is a critical task in industrial quality control. Addressing the current challenges of intense background texture interference and the difficulty in detecting defects with extreme aspect ratios on aluminum profiles, this research puts forward a complete end-to-end defect detection algorithm named WMC-DFINE (WIFA-MKSS-CSFF-DFINE) based on the DFINE framework. First, a Wavelet-Integrated Frequency Attention (WIFA) module is introduced, which utilizes a discrete wavelet transform to decouple features into the frequency domain, thereby dynamically suppressing high-frequency background noise and enhancing defect edge responses. Second, a Cross-Scale Feature Fusion (CSFF) module based on dual-channel pooling is designed to ensure the continuity of defect features, thereby resolving the semantic misalignment issue in traditional fusion. Third, a Multi-Kernel Strip Shuffle (MKSS) module is incorporated, utilizing decomposed convolution kernels to capture the geometric features of slender scratches. Finally, a knowledge distillation strategy is employed to transfer structured knowledge from a complex teacher model to a lightweight student model. Experiments on the Tianchi aluminum defect dataset demonstrate that WMC-DFINE achieves a mAP of 82.1%, which surpasses algorithms including YOLOv12, RT-DETR, and the baseline model DFINE. Furthermore, the distilled student model, WMC-DFINE-distill, improves the mAP by 3.2% compared to DFINE, reduces parameter count by 47%, and achieves an inference speed of 59.75 FPS on the experimental equipment. The proposed method effectively resolves the problem of balancing background suppression and defect detail feature preservation, offering a practical and efficient scheme for real-time industrial defect inspection.
Existing medical image generation tasks primarily employ Generative Adversarial Networks (GANs), which perform poorly on datasets with temporal characteristics and suffer from slow generation speed and mode collapse. In response to this question, this study puts forward a temporal conditional diffusion model based on a dual U-Net structure, which leverages the dual U-Net to extract rich detail information within a denoising diffusion framework while incorporating temporal information as a condition to guide the generation of 4D cardiac datasets with temporal features. Additionally, a deformation field is utilized to accelerate medical image generation. Experimental results show that compared to existing methods, the proposed approach can generate dynamic scan time frames while maintaining strong continuity and temporal consistency in both transverse and longitudinal spatial dimensions. In addition, the synthesized images are highly similar to those captured in reality. The proposed method effectively preserves anatomical structural details, making it highly suitable for medical image generation tasks.
The hierarchical organization of the brain's distributed network has received growing interest from the neuroscientific community, largely because of its potential to enhance our understanding of human cognition and behavior, in health and disease. This interest is motivated by the hypothesis that near-critical brain dynamics enable multiscale integration and segregation of neural dynamics. While most multiscale connectivity analyses focus on structural and functional networks, characterizing the effective connectome across multiple scales has been somewhat overlooked-primarily for computational reasons. The difficulty of estimating large cyclic causal models, together with the scarcity of theoretical frameworks for systematically moving between scales, has hindered progress in this direction. This technical note introduces a top-down multiscale parcellation scheme for dynamic causal models, with application to neuroimaging data. The method is based on Bayesian model comparison, as a generalization of the well-known Δ B I C method. To facilitate computation, recent developments in linear dynamic causal modeling (DCM) and Bayesian model reduction (BMR) are deployed. Specifically, a naïve version of BMR is introduced, enabling the parcellation scheme to scale to hundreds or thousands of regions. Notably, the derivations reveal an analytical relationship between reduced model evidence and minimum cut problem in graph theory. This duality puts the tools of graph theory at the service of model evidence optimization and significance testing. The proposed method was applied to simulated and empirical causal models to establish face and construct validity. Consequently, the large empirical causal network, inferred from a neuroimaging dataset, exhibited log-log scaling trends, suggestive of scale invariance in multiple dynamical measures. Future generalizations of this technique and its potential applications in systems and clinical neuroscience are discussed.
Stress levels within academic institutions are high and have continued to rise over recent decades. This can have a detrimental impact on the well-being of dental educators and puts them at a risk of burnout. It is vital to explore the factors that affect the well-being of staff and identify solutions to inform the development of strategies for the promotion of well-being. Participants attending the ADEE annual conference were invited to participate in a qualitative study using focus group discussions to explore this topic. Prompts for the four focus group discussions were identified from quantitative data collected in an earlier study conducted by the research group. The transcribed data were coded and analysed by two of the researchers to identify emerging themes. Four key themes emerged from the data analysis: (1) Workload with an additional three subthemes of poor collegiality, poor uptake of well-being services and gender; (2) stigma associated with poor well-being; (3) workplace culture; and (4) proposed solutions for promotion of staff well-being. It is vital for educational institutions to build strong foundations of basic well-being and resilience within their workforce by providing an environment and culture that supports both good mental health and good understandings of mental health. This requires a change in workplace culture in terms of the value institutions hold on the benefits of having good well-being and sustainable resilience at all levels of the workforce.
Volunteer teachers are an important supplementary force for the development of education in underdeveloped areas at present. Nevertheless, problems such as weak retention intention, low work enthusiasm and high turnover rate among volunteers cannot be ignored. It is important to examine how to effectively incentivize volunteers to participate in volunteer teaching. This study focuses on the issue of incentive failure among volunteer teachers. From the perspective of qualitative research and based on the grounded theory methodology, the research team conducted in-depth interviews with 36 volunteer teachers and carried out participatory observation of the daily work of 11 volunteer teachers. Through constant comparative analysis of the data and repeated review of analytic memos, categories and subcategories were clarified using the three-stage coding process of open coding, axial coding, and selective coding, and a model of the mechanism of incentive failure among volunteer teachers was constructed. The findings indicate that the extrinsic motivating factors leading to incentive failure among volunteer teachers include insufficient organizational vitality, deficiencies in the institutional framework, and imbalance in societal attention, while the intrinsic motivating factor is difficulty in self-worth identity. This study constructs a mechanism model of incentive failure for volunteer teachers. Based on the above research findings, this study discusses strategies to improve the volunteer incentive mechanism and puts forward suggestions from the perspectives of organizational vitality, institutional regulation, social public opinion and school education.
In consciousness science, theoretical predictions are often untestable, such as claims about phenomenal consciousness in other beings. This evidential underdetermination, in combination with the perceived moral significance of consciousness, puts consciousness science at risk of becoming a marketplace of rationalizations: a field that produces theories that reaffirm social practices and conventions.
The art of cytopathology lies not only in the interpretation of the slide but also careful consideration of demographic, clinical, radiological, and other relevant information of individual patients. While deep image learning may perform well in specific cytology image sets or uncover novel, significant cytomorphologic parameters, the lack of clinical context puts prediction models at a dangerous position where irrelevant and potentially harmful diagnoses may be issued. The increment in prediction accuracy by expanding the size of image sets diminishes and plateaus after reaching certain thresholds. This can be overcome by introducing multimodal data to deep learning, which has seen success in resection and small biopsy specimens from histopathological specimens and early adoption in cytopathology. In this study, a qualitative review on the current state of the potential sources of multimodal input-demographics, clinical investigations, and radiology; the diagnostic data that can be obtained in a cytology specimen-stains and preparations, immunocytochemistry, and molecular testing will be discussed with reference to the potential of multimodal deep learning in cytopathology.
This article focuses on the series of viewpoints on the prevention and treatment of medication-related osteonecrosis of the jaw issued by the American Association of Oral and Maxillofacial Surgery (AAOMS), and puts forward several uncertain issues from a clinical perspective. The points of doubt in diagnosis include the definition of drug categories and the threshold of bone exposure duration, as well as the significance of imaging features of bone lesions for diagnostic and treatment decisions. The concerns in terms of prevention include drug risks and disease incidence, tooth extraction and drug holiday, intervention measures promoting bone wound healing and their effectiveness. In terms of treatment, with integration of the author's practical experience, the discussion points focused on the dominant principles of non-surgical treatment, the identification and confirmation of the surgical bone incision boundary, the application of buccal fat pads, chin flaps and submandibular gland transposition, as well as the applicable conditions for permanent repair of fibular transplantation, temporary repair of reconstruction plate bridging, compromise mandibular resection and maxillary sinus opening, etc. The article suggests seeking evidence-based research on the above issues. 本文围绕美国口腔颌面外科学会（AAOMS）发布的关于药物相关性颌骨坏死防治的系列意见书，从临床角度对其带有不确定性的若干问题提出商榷意见。在诊断方面的质疑点包括对药物类别和骨暴露持续时间阈值的界定、骨病变影像学特征对诊断和治疗决策的意义；在预防方面的关注点包括药物风险与疾病发生率、拔牙与药物假期、促进骨创愈合的干预措施及其有效性；在治疗方面融入了笔者的实践体会，讨论点集中于非手术治疗的主导原则，手术中对手术切骨边界的确认，颊脂垫、颏瓣和颌下腺转位的应用，以及腓骨移植永久性修复、重建板桥接暂时性修复、姑息性下颌骨切除和上颌窦开放适用条件等。文章建议对上述问题寻求循证研究。.