Browse Articles

Viral replication dynamics: Coinfection and superinfection of Vero cells with Zika virus and West Nile virus or Zika virus and Mayaro virus

Globalization and climate change have contributed to the emergence and spread of arboviruses, with the possibility of their cocirculation in the same geographic region at the same time. Understanding the effects of concurrent infections within a mosquito vector or human host is therefore an emerging focus in arboviral research. Studies using mosquito- and mammalian-derived cell lines provide a controlled framework for studying viral replication dynamics. In this study, Vero cells were infected with either Zika virus (ZIKV) and West Nile virus (WNV) or ZIKV and Mayaro virus (MAYV). In nature, concurrent infections can occur either simultaneously (i.e., coinfections) or sequentially (i.e., superinfections), and both conditions were studied here. Arbovirus replication was assessed 6 days post-infection using RT-qPCR, along with corresponding cytopathic effects on cells. Single infections were used as comparators to the coinfection and superinfection assays. Coinfections (ZIKV/WNV and ZIKV/MAYV) resulted in viral interference, with ZIKV RNA levels reduced when cells were coinfected with either WNV or MAYV. Viral accommodation was observed when ZIKV was the first to infect the cells during superinfections. In contrast, superinfection exclusion was observed when ZIKV was introduced to cells that were already infected with another virus. RNA production of WNV and MAYV was not affected by the presence of ZIKV in any of the conditions investigated. Understanding replication dynamics in in vitro coinfection and superinfection scenarios is the first step toward addressing the knowledge gap concerning cocirculating arboviruses.

Prevalence and factors associated with metabolic syndrome among adults with stage 3–5 chronic kidney disease attending tertiary hospitals in Dodoma, Tanzania

Study protocol for a pilot evaluation of Talk With Me Baby (TWMB) for Well-Child Care: A primary care-integrated language promotion intervention in rural and underserved clinics

A child’s home language environment (HLE) refers to the quality of early language exposure and linguistic interactions with caregivers. A robust HLE is a primary public health consideration, as it positively influences a child’s early- and long-term academic, health and economic outcomes. Talk With Me Baby for Well-Child Care (TWMB) was developed to encourage a robust HLE within routine pediatric healthcare. While this approach has been deployed in a variety of settings, TWMB has not been rigorously evaluated in rural and underserved primary care settings. This paper describes a research protocol to evaluate the preliminary efficacy of TWMB in clinics serving rural or underserved families. At least 66 child-caregiver dyads will be recruited over 5 months (child age two to six months at enrollment) from two primary care clinics. All participants will receive the TWMB intervention during routine WCC visits at participating clinics during a 12-month period. Participants will complete baseline and post-intervention assessments of their HLE. Preliminary efficacy outcomes include changes in the caregivers’ language-promotion behaviors (as measured by Language Environment Analysis normative scores for daily Conversational Turn Count between the adult and child and daily Adult Word Count) from baseline to 12 months post-intervention. This trial has been prospectively registered as NCT06479278 on 2024-06-26.

Deficiency of DOCK2 delays development of spontaneous uveoretinitis in a lymphopenic mouse model of colitis

Abstract Spontaneous experimental autoimmune uveoretinitis (EAU) in double-transgenic (dTg) mice expressing retinal Hen Egg Lysozyme (HEL) and HEL-specific T cell receptor CD4 T cells is a severe CD4⁺ T cell-driven disease, occurs in 100% of dTg mice at post-partum day (p)20/21 and is accompanied by post-EAU colitis. Because DOCK2 (Dedicator of cytokinesis 2) is essential for Rac-dependent immune cell function, including T cell motility and migration from secondary lymphoid tissues, we investigated its effect on T cell behaviour in this model. dTg mice lacking DOCK2 (DOCK2 ⁻/⁻ dTg) were examined for evidence of uveitis (fundoscopy, histology and flow cytometry) and intestinal inflammation (colon length and histology). Retinal inflammation and ocular CD4⁺ T cell infiltration were assessed over time. Flow cytometry of lamina propria cells quantified CD4⁺ T cells and determined the balance of Helios + versus Helios − regulatory T cells. The eye-draining lymph nodes were also analysed for CD4⁺ T cell populations. DOCK2 ⁻/⁻ dTg mice showed delayed and attenuated uveitis. Retinal inflammation, consistently evident by p21 in DOCK2 +/+ dTg mice, and atrophy (~ p30) were absent until ~p60 and were associated with markedly reduced ocular CD4⁺ T cell infiltration. Despite profound lymphopenia, DOCK2 deficiency did not induce spontaneous colitis; colons were normal in length and lacked inflammation. Flow cytometry of lamina propria cells showed no change in overall CD4⁺ T cell numbers between DOCK2 ⁻/⁻ and DOCK2 ⁺/⁺ mice. However, the balance of Helios expression in Treg was altered, with DOCK2 ⁻/⁻ mice containing significantly higher numbers of Helios − Treg in the lamina propria compared to DOCK2 +/+ mice. In contrast, peripheral lymphoid tissues (eye-draining lymph nodes), showed disproportionately reduced CD4⁺ T cell numbers in DOCK2 ⁻/⁻ dTg mice. We conclude DOCK2 may be a potential target for control of uveoretinitis via expansion of eye-homing gut Helios − Treg.

G-protein coupled receptor, signal and signal-transduction related transcript mapping in nigral volume and pigmented neurons reveals a potential deficit of nigral feedback signals associated with Parkinson’s disease

Microarray and Next Generation Sequencing studies offer insight into gene regulation in Parkinson’s disease (PD). However, analysing vast numbers of genes can make the interpretation of data difficult when considering the platforms and techniques used across different studies. In this study, transcript expression, restricted to genes related to G-protein coupled receptors, activating agonists, agonist precursors, synthesis enzymes and transduction processes, as well as stress-related chaperones and solute carriers, was assessed across nine microarray platforms and two RNAseq studies of the substantia nigra (nigral volume). Changes in gene expression associated with PD were assessed by differential expression analysis while RNAseq studies were also used to calculate transcript per million values for each of the genes within the nigral volume. This analysis showed extensive changes in nigral volume signalling for several robustly expressed signal-related transcripts including tyrosine hydroxylase ( TH ), WNT signalling components ( SFRP1 , RSPO2 and DKK3 ), Kallikrein Related Peptidase 6 ( KLK6 ), neurexins ( NRXN1 , NRXN3 ), prostaglandin synthases ( PTGES2 , PTGES3 ) and fractalkine ( CX3CL1 ). The nigral volume signalling ligand data was then cross-referenced to the most abundant G-protein coupled receptor and signal transduction transcripts in the pigmented neurons using two RNAseq and two microarray studies. There were 32 significant changes in G-protein coupled receptor and associated signalling genes in pigmented neurons from PD tissue. Of these, 30 genes were upregulated. Analysis of the transcription factors likely regulating the 30 upregulated genes indicates a stimulation of cell stress pathways, particularly the JNK-MAP kinase pathway. Following this pathway back to changes in nigral signalling transcripts indicates that deficits in at least two autocrine/paracrine signalling systems; dopamine, via Gα o -coupled dopamine D 2 receptors and Dickkopf-3 (DKK3) appear likely to contribute to pigmented neuron stress in PD.

Efficient generation of adversarial examples and cross-channel quality optimization based on latent space distribution search

Spatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy

The intelligent identification of tea diseases is crucial for ensuring tea quality and reducing economic losses in the tea industry. However, the deployment of deep learning models on edge devices remains challenging due to the conflict between detection accuracy and computational overhead. To address this, we propose CA-YOLOv8n, a lightweight object detection model tailored for tea disease diagnosis. Specifically, we introduce a Path-Decoupling strategy to streamline the network structure and integrate the Coordinate Attention (CA) mechanism to enhance the model's spatial awareness of subtle pathological features. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 98.89% while reducing the parameter count by 32.6% and FLOPs by 24.1% compared to the baseline YOLOv8n. The model was integrated into a diagnostic platform with an automated reporting interface, demonstrating that real-time tea disease identification is feasible on commodity CPU hardware in resource-constrained agricultural environments.

rGO/RuO₂/PPy nanocomposite: a promising platform for repeatable, selective, long-term stable, and room-temperature TEA sensing with p-n transition

Robust point cloud lightweighting with multi-scale adaptive filtering and entropy-driven subdivision

Current point cloud simplification methods for complex ground objects face a persistent challenge: balancing noise robustness and geometric detail preservation. To resolve this, we present a lightweight simplification framework that integrates multi-scale adaptive filtering, entropy-driven spatial partitioning, and an enhanced medial axis transform (MAT). This framework incorporates three targeted technical innovations: (1) An adaptive sliding window polynomial fitting filter with multi-resolution weight adjustment, which achieves coordinated noise suppression and sharp feature preservation; (2) A curvature-weighted enhanced MAT algorithm that reduces skeletal artifacts and topological fractures; (3) An entropy-driven adaptive recursive axis-aligned bounding box (AABB) partitioning strategy, which mitigates the inherent trade-off of conventional uniform partitioning: memory waste in sparse regions and feature loss in dense areas. We validated this framework using self-collected datasets of buildings, vegetation, and roads, and further verified its generalization performance on the public STPLS3D benchmark. Our method achieves an average noise removal rate of 87.76%, representing an average improvement of 11.99% over the baseline method; edge retention is 83.3%, an average improvement of 7.35%; the topological integrity and branch accuracy of the skeleton extraction reached 0.93 and 0.95, respectively, both of which were the best among the tested algorithms; the mean error in normal estimation was as low as 3.34%, and the average point-to-surface distance and fracture rate in 3D reconstruction were the lowest. This method provides a point cloud processing solution that combines accuracy and efficiency for fields such as 3D geographic information modeling and scene reconstruction.

Three-dimensional printing of human scala tympani models: surface modification to reduce cochlear implant insertion forces with a physiological lubricant

When place and generation matter: Understanding resident satisfaction in rural tourism

The revitalization of traditional villages through rural tourism has emerged as an important pathway for achieving sustainable regional development worldwide. Although the benefits of tourism are widely acknowledged, limited research has explored how residents’ expectations and place-based perceptions influence satisfaction across generations. This study integrates expectation confirmation theory and place attachment theory to explore the influence of expectations, place identity, place attachment, and perceived value on resident satisfaction. Data from 245 residents in three traditional villages in Yongzhou, China, are analyzed using structural equation modeling and multi-group analysis. Results reveal a negative association between expectations and satisfaction, while perceived value mediates the effects of place identity and place attachment. Generational differences further moderate several key relationships, with older residents showing higher satisfaction. These findings deepen our understanding of resident attitudes toward rural tourism and provide practical guidance for inclusive, community-centered strategies that promote sustainable rural development in line with the United Nations Sustainable Development Goals 8, 11, and 12.

Cumulative human health risk from low-level PCB exposure in waste environments: a congener-specific assessment with implications for regulatory thresholds

Factors associated with severe acute respiratory syndrome coronavirus-2 infection in Hohoe Municipality, Ghana: A case-control study

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection was a major public health challenge globally and in Ghana. To prepare better for future pandemics, an evidence-based understanding of the determinants of coronavirus disease is essential to inform public health guidelines and surveillance. Thus, we identified the factors for SARS-CoV-2 infection in Hohoe Municipality. We conducted a facility-based, sex and age-matched (1:2) case-control study. Cases were persons with a laboratory-confirmed SARS-CoV-2 infection by Reverse Transcription Polymerase Chain Reaction (RT-PCR) or rapid antigen test, while controls tested negative with the same techniques. Data on sociodemographic, clinical, and exposure-related factors were collected through structured interviews. We employed a conditional regression model to establish the factors independently associated with SARS-CoV-2 infection using Stata version 17.0. All statistical tests were two-sided, and a p-value <0.05 in the final multivariate logistic model was considered statistically significant. A total of 234 participants were enrolled (78 cases, 156 controls). The mean age of the cases and controls was 39.7 (±14.6) and 39.4 (±14.4) years, respectively. Moderate/high levels of social interaction increased the odds of infection (aOR=3.00, 95% CI:1.05–8.56, p = 0.040). Having no underlying health condition (aOR=0.25, 95% CI:0.09–0.65, p = 0.004) and regular physical activity or exercise (aOR=0.18, 95% CI:0.04–0.70, p = 0.014) reduced the risk of infection. Public health interventions should therefore prioritize strengthening community awareness about the risks of close social interactions and the benefits of healthy lifestyles, including regular physical activity.

Genetic diversity and genetic parameter estimate among yam (Dioscorea cayenensis Lam. complex) landraces from southwest Ethiopia

Construction of a fine-grained retrieval model for archival text-image based on the integration of scene graph generation and attention mechanism

To tackle the challenges in fine-grained retrieval stemming from noise, official seal occlusions, small text blocks, and other issues prevalent in archival text images, and to fulfill the requirements of integrating both textual and visual dual features while enhancing retrieval accuracy and efficiency, this study has devised a five-tier architectural model. This model comprises an input layer, a preprocessing layer, a scene graph generation layer, an attention fusion layer, and a retrieval matching layer. The model incorporates a dedicated scene graph generation module tailored for archival data, aiming to enhance element detection. Additionally, it features a three-tier attention fusion module that integrates scene graph, text, and cross-modal features to ensure precise feature alignment. Training is carried out using a multi-task loss function, and an index is created to streamline retrieval and matching processes. Experimental results show that the proposed model achieves a Top-1 accuracy of 83.7% and an average precision of 88.3% on the test set, representing a 25.1% improvement over the Top-1 accuracy of an optical character recognition (OCR) combined with word frequency and inverse document frequency model. The proposed model achieves a Top-1 accuracy of 6.1% higher than the archival retrieval network model for examples with official seal occlusion and a Top-1 accuracy of 76.8% for small text blocks. The response time for a single retrieval is 52.6ms. Research provides technical support for efficient retrieval of large-scale archives in archives, effectively solving the problem of archive retrieval in complex scenarios, and significantly improving the efficiency of archive management and utilization.

Continuous metabolic syndrome score and cardiometabolic multimorbidity risk in three nationwide cohorts

Fundus image analysis of retinitis pigmentosa using artificial intelligence

Retinitis pigmentosa (RP) is a group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis would be helpful for effective management of the disease; however, many patients stay unaware of early symptoms. Meanwhile, fundus images are widely obtained during routine medical checkups but are underused for detecting RP. This study explores the effectiveness of finetuning deep learning models, pre-trained for general visual tasks, to identify RP from color fundus images. The dataset comprised 321 color fundus images from 201 Japanese subjects at Keio University Hospital, including 200 images from 107 patients with retinitis pigmentosa and 121 images from 94 non-retinitis pigmentosa subjects. Multiple images were available for some subjects. Using transfer learning, pretrained convolutional neural network models -VGG16, Resnet50, and InceptionV3- were finetuned to detect RP. As a result, Inception V3 achieved the best accuracy of 96.97%, which matches the average diagnostic accuracy of ophthalmologists. Gradient-weighted Class Activation Mapping (Grad-CAM) suggested that the model attended to clinically relevant fundus regions, including the peripheral retina and posterior pole, which may reflect features such as peripheral degenerative changes and retinal vascular attenuation. These findings support the potential interpretability of the finetuned model and suggest that deep learning may assist ophthalmologists in RP screening as a supportive tool.

Modality-mapping-guided customized CycleGAN filtering to enhance narrow-band imaging for improved colorectal lesion segmentation

Clinical and microbiological epidemiology of Candida infections in a high-complexity hospital in Tolima, Colombia (2014–2024)

Background Candida spp. infections are an increasing challenge in high-complexity hospitals, yet epidemiological data remain scarce in underrepresented in Colombian regions such as Tolima. Methods We conducted a retrospective observational study in a high-complexity hospital in Ibagué (Tolima, Colombia) from 2014 to 2024, integrating two institutional data sources: administrative/clinical records and the microbiology laboratory database (WHONET). Species identification relied on culture and VITEK, and antifungal susceptibility was interpreted using criteria from the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST). We summarized data using frequencies/proportions and medians (IQR), explored patterns with multiple correspondence analysis (MCA), and estimated associations with candidemia using penalized multivariable logistic regression due to low event frequency. Results We identified 987 candidiasis episodes and 776 fungal isolates, of which 314 were Candida (40.46%). Mucocutaneous disease predominated (vulvovaginal 50.7%; oropharyngeal 24.3%), while candidemia represented 2.0% of episodes. Among isolates, Candida albicans was most frequent (58.9%), followed by C. parapsilosis (16.6%), C. tropicalis (12.1%), and Nakaseomyces glabratus (6.4%); Candida auris was detected once. In exploratory clinical/administrative models, clinically recorded candidemia showed associations with invasive devices (OR 5.54, 95% CI 2.01–15.64), recent surgery (OR 7.11, 95% CI 1.20–30.88) and tumor (OR 19.88, 95% CI 3.14–97.51). Susceptibility data were available for 196/314 isolates (62.4%); echinocandin activity was high, whereas azole susceptibility was more variable. Conclusions Candidiasis showed a sustained recorded burden and substantial non- albicans diversity, supporting local surveillance, species-level identification, and isolate-level susceptibility testing.

Integration of gene expression and alternative splicing enhances IGHV mutation status and survival risk prediction in chronic lymphocytic leukemia

Abstract The immunoglobulin heavy chain variable region (IGHV) mutation status is a key prognostic marker in chronic lymphocytic leukemia (CLL), shaping disease progression and therapeutic response. Current IGHV testing relies on molecular genomic analyses and requires specific laboratory tests, limiting its accessibility. Here, we developed an unbiased computational approach to accurately predict IGHV mutation status based on gene expression and alternative splicing profiles derived from transcriptome data. We showed that predicted IGHV status had a superior predictive power for overall and failure-free survival compared to conventional IGHV testing methods. Moreover, we identified significant novel associations between key transcriptomic features and genetic lesions, indicating potential but unexplored roles in CLL pathogenesis. Our results thus highlight a novel strategy for integrating molecular features to improve CLL prognosis, underscoring the prognostic value of RNA alternative splicing in CLL biology.