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Tissue-specific amino acid accumulation in Cynomorium songaricum stem and epidermis responds to distinct water and soil cues
This study provides the first evidence, from the perspective of intra-organ metabolic division of labour, that Cynomorium songaricum adapts to the heterogeneity of desert habitats through a synergistic strategy of “stem epidermis perceiving the water environment and fleshy stem buffering soil stress.” The research found a significant spatial metabolic division within the organs of C. songaricum : the stem epidermis is enriched in various essential and functional amino acids, including threonine and serine, with their content significantly higher than in the fleshy stem. In contrast, the fleshy stem specifically accumulates proline and cysteine, showing a significant difference. Environmental driving analysis indicates that, although C. songaricum is a holoparasitic root parasite, the metabolic division within its organs is still significantly driven by differential rhizosphere soil-water environmental factors: the amino acid profile of the stem epidermis is sensitive to water environmental factors such as electrical conductivity and carbonate ions, showing a positive correlation; whereas the amino acid composition of the fleshy stem is mainly regulated by soil factors such as water-soluble salts, showing a negative correlation. This suggests that, even while relying on the host for nutrition, the metabolic characteristics of C. songaricum are still influenced by its growth micro-environment. This finding breaks through the research paradigm of treating plants as homogeneous entities, providing new insights into understanding plant ecological adaptability and offering a key scientific basis for the artificial adaptive management and comprehensive utilization of C. songaricum resources.
Diagnostic accuracy of circulating microRNAs and lipidomic biomarkers for early breast cancer detection: A systematic review and meta-analysis
Background Breast cancer outcomes improve substantially with earlier detection, yet mammography performance can be limited in dense breasts and may lead to false-positive investigations. Circulating microRNAs (miRNAs) and lipidomic/metabolomic signatures have emerged as promising minimally invasive biomarkers that could complement imaging for early-stage detection. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of circulating microRNAs and lipidomic/metabolomic biomarkers for early breast cancer detection and to explore between-study heterogeneity. Materials and methods A PRISMA 2020–compliant systematic review and meta-analysis were conducted. MEDLINE (PubMed), Web of Science, Scopus, Springer, ScienceDirect, and the Cochrane Library were searched for eligible diagnostic studies assessing circulating microRNAs and lipidomic/metabolomic biomarkers measured in serum or plasma. No language restrictions were applied. Non-English reports were screened and, when potentially eligible, translated for full-text assessment and data extraction. Studies using whole blood were excluded. Study quality was assessed using QUADAS-2. Random-effects models (DerSimonian–Laird) pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and summary ROC (SROC) area under the curve (AUC). Heterogeneity was evaluated using Q and I 2 statistics, and small-study effects were assessed using Deeks’ test. Results Thirty-two studies (2015–2025) comprising 6,935 participants (3,697 breast cancer cases; 3,238 controls) were included. Pooled sensitivity was 0.87 (95% CI [0.83, 0.90]) and pooled specificity was 0.84 (95% CI [0.79, 0.88]), with pooled DOR 46.10 (95% CI [27.80, 76.70]), PLR 5.17 (95% CI [3.99, 6.68]), NLR 0.16 (95% CI [0.13, 0.21]), and SROC AUC 0.92 (95% CI [0.87, 0.94]). Heterogeneity was substantial (I 2 = 88.29% for sensitivity; I 2 = 90.20% for specificity). In subgroup analyses, serum-based studies showed higher pooled specificity than plasma-based studies. A formal threshold effect assessment did not reveal a statistically significant correlation between sensitivity and false-positive rate (Spearman ρ = − 0.334, p = 0.062). Deeks’ test suggested potential small-study effects (p = 0.043). Conclusions Circulating microRNA and lipidomic/metabolomic biomarkers demonstrate strong overall diagnostic performance for breast cancer detection; however, substantial heterogeneity and potential small-study effects limit their immediate clinical translation. Given that most included studies used retrospective case-control designs rather than prospective screening cohorts, these biomarkers are best regarded as investigational, complementary tools rather than replacements for mammography at this stage. Future large, prospective, standardized studies with harmonized pre-analytics and prespecified thresholds are needed to support the implementation of screening or triage pathways.
The news media and the agenda for noncommunicable diseases before and during the COVID-19 pandemic: Losing the competition for coverage and framing responsibility for action in Malawi
Chronic noncommunicable diseases (NCDs, such as cardiovascular and diabetes) pose an increasing burden in low-and middle-income countries, accounting for ~80% of premature (under age 70) NCD deaths globally. Despite their significant and rapidly growing burden—and status under United Nations Sustainable Development Goal 3.4—little is known about the agenda status of NCDs in lower-income countries. Though coverage of health issues shapes policymaker and public awareness and opinions, news media is an under-analyzed agenda setting arena. This study explores levels of priority for 10 NCDs compared to four infectious diseases and their burdens in the news media arena in Malawi; shifts in the agenda during the COVID-19 emergency; and media framing of actors responsible for addressing Malawi’s NCD burden. The study is informed by analysis of the contents of headlines (an indicator of media priority) and a purposefully selected subset of articles (framing analysis) published in two news outlets between 2015 and 2023. News media priority for NCDs in Malawi remained relatively low despite elevation to Sustainable Development Goal status. Between 2015 and 2019, news media priority in Malawi heavily favored HIV/AIDS, tuberculosis and malaria over 10 NCDs. Media priority shifted to COVID-19 and headlines on mental health surged ahead of those on HIV/AIDS and tuberculosis during the emergency (2020–2023). Alignment between the burden of disease and media priority was inconsistent. News media framed nongovernmental actors (primarily domestic) as responsible for addressing NCDs (predominantly cancers) far more frequently than other actors, including government, politicians and development partners. Featuring patterns of low attention and framing that fails to hold authorities responsible for addressing NCDs, media priority appears unlikely to substantially shift public opinion or policymaker agendas in their favor in Malawi. This study sets the stage for further analysis of media priority for health issues in lower-income countries.
Co-occurrence of antibiotic residues and Antimicrobial resistance genes in animal manure and agricultural soils from Machakos, Kiambu, and Kajiado Counties, Kenya
The excessive and often unregulated use of antibiotics in livestock production and human health has led to the dissemination of antibiotic residues and antibiotic resistance genes (ARGs) posing a serious threat to the environment and public health. This study investigates the co-occurrence and spatial distribution of antibiotic residues and ARGs in livestock manure and agricultural soils from Machakos, Kiambu, and Kajiado counties in Kenya, regions characterized by intensive livestock farming. A total of 180 animal manure and soil samples from 30 farms across the three counties were collected and pooled into 18 samples. Antibiotic residues were extracted and quantified using high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Nine ARGs ( aadA, ermB , sul1, tetQ, tetW, dfrA1, blaMOX, blaOXA and qnrB were quantified via absolute and relative qPCR, normalized to 16S rRNA gene copy numbers. Multivariate analyses, including PCoA and Spearman Correlation, were conducted to explore ARG structure and co-occurrence. Tetracycline, particularly oxytetracycline, was the most abundant antibiotic in manure (up to 1150 ng mL −1 ), especially in pig manure from Kiambu. Sulfadimethoxine was undetectable in nearly all samples except in soil mixed with pig manure from Kajiado county, which showed a concentration above 70 ng mL −1 , the pig manure from the same county had levels below 2 ng mL −1 . Sulfadimidine, sulfamethoxanole and erythromycin were not detected in any of the samples. A three-way ANOVA showed that animal source (cattle, pigs, poultry) and sample type (manure, soil) were generally not significant factors for antibiotic concentration. Only oxytetracycline (P = 0.0421, 0.0901) and sulfadimethoxine (P = 0.0904, 0.044, 0.054) showed marginal significance by sample type (P = 0.0785). Antibiotic resistance genes were widely distributed, with aadA, ermB and sul1 being the most prevalent, especially in soils from Kiambu and Machakos. The tetQ showed extremely high relative abundance, indicating intense selection pressure from tetracyclines. Strong positive correlations were observed between co-occurring ARGs, including tetQ and tetW as well as erm and sul1 . The concurrent detection of persistent antibiotic residues and high ARG loads in both manure and soils underscores the urgent need for improved antibiotic stewardship, sustainable manure management, and environmental monitoring in Kenyan agroecosystems.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Occupational interventions and their effects on work-related and mental health outcomes: A scoping review protocol
Background The well-being of individuals within their workplace (i.e., workplace mental health) has important implications for their productivity and mental health. The extensive literature on the effectiveness of individual workplace interventions does not yet include a comprehensive synthesis of occupational interventions delivered in workplace settings. Objective The proposed scoping review is designed to identify and categorize occupational interventions studied in the literature; and 2) map reported effects on work-related outcomes and mental health outcomes. Methods The review will examine five online databases to identify relevant articles: Embase, CINAHL, APA PsycINFO, MEDLINE, and Web of Science. The inclusion criteria will encompass peer-reviewed English-language studies published from 2005 to 2025, focusing on the effects of occupational interventions designed to improve workplace mental health. The results will support work participation among working adults. Discussion The review will examine the effects of current occupational interventions on workers’ mental health and well-being, as well as on work-related outcomes (e.g., work participation, productivity). The review results can inform researchers designing future studies assessing the effectiveness of occupational interventions on mental health and guide the development of new interventions.
Bactericidal and anti-inflammatory activity of defined hypochlorite solution
Background Hypochlorite is an antiseptic that has been used for over a century for industrial sterilisation and antisepsis. More recently, the topical application of hypochlorite to severe burn patients increased their rate of survival post infection with Gram-negative bacteria, of which antisepsis cannot be solely responsible. Methodology Broth microdilution experiments were performed to confirm anti-microbial activity. Cytokine exposure was performed by dialytic exposure of serum spiked with either interleukin-6 or interleukin-10, accounting for contextually appropriate organic load. Cytokine degradation was measured by immunoassay and by functional bioassay utilising HEK-Blue cells. Hypochlorite mediated cytotoxicity against dermal cells was assessed via direct exposure, followed by a 1-hour attenuation period. Resazurin was utilised to assay the impact of hypochlorite on cell viability. Results We have shown that this formulation of hypochlorite in isotonic saline has multiple mechanisms. The first is that 7 mmol/L hypochlorite is capable of eliminating both Gram-negative and Gram-positive bacteria, and that the same concentration differentially degrades the function of IL-6 and IL-10. IL-6 was seen to be 3 times more susceptible to loss of function than IL-10 after only 5 minutes of exposure ( P < 0.005). The asymmetrical effect on these cytokines was observed between 7 and 1.75 mmol/L, and across the entire time scale examined. This hypochlorite formulation has the capability to sterilize a wound, and by altering the cytokine profile reduce inflammation and scarring, and improve wound healing. Conclusions These findings, taken with the burn survival study, suggest that the increased survival rate after application of hypochlorite could be due to the combined effect of elimination of infective pathogens, and the differential degradation of interleukin-6 and interleukin-10 at the site of injury.
Global and local impacts of multi-dimensional urban form on PM2.5 concentrations: A case study of the downstream urban agglomeration in the Yangtze River Delta
The acceleration of urbanization and the expansion of population scale have led to increasingly prominent PM2.5 pollution. Taking the urban agglomeration in the lower reaches of the Yangtze River within the Yangtze River Delta as the study area, this research innovatively constructs a 1D-2D-3D multi-dimensional urban form indicator system. By integrating spatial autocorrelation analysis, hot spot analysis, the Optimal parameters-based geographical detector (OPGD) model, and the Geographically Weighted eXtreme gradient boosting (GW-XGBoost) model, this study systematically reveals the spatiotemporal evolution characteristics of PM2.5 concentration from 2014 to 2022 and the global and local driving mechanisms of multi-dimensional urban form on PM2.5 concentration. This study aims to fill existing research gaps and provide scientific support for the precise prevention and control of PM2.5 pollution in urban agglomerations. The results indicate that: Multi-dimensional urban form exhibits significant spatial differentiation, with high values of 1D and 2D forms concentrated east of Nanjing, while high values of 3D forms are distributed west of Nanjing. The correlation coefficients of Building density (BD) with Mean building height (MBH) and Floor area ratio (FAR) reach 0.78 and 0.85, respectively, indicating a distinct characteristic of urban vertical expansion. The regional annual average PM2.5 concentration shows a continuous downward trend (decreasing from 60.77 μg/m 3 in 2014 to 29.52 μg/m 3 in 2022), with reductions ranging from 29.55% to 40.61% across individual cities. Spatially, a hot spot area at the 99% confidence level, centered around Nanjing and Ma’anshan, is formed, presenting a stable pattern of “high in the middle and low on both sides.” Regarding the global driving mechanisms, River density (RD), Digital elevation model (DEM), and Road network density (RND) are the core factors influencing PM2.5 concentration (with q -values of 0.3232, 0.2604, and 0.1852, respectively). Pollution risk is highest when DEM is in the 19–34 m elevation zone (concentration reaching 30.06 μg/m 3 ), and all factor interactions exhibit nonlinear enhancement effects. Significant spatial non-stationarity exists in the local driving mechanisms. The regulatory effects of urban form are stronger in core cities (Nanjing, Shanghai), with local R 2 values ranging from 0.30 to 0.35. Specifically, RD exhibits a significant positive driving effect in the central region of Nanjing–Ma’anshan–Wuhu (coefficient 0.80–1.00). In key transportation areas such as northern Shanghai, the coefficient of RND reaches 0.50–0.70. The positive effect of Proportion of transportation land (PTL) is prominent along expressways and around logistics hubs (coefficient 0.30–0.50). In contrast, in peripheral cities (Anqing, Chizhou), the local coefficients of determination (R 2 ) are only 0.16–0.20. The mitigating negative effect of the Proportion of water body (PWB) exhibits a “water-adjacent attenuation” characteristic. This study effectively compensates for the shortcomings of traditional research in the systematic integration and methodological applications of characterizing nonlinear relationships, accounting for spatial heterogeneity, and analyzing multi – dimensional urban form systems. It provides scientific support and specific pathway references for the precise prevention and control of PM2.5 pollution and urban form optimization at the urban agglomeration scale. The findings carry important practical value for air quality improvement and sustainable development in similar high – density urban agglomerations.
Breast cancer clustering integrating complete gene expression profiles and genetic ancestry
Breast cancer (BC) remains the leading cause of cancer-related mortality among women globally. Precise subtyping of BC is critical for optimizing treatment strategies. This study explored the capacity of bulk RNA-seq data to improve breast cancer characterization by analysis of complete expression profiles. We analyzed RNA-seq data for 274 tumor samples and six healthy tissue samples from diverse geographical origins. Using over 9,800 SNPs directly genotyped from RNA-seq data, we successfully predicted broad genetic ancestry, identifying European, African, Asian, South Asian, and Admixed American origins. Molecular subtyping through PAM50 showed some level of ambiguity, depending on the amount of samples provided as input. In silico drug sensitivity analysis identified potential therapeutic strategies, including Etoposide and Mistaurin, with cluster-specific efficacy. Our findings emphasize the integration of ancestry-informed data and complete transcriptomic profiles to redefine BC subtyping. These insights offer a foundation for more equitable, ancestry-informed therapeutic strategies and highlight the importance of diversity in cancer research.
Quantitative assessment of synergistic effects in push-pull tactical ventilation during nonlinear space fires
Smoke generated in compartment fires rapidly degrades visibility and tenability, making mechanical tactical ventilation a critical measure for restoring operable conditions. This study provides a full-scale experimental assessment of the mechanical (fan-driven) component of tactical ventilation in a two-story, non-linear layout with stairwell connectivity. Following the cold-flow experimental methodology established in prior PPV studies, smoke was produced using commercially available simulated smoke cakes to generate cold, thereby isolating the aerodynamic ventilation performance from buoyancy-driven fire dynamics. The performance of positive pressure ventilation (PPV), negative pressure ventilation (NPV), and their coordinated push-pull operation was quantified using illuminance recovery, local airspeed measurements at openings, and clearance time to 90% illuminance recovery. Each condition was repeated three times. Results show that the push-pull mode achieved the shortest mean clearance time (101.4 ± 16.9 s, mean ± SD, n = 6), reducing clearance time by approximately 25.0% compared with PPV (135.1 ± 15.9 s) and 31.9% compared with NPV (149.0 ± 15.9 s). One-way ANOVA on the overall clearance time confirmed statistically significant differences among the three conditions (F(2,15) = 13.67, p < 0.001). However, the synergy gain factor (SGF = 0.666) indicates sub-linear gains relative to ideal linear superposition. Importantly, the findings are intended as a ventilation-driven baseline relevant to training scenarios and post-knockdown smoke management where thermal driving is reduced; extrapolation to fully developed fires requires coupled thermal-fluid validation.
The impact of Kamala Harris’s social identities during the 2024 presidential election
The social identities of Democratic Party presidential nominee Kamala Harris were noted frequently prior to the 2024 United States Election. We examined how making salient her marginalized racial and gender identities affected evaluations of her prior to the election. In Study 1, a stratified U.S. sample viewed a paragraph describing only Harris’s qualifications or her qualifications along with her gender, race, or both. Mentioning her race led to less favorable perceptions regardless of participants’ political orientation, suggesting subtle identity cues shape impressions. Political orientation did moderate responses when Harris was compared to an unnamed candidate, with more conservative participants rating Harris less favorably. Study 2 disaggregated Harris’s racial background showing that her Black identity, not her South Asian or combined racial identity, drove the negative evaluations across participant political orientation. As in Study 1, conservatives rated her more negatively than an unnamed candidate, but only when her Black identity was made salient. Together, these studies demonstrate that identity salience, particularly Black racial identity, can meaningfully influence candidate evaluations.