Background In the context of global climate change, heatwaves pose an increasing threat to human health. Emergency ambulance calls are an important outcome indicator of acute health response in populations during heatwave weather. However, studies on the association between emergency ambulance calls and heatwaves in China have primarily focused on the southern regions, and less attention is paid to the northern regions, which hinders a comprehensive assessment of acute health impact posed by extreme heat. Objective To quantify the association between heatwaves and emergency ambulance calls in Dezhou City, Shandong Province. Methods The data on daily records of emergency ambulance calls, meteorological factors, and air pollution from May to September of 2020 to 2022 in Dezhou City, Shandong Province were collected. Heatwaves were defined by combining thresholds at the 90th, 92.5th, 95th, and 97.5th percentiles (P₉₀, P_92.5, P₉₅, and P_97.5) of the year-round daily mean temperature and durations of ≥2, 3, or 4 consecutive days, respectively. A generalized additive model with a distributed lag nonlinear model was used to estimate the relative risk of emergency ambulance calls during heatwave days compared with non-heatwave days. Results During the study period, a total of 143393 emergency ambulance calls were analyzed, with 59.75% of individuals aged <65 years, 53.38% male, and 44.94% due to respiratory causes. Compared to non-heatwave periods, all 12 types of defined heatwaves were significantly associated with an increased risk of emergency ambulance calls. The heatwave effect intensified progressively with higher temperature thresholds and longer durations. The cumulative relative risk rose from 1.12 (95%CI: 1.07, 1.17) for the P₉₀_2d type to 1.26 (95%CI: 1.14, 1.40) for the P_97.5_4d type. No significant gender difference was observed in the association between heatwaves and emergency ambulance calls. Regarding age, older adults were more vulnerable, with a relative risk of 1.60 (95%CI: 1.42, 1.81) for the most severe heatwave type P_97.5_4d compared with non-heatwave periods. Among the different causes of calls, the association between respiratory diseases and heatwaves was the most pronounced, with the relative risk increasing from 1.22 (95%CI: 1.15, 1.29) for the mildest heatwave type P₉₀_2d to 1.72 (95%CI: 1.54, 1.93) for the most severe heatwave type P_97.5_4d. Conclusion In Dezhou City, Shandong Province, heatwaves are significantly associated with an increased risk of emergency ambulance calls. The effects of heatwaves on emergency ambulance calls vary by age and disease type, with the elderly and patients with respiratory diseases being more susceptible. These groups should take protective measures.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

ObjectiveBased on the TCM theory of "Yang transforms materials to Qi while Yin constitutes material form"， this paper explored the effects of Zuogui Wan and Yougui Wan on the molecular mechanism of mitochondrial biogenesis during the adipogenic differentiation process of rat bone marrow mesenchymal stem cells （BMSCs） by mediating peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α） and peroxisome proliferators-activated receptor γ （PPARγ）， providing theoretical support for the prevention and treatment of postmenopausal osteoporosis （PMOP） using Zuogui Wan and Yougui Wan. MethodsBMSCs were divided into a blank group， Zuogui Wan （ZGW） group， Yougui Wan （YGW） group， and Progynova group. Cell identification was performed using flow cytometry. The growth curves of BMSCs were plotted using the methylthiazolyldiphenyl-tetrazolium bromide （MTT） method， and the effects of Zuogui Wan and Yougui Wan on the proliferation of BMSCs were detected. The Oil red O staining method was used to detect lipid droplet formation. The Western blot method was used to detect the expression of adipogenesis-related factors PPARγ， CCAAT/enharcer-binding protein （C/EBP）α， C/EBPβ， lipoprotein lipase （LPL） protein， brown adipose tissue-related （BAT） proteins PGC-1α， uncoupcing protein 1 （UCP1）， PR domdin-containing protein 16 （PRDM16）， mitochondrial biogenesis-related PGC-1α， nuclear respiratory factor 1 （Nrf1）， nuclear factor E₂-related factor 2 （Nrf2）， and mitochondrial transcription factor A （TFAM）. The expression of adipogenesis-related factors PPARγ， C/EBPα， C/EBPβ， LPL genes， and the copy number of cytochrome B （CytoB mtDNA） gene was detected using real-time polymerase chain reaction （Real-time PCR）. Mitochondrial ultrastructure was detected using transmission electron microscopy. ResultsCompared with that in the blank group， the proliferation ability of BMSCs in each treatment group increased continuously as the intervention progressed， and lipid droplets significantly decreased after the drug intervention. The mRNA and protein expression levels of adipogenesis-related factors PPARγ， C/EBPα， C/EBPβ， and LPL were significantly downregulated （P<0.01）， while those of the BAT-related factors PGC-1α， UCP1， PRDM16 were significantly upregulated （P<0.01）. The number of mitochondria increased， accompanied by reduced swelling. The double membrane and cristae structure were clear， and the internal cristae rupture was reduced. The copy number of CytoB mtDNA in each treatment group was significantly increased （P<0.01）. The protein expression levels of mitochondrial biogenesis-related PGC-1α， Nrf1， Nrf2， and TFAM in each treatment group were significantly increased （P<0.01）. ConclusionBoth Zuogui Wan and Yougui Wan can prevent and treat PMOP by intervening in mitochondrial biogenesis in BMSCs through PGC-1α/PPARγ.

Objective:To investigate the diagnostic efficacy of time-dependent diffusion MRI (t d-dMRI)-derived microstructural parameters for clinically significant prostate cancer (csPCa) and their associations with the pathological grade of prostate cancer(PCa) based on the International Society of Urological Pathology (ISUP) grades. Methods:This cross-sectional study prospectively enrolled 196 patients suspected of PCa from March 2023 to March 2024 at the First Affiliated Hospital, Sun Yat-Sen University. All patients underwent multiparametric MRI and t d-dMRI to obtain microstructural parameters, including cell diameter (d), intracellular volume fraction (f in), extracellular diffusion coefficient (D ex), cellularity, and apparent diffusion coefficient (ADC) value at oscillation frequencies of 33 Hz, 17 Hz, 0 Hz (ADC 33, ADC 17, and ADC 0). Pathologically, 95 cases were classified as csPCa (ISUP 2-5), and the rest 101 cases were classified as non-csPCa (benign or ISUP 1). Comparison of these microstructural metrics was made between csPCa and non-csPCa groups by independent t-tests or Mann-Whitney U tests, and multivariable logistic regression was used to identify independent predictors. A combined diagnostic model was then constructed based on the independent predictors. The receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. Finally, in PCa, the correlation between microstructural parameters and ISUP grades was investigated by Spearman correlation. Results:The t d-dMRI measurements, including d, f in, cellularity, ADC 33,ADC 17 and ADC 0, were significantly different between csPCa and non-csPCa groups (All P<0.05). But D ex was not significantly different between the two groups ( Z=-1.27, P=0.204). The area under the receiver operating characteristic curve (AUC) for diagnosing csPCa were 0.701 (95% CI 0.628-0.775) for d, 0.869 (95% CI 0.819-0.920) for f in, 0.884 (95% CI 0.835-0.932) for cellularity, 0.777 (95% CI 0.712-0.842) for ADC 33, 0.852 (95% CI 0.799-0.905) for ADC 17, and 0.840 (95% CI 0.786-0.894) for ADC 0. Cellularity ( OR=6.142, 95% CI 2.920-12.929, P<0.001) and ADC 17 ( OR=0.108, 95% CI 0.027-0.429, P=0.002) were identified as the independent predictors, and their combined model achieved an AUC of 0.896 (95% CI 0.852-0.941). In PCa f in and cellularity were positively correlated with ISUP grades ( r=0.490 and 0.397, P<0.001), while ADC 33, ADC 17, and ADC 0 were negatively correlated with ISUP grades ( r=-0.198, -0.345, -0.360; P=0.041,<0.001,<0.001). d and D ex were not correlated with ISUP grades ( P>0.05). Conclusion:t d-dMRI based microstructural mapping correlates with ISUP grades of PCa and may be useful for the differential diagnosis of csPCa.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Objective:To summarize the drug resistance characteristics of clinical pathogen isolates in a tuberculosis specialized hospital from 2017 to 2023，and understand the distribution characterisitics of pathogens and their resistance to antimicrobial agents.Methods:The isolates from January 2017 to December 2023 were identified and subjected to drug susceptibility tests using the VITEK 2-compact system. The susceptibilities of the isolates to antimicrobial agents were determined by the minimum inhibitory concentration（MIC）methods according to the CLSI（2023）guideline. The data of detection of different kinds of bacteria，specimen distribution and the detection rates of major drug-resistant gram-negative bacteria in hospital antimicrobial resistance were analyzed. Chi square test were performed to analyze one or more sets of data related to detection rate or drug resistance rate by SPSS 17.0.Results:A total of 9 993 isolates were detected，among which 9 079（90.9%）were gram-negative bacteria and 914（9.1%）were gram-positive bacteria. From 2017 to 2022，among all gram-negative isolates，the top three most frequently isolated pathogens were Klebsiella pneumonia，followed by Pseudomonas aeruginosa and Acinetobacter baumannii. The overall resistance rates to carbapenems in Acinetobacter baumannii， Pseudomonas aeruginosa， Klebsiella pneumonia，and Escherichia coli were 43.7%（495/1 133），19.4%（276/1 423），8.4%（279/3 304），3.6%（19/521），respectively. The overall detection rates of ceftriaxone/ciprofloxacin-resistant Klebsiella pneumonia（CTX/CRO-R-KPN），or Escherichia coli（CTX/CRO-R-ECO），and quinolone-resistant Escherichia coli（QNR-ECO）were 19.5%（644/3 304），57.6%（300/521），77.4%（403/521），respectively. The difference in resistance rates between imipenem-resistant and imipenem-susceptible Klebsiella pneumoniae was statistically significant（ P<0.05）. Except for CTX/CRO-R-ECO（ χ2=7.9 ，P>0.05），significant differences were observed in the detection rates of other major drug-resistant gram-negative bacteria in hospital（ P<0.05）. The detection of some drug-resistant strains showed an upward trend in recent years. Carbapenem-resistant Acinetobacter baumannii， Pseudomonas aeruginosa，and Escherichia coli exhibited higher resistance rate to other antibiotics. Conclusions:The detection rates of major drug-resistant gram-negative bacteria have increased in the past two years. Carbapenem-resistant strains exhibit relatively high resistance rate to cephalosporin and quinolone antibiotics. Although carbapenem antibiotics remain effective against Enterobacteriaceae，strengthened antimicrobial resistance monitoring and control of multidrug-resistant bacteria spread are necessary.

Objective:To analyze the clinical characteristics of the Chikungunya fever outbreak in Shunde District, Foshan City, Guangdong Province in July 2025 and the risk factors associated with delayed viral RNA clearance.Methods:A total of 562 patients with Chikungunya fever admitted to three designated hospitals in Shunde District from July 10 to 30, 2025 were enrolled. Demographic data, clinical manifestations, and laboratory findings were collected. Patients were categorized into four age groups including minors (<18 years), young adults (18 to 39 years), middle-aged adults (40 to 64 years) and elderly adults (≥65 years). The differences of clinical characteristics among these age groups were analyzed. Intergroup comparisons were performed using chi-square test, one-way analysis of variance, or Kruskal-Wallis H test. Pairwise comparisons between groups were conducted using the Bonferroni or Games-Howell or Dunn method. Binary logistic regression was employed to analyze risk factors associated with delayed viral RNA clearance (>7 days). Results:The mean age of the 562 enrolled Chikungunya fever patients was (44.8±21.3) years. Fever, arthralgia and rash were the three core symptoms, with incidence rates of 87.5% (492/562), 88.4%(497/562) and 69.6%(391/562), respectively. At discharge, only 54.1%(304/562) of patients achieved complete symptom resolution, while 26.5%(149/562) still had arthralgia and 36.1%(203/562) had residual rash. Significant differences were observed among age groups in the incidence of fever ( χ2=9.43, P=0.024), peak body temperature ( F=6.54, P<0.001), incidence of arthralgia ( χ2=26.89, P<0.001), duration of arthralgia ( F=12.68, P=0.001), incidence of rash ( χ2=68.99, P<0.001), rate of residual rash at discharge ( χ2=32.37, P<0.001), lymphocyte count ( F=12.94, P<0.001), platelet count ( F=14.95, P<0.001), and C-reactive protein levels (CRP) ( H=94.18, P<0.001). Further pairwise comparisons revealed that compared to the middle-aged and elderly groups, the minor group had a higher incidence of fever and a lower incidence of arthralgia, and the duration of arthralgia was shorter than the elderly group (all P<0.008 3). Compared with the other three groups, the elderly group had lower incidence and residual rate of rash, and lower platelet counts (all P<0.008 3), and higher levels of CRP (all P<0.05). The elderly group had lower lymphocyte counts compared to the minor and young adult groups (both P<0.05). Significant differences were found among age groups in the time to viral RNA clearance ( F=5.77, P=0.003) and length of hospital stay ( F=11.64, P<0.001), with the elderly group having significantly longer duration for both compared to the other three groups (all P<0.05). Multivariate analysis showed that advanced age (odds ratio ( OR)=1.049, 95% confidence interval ( CI) 1.015 to 1.083), longer duration of fever ( OR=1.529, 95% CI 1.086 to 2.155) and longer duration of arthralgia ( OR=1.927, 95% CI 1.318 to 2.817) were independent risk factors for delayed viral RNA clearance (all P<0.05). Conclusions:Patients with Chikungunya fever in Shunde District primarily present with fever, arthralgia and rash. The incidence and characteristics of these three core symptoms show age-related variations. Elderly patients and those with longer durations of fever or arthralgia are more likely to experience delayed viral clearance.

Computational analysis can accurately detect drug-gene interactions(DGIs)cost-effectively.However,transductive learning models are the hotspot to reveal the promising performance for unknown DGIs(both drugs and genes are present in the training model),without special attention to the unseen DGIs(both drugs and genes are absent in the training model).In view of this,this study,for the first time,proposed an inductive learning-based model for the precise identification of unseen DGIs.In our study,by integrating disease nodes to avoid data sparsity,a multi-relational drug-disease-gene(DDG)graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions.Following the extraction of graph features by utilizing graph embedding algorithms,our next step was the retrieval of the attributes of individual gene and drug nodes.In this way,a hybrid feature charac-terization was represented by integrating graph features and node attributes.Machine learning(ML)models were built,enabling the fulfillment of transductive predictions of unknown DGIs.To realize inductive learning,this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights,enabling inductive predictions for the unseen DGIs.Consequently,the final model was superior to existing models,with significant improvement in predicting both external unknown and unseen DGIs.The practical feasibility of our model was further confirmed through case study and molecular docking.In summary,this study establishes an efficient data-driven approach through the proposed modeling,suggesting its value as a promising tool for accelerating drug discovery and repurposing.