Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

BackgroundSuicide is the second leading cause of death in adolescents. Patients with depression have a high risk of suicide， and family dysfunction is an important contributing factor of suicide. However， the mechanisms among these variables remain unclear. ObjectiveTo explore the influence of family cohesion and adaptability on the risk of suicide in adolescent inpatients with depression， and the mediating effect of depressive symptoms and the moderating effect of resilience， so as to provide references for suicide prevention and intervention. MethodsA total of 335 adolescent inpatients with depression were consecutively enrolled from October 2024 to August 2025 in a tertiary Grade A psychiatric hospital in Guangzhou. All participants met the diagnostic criteria for depression in the International Classification of Diseases， tenth edition （ICD-10）. They were assessed using the Family Adaptability and Cohesion Scale Ⅱ-Chinese Version （FACES Ⅱ-CV）， the Self-rating Depression Scale （SDS）， the Connor-Davidson Resilience Scale （CD-RISC）， the Suicidal Behaviors Questionnaire-Revised （SBQ-R） and the suicide module of the Mini-International Neuropsychiatric Interview （MINI）. A comprehensive suicide risk index was constructed. Model 4 and model 7 in the SPSS macro program Process 4.1 were used to test the mediating effect and the moderating effect， respectively. ResultsCorrelation analysis revealed that the SDS score was positively correlated with the composite suicide risk index （r_s=0.663， P<0.01） but negatively correlated with the family cohesion and adaptability dimensions scores of the FACES II-CV and the CD-RISC score （r_s=-0.448， -0.386， -0.576， P<0.01）. Depressive symptoms fully mediated the association between family cohesion and suicide risk， with an indirect effect of -0.491 （95% CI： -0.646–-0.352）， accounting for 89.76% of the total effect. Depressive symptoms also fully mediated the association between family adaptability and suicide risk， with an indirect effect of -0.442 （95% CI： -0.603–-0.293）， accounting for 89.11% of the total effect. Resilience significantly moderated the effect of family cohesion on depressive symptoms （β=-0.006， P<0.01） and the effect of family adaptability on depressive symptoms （β=-0.007， P<0.05）. ConclusionIn adolescent inpatients with depression， depressive symptoms may play a fully mediating role in the relationships between family cohesion/family adaptability and suicide risk， respectively， and resilience moderated the first stage of this mediating pathway. ［Funded by Guangzhou Science and Technology Plan Project （number， 2024A03J0299）］

Objective The aim of the study is to analyze the incidence patterns and epidemiological characteristics of parasitic and vector-borne diseases in the Minhang and Jinshan districts of Shanghai, and provide evidence-based information for optimizing prevention and control measures. Methods We collected information from epidemic reports and epidemiological investigation forms for cases of parasitic and vector-borne diseases from 2005 to 2024 in Shanghai's Minhang and Jinshan districts. Descriptive epidemiological method were adopted to conduct a comparative analysis of incidence trends, epidemic characteristics, and infection sources. Results A total of 229 cases in 7 categories were reported between 2005 and 2024, with an average annual incidence rate of 0.36/100 000. The average annual incidence rate in Minhang District was significantly higher than that in Jinshan District. The most prevalent diseases were malaria and dengue fever, followed by Japanese encephalitis. Monthly case reports were available, predominantly from June to October, with a peak in July-August. A statistically significant difference was observed between the two districts in case distribution during June-October (P＜0.001), whereas no significant difference was found for July-August (P=0.331). The majority of cases were young men (111/229,48.47%), aged 20-39 years. Migrant workers/farmers constituted the largest occupational group (55/229, 24.02%). Registered residents of other provinces and cities (147/229,64.19%), and the main source of infection was overseas importation (112/229,48.91%). There were no statistically significant differences in gender and household-registration composition between the two districts (P＞0.05). Significant differences existed in age, occupation, and source of infection (P＜0.05). The imported diseases were malaria and dengue fever, with Africa and Southeast Asia being the primary source locations. Malaria was the predominant disease imported from other provinces and cities, with cases having been imported from abroad since 2012. Local infections were mainly Japanese encephalitis, followed by malaria (with the last local case reported in 2010). Cases between 2005 and 2009 were predominantly imported from other provinces and cities. The proportion of cases imported from abroad increased sharply after 2010, particularly in Minhang District. Local infections sometimes arise in Jinshan District, with Japanese encephalitis serving as the primary type. Conclusions Incidence rates of parasitic and vector-borne diseases in Minhang and Jinshan districts have generally remained low. To further reduce this disease burden, it will be necessary to strengthen joint measures for prevention and control, eliminate the environmental risk factors of vector organisms, enhance monitoring and precise control of the environment and inbound personnel to prevent the occurrence and transmission of parasitic and vector-borne diseases.

Lymphoma is a highly heterogeneous malignancy characterized by complex molecular regulatory mechanisms that result in significant differences in aggressiveness and prognosis across its subtypes.Gene copy number alteration(CNA)analysis,an emerging technology,has become a pivotal tool in the precision re-search and management of lymphoma.By detecting DNA deletions,amplifications,and chromosomal copy number changes,CNA analysis addresses the limitations of traditional cytogenetic techniques,enhances the ac-curacy of subtype classification,and aids in evaluating tumor heterogeneity and disease progression.This re-view provides a comprehensive summary of CNA detection methods and their applications in lymphoma,with a focus on recent advancements in the field.It offers a comparative analysis of CNA detection techniques and discusses their role in precision diagnosis,subtype classification,monitoring disease progression,predicting therapeutic resistance,and assessing prognosis.Additionally,the review explores the potential applications of CNA analysis in uncovering molecular regulatory mechanisms,optimizing therapeutic strategies,and impro-ving patient survival outcomes.

Objective To investigate the refractive development parameters of primary school students aged 7 to 12 years in Chuxiong City,Yunnan Province,analyze their relationship with equivalent spherical power(SE),and evaluate the monitoring role of refractive parameters in myopia.Methods A total of 1463 primary school students aged 7-12 from Beipu Primary School in Chuxiong City,Yunnan Province were selected.Spherical degree(DS),cylindrical degree(DC),and equivalent spherical degree(SE)were obtained using a computer autorefractor.Axial length(AL),corneal curvature(K1,K2),anterior chamber depth(ACD),central corneal thickness(CCT),lens thickness(LT),and vitreous depth(VD)were measured using a biometer.The corneal curvature radius(CR),the ratio of axial length to corneal curvature radius(AL/CR),and lens power(LP)were calculated.Participants were divided into 7～8 years old group(n=518),9～10 years old group(n=547),and 11～12 years old group(n=398)according to age;and categorized into myopic group(n=647),emmetropic group(n=532),and hyperopic group(n=284)based on refractive status to analyze the characteristics of refractive parameters and their impact across different age groups and refractive categories.Results(1)SE,DS,LT,and LP decreased with age(P<0.05);(2)AL,AL/CR,ACD,and VD increased with age(P<0.05);(3)K,CR and CCT showed no significant change with age(P>0.05);(4)In both myopic and hyperopic groups,AL was negatively correlated with SE(rs=-0.617,rs=-0.318,both P<0.05),AL/CR was negatively correlated with SE(rs=-0.737,rs=-0.406,both P<0.05),LP was positively correlated with SE(rs=0.412,rs=0.182,both P<0.05),while CR showed no correlation with SE(P>0.05).Conclusion The refractive development parameters AL,AL/CR,ACD,and VD increased with age in primary school students aged 7 to 12,while SE,DS,LT,and LP decreased with age,and K,CR,and CCT showed no significant changes.AL/CR and AL can serve as monitoring indicators for myopia,with AL/CR demonstrating a higher correlation with SE than AL.

Cardiovascular disease and cancer are the two leading chronic conditions contributing to global mortality. With the rising incidence of cancer, the prevalence of cancer therapy-related cardiovascular complications has also increased, driving the development of the emerging field of cardio-oncology. The advancement of precision medicine offers new opportunities for the individualized and targeted management of cardiovascular toxicities associated with cancer treatment. Artificial intelligence (AI) has the potential to overcome traditional limitations in medical data integration, dynamic monitoring, and interdisciplinary collaboration, thereby accelerating the application of precision medicine in cardio-oncology. By enabling personalized treatment and reducing cardiovascular complications in cancer patients, AI serves as a critical tool in this domain. This article provides an in-depth interpretation of the 鈥淎rtificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association鈥?aiming to inform the integration of AI into precision medicine in China. The goal is to promote its application in the management of cardiovascular diseases related to cancer therapy and to achieve precision management in this context.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

China's vast territory has experienced significant climate change trends in recent years,characterized by reduced regional temperature differences and prolonged high-temperature durations.In some areas,summer temperatures can exceed 39℃.Consequently,the incidence of heat stroke has risen annually,emerging as a critical and life-threatening emergency threatening public health.Ethnomedicine,developed through long-term practice across diverse ethnic groups,has cultivated regionally tailored therapeutic approaches for heat stroke based on local climatic conditions and population-specific physiological traits,offering varied treatment options for patients in different areas.However,the absence of unified ethnic medical diagnostic and therapeutic guidelines has constrained the broader application and advancement of these practices.To leverage the unique advantages of ethnic medicine in heat stroke management and enhance clinical standardization,led by the China Medical Association of Minorities:collaborated with authoritative institutions and experts in multi-ethnic medicine.Through systematic collation of ethnic medical experiences in heat stroke prevention and treatment,the team formulated the expert consensus on multi-ethnic medical diagnosis and treatment of heat stroke(2024).This consensus synthesizes shared principles across ethnic medical systems while integrating their distinct theoretical frameworks and clinical practices,yielding scientifically rigorous,practical,and widely applicable recommendations to guide clinical interventions in traditional Chinese medicine(TCM)and ethnic medicine.While ethnic medical systems differ in their etiological and pathophysiological interpretations of heat stroke,they collectively emphasize holistic regulation and adaptive treatment strategies aligned with seasonal and geographical contexts.For instance,TCM,Zhuang medicine,Miao medicine,Mongolian medicine,and Tibetan medicine employ unique methodologies:such as harmonizing qi and blood,balancing body fluids,and dispelling dampness:to address heat stroke through comprehensive interventions.These approaches include oral formulations,external therapies,specialized acupuncture-moxibustion techniques,and herbal baths,demonstrating rich clinical value.The establishment of this consensus lays a foundational framework for the standardized application of ethnic medicine in heat stroke prevention and treatment.It provides innovative insights and practical evidence for optimizing integrated rescue protocols and advancing the evidence-based integration of traditional and modern medical practices.

Compared with matrix-assisted laser desorption ionization mass spectrometry(MALDI-MS)using organic small molecule matrix,surface-assisted laser desorption ionization mass spectrometry(SALDI-MS)based on nanomaterial matrix is more effective in analysis of small molecule compounds.Ion sputtering instruments have obvious advantages for applying inorganic nanomatrix.In this work,the carbon-platinum material was sputtered onto a glass cover slip using an ion sputtering instrument to form a carbon-platinum(C-Pt)composite nanomatrix,and an SALDI-MS analytical method was thus established based on the prefabricated C-Pt composite matrix.The experimental results showed that the C-Pt composite nanomatrix could significantly improve the signal intensity and signal-to-noise ratio of the mass spectrum peaks of the components to be measured.The ratio of carbon to platinum,the duration of ion sputtering,and the laser power in mass spectrometer were investigated to select the optimal C-Pt matrix prefabricated conditions and SALDI-MS experimental conditions.Using the prefabricated C-Pt composite matrix,the melittriose and daidzein sample solution were applied as sample to examine repeatability.The results showed that the intra-point repeatability(RSD)was≤4.8%and the inter-point repeatability(RSD)was≤6.4%.The quercetin and melitriose were applied as model samples,and a linearity between MS peak intensity and respective concentration in the range of 0.05-1.0 mg/mL was found,with linear correlation coefficients(R2)greater than 0.994,showing good potential for quantitative and imaging analysis.Then,the prefabricated C-Pt composite matrix was applied to SALDI-MS analysis of the 50%ethanol extract of soybean,and 15 kinds of compounds including oligosaccharides and triglycerides were identified from the mass spectra.Furthermore,the C-Pt matrix was employed in SALDI-MS imaging for the compositions in corn tissue section,and the results showed that diglycerides and triglycerides were mainly distributed in the corn embryo and around the embryo,and the distribution of oligosaccharides was relatively even.