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.

Objective: To investigate the relationship between pubertal timing and depressive symptoms among high school students in Suzhou, so as to provide scientific evidence for promoting adolescents mental health. Methods: From October 2023 to January 2024, 3 369 students were selected from 20 high schools in Suzhou using stratified cluster random sampling method. Physical examinations and questionnaire surveys were conducted. The Preece & Baines growth Model 1 was used to calculate the age at take off of height velocity (ATO) and age at peak height velocity (APHV), categorizing students into three groups: early pubertal timing group (< P 15 ), ontime group ( P 15 - P 85 ), and delayed group (> P 85 ). Binary Logistic regression was used to analyze its association with depressive symptoms. Results: The ATO for male and female high school students in Suzhou was (9.35±1.23) and ( 8.12 ±1.52) years old, respectively. The mean APHV was (12.35±0.74) years old for boys and (10.91±0.82) years old for girls. The overall prevalence of depressive symptoms was 34.22%, with no statistically significant gender difference ( χ 2=0.42, P =0.52). Significant differences in depressive symptom prevalence were observed across grade levels, breakfast frequency, weekly days of moderate to vigorous physical activity, daily sleep duration, history of school bullying, and the presence of Internet addiction ( χ 2=5.03-69.21, all P < 0.05 ). After adjusting for age, body mass index, region, boarding status, breakfast frequency, weekly moderate to vigorous physical activity days, sleep duration, campus bullying, and presence of Internet addiction, Logistic regression analysis revealed that when ATO was used to evaluate pubertal timing, the risk of depressive symptoms in the delayed group of boys was 1.65 times that of the on time group (95% CI =1.24-2.19); when APHV was used to evaluate pubertal timing, the risks of depressive symptoms in the early pubertal timing group and delayed group of boys were 1.43 times (95% CI =1.07-1.91) and 1.41 times (95% CI =1.05-1.88) of that of the on time group, respectively (all P <0.05). No statistically significant associations were found among females (all P > 0.05 ). Conclusion The prevalence of depressive symptoms among high school students in Suzhou is relatively high, and both early and delayed puberty timing in boys are associated with depressive symptoms.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

Background and purpose:High-throughput detection of plasma cell-free DNA(cfDNA)is widely used for multi-cancer targeted therapy drug screening,and this study investigated the relationship between the type and number of plasma cfDNA class Ⅰ and Ⅱ targeted therapy-related gene variants and cancer survival in patients with non-small cell lung cancer(NSCLC).Methods:The sequencing results and clinical data of NSCLC patients who underwent tumor plasma cfDNA high-throughput sequencing projects in Sun Yat-sen University Cancer Center from 2021 to 2023 were collected.The survival follow-up of enrolled patients was carried out from the day of plasma collection on June 1,2021 to May 27,2024,and GraphPad Prism 8.0 and SPSS Statistics 25.0 were used.Univariate and multivariate statistical analyses were conducted on the types and numbers of class Ⅰ and class Ⅱ targeted therapy-related genes in the survival and clinical data of patients and sequencing results(Ethical approval:B2024-359-01).Results:A total of 313 patients included in this study with NSCLC were categorized into stage Ⅰ 25 patients(7.98%),stageⅡ 20 patients(6.39%),stage Ⅲ 38patients(12.14%),and stage Ⅳ 230 patients(73.48%).Pathological diagnosis results showed that adenocarcinoma accounted for 90.10%,squamous cell carcinoma accounted for 5.11%,large cell carcinoma accounted for 2.87%and other classifications accounted for 1.92%.The number and the percentage of class Ⅰ and class Ⅱ targeted therapy drug-related genes in the plasma cfDNA NSCLC patients were 0(25.24%),1(17.57%),2(19.17%),3(14.38%),4(8.31%),and 5 or more(15.34%).The results of statistical analysis showed that 3 genes with the highest mutation frequencies were EGFR,TP53 and ERBB2,and the mutation frequency of EGFR gene was 36.04%.The mutation frequency of TP53 gene was 30.63%.The mutation frequency of ERBB2 gene was 4.95%.The survival time of patients is related to not only the expression of hotspot targeted genes,but also the number of class Ⅰ and Ⅱ target-related gene variants detected by plasma cfDNA high-throughput sequencing.The survival time of the patients with no targeted therapy-related locus variants after treatment was longer compares with targeted therapy-related locus variants,which can reduce the risk of death by 63.2%.However,patients with a single gene locus variant had longer survival time and lower risk of death than those with multiple driver locus variants,and the measured class Ⅰ and Ⅱ targeted therapy drugs were within 3 genes.Overall,the smaller the number of genes,the longer the survival.Conclusions:The number of class Ⅰ and class Ⅱtargeted therapy-related gene variants in plasma cfDNA high-throughput sequencing also has an effect on the survival of patients after treatment.Plasma cfDNA level detected by high-throughput sequencing could be a prognostic factor for the NSCLC patients.

Objective:To evaluate the clinical rehabilitation effect of external counterpulsation(ECP)on patients with coronary heart disease(CHD)by meta-analysis.Methods:We searched the databases of CNKI,WanFang,VIP,CBM,PubMed,Web of Science,Cochrane Library and Embase for randomized controlled trials(RCTs)and prospective cohort studies upon rehabilitative effect of ECP on CHD patients before May 2024.And meta-analysis was conducted to calculate the pooled MD and 95％CI using the random(P＜0.5 or I2≥50％)or fixed effect models(other situations).Results:13 eligible literatures were finally included in the meta-analysis.Compared with participants in control group,those in trial group had significant higher left ventricular ejection fraction(MD=4.15,95％CI 2.55～5.76,P＜0.001),stroke volume(MD=9.11,95％CI 7.59～10.64,P＜0.001),peak oxygen uptake(MD=5.42,95％CI 2.53～8.32,P＜0.001),6-min walking distance(MD=31.14,95％CI 24.89～37.40,P＜0.001),metabolic equiv-alent(MD=0.58,95％CI 0.45～0.71,P＜0.001),exercise duration time(SMD=0.77,95％CI 0.55～0.99,P＜0.001),oxygen pulse(MD=0.88,95％CI 0.68～1.09,P＜0.001),and significant lower left ventricular end-diastolic diameter(MD=-3.19,95％CI-5.20～-2.61,P＜0.001).Conclusion:This study showed that ECP could effectively improve heart function,exercise capacity and tolerance of CHD patients.

Objective To investigate the risk factors of the infarct growth rate(IGR)in patients with acute anterior circulation large vessel occlusion.Methods This is a retrospective analysis of consecutive patients having acute anterior circulation large-vessel occlusion and being admitted to the Department of Neurointerventional Intervention,Central Hospital affiliated to Dalian University of Technology between September 2021 and July 2023.Patients were dichotomized into rapid and slow growth groups based on the median value of IGR,with 145 cases in each group.Univariate and multivariate Logistic regression models were used to analyze the risk factors of preoperative IGR.Results Multivariable Logistic regression analysis,after univariate screening,showed that hypertension(OR=2.27,95%CI:1.39-3.71),atrial fibrillation(OR=1.95,95%CI:1.22-3.12),and intracranial internal carotid artery occlusion(OR=1.98,95%CI:1.19-3.28)were risk factors of preoperative IGR(P<0.05).Hypertension,atrial fibrillation,and internal carotid artery occlusion all exhibited relatively low predictive value for IGR(P<0.05).The area under the curve for predicting IGR by combining these three factors was 0.642(95%CI:0.579-0.704),indicating a slight improvement in predictive performance,yet it remained relatively low.Conclusion Hypertension,atrial fibrillation,and proximal occlusion are risk factors of IGR in patients having acute anterior circulation large vessel occlusion.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

Objective:To investigate the epidemiological dynamics and spatiotemporal diffusion trend of brucellosis in China from 2010 to 2024.Methods:Data on reported human brucellosis cases in mainland China from January 1, 2010, to December 31, 2024, were collected via the"China Information System for Disease Control and Prevention", including detailed information on the date of onset, gender, age, occupation, and residential address of the cases. The Joinpoint regression and spatial interpolation techniques were used to investigate the spatiotemporal dynamics and population distribution characteristics of human brucellosis in pastoral/semi-pastoral areas and other regions, as well as urban and rural areas, and explore the epidemic trends of the disease.Results:From 2010 to 2024, pastoral/semi-pastoral regions reported 252 094 brucellosis cases, with a mean annual incidence rate of 36.57±7.28 per 100 000. In contrast, other regions cumulatively recorded 519 748 cases during the same period, demonstrating a significantly lower mean annual incidence rate of 2.54±0.74 per 100 000. The incidence rate of human brucellosis in pastoral/semi-pastoral regions exhibited a declining-rebounding-declining trend. Specifically, the incidence rate decreased significantly from 2010 to 2017 (APC=-7.20; P<0.001) and increased notably from 2017 to 2021 (APC=18.00; P=0.015) with a decline again from 2021 to 2024 (APC=-7.53; P=0.027). In other regions, the incidence rate showed a fluctuating upward trend. Specifically, the incidence rate increased significantly from 2010 to 2015 (APC=20.37; P<0.001) and decreased notably from 2015 to 2018 (APC=-21.78; P<0.001), followed by an increase again from 2018 to 2024, a significant upward trend in incidence rate from 2018 to 2021 (APC=26.73; P<0.001) and a non-significant decline from 2021 to 2024 (APC=-0.99; P=0.735), resulting in the maintenance of a relatively high incidence level. Rural areas demonstrated significantly higher brucellosis incidence rates than urban settings (all P<0.001). Brucellosis exhibited a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions between 2010 and 2024. The age structure of patients in pastoral/semi-pastoral areas differed significantly from that in other regions. Specifically, in pastoral/semi-pastoral areas, the incidence rate was higher among the 35-49 age groups, while in other regions, the incidence rate was higher among those aged 55-64. Conclusion:There are notable disparities in the incidence of human brucellosis between pastoral/semi-pastoral areas and other regions in China. Human brucellosis exhibits a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions.

Background and purpose:High-throughput detection of plasma cell-free DNA(cfDNA)is widely used for multi-cancer targeted therapy drug screening,and this study investigated the relationship between the type and number of plasma cfDNA class Ⅰ and Ⅱ targeted therapy-related gene variants and cancer survival in patients with non-small cell lung cancer(NSCLC).Methods:The sequencing results and clinical data of NSCLC patients who underwent tumor plasma cfDNA high-throughput sequencing projects in Sun Yat-sen University Cancer Center from 2021 to 2023 were collected.The survival follow-up of enrolled patients was carried out from the day of plasma collection on June 1,2021 to May 27,2024,and GraphPad Prism 8.0 and SPSS Statistics 25.0 were used.Univariate and multivariate statistical analyses were conducted on the types and numbers of class Ⅰ and class Ⅱ targeted therapy-related genes in the survival and clinical data of patients and sequencing results(Ethical approval:B2024-359-01).Results:A total of 313 patients included in this study with NSCLC were categorized into stage Ⅰ 25 patients(7.98%),stageⅡ 20 patients(6.39%),stage Ⅲ 38patients(12.14%),and stage Ⅳ 230 patients(73.48%).Pathological diagnosis results showed that adenocarcinoma accounted for 90.10%,squamous cell carcinoma accounted for 5.11%,large cell carcinoma accounted for 2.87%and other classifications accounted for 1.92%.The number and the percentage of class Ⅰ and class Ⅱ targeted therapy drug-related genes in the plasma cfDNA NSCLC patients were 0(25.24%),1(17.57%),2(19.17%),3(14.38%),4(8.31%),and 5 or more(15.34%).The results of statistical analysis showed that 3 genes with the highest mutation frequencies were EGFR,TP53 and ERBB2,and the mutation frequency of EGFR gene was 36.04%.The mutation frequency of TP53 gene was 30.63%.The mutation frequency of ERBB2 gene was 4.95%.The survival time of patients is related to not only the expression of hotspot targeted genes,but also the number of class Ⅰ and Ⅱ target-related gene variants detected by plasma cfDNA high-throughput sequencing.The survival time of the patients with no targeted therapy-related locus variants after treatment was longer compares with targeted therapy-related locus variants,which can reduce the risk of death by 63.2%.However,patients with a single gene locus variant had longer survival time and lower risk of death than those with multiple driver locus variants,and the measured class Ⅰ and Ⅱ targeted therapy drugs were within 3 genes.Overall,the smaller the number of genes,the longer the survival.Conclusions:The number of class Ⅰ and class Ⅱtargeted therapy-related gene variants in plasma cfDNA high-throughput sequencing also has an effect on the survival of patients after treatment.Plasma cfDNA level detected by high-throughput sequencing could be a prognostic factor for the NSCLC patients.