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.

Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

Objective To investigate the epidemiological characteristics and changing trend of typhus in Shaanxi Province from 2005 to 2023 to provide a scientific basis for its prevention and control. Methods Excel 2007, SPSS 25.0, Joinpoint 4.9.1.0, and Geoda 1.6 were used for data collection and statistical analysis. Super Map was used for data visualization to describe the changing characteristics of the disease. Results A total of 394 typhus cases were reported in Shaanxi Province from 2005 to 2023. The average annual incidence of typhus was 0.054/100 000, showing a dynamic fluctuation trend(AAPC=-3.3, t=-0.3, P>0.05). The cases were mainly concentrated in Baoji, Hanzhong and Xi′an, accounting for 78.68%. The incidence peak was from May to October, accounting for 64.21% of annual incidence. The epidemic season was from May to October and December. The incidence of the disease was concentrated in the 40-69 age group, accounting for 58.88%, and the sex was 1.07:1. The main occupation was farmers, accounting for 72.08%. The median time from onset to diagnosis was 7 days. Global spatial autocorrelation analysis showed that there were significant spatial autocorrelations in 11 years from 2005 to 2023(P<0.05). Local spatial autocorrelation analysis detected a total of 43“high-high”clustering areas, mainly concentrated in Baoji City. Conclusions The overall incidence of typhus in Shaanxi Province showed a dynamic fluctuation trend, with notable seasonal and regional aggregation. The incidence of typhus was higher in middle-aged and elderly people in rural areas. Surveillance should be strengthened in typhus endemic areas in summer and autumn, and health education should be conducted for key population to form good health habits and reduce the incidence of typhus.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective To investigate the regulatory mechanism of the central nervous system in patients with refractory overactive bladder (rOAB) using diffusion tensor imaging (DTI) and graph theory analysis. Methods A total of 43 rOAB patients (rOAB group) and 46 matched healthy controls (HC group) were recruited during May and Nov.2024. All participants were scanned with DTI, and surveyed with the overactive bladder symptom score (OABSS), and overactive bladder questionnaire (OAB-q). Their age, gender, height, weight, and educational years were collected.DTI plus graph theory analysis was employed to explore the alterations in global and local topological properties of the brain structural network in rOAB patients. Brain regions showing significant group differences in structural metrics [specifically, the right paracentral lobule (PCL.R) ]were further used as seed points for functional connectivity (FC) analysis. Correlations between the nodal clustering coefficient (NCp) of the identified region, FC strength, OABSS, and OAB-q score were investigated. Results The OABSS [8 (6,10) vs.0 (0,1) ]and OAB-q [71 (53,80) vs.20 (19,24) ]were higher in the rOAB group than the HC group (P<0.001). Graph theory analysis revealed no statistically significant differences in global network metrics between the two groups (P>0.05). However, the NCp was significantly higher in the PCL.R of rOAB group compared to HC group (P<0.05, FDR-corrected).FC analysis using the PCL.R as a seed region demonstrated significantly reduced FC value in the left cerebellar crus Ⅱ (Cerebelum_Crus2_L) of the rOAB group (P<0.05, FDR-corrected). Partial correlation analysis showed that the NCp of PCL.R was positively correlated with both OABSS (r=0.255, P=0.018) and OAB-q score (r=0.257, P=0.017). Conversely, the FC of Cerebelum_Crus2_L was significantly negatively correlated with OABSS (r=-0.545, P<0.001) and OAB-q score (r=-0.535, P<0.001). Conclusion Patients with rOAB exhibit distinct brain structural network alterations compared to healthy individuals, primarily manifestation in the NCp value of PCL.R increased, and the FC intensity of Cerebelum_Crus2_L is significantly weakened. These alterations in the topological properties of the structural network may be implicated in the pathogenesis of rOAB.

Pancreatic extracorporeal shock wave lithotripsy(P-ESWL),a non-invasive treatment,is widely accepted worldwide as the preferred option for pancreatic duct stone(PDS)treatment.P-ESWL provides significant relief of painful symptoms and improves patients' quality of life through efficient lithotripsy and catheter removal.Although there is risk of post-operative complications such as pancreatitis,the overall incidence is low and can be further minimized by effective management strategies.It is worth noting that computed tomography-based quantitative analysis and radiomics prediction model provide a scientific basis for personalized P-ESWL,heralding more precise and efficient treatment in the future.P-ESWL for treating PDS will be further improved by future multi-center and large-sample studies,as well as by the integration of artificial intelligence and machine learning algorithms,which may lead to significant therapeutic effects and improvements in patients' quality of life.

Objective To identify risk factors for pathological upgrading after radical prostatectomy in patients with biopsy-confirmed International Society of Urological Pathology(ISUP)grade 1 prostate cancer and to develop a predictive nomogram.Methods A total of 256 patients with ISUP grade 1 prostate cancer diagnosed by biopsy and undergoing radical prostatectomy in The First Affiliated Hospital of Naval Medical University between Jan.2017 and May 2024 were retrospectively enrolled.Clinical,imaging,and biopsy data were collected.Independent predictors were identified using univariate and multivariate binary logistic regression,and a nomogram model was constructed.Model performance was evaluated using receiver operating characteristic curve,clinical impact curve,and decision curve analysis.The stability of the model was evaluated by Hosmer-Lemeshow test.Results Multivariate binary logistic regression analysis revealed that the number of positive puncture cores(odds ratio[OR]=1.80),prostate imaging and reporting data system(PI-RADS)score(OR=1.88),and prostate specific antigen density(PSAD)stage(OR=1.43)were independent predictors of pathological upgrading(all P＜0.01).The area under curve(AUC)value of the nomogram model based on the above 3 predictors was 0.82(95％confidence interval 0.77-0.87).Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.01-0.99.Clinical impact curve analysis showed that at a threshold probability of 0.40,the model could avoid 45 unnecessary interventions(12％reduction in false-positive rate)with a net clinical benefit of 0.46.The Hosmer-Lemeshow test indicated good model fit(P=0.45).Conclusion The constructed nomogram model can accurately predict the risk of pathological upgrading after radical prostatectomy in patients with ISUP grade 1 prostate cancer,providing a quantitative tool to support individualized decision-making for active surveillance.

Objective:To establish the quality for Kangjun Xiaoyan preparations.Methods:Rheum officinale Baill,Isatidis indigotica Fort,Miq.Were identified by TLC;microscopic identification method was used to distin-guish Lysimachia christinae Hance and Scutellaria baicalensis Georgi;using maranthoidin B as an indicator compo-nent for the detection of adulteration in Lonicera macranthoides Hand.;the content of chlorogenic acid and baicalin were determined by HPLC.Results:Four types of medicinal herbs in the sample can identify by TLC and negative sample without interference,the examination item for maranthoidin B can accurately determine whether adulterated L.macranthoides is present in the sample;the methodological validation of content determination showed that there was a good linear relationship between chlorogenic acid and baicalin injection mass in the range of 89.8-5 845.4 and 108.2-7 047.0 ng respectively(r=0.999 3 and 0.999 9).The average recoveries were 97.55％and 102.05％with RSDs 1.2％and 1.6％.Conclusion:The established methods were simple,accurate,reliable and reproducible.It can be effectively used for the quality control of Kangjun Xiaoyan preparations.

Objective:To investigate the predictive value of serum 25 hydroxyvitamin D3[25 (OH) D3], insulin-like growth factor 1 (IGF-1) and bone mineral density (BMD) in the occurrence of osteoporosis (OP) in postmenopausal women.Methods:A total of 87 postmenopausal women admitted to Shanxi Children’s Hospital from Jan. 2020 to Dec. 2023 were chosen and separated into OP group ( n=40) and non-OP group ( n=47) . The differences of clinical features, serum 25 (OH) D3, IGF-1 and BMD were compared, and the correlation analysis between serum 25 (OH) D3, IGF-1 and BMD (including L 1-4 BMD, femoral neck BMD and total hip BMD) was analyzed. Multivariate Logistic regression model and receiver operating characteristic (ROC) curve were used to analyze the influencing factors of OP occurrence in postmenopausal women and the predictive value of serum 25 (OH) D3 and IGF-1 combined with BMD for OP occurrence. Results:Compared with non-OP group, there were no significant differences in age, body mass index (BMI) , menopause time, serum calcium, serum phosphorus, alanine aminotransferase (ALT) , or aspartate aminotransferase (AST) levels in OP group ( t=1.42, 1.03, 1.71, 0.93, 0.76, 0.43, 0.04; P=0.161, 0.306, 0.092, 0.354, 0.452, 0.670, 0.966) , but the proportion of diabetes mellitus significantly increased ( χ2=4.37, P=0.037) . Compared with non-OP group, the levels of serum 25 (OH) D3 and IGF-1 and the values of L1-4 BMD, femoral neck BMD and total hip BMD in OP group significantly decreased ( t=5.37, 4.83, 8.31, 2.01, 3.11; P<0.001, P<0.001, P<0.001, P=0.048, P=0.003) . Correlation analysis showed that serum 25 (OH) D3 and IGF-1 were significantly positively correlated with L 1-4 BMD, femoral neck BMD and total hip BMD ( r=0.37, 0.42, 0.29, 0.33, 0.28, 0.29; P<0.001, P<0.001, P=0.024, P=0.015, P=0.032, P=0.021) . Multivariate Logistic analysis showed that serum 25 (OH) D3, IGF-1 and L 1-4 BMD were independent influencing factors for OP occurrence in postmenopausal women ( P=0.007, 0.019, 0.001) ROC curve showed that the area under the curve (AUC) values of serum 25 (OH) D3, IGF-1, L 1-4 BMD and their combination in the prediction of the occurrence of OP in postmenopausal women were 0.764, 0.752, 0.957 and 0.985, respectively. Conclusion:Serum 25 (OH) D3, IGF-1 and L 1-4 BMD can be used as predictors of OP occurrence in postmenopausal women, and the combined value of the three is higher.