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:A prognosis prediction model for pancreatic cancer was constructed based on multi-scale radiomics combined with deep learning, and the prediction effect of the model was evaluated.Methods:A retrospective analysis was conducted on the clinical data of 215 patients who underwent radical resection of pancreatic cancer at the First Affiliated Hospital of Naval Medical University from January 2017 to December 2017. Among them, 134 were male and 81 were female, with an age of (61.9±9.2) years. Patients were randomly divided into the training set ( n=151) and the test set ( n=64) in a ratio of 7: 3. Habitat features, peritumoral radiomics features, 3D radiomics features, and 2.5D deep learning features were extracted from preoperative CT images respectively. After feature screening, a survival prediction model was constructed using the CoxBoost machine learning algorithm that integrated the Boosting algorithm and the Cox proportional hazards model. The performance of the model was evaluated using the area under the time-dependent receiver operating characteristic curve and the consistency index. The clinical benefits of the model were evaluated using decision curve analysis. The survival curves were plotted using the Kaplan-Meier method, and the log-rank test was used for the comparison of survivals between groups. Results:The LASSO, random forest and extreme gradient boosting models were each used to screen out the top 10 most important features and take the union, ultimately obtaining 20 radiomics features for modeling. In the training set and test set, the consistency index of the CoxBoost model in predicting overall survival was 0.717 (95% CI: 0.669-0.765) and 0.688 (95% CI: 0.610-0.766), respectively, and the area under the curve for predicting overall survival at 1, 2, and 3 years after surgery was 0.830 (95% CI: 0.752-0.898), 0.753 (95% CI: 0.665-0.833), 0.828 (95% CI: 0.735-0.908) and 0.690 (95% CI: 0.549-0.824), 0.780 (95% CI: 0.649-0.887 and 0.793 (95% CI: 0.660-0.897), respectively. The area under the curve for predicting long-term survival after surgery (≥40 months) was above 0.8. Based on the optimal cutoff value of -0.19 for the predicted value of the CoxBoost model calculated by the R package " survminer", the patients were divided into high-risk (predicted value >-0.19) and low-risk (predicted value <-0.19) groups. In both the training set and the test set, the survival of patients in the low-risk group was better than that in the high-risk group (training set: χ2=39.01, P<0.001; test set: χ2=12.34, P<0.001). The median survival period of patients in the high-risk group was lower than that in the low-risk group (training set: 15.80 vs 34.07 months; test set: 16.87 vs 43.07; months). Decision curve analysis shows that patients obtain survival benefit when the threshold probability of the training set is greater than 0.25 and that of the test set is greater than 0.45. Conclusion:The CoxBoost model has a good predictive ability for the overall survival of pancreatic cancer patients after surgery and can effectively screen out patient subgroups that may significantly benefit from surgical treatment.

Objective:By analyzing the genetic risk of triglyceride-glucose index(Tyg)and insulin resistance(IR)for cardiovascular disease(CVD), to elucidate the extent to which the contribution of Tyg to the risk of CVD development is influenced by IR genetic risk.Methods:In this study, we selected data from a cohort of elderly people in the Kunshan community, screened 7, 385 individuals with both clinical and genomic data, and calculated the polygenic risk score of insulin resistance(IRPRS)for each participant based on publicly available IR genome-wide association data, and assessed the effect of genetic risk and Tyg level on the risk of developing CVD using a multivariate Cox proportional risk model.Calculating interactions to assess the effects of genetic risk and Tyg levels on the risk of developing CVD, the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD were assessed using a multivariate Cox proportional risk model, and subgroup analyses were performed for gender to assess the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD by gender.Results:In the univariate Cox model, Q3 and IRPRS with the highest TYG levels were significantly associated with the risk of CVD, respectively( HR=1.59, 95% CI: 1.33-1.89; P<0.001; HR=1.61, 95% CI: 1.18-2.20; P=0.003).After adjusting for multiple confounders, the Q3 Group with the highest TYG level was still significantly associated with the risk of CVD( HR=1.28, 95% CI: 1.05-1.57; P=0.014), the Association of TYG with the risk of CVD did not change significantly( HR=1.29, 95% CI: 1.05-1.57; P=0.014).We conducted a subgroup analysis by sex and found that among older men, 13, the highest levels of TYG and IRPRS were significantly associated with CVD risk, respectively( HR=1.70, 95% CI: 1.31.2.20; P<0.001; HR=1.98, 95% CI: 1.24-3.15; P=0.004).After adding IRPRS to the model, the Association of TYG with the risk of CVD remained unchanged( HR=1.69, 95% CI: 1.31-2.19; P<0.001).After adjusting for various confounders, Tyg remained significantly associated with the risk of CVD( HR=1.39, 95% CI: 1.04-1.88; P=0.028), the results showed that TYG remained significantly associated with the risk of CVD( HR=1.41, 95% CI: 1.05-1.90; P=0.023), and the association did not decrease.No Association of IRPRS with CVD risk was found in older women. Conclusions:IRPRS and TYG are the risk factors of CVD, and diet, exercise, drugs and other external factors on TYG are the main risk factors of CVD.For individuals with high genetic factors, the risk of CVD can still be reduced by lifestyle adjustments such as diet, exercise and drug intervention.

Objective To explore the expression of epidermal growth factor and nuclear transcrip-tion factor kappa B(NF-κB)in elderly gliomas and their roles in malignant progression of the dis-ease.Methods A total of 240 elderly glioma patients undergoing surgical resection in our hospital from January 2020 to January 2023 were enrolled and served as an observation group.Another 100 patients receiving surgical treatment due to traumatic brain injury in our hospital during the same period were recruited and served as the control group.Immunohistochemical assay was used to detect the expression of epidermal growth factor and NF-κB in brain tissue.Cox regression analy-sis was employed to determine the independent influencing factors of malignant progression in the elderly glioma patients.Results The observation group had significantly higher epidermal growth factor expression score and NF-κB expression score than the control group(7.44±1.16 vs 3.68±0.51,6.49±1.02 vs 3.56±0.64,P＜0.01).The age,tumor pathological grade,surgical approach,postoperative radiotherapy,and epidermal growth factor and NF-κB expression levels were inde-pendent factors affecting the progression of elderly glioma patients(P＜0.05,P＜0.01).Conclusion The epidermal growth factor and NF-κB expression levels are independent influen-cing factors of progressive disease in elderly glioma patients,and can serve as evaluation indicators for postoperative progression in the patients.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

OBJECTIVE To carry out regular monitoring of carbapenem-resistant Klebsiella pneumoniae(CRKP)contamination status in the environment of intensive care unit(ICU)and take targeted prevention and control measures so as to reduce the incidence of hospital-associated infections with multidrug-resistant organisms(MDROs).METHODS The surfaces of surroundings of the patients who were colonized and infected with CRKP in the ICU of grade A tertiary hospital of Shanghai and the hands of relevant staff were sampled by stages from Jan 1,2021 to Jun 30,2024.The distribution of the CRKP strains in the surroundings were analyzed according to the locations positive for CRKP,and the disinfection measures were accordingly and continuously modified.The trend of isolation rate of CRKP strains from the ICU patients was analyzed during the time period when the measures were implemented.RESULTS Totally 266 environmental samples were collected during the baseline period(from Jan.1 2021 to Dec.31 2021),265 during intervention period(from Jan.1 2022 to Dec.31 2023),274 during con-solidation period(from Jan.1 to Jun.30 2024);the isolation rates of the CRKP strains were 4.51％,4.91％and 3.65％,respectively.The isolation rate of the strains was highest from the bed unit(10.40％),followed by the article for public use(6.74％),articles used by health care workers(2.98％)and diagnosis and treatment arti-cles(1.91％).The isolation rate of CRKP of the patients was 24.75％during the baseline period,15.48％during the intervention period,5.69％during the consolidation period,showing a continuously downward trend(x2=30.330,P＜0.001).CONCLUSION It is necessary to regularly carry out the environmental monitoring of CRKP strains,seek for the weak links of environmental disinfection and implement the intensified prevention and control measures so as to reduce the incidence of CRKP infection,which may provide theoretical bases for effective control of the CRKP strains.

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:By analyzing the genetic risk of triglyceride-glucose index(Tyg)and insulin resistance(IR)for cardiovascular disease(CVD), to elucidate the extent to which the contribution of Tyg to the risk of CVD development is influenced by IR genetic risk.Methods:In this study, we selected data from a cohort of elderly people in the Kunshan community, screened 7, 385 individuals with both clinical and genomic data, and calculated the polygenic risk score of insulin resistance(IRPRS)for each participant based on publicly available IR genome-wide association data, and assessed the effect of genetic risk and Tyg level on the risk of developing CVD using a multivariate Cox proportional risk model.Calculating interactions to assess the effects of genetic risk and Tyg levels on the risk of developing CVD, the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD were assessed using a multivariate Cox proportional risk model, and subgroup analyses were performed for gender to assess the effects of Tyg tertile grouping and IRPRS on the risk of developing CVD by gender.Results:In the univariate Cox model, Q3 and IRPRS with the highest TYG levels were significantly associated with the risk of CVD, respectively( HR=1.59, 95% CI: 1.33-1.89; P<0.001; HR=1.61, 95% CI: 1.18-2.20; P=0.003).After adjusting for multiple confounders, the Q3 Group with the highest TYG level was still significantly associated with the risk of CVD( HR=1.28, 95% CI: 1.05-1.57; P=0.014), the Association of TYG with the risk of CVD did not change significantly( HR=1.29, 95% CI: 1.05-1.57; P=0.014).We conducted a subgroup analysis by sex and found that among older men, 13, the highest levels of TYG and IRPRS were significantly associated with CVD risk, respectively( HR=1.70, 95% CI: 1.31.2.20; P<0.001; HR=1.98, 95% CI: 1.24-3.15; P=0.004).After adding IRPRS to the model, the Association of TYG with the risk of CVD remained unchanged( HR=1.69, 95% CI: 1.31-2.19; P<0.001).After adjusting for various confounders, Tyg remained significantly associated with the risk of CVD( HR=1.39, 95% CI: 1.04-1.88; P=0.028), the results showed that TYG remained significantly associated with the risk of CVD( HR=1.41, 95% CI: 1.05-1.90; P=0.023), and the association did not decrease.No Association of IRPRS with CVD risk was found in older women. Conclusions:IRPRS and TYG are the risk factors of CVD, and diet, exercise, drugs and other external factors on TYG are the main risk factors of CVD.For individuals with high genetic factors, the risk of CVD can still be reduced by lifestyle adjustments such as diet, exercise and drug intervention.

Objective To explore the expression of epidermal growth factor and nuclear transcrip-tion factor kappa B(NF-κB)in elderly gliomas and their roles in malignant progression of the dis-ease.Methods A total of 240 elderly glioma patients undergoing surgical resection in our hospital from January 2020 to January 2023 were enrolled and served as an observation group.Another 100 patients receiving surgical treatment due to traumatic brain injury in our hospital during the same period were recruited and served as the control group.Immunohistochemical assay was used to detect the expression of epidermal growth factor and NF-κB in brain tissue.Cox regression analy-sis was employed to determine the independent influencing factors of malignant progression in the elderly glioma patients.Results The observation group had significantly higher epidermal growth factor expression score and NF-κB expression score than the control group(7.44±1.16 vs 3.68±0.51,6.49±1.02 vs 3.56±0.64,P＜0.01).The age,tumor pathological grade,surgical approach,postoperative radiotherapy,and epidermal growth factor and NF-κB expression levels were inde-pendent factors affecting the progression of elderly glioma patients(P＜0.05,P＜0.01).Conclusion The epidermal growth factor and NF-κB expression levels are independent influen-cing factors of progressive disease in elderly glioma patients,and can serve as evaluation indicators for postoperative progression in the patients.