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 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.

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.

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.

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 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.

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 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.