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 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.

OBJECTIVES: To investigate the application value of targeted next-generation sequencing (tNGS) in the etiological diagnosis of moderate to severe respiratory distress syndrome (RDS) in neonates. METHODS: A prospective randomized controlled trial was conducted, enrolling 81 term and late-preterm neonates with moderate to severe RDS admitted to Fujian Children's Hospital between December 2023 and December 2024. Patients were randomly assigned to the conventional microbiological test (CMT) group (n=42) or the tNGS group (n=39). For routine pathogen detection, bronchoalveolar lavage fluid was obtained via bronchoscopy, and lower respiratory tract specimens were collected via the endotracheal tube; all specimens underwent culture, and some specimens additionally underwent polymerase chain reaction or antigen testing. In the tNGS group, tNGS was performed in addition to routine pathogen detection on the same specimen types. The detection rate of pathogens, the detection rate of co-infections, and the duration of antibiotic use were compared between the two groups. RESULTS: The pathogen detection rate in the tNGS group (18/39, 46%) was significantly higher than that in the CMT group (8/42, 19%) (P=0.009). The co-infection detection rate was 13% (5/39) in the tNGS group, while no co-infections were identified in the CMT group (P=0.024). Regarding treatment, the duration of antibiotic use in the tNGS group was shorter than that in the CMT group [(12±4) days vs (15±5) days, P=0.003]. CONCLUSIONS tNGS significantly improves the pathogen detection rate in neonates with moderate to severe RDS and offers advantages in the rapid identification of co-infections and reduction of antibiotic treatment duration, suggesting it has clinical utility and potential for wider adoption.
Humans ; Prospective Studies ; Infant, Newborn ; Female ; Respiratory Distress Syndrome, Newborn/etiology* ; Male ; High-Throughput Nucleotide Sequencing/methods*

OBJECTIVE: To investigate the efficacy and safety of chidamide combined with DICE regimen (cisplatin+ ifosfamide + etoposide + dexamethasone) for relapsed/refractory diffuse large B-cell lymphome(R/R DLBCL). METHODS: The clinical data of 31 R/R DLBCL patients treated by chidamide combined with DICE regimen in the Hematology Department of the Fourth Hospital of Hebei Medical University from October 2016 to October 2020 were retrospectively analyzed. The clinical efficacy and adverse events were observed. RESULTS: Among the 31 patients, 20 were male and 11 were female. The median age of the patients was 55 (range: 27-71) years old, 21 cases were < 60 years old, 10 cases were ≥60 years old. 26 cases were refractory and 5 cases were relapsed. There were 13 cases of germinal center B-cell like (GCB), 17 cases of non-GCB, and 1 case had missing Hans type. There were 17 cases of double-expression lymphoma (DEL) and 14 cases of non-DEL. The complete response rate of patients was 38.7%(12/31), the overall response rate was 67.7%(21/31). The median progression-free survival time and the median overall survival time were 9.8(95%CI : 4.048-15.552) months, 13.9(95%CI : 9.294-18.506) months, respectively. Multipvariate analysis showed that GCB and DEL reduced the risk of disease recurrence in R/R DLBCL patients. The main grade 3/4 hematological adverse events in this study were thrombocytopenia, agranulocytosis, anemia and leukopenia. CONCLUSION The chidamide combined with DICE regimen is effective in the treatment of R/R DLBCL, and hematological adverse events should be closely monitored.
Humans ; Lymphoma, Large B-Cell, Diffuse/drug therapy* ; Middle Aged ; Female ; Male ; Adult ; Aged ; Retrospective Studies ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Benzamides/administration & dosage* ; Aminopyridines/administration & dosage* ; Etoposide/therapeutic use* ; Cisplatin/administration & dosage* ; Ifosfamide/administration & dosage* ; Dexamethasone/therapeutic use*

OBJECTIVE: To evaluate the clinical efficacy and safety of Yangxue Qingnao Pills (YXQNP) combined with amlodipine in treating patients with grade 1 hypertension. METHODS: This is a multicenter, randomized, double-blind, and placebo-controlled study. Adult patients with grade 1 hypertension of blood deficiency and Gan (Liver)-yang hyperactivity syndrome were randomly divided into the treatment or the control groups at a 1:1 ratio. The treatment group received YXQNP and amlodipine besylate, while the control group received YXQNP's placebo and amlodipine besylate. The treatment duration lasted for 180 days. Outcomes assessed included changes in blood pressure, Chinese medicine (CM) syndrome scores, symptoms and target organ functions before and after treatment in both groups. Additionally, adverse events, such as nausea, vomiting, rash, itching, and diarrhea, were recorded in both groups. RESULTS: A total of 662 subjects were enrolled, of whom 608 (91.8%) completed the trial (306 in the treatment and 302 in the control groups). After 180 days of treatment, the standard deviations and coefficients of variation of systolic and diastolic blood pressure levels were lower in the treatment group compared with the control group. The improvement rates of dizziness, headache, insomnia, and waist soreness were significantly higher in the treatment group compared with the control group (P<0.05). After 30 days of treatment, the overall therapeutic effects on CM clinical syndromes were significantly increased in the treatment group as compared with the control group (P<0.05). After 180 days of treatment, brachial-ankle pulse wave velocity, ankle brachial index and albumin-to-creatinine ratio were improved in both groups, with no statistically significant differences (P>0.05). No serious treatment-related adverse events occurred during the study period. CONCLUSIONS Combination therapy of YXQNP with amlodipine significantly improved symptoms such as dizziness and headache, reduced blood pressure variability, and showed a trend toward lowering urinary microalbumin in hypertensive patients. These findings suggest that this regimen has good clinical efficacy and safety. (Registration No. ChiCTR1900022470).
Humans ; Amlodipine/adverse effects* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Hypertension/complications* ; Middle Aged ; Treatment Outcome ; Drug Therapy, Combination ; Adult ; Blood Pressure/drug effects* ; Double-Blind Method ; Aged ; Antihypertensive Agents/adverse effects*

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.

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 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.