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 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 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.

Ambient air pollution is increasingly being recognized as a risk factor for heart failure; however, its effects on cardiac biomarkers remain unclear. This scoping review assessed the existing evidence on the association between air pollution and cardiac biomarkers in heart failure, described the key concepts, synthesized data, and identified research gaps. Following the PRISMA-ScR guidelines, PubMed, Embase, Web of Science, and CNKI databases were searched for studies on air pollution, heart failure, and biomarkers. A total of 765 records were screened, and 81 full texts were assessed for eligibility, resulting in 15 studies. The results showed that the exposure to particulate matter was associated with elevated N-terminal pro-B-type natriuretic peptide and troponin levels. Several studies have linked particulate matter exposure to a higher cardiovascular risk and heart failure biomarkers. Inflammatory and oxidative stress markers were consistently elevated across studies, supporting the biological relevance of these associations. However, few studies have focused specifically on populations with heart failure or clinically relevant biomarkers, and the evidence for gaseous pollutants remains inconclusive. These findings highlight the need to integrate environmental risk assessment into heart failure care and inform policy efforts to reduce the pollution-related cardiovascular burden. Further research should address these gaps through improved exposure assessments and the integration of mechanistic evidence.
Heart Failure/epidemiology* ; Biomarkers/metabolism* ; Humans ; Air Pollution/adverse effects* ; Air Pollutants/adverse effects* ; Particulate Matter/adverse effects* ; Environmental Exposure ; Natriuretic Peptide, Brain/blood* ; Oxidative Stress ; Troponin/blood*

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 investigate the current status of knowledge, attitude, and practice (KAP) of intensive care unit (ICU) medical staff for post-intensive care syndrome (PICS) and explore its influencing factors.Methods:The cross-sectional investigation study with stratified sampling was conducted. From June to September 2024, ICU medical staff from general hospitals in 5 regions (Chongqing, Beijing, Shaanxi, Jiangsu, and Gansu) were selected as the research subjects. The KAP of PICS questionnaire was distributed in the form of an electronic questionnaire. Observation indicators: (1) results of the questionnaire survey; (2) general information of ICU medical staff; (3) KAP scores of PICS and the correlation among various dimensions; (4) analysis of influencing factors for KAP of PICS. Comparison of measurement data with normal distribution between groups was conducted using the independent samples t test. One-way analysis of variance (ANOVA) was applied for com-parison among multiple groups, and post-hoc LSD test was used for pairwise comparison. Comparison of count data between groups was conducted using the chi-square test. Pearson correlation analysis was adopted for correlation analysis. Multiple linear regression analysis was used for univariate and multivariate analyses. Results:(1) Results of questionnaire survey. A total of 410 questionnaires were distributed and retrieved, among which 408 were valid, with an effective rate of 99.512%(408/410). (2) General information of ICU medical staff. Among the 408 ICU medical staff, there were 79 males and 329 females. Eight cases were under 25 years old, 248 cases were 25-35 years old, 132 cases were 36-40 years old, and 20 cases were over 40 years old. In terms of professional title, there were 10 junior nurses, 130 junior nurse practitioners, 228 intermediate nurse practitioners, and 40 senior nurse practitioners. About the educational background, 34 cases had a junior college degree, 347 cases had a bachelor's degree, and 27 cases had a master's degree or above. Regarding the hospital level, 25 nurses worked in secondary hospitals and 383 cases in tertiary hospitals. In terms of ICU type, 181 cases were from specialized ICU and 227 cases from general ICU. About working experience in ICU, 41 nurses had less than 5 years, 207 cases had 5-10 years, and 160 cases had more than 10 years. (3) KAP scores of PICS and the correlation among various dimensions. The total KAP score of PICS among the 408 ICU medical staff was 88.7±14.2, with 40.2±9.2 for the knowledge dimension, 22.0±5.6 for the attitude dimension, and 26.5±6.3 for the practice dimension. Pearson correlation analysis showed that the knowledge dimension of PICS among ICU medical staff was significantly positively correlated with both the attitude dimension and the practice dimension ( r=0.15, 0.69, P<0.05); the attitude dimension was positively correlated with the practice dimension ( r=0.23, P<0.05).(4) Analysis of influencing factors for KAP of PICS. Results of multivariate analysis showed that age (25-35 years old, 36-40 years old, over 40 years old), educational background and hospital level were independent influencing factors for the KAP of PICS among ICU medical staff ( t=2.23, 1.97, 2.84, 0.15, 2.04, P<0.05). Conclusions:The KAP of PICS among ICU medical staff is relatively good, while their practical ability still needs to be improved. Age, educational background, and hospital level are independent influencing factors for the KAP of PICS among ICU medical staff.

Objective:To investigate the current status of knowledge, attitude, and practice (KAP) of intensive care unit (ICU) medical staff for post-intensive care syndrome (PICS) and explore its influencing factors.Methods:The cross-sectional investigation study with stratified sampling was conducted. From June to September 2024, ICU medical staff from general hospitals in 5 regions (Chongqing, Beijing, Shaanxi, Jiangsu, and Gansu) were selected as the research subjects. The KAP of PICS questionnaire was distributed in the form of an electronic questionnaire. Observation indicators: (1) results of the questionnaire survey; (2) general information of ICU medical staff; (3) KAP scores of PICS and the correlation among various dimensions; (4) analysis of influencing factors for KAP of PICS. Comparison of measurement data with normal distribution between groups was conducted using the independent samples t test. One-way analysis of variance (ANOVA) was applied for com-parison among multiple groups, and post-hoc LSD test was used for pairwise comparison. Comparison of count data between groups was conducted using the chi-square test. Pearson correlation analysis was adopted for correlation analysis. Multiple linear regression analysis was used for univariate and multivariate analyses. Results:(1) Results of questionnaire survey. A total of 410 questionnaires were distributed and retrieved, among which 408 were valid, with an effective rate of 99.512%(408/410). (2) General information of ICU medical staff. Among the 408 ICU medical staff, there were 79 males and 329 females. Eight cases were under 25 years old, 248 cases were 25-35 years old, 132 cases were 36-40 years old, and 20 cases were over 40 years old. In terms of professional title, there were 10 junior nurses, 130 junior nurse practitioners, 228 intermediate nurse practitioners, and 40 senior nurse practitioners. About the educational background, 34 cases had a junior college degree, 347 cases had a bachelor's degree, and 27 cases had a master's degree or above. Regarding the hospital level, 25 nurses worked in secondary hospitals and 383 cases in tertiary hospitals. In terms of ICU type, 181 cases were from specialized ICU and 227 cases from general ICU. About working experience in ICU, 41 nurses had less than 5 years, 207 cases had 5-10 years, and 160 cases had more than 10 years. (3) KAP scores of PICS and the correlation among various dimensions. The total KAP score of PICS among the 408 ICU medical staff was 88.7±14.2, with 40.2±9.2 for the knowledge dimension, 22.0±5.6 for the attitude dimension, and 26.5±6.3 for the practice dimension. Pearson correlation analysis showed that the knowledge dimension of PICS among ICU medical staff was significantly positively correlated with both the attitude dimension and the practice dimension ( r=0.15, 0.69, P<0.05); the attitude dimension was positively correlated with the practice dimension ( r=0.23, P<0.05).(4) Analysis of influencing factors for KAP of PICS. Results of multivariate analysis showed that age (25-35 years old, 36-40 years old, over 40 years old), educational background and hospital level were independent influencing factors for the KAP of PICS among ICU medical staff ( t=2.23, 1.97, 2.84, 0.15, 2.04, P<0.05). Conclusions:The KAP of PICS among ICU medical staff is relatively good, while their practical ability still needs to be improved. Age, educational background, and hospital level are independent influencing factors for the KAP of PICS among ICU medical staff.

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 evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) .Methods:A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results:Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P=0.02, OR=0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P=0.02, OR=0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P=0.036, HR=0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P=0.024, HR=2.14, 95% CI 1.10-4.15) , leukemia transformation ( P<0.001, HR=2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P=0.012, HR=2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P=0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion:Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.