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 explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

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.

To explore the application effectiveness of generative artificial intelligence(GAI) in the standardized training assessment of critical care medicine residents.

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

Objective:To analyze the evolutionary patterns of China's health workforce policies following China's healthcare system reform and assess their alignment with reform objectives.Methods:This study examined health workforce-related policies implemented during China's healthcare system reform.Cross-referencing analysis and content evaluation were conducted within Health Worker-Centered Framework.Results:A total of 196 policies were analyzed,revealing two evolution patterns:(1)alignment with systemic reform goals,ensuring integration with broader healthcare transformation;(2)incremental optimization within the health workforce domain,emphasizing continuity and phased development.Thematic priorities included education/training,performance incentives,and human resource mobility,which closely coordinate with key reform targets such as strengthening primary care,reforming public hospitals,and establishing hierarchical diagnosis and treatment system.Conclusions:Since the healthcare system reform,China's health workforce policies reveals their dynamic alignment with reform goals.Policy evolution closely synergizes with reform objectives,providing institutional support for health talent development.However,Sectoral synergy dilemmas remain in health workforce policies,future efforts should strengthen policy integration and dynamic adjustment mechanisms to achieve high-quality development of health human resources.

Aim To investigate the effects of Mdivi-1 on A1 astrocyte activation and its associated signaling molecules.Methods CTX-TNA2 astrocytes were di-vided into the control,ACM,and low-,medium-,and high-dose Mdivi-1 groups based on concentration screening via CCK-8 assay.ACM,a DMEM high-glu-cose medium containing preset concentrations of IL-1α,TNF-α,and C1q,was used to induce A1 activa-tion.The ACM group was stimulated with ACM for 24 hours.Mdivi-1 groups were pretreated with correspond-ing concentrations of Mdivi-1 for 2 hours,followed by ACM stimulation for 24 hours.Real-time quantitative PCR and Western blot were employed to assess mRNA levels and protein expression of IL-1β,C3,and iNOS in all groups.Immunofluorescence and Western blot were used to detect the expression of signaling molecules NLRP3,caspase-1,and ASC.DHE labeling was used to assess and flow cytometry was used to examine reac-tive oxygen species(ROS)levels.Results The CCK-8 assay identified 5,10,and 25 μmol·L-1as ap-propriate concentrations for Mdivi-1 intervention in CTX-TNA2 cells.Real-time quantitative PCR and Western blot results indicated that,compared to the control group,IL-1 β,C3,and iNOS mRNA levels and protein expression were significantly elevated in the ACM group(P＜0.05).In contrast,these levels were significantly reduced in the 10 and 25 μmnol·L-1 Mdi-vi-1 groups compared to the ACM group(P＜0.05).Immunofluorescence and Western blot results confirmed that ACM stimulation significantly activated the NLRP3 inflammasome in astrocytes,while Mdivi-1 intervention effectively reversed the ACM-induced upregulation of NLRP3,caspase-1,and ASC.DHE staining results demonstrated that 5,10,and 25 μmol·L-1Mdivi-1 in-terventions partially reversed the ACM-induced in-crease in ROS levels in a dose-dependent manner.Conclusion Mdivi-1 effectively inhibits A1 astrocyte activation,potentially through modulation of ROS and the NLRP3 inflammasomes.

Objective:To investigate the molecular characteristics of low-risk acute myeloid leukemia(AML)at recurrence,and analyze the factors affecting retreatment efficacy and prognosis.Methods:A retrospective analysis was conducted on the clinical and laboratory data of 31 patients with newly diagnosed low-risk AML who relapsed during consolidation treatment or follow-up after treatment in our hospital from April 2017 to January 2023.Gene mutations before and after relapse were compared,retreatment efficacy following relapse was evaluated,and univariate and multivariate analyses were performed to identify factors influencing treatment efficacy and prognosis.Results:Gene sequencing results after relapse showed that the most common newly acquired mutation was FLT3-ITD,while RAS mutation detected at initial diagnosis were predisposed to loss of expression during relapse.The median overall survival(OS)after relapse for the entire cohort was 349(170-528)days,with non-hematopoietic stem cell transplantation(HSCT)group and HSCT group demonstrating median survival times of 210(106-314)days and not reached,respectively(P=0.001).Multivariate analysis revealed that age ≥60 years was a significant risk factor for achieving remission after retreatment in initially diagnosed low-risk AML patients who experienced relapse(OR=18.222,95％CI:1.188-279.597,P=0.037).Additionally,DNMT3A mutation was identified as an independent risk factor for OS(HR=13.165,95％CI:2.018-85.877,P=0.007),while HSCT post-relapse demonstrated significant survival benefits(HR=0.133,95％CI:0.025-0.698,P=0.017)and served as an independent protective factor for OS.Conclusion:Relapsed low-risk AML is often associated with loss of RAS and novel mutations in FLT3-ITD.Age ≥ 60 years and DNMT3A mutations were identified as independent adverse factors for achieving subsequent remission and post-relapse survival,respectively,while HSCT significantly improved patient outcomes.