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.

Objective: To investigate the damaging effects of red blood cell supernatant (RBC-S) stored for different durations (7 d, 14 d, and 28 d) on vascular endothelial cells, and to explore the underlying mechanisms using bioinformatics analysis, so as to provide references for optimizing red blood cell transfusion strategies. Methods: Human umbilical vein endothelial cells (HUVECs) were co-cultured with RBC-S stored for 7, 14 and 28 days, designated as the 7 d group, 14 d group and 28 d group respectively, which were collectively defined as the experimental groups. Cell damage was evaluated by cell proliferation assay (Cell Counting Kit8, CCK8), lactate dehydrogenase (LDH) release assay, 4′, 6diamidino2phenylindole (DAPI) staining, and flow cytometry for apoptosis and reactive oxygen species (ROS) levels. The damage degree of RBC-S on vascular endothelial cells was assessed by statistical analysis of damage data among different groups. Since the damage effect reached a plateau at all time points, the 28 d storage group was selected as the representative for further mechanistic studies. Transcriptomic analysis was performed to explore the role of frizzled class receptor 1 (FZD1) and Wnt signaling pathway in red blood cell storagerelated endothelial dysfunction. Results: Compared with the control group, the storage groups treated with 7 d, 14 d, and 28 d RBC-S showed significantly decreased cell proliferation rates [control group 100%, 7 d group (69.51±2.30)%, 14 d group (74.54±2.89)%, 28 d group (73.59±2.36)%, P<0.05], significantly reduced numbers of DAPI-stained cell nuclei [control group (213±12.5) per field, 7 d group (140.33±17.04) per field, 14 d group (152.00±23.72) per field, 28 d group (144.33±19.09) per field, P<0.05] and significantly increased LDH release [control group (1), 7 d group (8.33±1.41), 14 d group (9.23±0.83), 28 d group (9.16±0.60), P<0.05]. There was no significant difference in the degree of damage caused by RBC-S among different storage groups (P>0.05). With the prolongation of storage time, free hemoglobin (FHb) gradually increased [control group (not detected), 7 d (16.57±6.38) mg/L, 14 d (76.80±22.83) mg/L, 28 d (286.97±29.02) mg/L, P<0.05]. The apoptotic rate (20.53±2.94)% and ROS relative intensity (5.13±0.91) in the 28 d storage group were significantly higher than those in the control group (P<0.05). Transcriptomic analysis showed that FZD1 played a key role in vascular endothelial dysfunction induced by red blood cell storage and was closely related to the Wnt signaling regulatory network. Conclusion: RBC-S stored for 7 d, 14 d, or 28 d can all significantly damage vascular endothelial cells, and the damaging effect reaches a plateau at 7 d of storage. Mechanistic investigation of the 28 d group indicated that the downregulation of the FZD1/Wnt signaling pathway may play a critical role in vascular endothelial dysfunction induced by red blood cell storage, providing a theoretical basis for further optimizing red blood cell storage and transfusion strategies.

Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

Objective To investigate the toxic effects of combined yttrium and lead exposure on liver and kidney functions in male Sprague-Dawley (SD) rats. Methods Adult male specific pathogen-free SD rats were randomly divided into four groups. Rats in the low-, medium-, and high-dose groups were co-administered with yttrium acetate (10.0, 50.0, and 100.0 mg/kg body weight, respectively) and lead acetate (4.0, 20.0, and 40.0 mg/kg body weight, respectively) by intragastric gavage, once per day for 28 consecutive days. Rats in the control group received an equal volume of deionized water. Yttrium and lead levels in whole blood, liver, and kidney tissues of the rats were measured using inductively coupled plasma mass spectrometry. Serum index of liver and kidney function were measured by a double reagent method, and histopathological changes in the liver and kidney were observed. Results The body weights of rats in all the three exposure groups were lower than those in the control group from day 14 of exposure until the end of exposure (all P<0.05). Body weight gain of rats was observed only in the low-dose group at day 28, whereas no body weight gain was observed throughout the exposure period in the medium- and high-dose groups. Histopathological examination showed dose-dependent liver and kidney injuries in rats of the three exposure groups compared with the control group. Specifically, the liver exhibited widening of intercellular spaces and increased inflammatory cell infiltration, while the kidney showed increased tissue hemorrhage and increased damage to glomeruli and renal tubules. Lead levels in whole blood, liver, and kidney tissues of the rats were higher than yttrium levels in all the three exposure groups (all P<0.05). Blood yttrium levels in rats of the high-dose group were higher than those in the other three groups (all P<0.05), and yttrium levels in the liver and kidney tissues of the three exposure groups were higher than those in the control group (all P<0.05). Lead levels in whole blood, liver, and kidney tissues of the rats increased with increasing exposure dose (all P<0.05). At the end of exposure, the kidney organ coefficient of rats in the high-dose group was higher than those in the other three groups (all P<0.05). The serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST) ratio of rats in the high‑dose group was lower than that in the control group (P<0.05). The serum urea and uric acid levels in the medium‑ and high‑dose groups were lower than those in the control group (both P<0.05). The serum cystatin C level in the high‑dose group was higher than that in the control group (P<0.05). Blood yttrium and blood lead levels were each negatively correlated with serum ALT/AST ratio, serum urea nitrogen, and uric acid levels (both P<0.05). Both yttrium and lead levels in liver tissue were each negatively correlated with serum ALT/AST ratio (all P<0.05). Both yttrium and lead levels in kidney tissue were each negatively correlated with both serum urea nitrogen and uric acid levels (all P<0.05), and each positively correlated with serum cystatin C levels (both P<0.05). Conclusion Co-exposure to yttrium and lead induces dose‑dependent hepatotoxicity and nephrotoxicity in male SD rats. Lead exhibits a more pronounced accumulation effect and contributes more substantially to the observed toxicity.

OBJECTIVE To investigate the mechanism by which resveratrol improves renal injury in rats with uric acid nephropathy （UAN） through inhibition of the protein kinase R-like endoplasmic reticulum kinase （PERK）/eukaryotic translation initiation factor 2α （eIF2α） pathway.METHODS A UAN rat model was established via oral administration of adenine plus potassium oxonate， and the rats were divided into a UAN model group， a positive control group （allopurinol 10 mg/kg）， a resveratrol group （100 mg/kg）， and a resveratrol+tunicamycin group [resveratrol 100 mg/kg+tunicamycin （PERK activator） 1 mg/kg]. A control group （without modeling） was also established， with 6 rats in each group. Rats in each group were administered the corresponding drug solution and/or normal saline via gavage or intraperitoneal injection once daily for 28 consecutive days. The contents of renal function-related parameters （24-hour urinary total protein， and uric acid， creatinine and blood urea nitrogen in serum）， the levels of inflammation-related markers [interleukin-6 （IL-6）， IL-18， tumor necrosis factor-α （TNF-α）， IL-10， and nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3）]， and pathological changes in renal tissue were determined， and the score for tubular damage， the score for inflammatory infiltration， and the degree of mesangial proliferation were assessed. The mRNA expressions of neutrophil gelatinase-associated lipocalin （NGAL） and kidney injury molecule-1 （KIM-1）， as well as the expressions of proteins associated with the PERK/eIF2α pathway， were measured in renal tissue from each group.RESULTS Compared with the control group， rats in the UAN model group exhibited marked tubular damage， inflammatory cell infiltration， and mesangial proliferation. The contents of renal function-related parameters， levels of IL-6， IL-18， TNF-α and NLRP3， scores for tubular damage and inflammatory infiltration， degree of mesangial proliferation， mRNA expressions of NGAL and KIM-1， and phosphorylation levels of PERK and eIF2α， as well as the expressions of activating transcription factor 4 and C/EBP homologous protein， were all significantly increased or upregulated， while IL-10 levels were significantly decreased （ P ＜0.05）. Compared with the UAN model group， the resveratrol group and the positive control group showed significant improvements in all of the aforementioned parameters （ P ＜0.05）. Tunicamycin could significantly reverse the beneficial effects of resveratrol on all quantitative parameters （ P ＜0.05）.CONCLUSIONS Resveratrol can reduce inflammatory responses in renal tissue of UAN rats， and improve tubular damage and renal function. These effects may be related to the inhibition of endoplasmic reticulum stress mediated by the PERK/eIF2α pathway.

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 analyze the current status of occupational stress among primary and secondary school teachers, and explore the dose-response relationship between weekly work hours and occupational stress. Methods A total of 1 252 teachers from 13 primary and secondary schools in three prefecture-level cities of a central province of China were selected as the research subjects by the convenience sampling method. The Core Occupational Stress Scale was used to assess occupational stress levels of the teachers. Multivariate logistic regression analysis combined with restricted cubic spline models was applied to study the dose-response relationship between weekly work hours and occupational stress. Results The average weekly work hours were (55.3±15.9) hours, with 78.6% of teachers working more than 40.0 hours per week. The total score of occupational stress was (40.3±8.2) points, and the detection rate of occupational stress was 29.8%. Multivariate logistic regression analysis combined with restricted cubic spline models revealed a linear dose-response relationship in weekly work hours and occupational stress among teachers (P for overall trend <0.05, P for nonlinearity was 0.22). Result of age-stratified analysis showed that weekly work hours had a linear dose-response relationship with occupational stress risk in teachers aged 21-<31 and 31-<46 years (P for overall trend <0.05, P for nonlinearity was 0.71 and 0.27, respectively). However, no association was found between weekly work hours and occupational stress risk among teachers aged ≥46 years (P for overall trend =0.08, P for nonlinearity was 0.09). Conclusion There is a linear dose-response relationship between weekly work hours and occupational stress among primary and secondary school teachers in the province, with younger teachers being more susceptible to suffer occupational stress due to long working hours.

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.