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.

BackgroundSuicide is the second leading cause of death in adolescents. Patients with depression have a high risk of suicide， and family dysfunction is an important contributing factor of suicide. However， the mechanisms among these variables remain unclear. ObjectiveTo explore the influence of family cohesion and adaptability on the risk of suicide in adolescent inpatients with depression， and the mediating effect of depressive symptoms and the moderating effect of resilience， so as to provide references for suicide prevention and intervention. MethodsA total of 335 adolescent inpatients with depression were consecutively enrolled from October 2024 to August 2025 in a tertiary Grade A psychiatric hospital in Guangzhou. All participants met the diagnostic criteria for depression in the International Classification of Diseases， tenth edition （ICD-10）. They were assessed using the Family Adaptability and Cohesion Scale Ⅱ-Chinese Version （FACES Ⅱ-CV）， the Self-rating Depression Scale （SDS）， the Connor-Davidson Resilience Scale （CD-RISC）， the Suicidal Behaviors Questionnaire-Revised （SBQ-R） and the suicide module of the Mini-International Neuropsychiatric Interview （MINI）. A comprehensive suicide risk index was constructed. Model 4 and model 7 in the SPSS macro program Process 4.1 were used to test the mediating effect and the moderating effect， respectively. ResultsCorrelation analysis revealed that the SDS score was positively correlated with the composite suicide risk index （r_s=0.663， P<0.01） but negatively correlated with the family cohesion and adaptability dimensions scores of the FACES II-CV and the CD-RISC score （r_s=-0.448， -0.386， -0.576， P<0.01）. Depressive symptoms fully mediated the association between family cohesion and suicide risk， with an indirect effect of -0.491 （95% CI： -0.646–-0.352）， accounting for 89.76% of the total effect. Depressive symptoms also fully mediated the association between family adaptability and suicide risk， with an indirect effect of -0.442 （95% CI： -0.603–-0.293）， accounting for 89.11% of the total effect. Resilience significantly moderated the effect of family cohesion on depressive symptoms （β=-0.006， P<0.01） and the effect of family adaptability on depressive symptoms （β=-0.007， P<0.05）. ConclusionIn adolescent inpatients with depression， depressive symptoms may play a fully mediating role in the relationships between family cohesion/family adaptability and suicide risk， respectively， and resilience moderated the first stage of this mediating pathway. ［Funded by Guangzhou Science and Technology Plan Project （number， 2024A03J0299）］

BackgroundCurrently， the problem of depressed mood in college students is becoming more prominent. The experience of childhood maltreatment is a significant contributor to depression among college students. Although the association between the two has been confirmed， the specific psychosocial mechanisms underlying how childhood maltreatment affects college students' mental health remain insufficiently evidenced. ObjectiveTo explore the mediating role of emotion regulation difficulties in the relationship between childhood maltreatment and depression among college students， and to investigate the moderated effects of psychological resilience and family socioeconomic status， aiming to provide references for improving depressive symptoms in college students. MethodsOn 14 March 2024， a cluster sampling method was employed to recruit 751 college students from a university in Heilongjiang Province. Participants were assessed with Childhood Trauma Questionnaire （CTQ）， Difficulties in Emotion Regulation Scale （DERS）， Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， 10-item Connor-Davidson Resilience Scale （CD-RISC-10） and Family Socioeconomic Status Questionnaire. Pearson correlation analysis was adopted to examine the correlation between the scores of scales. Model 4 and model 7 in Process 4.2 were used to test the mediating effects of emotional regulation difficulties and the moderated effects of psychological resilience and family socioeconomic status. Results① A total of 712 （94.81%） valid questionnaires were collected. ② College students' CTQ score was positively correlated with DERS score and PHQ-9 score （r=0.296， 0.507， P<0.01）， and negatively correlated with CD-RISC-10 score and Family Socioeconomic Status Questionnaire score （r=-0.148， -0.229， P<0.01）. ③ The indirect effect value of difficulties in emotion regulation on the relationship between childhood maltreatment and depression was 0.091 （95% CI： 0.018~0.046）， accounting for 17.95% of the total effect. ④ The first half of the mediation model "childhood maltreatment → difficulties in emotion regulation → depression" （childhood maltreatment → difficulties in emotion regulation） was moderated by psychological resilience （β=-0.030， t=-6.147， 95% CI： -0.040~-0.020） and family socioeconomic status （β=-0.051， t=-3.929， 95% CI： -0.077~-0.026）. ConclusionChildhood maltreatment exerts both a direct effect on college students' depression and an indirect effect through emotion regulation difficulties. The childhood maltreatment → emotion regulation difficulties pathway in this mediation model is moderated by psychological resilience and family socioeconomic status. ［Funded by Qiqihar Medical University Graduate Student Innovation Fund Project （number， QYYCX2023-48）； Special Research Fund Project for Young Doctors of Qiqihar Academy of Medical Sciences （number， QMSI2021B-08）］

As a mercury-containing elixir， Hongshengdan has been known as a sacred medicine for surgery by ancient medical practitioners because of its precise curative effects. It originated from Yizong Shuoyue in the Qing dynasty， Qing dynasty and modern medical practitioners have adapted and modified its formula for clinical application. Employing bibliometric methods， the authors systematically organized relevant ancient literature of the Qing dynasty and modern literature， and analyzed the composition and dosage， preparation method， and clinical application. Among the 25 ancient books concerning Hongshengdan， a total of 12 medicinal formulas， 15 refining methods and 9 clinical applications were obtained. Research confirms that Hongshengdan consisted of mercury， saltpeter， alum， soap alum， cinnabar and realgar. Using measurement conversion standards of Qing dynasty， the modern single-batch formulation comprised 37.30 g of mercury， 149.20 g of saltpeter， 37.30 g of alum， 22.38 g of soap alum， 18.65 g of cinnabar， and 18.65 g of realgar. In modern refining of Hongshengdan， most medical practitioners take the core medicines， with dosages approximately 30 g of mercury， 30 g of saltpeter， and 30 g of alum. Refining method involves pretreatment stewing the materials during preparation， and alum， soap alum， and saltpeter are first ground together， then combined with mercury， cinnabar， and realgar for grinding until mercury and other drugs grind to the degree of no star points. The mixture is then placed in a pot or vessel by cold-forming method. After covering， the opening is sealed using either raw gypsum salt mud or honey-dipped cotton paper strips. Sand is packed around the vessel and then pressurized. During the calcination process， begin with a low flame（30 min）， then increase to a medium flame（30 min）， followed by a high flame（30 min）， after removing fire toxins， collect the final product. Hongshengdan has the efficacy of lifting the poison， removing the corrosion， producing muscle and dispersing， and is often used in the treatment of surgical sore and carbuncle type of diseases. Modern research indicates that Hongshengdan is commonly used to treat skin system diseases such as ulcers and herpes. The aforementioned findings provide a reference basis for the subsequent refining method and clinical application of Hongshengdan.

Hand, foot and mouth disease(HFMD) is an acute infectious disease that seriously affects children's health, and can easily lead to large scale epidemic with a fast transmission rate. As a public health emergency of concern in China in 2024, HFMD has become a key focus of the prevention and control of childhood disease in China. HFMD vaccination is an important and effective measure for the prevention and control of HFMD. In order to understand the research and development priorities of HFMD vaccine related technologies, the patent database was used to analyze the research progress and development trend of HFMD vaccine from the patent perspective. The patent technology of HFMD vaccine is summarized from the perspectives of global patent development status, research achievements in China, and important applicants, so as to better grasp the research and development status and research priorities of HFMD vaccine, and provide scientific basis for the prevention and control of HFMD in China.

Objective To investigate the refractive development parameters of primary school students aged 7 to 12 years in Chuxiong City,Yunnan Province,analyze their relationship with equivalent spherical power(SE),and evaluate the monitoring role of refractive parameters in myopia.Methods A total of 1463 primary school students aged 7-12 from Beipu Primary School in Chuxiong City,Yunnan Province were selected.Spherical degree(DS),cylindrical degree(DC),and equivalent spherical degree(SE)were obtained using a computer autorefractor.Axial length(AL),corneal curvature(K1,K2),anterior chamber depth(ACD),central corneal thickness(CCT),lens thickness(LT),and vitreous depth(VD)were measured using a biometer.The corneal curvature radius(CR),the ratio of axial length to corneal curvature radius(AL/CR),and lens power(LP)were calculated.Participants were divided into 7～8 years old group(n=518),9～10 years old group(n=547),and 11～12 years old group(n=398)according to age;and categorized into myopic group(n=647),emmetropic group(n=532),and hyperopic group(n=284)based on refractive status to analyze the characteristics of refractive parameters and their impact across different age groups and refractive categories.Results(1)SE,DS,LT,and LP decreased with age(P<0.05);(2)AL,AL/CR,ACD,and VD increased with age(P<0.05);(3)K,CR and CCT showed no significant change with age(P>0.05);(4)In both myopic and hyperopic groups,AL was negatively correlated with SE(rs=-0.617,rs=-0.318,both P<0.05),AL/CR was negatively correlated with SE(rs=-0.737,rs=-0.406,both P<0.05),LP was positively correlated with SE(rs=0.412,rs=0.182,both P<0.05),while CR showed no correlation with SE(P>0.05).Conclusion The refractive development parameters AL,AL/CR,ACD,and VD increased with age in primary school students aged 7 to 12,while SE,DS,LT,and LP decreased with age,and K,CR,and CCT showed no significant changes.AL/CR and AL can serve as monitoring indicators for myopia,with AL/CR demonstrating a higher correlation with SE than AL.

Cardiovascular disease and cancer are the two leading chronic conditions contributing to global mortality. With the rising incidence of cancer, the prevalence of cancer therapy-related cardiovascular complications has also increased, driving the development of the emerging field of cardio-oncology. The advancement of precision medicine offers new opportunities for the individualized and targeted management of cardiovascular toxicities associated with cancer treatment. Artificial intelligence (AI) has the potential to overcome traditional limitations in medical data integration, dynamic monitoring, and interdisciplinary collaboration, thereby accelerating the application of precision medicine in cardio-oncology. By enabling personalized treatment and reducing cardiovascular complications in cancer patients, AI serves as a critical tool in this domain. This article provides an in-depth interpretation of the 鈥淎rtificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association鈥?aiming to inform the integration of AI into precision medicine in China. The goal is to promote its application in the management of cardiovascular diseases related to cancer therapy and to achieve precision management in this context.

Interferon regulatory factor 4 (IRF4) is a critical transcription factor that governs the differentiation of cluster of differentiation 4⁺ (CD4⁺) T cells. The pathogenesis and progression of psoriasis are primarily attributed to an immune imbalance stemming from the overproduction of interleukin-17A (IL-17A) by T lymphocytes. However, the role of IRF4 in psoriasis remains unexplored. In this study, we found that IRF4 activity is increased in the cutaneous lesions of patients with psoriasis in response to stimulation by IL-23A and IL-1β. This IRF4 elevation heightens its binding to the E1A binding protein p300 (EP300) promoter, triggering the transcription of downstream retinoic acid receptor-related orphan receptor-γt (RORγt) and increasing the secretion of IL-17A, thereby establishing the IL-1β/IL-23A-IRF4-EP300-RORC-IL-17A inflammatory cascade in psoriasis. The alleviation of imiquimod (IMQ)-induced psoriatic-like symptoms was achieved through the creation of a Irf4 ^-/- gene deletion mouse model and pharmacological inhibition using antisense oligonucleotides targeted for Irf4. This amelioration was accompanied by a decreased number of IL-17A-producing CD4⁺ T cells in the skin. The findings of this study suggest that IRF4 plays a crucial role in the promotion of inflammation and exacerbation of IMQ-induced psoriasiform dermatitis. Consequently, IRF4 targeting could be a promising therapeutic strategy.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional