OBJECTIVE To provide a scientific basis for the quality evaluation and clinical application of Qingwen hufei granules. METHODS Fourteen batches of Qingwen hufei granules were used as samples to establish high-performance liquid chromatography （HPLC） fingerprints using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 Edition）. The chromatographic peaks were identified and the similarity was evaluated. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were used to conduct chemical pattern recognition analysis on the 14 batches of samples. Meanwhile， the contents of neochlorogenic acid （NGA）， chlorogenic acid （CHA）， cryptochlorogenic acid （CGA）， forsythoside A （FTA）， 3，5-O-dicaffeoylquinic acid （3，5-O- DA）， 4，5-O-dicaffeoylquinic acid （4，5-O-DA）， and angoroside C （AGC） in the samples were determined by HPLC. RESULTS The methodological investigation results of both the fingerprint and the content determination complied with the relevant requirements. Fourteen common peaks were indicated in the HPLC fingerprints of the 14 batches of samples， and 7 of them were identified [NGA （peak 2）， CHA （peak 3）， CGA （peak 5）， FTA （peak 11）， 3，5-O-DA （peak 12）， 4，5-O-DA （peak 13）， and AGC （peak 14）]； the similarity of each sample was greater than 0.94. The results of CA and PCA showed that the samples could be classified into 3 categories； the results of OPLS-DA indicated that peak 4 （unknown）， peak 11 （FTA）， peak 8 （unknown）， peak 9 （unknown）， and peak 1 （unknown） were the differential components. The content ranges of NGA， CHA， CGA， 3，5-O-DA， FTA， 4，5-O-DA and AGC in the 14 batches of samples were 0.210 4-0.458 7， 0.269 1-0.506 3， 0.228 1-0.461 1， 0.443 9-1.044 6， 0.066 7-0.155 7， 0.062 8-0.143 8， and 0.057 4-0.105 7 mg/g， respectively. CONCLUSIONS The HPLC fingerprint and multi-component content determination methods established in this study are efficient and reliable， and can be used for the quality evaluation of Qingwen hufei granules.

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.

Transmembrane proteins (TMEM) are a type of membrane protein. Most proteins in this family are located in the phospholipid bilayer of the cell membrane, while a smaller portion is found in the membranes of cellular organelles. Transmembrane protein 43 (TMEM43) is a member of the TMEM protein family and is encoded by the TMEM43 gene. This protein consists of 400 amino acids and has 4 transmembrane domains and 1 membrane-associated domain. TMEM43 is localized to various biological membranes within the cell, such as the cell membrane and nuclear membrane, where it forms transmembrane channels for various ions. Additionally, TMEM43 is expressed in many species, showing high genetic similarity, especially with the four transmembrane domains being highly conserved. Current studies on the TMEM43 gene are still in its early stages, mainly focusing on its association with arrhythmogenic right ventricular cardiomyopathy (ARVC) and cancer. However, recent studies suggest that pathogenic mutations in TMEM43 may cause auditory neuropathy spectrum disorder (ANSD). Patients with TMEM43 p.Ser372Ter exhibited late-onset progressive ANSD. Impact of TMEM43 pathogenic mutations on individual hearing was likely mediated through effects on gap junction (GJ) structures on glia-like supporting cells (GLS), cell membranes. The TMEM43 p.Arg372Ter pathogenic mutation primarily affected the structure and function of TMEM43 protein, leading to premature termination of protein translation and the production of a truncated protein. Abnormal TMEM43 protein significantly reduced K⁺ influx in GLS cells, disrupting the endolymphatic K⁺ circulation and cochlear microenvironment homeostasis. When K⁺ circulation was obstructed, the endocochlear potential (EP) became abnormal, impairing the physiological function of hair cells and potentially leading to hearing impairment. However, it is important to note that studies on the mechanism is limited, and more experimental evidence is needed to confirm this hypothesis. Currently, there is a significant gap in research on TMEM43 and hearing loss, with many issues remaining unresolved. While TMEM43 has been studied in relation to hearing loss in humans, zebrafish, mice, and rats, the research is still preliminary. Detailed investigations into the molecular pathogenic mechanisms, the impact of mutations on hearing damage, and related therapeutic strategies are needed. Additionally, as a newly identified hearing loss-related gene, the mutation frequency and incidence of hearing disorders associated with TMEM43 have not been effectively quantified. For example, the ClinVar database listed 829 mutation sites for the TMEM43 gene, with only three mutations related to auditory neuropathy: c.605A>T (p.Asn202Ile), c.889T>A (p.Phe297Ile), and c.1114C>T (p.Arg372Ter). Aside from the aforementioned TMEM43 c.1114C>T (p.Arg372Ter) mutation observed in patients, the other two mutations were experimentally induced and have not been found in patients. Consequently, these mutations have been classified as unknown significance. We reviewed the current understanding of TMEM43 and hearing loss, analyzed its role in ear development and sound conduction, and explored the impact of TMEM43 gene variations on hearing loss, aiming to provide new insights for future research and precision medicine related to TMEM43.

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 explore the relationship among sleep quality, fatigue and work-related musculoskeletal disorders (WMSDs) in firefighters, and to examine the mediating effect of fatigue between sleep quality and the risk of WMSDs. Methods A total of 271 firefighters from three prefecture-level cities in Jiangsu Province were selected as the study subjects by a convenient sampling method. The Chinese Musculoskeletal Disorders Questionnaire, Pittsburgh Sleep Quality Index (PSQI), and Fatigue Scale-14 were used to assess WMSDs, sleep quality and fatigue status among the study subjects. The effect of sleep quality and fatigue on WMSDs was analyzed, as well as the mediating role of fatigue between sleep quality and the risk of WMSDs. Results The annual prevalence of WMSDs among the firefighters was 68.6%. The firefighters who had poor and very poor sleep quality accounted for 36.2% and 7.7%, respectively. There were 88.6% of firefighters reported fatigue. The total scores of PSQI and fatigue of firefighters in WMSDs group were higher than those in non-WMSDs group (all P<0.01). Binary logistic regression analysis showed that the higher the total score of PSQI, the worse the sleep quality, and the higher the risk of WMSDs in firefighters (all P<0.01). The higher the total score of fatigue, the higher the risk of WMSDs (P<0.01), and the risk of WMSDs in the fatigue group was higher than that in the non-fatigue group among the firefighters (P<0.01). The direct effect of sleep quality on WMSDs in firefighters was 0.028 [95% confidence interval (CI): 0.012-0.050, P<0.01], and its indirect effect on WMSDs mediated by fatigue was 0.027 (95%CI: 0.018-0.040, P<0.01). Fatigue played a mediating role in sleep quality and WMSDs, with the mediating ratio of 0.491 (95%CI: 0.304-0.740, P<0.01). Conclusion Poor sleep quality and fatigue are related to an increased risk of WMSDs in firefighters in a dose-response manner. Fatigue plays a mediating role between sleep quality and the risk of WMSDs.

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.

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 re-mains 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 mitochon-drial 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.

Objective:To explore the clinical value of the three-dimensional fat suppression fast spoiled gradient echo (3D-FS-FSPGR) MRI sequence in the preoperative evaluation of congenital radial polydactyly.Methods:The data of children with congenital radial polydactyly who underwent surgical treatment in the Department of Orthopedics at Shanxi Children’s Hospital from May 2021 to April 2022 were retrospectively analyzed. Preoperative X-ray examinations and MRI 3D-FS-FSPGR sequence scans were performed on the children. Radiologists first described the morphological characteristics of the articular cartilage, and then orthopedic surgeons performed the Wassel classification based on the X-ray and MRI imaging result, focusing on the bifurcation level, morphology, and articular surface of the phalangeal and metacarpal cartilage. The corresponding surgical method was selected according to the Wassel classification, and intraoperative incision exploration was used as the gold standard. Six months after surgery, the surgical outcome was evaluated using the modified Tada scoring system [with a total score of 0-7, and classified as excellent: >5 points, good: 3-5 points, and poor: <3 points; the excellent and good rate = (excellent + good) cases/total number of cases × 100%]. The appearance, function and recurrence of the operated finger were evaluated 1 year after surgery. Descriptive statistics were performed using SPSS 26.0 software, and the Kappa coefficient was used to evaluate the consistency of the Wassel classification result between radiographs, MRI 3D-FS-FSPGR sequences and intraoperative exploration respectively.Results:A total of 45 children (55 fingers) with congenital radial polydactyly were enrolled, including 25 males and 20 females, aged 5 to 60 months, with the median age of 9 months. Unilateral findings were seen in 35 cases and bilateral findings in 10 cases. MRI 3D-FS-FSPGR imaging sequences clearly demonstrated the level of cartilage bifurcation and bone tissue growth and development, which were consistent with intraoperative exploration findings. The accuracy of the MRI 3D-FS-FSPGR Wassel classification was 100% (55/55), and the accuracy of the X-ray Wassel classification was 81.8% (45/55). Disagreements were found in the classifications of five fingers: three with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅲ, and two with X-ray classifications of Wassel type Ⅳ but actually classified as type Ⅴ. The Kappa coefficients were all >0.85. All patients were followed up for 1 year. The modified Tada score showed excellent in 41 fingers, good in 6 fingers, and poor in 8 fingers, for an excellent and good rate of 85.5% (47/55). At final follow-up, the reconstructed thumbs showed significant improvement in appearance, with normal bone axis restoration, no deviation of the digits, and normal nail appearance. There was no significant scarring or contracture. Functions of thumb flexion, extension, grasping, and opposition were good. There was no postoperative deformity or recurrence.Conclusion:MRI 3D-FS-FSPGR sequences can accurately classify congenital radial polydactyly preoperatively, optimize the surgical incision and osteotomy alignment, and achieve excellent surgical outcomes.