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.

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.

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*

This work was aimed at analyzing the protein characteristics of Coiled-Coil Domain-Containing Protein 115(CCDC115)and using Ccdc115-deficient mouse monocyte-macrophage leukemia cells(RAW264.7)to explore the influence of CCDC115 on the intracellular survival of Salmonella Typhimurium.Bioinformatics analysis was conducted to examine the fundamental attributes of CCDC115,which was determined to be an unstable protein consisting of two α-helices and an intervening disordered re-gion,devoid of any transmembrane structural domains.A RAW264.7-Ccdc115-KO cell line was successfully established with CRISPR/Cas9 gene-editing technology.To elucidate the effects of CCDC115 on the intracellular survival of Salmonella Typhimurium,we infected RAW264.7 cells with Salmonella Typhimurium.The expression of CCDC115 was found to be upregulated at both the mRNA and protein levels post-infection,according to RT-qPCR and western blot analysis.Via counting of colony-forming units(CFU),the proliferation rate of Salmonella Typhimurium within RAW264.7-Ccdc115-KO cells was found to be 1.5-fold higher than that in RAW264.7 cells.Acidification imaging studies indicated that,whereas Salmonella Typhimurium phagosomes underwent acidifi-cation in RAW264.7 cells,this process was absent in RAW264.7-Ccdc115-KO cells.In conclusion,the study successfully estab-lished a RAW264.7-Ccdc115-KO cell line and demonstrated that the expression of CCDC115 is elevated during Salmonella Ty-phimurium infection,thus potentially inhibiting the intracellular survival of Salmonella Typhimurium by facilitating phagosome acidifi-cation.This study lay a theoretical foundation for functional studies of CCDC115 and the investigation of mechanisms regulating the survival of intracellular Salmonella Typhimurium.

Objective:To evaluate the changes in cortical electroencephalogram (EEG) burst suppression rate (BSR) during sevoflurane anesthesia in septic mice and the correlation with cerebral blood perfusion.Methods:Forty SPF male C57BL/6J mice, aged 8-10 weeks, weighing 22-25 g, were divided into 2 groups ( n=20 each) by the random number table method: sham operation group (Sham group) and cecal ligation perforation group (CLP group). The sepsis model was established by cecal ligation and puncture in anesthetized animals. Mice in both groups inhaled 2% sevoflurane for 2 h. During sevoflurane anesthesia, BSR (30 min as an epoch) on electroencephalogram was recorded, and the cortical cerebral blood perfusion was recorded using the laser speckle flow imaging at 30, 60, 90 and 120 min of anesthesia. Results:Compared with Sham group, the cortical EEG BSR was significantly increased, and the cortical cerebral blood perfusion was decreased during sevoflurane anesthesia in CLP group ( P<0.05). Cortical EEG BSR was negatively correlated with cortical cerebral blood perfusion ( P<0.05). Conclusions:Cortical EEG BSR increases during sevoflurane anesthesia in septic mice, which may be related to decreased cortical cerebral blood perfusion.

The medical mask,which is used as an important tool of preventing the spread of respiratory diseases,can effectively block the transmission of biological aerosols.The detection for the filtration efficiency of bacteria in medical mask is particular importance.The Andersen sampler,is one kind of device that samples microbial aerosols,is widely used in the inspection for the filtration efficiency for bacteria in medical masks.It mainly consists of six impactors with different pore sizes.It simulates the deposition process of the most of particles at different positions in respiratory system through the bacterial particles in biological aerosols impact respectively the surface of petri dishes with agar under different pore sizes.This paper explored the development background,structure and sampling principle,operation and counting procedures of the Andersen sampler,as well as its application and importance in the inspection for the filtration efficiency for bacteria in medical mask.

Objective To investigate the factors influencing the occurrence of coma in patients with acute basilar artery occlusion(BAO)and the favorable prognosis in the coma patients after receiving mechanical thrombectomy(MT).Methods The clinical data and imaging materials of 102 patients with acute BAO,who received MT at the First Affiliated Hospital of Soochow University of China from January 2016 to April 2024,were retrospectively analyzed.According to whether the patient had a coma or not on admission,the patients were divided into non-coma group and coma group.The clinical data and imaging findings were compared between the two groups.Multivariate logistic regression analysis was performed to identify the factors influencing the occurrence of coma.The modified Rankin scale(mRS)score was used to evaluate 90-day clinical prognosis.The patients of coma group were further divided into favorable prognosis subgroup(mRS:0-3 points)and poor prognosis subgroup(mRS:4-6 points).Baseline date and surgical data were compared between the two subgroups,and multivariate logistic regression analysis was conducted to identify the factors associated with a favorable prognosis in coma patients after receiving mechanical thrombectomy.Results Of the 102 patients with acute BAO,54 were in unconscious state on admission(coma group)and 48 were in conscious state(non-coma group)on admission.Multivariate logistic regression analysis revealed that coexisting cardiovascular diseases with severe cardiac insufficiency or moderate to severe coronary artery stenosis(P=0.009)and low BATMAN score(P＜0.001)were the independent risk factors for the occurrence of coma in acute BAO patients.Among the 54 unconscious patients who received MT treatment,favorable prognosis was obtained in 13 and poor prognosis was seen in 41.Multivariate logistic regression analysis indicated that high BATMAN score(P=0.017)was the independent influencing factor for favorable prognosis in acute BAO patients with coma after receiving MT therapy.Conclusion Acute BAO patients having coexisting cardiovascular diseases with severe cardiac insufficiency or moderate to severe coronary artery stenosis or having low BATMAN score are more likely to develop coma.Acute BAO patients with coma having high BATMAN score are more likely to obtain a favorable prognosis after receiving MT therapy.

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.