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.

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.

Aim To explore the compatibility and po-tential mechanism of effective components of Biejia-Ezhu against triple negative breast cancer(TNBC)and verify it by experiments.Methods Effective compo-nents and targets of Biejia-Ezhu were obtained by TC-MSP and Swiss Target Prediction.Disease targets of TNBC were obtained from OMMI and GeneCards data-bases.The PPI network was constructed using STRING database.GO and KEGG path enrichment analysis was performed using DAVID database.Cytoscape3.9.1 software was used to construct the"drug-component-target-disease"network,screen key targets and compo-nents for molecular docking,and further verify the com-patibility of key components and targets in vitro.Re-sults ① A total of 71 effective components were iden-tified in the Biejia-Ezhu drug pair.There were 146 drug targets associated with the disease.A total of 113 signaling pathways were identified by KEGG analysis.The 71 potential active components of Biejia-Ezhu mainly acted on key targets such as mTORC1,ULK1,TNF,EGFR,ESR1,STAT3,HIF1A,and PTGS2.Mo-lecular docking results showed that glycine and curcu-min were the key active components of Biejia-Ezhu,and both had strong docking activity against key target proteins mTORC1 and ULK1.②The results of in vitro experiment showed that glycine combined with curcu-min significantly inhibited the proliferation and clonal formation ability of TNBC cells(P＜0.05),up-regula-ted the expression of autophagy marker LC3 Ⅱ/Ⅰ,down-regulated the expression of EGFR,down-regula-ted the expression of pathway protein mTORC1,p-mTOR,p-ULK1,and promoted the expression of path-way protein ULK1(P＜0.05).Conclusion The key component of Biejia-Ezhu against triple-negative breast cancer is glycine-curcumin,the mechanism of which may be related to the regulation of the mTORC1/ULK1 signaling pathway to promote autophagy.

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*

Aortic regurgitation and mitral regurgitation are more common in elderly heart valve disease,and both may be present in some patients.Severe aortic regurgitation complicated with severe mitral regurgitation often requires surgical valve replacement,but in patients at high risk of surgery,the risk of perioperative mortality is significantly increased.Therefore,for such patients,minimally invasive interventions can significantly improve long-term patient outcomes while reducing surgical risk.This article report a case of transcatheter aortic valve replacement combined with transcatheter edge-to-edge repair in the treatment of severe aortic regurgitation combined with mitral valve prolapse,in order to explore new treatment ideas for similar cases.

Aim To explore the compatibility and po-tential mechanism of effective components of Biejia-Ezhu against triple negative breast cancer(TNBC)and verify it by experiments.Methods Effective compo-nents and targets of Biejia-Ezhu were obtained by TC-MSP and Swiss Target Prediction.Disease targets of TNBC were obtained from OMMI and GeneCards data-bases.The PPI network was constructed using STRING database.GO and KEGG path enrichment analysis was performed using DAVID database.Cytoscape3.9.1 software was used to construct the"drug-component-target-disease"network,screen key targets and compo-nents for molecular docking,and further verify the com-patibility of key components and targets in vitro.Re-sults ① A total of 71 effective components were iden-tified in the Biejia-Ezhu drug pair.There were 146 drug targets associated with the disease.A total of 113 signaling pathways were identified by KEGG analysis.The 71 potential active components of Biejia-Ezhu mainly acted on key targets such as mTORC1,ULK1,TNF,EGFR,ESR1,STAT3,HIF1A,and PTGS2.Mo-lecular docking results showed that glycine and curcu-min were the key active components of Biejia-Ezhu,and both had strong docking activity against key target proteins mTORC1 and ULK1.②The results of in vitro experiment showed that glycine combined with curcu-min significantly inhibited the proliferation and clonal formation ability of TNBC cells(P＜0.05),up-regula-ted the expression of autophagy marker LC3 Ⅱ/Ⅰ,down-regulated the expression of EGFR,down-regula-ted the expression of pathway protein mTORC1,p-mTOR,p-ULK1,and promoted the expression of path-way protein ULK1(P＜0.05).Conclusion The key component of Biejia-Ezhu against triple-negative breast cancer is glycine-curcumin,the mechanism of which may be related to the regulation of the mTORC1/ULK1 signaling pathway to promote autophagy.

Medical therapy and surgical intervention are the two primary approaches for treating myocardial bridge.However,there remains controversy regarding the use of coronary artery bypass grafting(CABG)and myocardial bridge unroofing.Here,we report a case of myocardial infarction following CABG in a patient with a myocardial bridge.The patient was admitted to Lanzhou First Peopie's Hospital with persistent chest pain,chest tightness,and shortness of breath lasting 2 hours.Physical examination revealed no significant abnormalities.Electrocardiography(ECG)indicated extensive anterior wall myocardial infarction.Laboratory findings showed myoglobin levels of 140.1 ng/ml and troponin Ⅰ levels of 2.59 ng/ml,with no other significant abnormalities.The initial diagnosis was acute extensive anterior wall myocardial infarction.Emergency coronary angiography revealed a myocardial bridge in the mid-segment of the left anterior descending artery(LAD).Emergency CABG using the left internal mammary artery to the LAD was performed,leading to symptomatic improvement,and the patient was discharged in stable condition.However,the patient experienced a recurrent myocardial infarction seven years post-surgery and received secondary preventive medical therapy.The patient is currently under ongoing follow-up care.CABG is an effective treatment for myocardial bridge.However,based on the case reported in this study,we recommend careful evaluation of whether a patient may benefit from CABG.

Objective:To develop a formula correlating body weight and the insertion length of peripherally inserted central catheters (PICC) in very low birth weight infants (VLBWI) undergoing lower limb PICC placement, aiming to improve the success rate of one-time catheter placement.Methods:A prospective research method was adopted, 205 VLBWI patients who were hospitalized in the NICU of Shenzhen Maternal and Child Health Hospital from May 2023 to February 2024 and had indwelling PICC in the lower extremities were selected as the research subjects using the convenience sampling method. Data including gender, insertion vein, body weight on the day of catheterization, and the vertical distance from the puncture site to the first popliteal crease were collected. Pearson correlation analysis and linear regression analysis were used to construct a predictive formula for estimating the insertion length of PICC based on body weight. The Bland-Altman plot was applied to evaluate the consistency between the predicted insertion depth using the formula and the actual correct insertion length.Results:A total of 182 VLBWI were included in the study, comprising 102 male infants and 80 female infants. The catheterization sites were the right lower extremity in 166 cases and the left lower extremity in 16 cases. The body weight was (1.08 ± 0.26) kg. The vertical distance from the puncture site to the first popliteal crease was (5.58 ± 1.72) cm. The prediction formula for the length of lower extremity PICC catheterization was constructed through linear regression analysis: PICC insertion length = 8.013 + [5.194×body weight (kg)] ± vertical distance from the puncture site to the popliteal crease (cm). Consistency evaluation showed good agreement between the predicted insertion depth and the actual correct insertion length, with 95% of the measurement differences falling within the limits of agreement. The mean difference was 0.07 cm ( P > 0.05). Conclusions:The weight-based formula derived for VLBWI provides a basis for predicting the optimal insertion length of PICC in clinical practice.

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 observe the predictive factors of poor prognosis in patients with acute basilar artery occlusion(BAO)who got first-pass effect(FPE)after mechanical thrombectomy(MT).Methods Eighty-two acute BAO patients who got FPE following MT were retrospectively collected and divided into good prognosis group(modified Rankin scale[mRS]score≤3,n=48)and poor prognosis group(mRS score＞3,n=34)90 days after treatments.The data were compared between groups,and variables which showed P＜0.1 were included in multivariate logistic regression analysis to identify independent predictors of poor prognosis in acute BAO patients who got FPE after MT.Results Higher age of patients,pre-treatment National Institute Health stroke scale(NIHSS)and neutrophil-to-lymphocyte ratio(NLR),also higher proportions of patients with diabetes mellitus,atrial fibrillation(AF)and cardioembolic stroke in trial of org 10 172 in acute stroke treatment(TOAST)classification were found in poor prognosis group than those in good prognosis group(all P＜0.05).Conversely,patients in poor prognosis group had lower pre-treatment Glasgow coma scale(GCS)scores,lower posterior circulation-Alberta stroke program early CT score(pc-ASPECTS)and basilar artery on CT angiography(BATMAN)scores(all P＜0.05).Multivariate logistic regression analysis revealed patients complicated with AF(OR[95%CI]=29.769[1.470,602.943])and elevated pre-treatment NLR(OR[95%CI]=1.212[1.016,1.446])had relatively poor prognosis(both P＜0.05),whereas those with increased pre-treatment GCS score(OR[95%CI]=0.615[0.429,0.882]),elevated pc-ASPECTS(OR[95%CI]=0.263[0.092,0.748])and higher BATMAN score(OR[95%CI]=0.260[0.085,0.796])had relatively better prognosis(all P＜0.05).Conclusion Complicated with AF,low pre-treatment GCS score,high NLR,low pc-ASPECTS and low BATMAN score were all predictive factors for poor prognosis in acute BAO patients who got FPE after MT.