This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK _a values. The high reliability and interpretability of GraFpK _a ensure accurate pK _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK _a values, making it a valuable tool in the field of pK _a prediction.

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.

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*

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.

Negative logarithm of the acid dissociation constant(pKa)significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pKa prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pKa prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pKa values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pKa values,making it a valuable tool in the field of pKa prediction.

Objective:To explore the application effect of family nursing based on Noddings′ caring education theory in children with bronchopneumonia, so as to provide reference for clinical nursing of children with bronchopneumonia.Methods:A randomized controlled study was conducted, 120 children with bronchopneumonia admitted to Anqing Municipal Hospital from August 2023 to March 2024 were enrolled as the research objects by convenience sampling method. According to random number table method, they were divided into the control group and the experimental group. The control group was given routine nursing, while the experimental group was given family nursing based on Noddings′ caring education theory on the basis of routine nursing. The symptoms remission (disappearance time of cough and wheezing), length of hospital stay, lesion absorption, compliance of aerosol inhalation, treatment compliance, etc in 2 groups were observed.Results:A total of 60 children with bronchopneumonia were included in the control group, including 37 males and 23 females, aged (5.75 ± 1.09) years old, and 60 cases were included in the experimental group, including 36 males and 24 females, aged (5.65 ± 1.06) years old. The disappearance time of cough and wheezing, and length of hospital stay in the experimental group were (5.98 ± 0.61), (4.31 ± 0.45), (5.50 ± 0.65) d, which were shorter than (6.21 ± 0.63), (4.52 ± 0.46), (6.38 ± 0.89) d in the control group, the differences were statistically significant ( t = 2.03, 2.53, 6.19, all P<0.05). The lesion absorption rate and compliance of aerosol inhalation in the experimental group were 95.00% (57/60) and 91.67% (55/60), higher than 83.33% (50/60) and 76.67% (46/60) in the control group, the differences were statistically significant ( χ2 = 4.23, 5.07, both P<0.05). The scores of treatment compliance (medication according to time and dosage, moderate exercise, reasonable diet, self-care) in the experimental groups were (4.02 ± 0.41), (3.39 ± 0.37), (3.87 ± 0.48), (3.13 ± 0.37) points, higher than (3.82 ± 0.39), (3.25 ± 0.35), (3.68 ± 0.45), (2.97 ± 0.34) points in the control group, the differences were statistically significant ( t value were 2.13-2.74, all P<0.05). Conclusions:Family nursing based on Noddings′ caring education theory is beneficial to alleviate clinical symptoms and medical fear, improve compliance of aerosol inhalation and treatment compliance in children with bronchopneumonia.

Objective:To investigate the evidence-based practice of prevention and care of nasal and facial pressure injuries in patients with non-invasive positive pressure ventilation (NPPV) using the knowledge to action framework (KTA), and to explore its effectiveness.Methods:Using an evidence-based nursing approach, an evidence-based practice group was established to formulate a clinical problem, the literature from domestic and international databases were researched for relevant evidence, the evidence was introduced into clinical scenarios, an evidence-based practice plan was developed, and a strategy for applying the best evidence was constructed by conducting a baseline review of healthcare professionals and patients with NPPV, analyzing barriers and promoting factors, and making changes in clinical practice at the organizational level, the practitioner level, and the patient level. Purposive sampling method was used to select the healthcare staff of the cardiac surgical intensive care unit (CSICU) of the Shandong Provincial Hospital Affiliated to Shandong First Medical University, as well as the patients with NPPV admitted from October 1 to November 15, 2023 (pre-evidence-based practice) and November 16 to December 31 (post-evidence-based practice), as the subjects of the study. Through questionnaire analysis, the incidence of nasal and facial pressure injury of NPPV patients, the implementation rate of review indicators of medical staff, the score of the knowledge, belief and conduct of medical staff, and the compliance and comfort of patients before and after evidence-based practice were compared.Results:A total of 52 medical staff were included, aged (28.54±6.50) years old, with 3.00 (1.00, 12.75) years of working experience; 2 doctoral degree holders (3.85%), 4 master degree holders (7.69%), 46 bachelor degree holders (88.46%); 2 with senior title (3.85%), 17 with intermediate title (32.69%), and 33 junior titles (63.46%). Fifty patient questionnaires were collected before and after evidence-based nursing practice; the differences between before and after evidence-based practice in terms of gender, age, body weight, duration of ventilator usage, 24-hour bleeding and total bleeding were not statistically significant and were comparable. Compared with the pre-evidence-based practice, after carrying out the corresponding evidence-based nursing practice, the incidence of nasal and facial pressure injuries of NPPV patients decreased from 16.00% (8/50) to 4.00% (2/50, P < 0.05), the total implementation rate of review indicators of medical staff increased from 79.73% to 94.08% ( P < 0.01), and the total scores of knowledge, belief and conduct were significantly improved (141.96±13.88 vs. 114.65±19.72, P < 0.05), and compliance and comfort of patients were significantly improved (compliance score: 4.60±0.99 vs. 5.82±1.42, comfort score: 4.10±1.63 vs. 6.92±2.33, both P < 0.05]. Conclusion:The application of an evidence-based nursing approach to obtain evidence related to the prevention of nasal and facial pressure injuries in patients with NPPV can be used to guide clinical practice, significantly reducing the incidence of nasal and facial pressure injuries in such patients, improving the implementation rate of review indicators and the knowledge, belief, and conduct scores of medical staff, and enhancing compliance and comfort of NPPV patients.

Background/Aims: Low educational attainment is a well-established risk factor for nonalcoholic fatty liver disease (NAFLD) in developed areas. However, the association between educational attainment and the risk of NAFLD is less clear in China. Methods: A cross-sectional study including over 200,000 Chinese adults across mainland China was conducted. Information on education level and lifestyle factors were obtained through standard questionnaires, while NAFLD and advanced fibrosis were diagnosed using validated formulas. Outcomes included the risk of NAFLD in the general population and high probability of fibrosis among patients with NAFLD. Logistic regression analysis was employed to estimate the risk of NAFLD and fibrosis across education levels. A causal mediation model was used to explore the potential mediators. Results: Comparing with those receiving primary school education, the multi-adjusted odds ratios (95% confidence intervals) for NAFLD were 1.28 (1.16 to 1.41) for men and 0.94 (0.89 to 0.99) for women with college education after accounting for body mass index. When considering waist circumference, the odds ratios (95% CIs) were 0.94 (0.86 to 1.04) for men and 0.88 (0.80 to 0.97) for women, respectively. The proportions mediated by general and central obesity were 51.00% and 68.04% for men, while for women the proportions were 48.58% and 32.58%, respectively. Furthermore, NAFLD patients with lower educational attainment showed an incremental increased risk of advanced fibrosis in both genders. Conclusions In China, a low education level was associated with a higher risk of prevalent NAFLD in women, as well as high probability of fibrosis in both genders.