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.

OBJECTIVE: To investigate the application value of pediatric sepsis-induced coagulation (pSIC) score and mean platelet volume/platelet count (MPV/PLT) ratio in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. METHODS: A retrospective cohort study was conducted, selecting 112 children with sepsis (sepsis group) admitted to pediatric intensive care unit (PICU) of Liaocheng Second People's Hospital from January 2020 to December 2023 as the study objects, and 50 children without sepsis admitted to the pediatric surgery department of our hospital during the same period for elective surgery due to inguinal hernia as the control (control group). The children with sepsis were divided into two groups according to the pediatric critical case score (PCIS). The children with PCIS score of ≤ 80 were classified as critically ill group, and those with PCIS score of > 80 was classified as non-critically ill group. pSIC score, coagulation indicators [prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and fibrinogen (FIB)], and platelet related indicators (PLT, MPV, and MPV/PLT ratio) were collected. Pearson correlation method was used to analyze the correlation between pSIC score and MPV/PLT ratio as well as their correlation with coagulation indicators. Multivariate Logistic regression analysis was used to screen the independent risk factors for pediatric sepsis and critical pediatric sepsis. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the application value of the above independent risk factors on the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. RESULTS: 112 children with sepsis and 50 children without sepsis were enrolled in the final analysis. pSIC score, PT, INR, APTT, FIB, MPV, and MPV/PLT ratio in the sepsis group were significantly higher than those in the control group [pSIC score: 0.93±0.10 vs. 0.06±0.03, PT (s): 14.76±0.38 vs. 12.23±0.15, INR: 1.26±0.03 vs. 1.06±0.01, APTT (s): 40.08±0.94 vs. 32.47±0.54, FIB (g/L): 3.51±0.11 vs. 2.31±0.06, MPV (fL): 8.86±0.14 vs. 7.62±0.11, MPV/PLT ratio: 0.037±0.003 vs. 0.022±0.001, all P < 0.01], and PLT was slightly lower than that in the control group (×10⁹/L: 306.00±11.01 vs. 345.90±10.57, P > 0.05). Among 112 children with sepsis, 46 were critically ill and 66 were non-critically ill. pSIC score, PT, INR, APTT, MPV, and MPV/PLT ratio in the critically ill group were significantly higher than those in the non-critically ill group [pSIC score: 1.74±0.17 vs. 0.36±0.07, PT (s): 16.55±0.80 vs. 13.52±0.23, INR: 1.39±0.07 vs. 1.17±0.02, APTT (s): 43.83±1.72 vs. 37.77±0.95, MPV (fL): 9.31±0.23 vs. 8.55±0.16, MPV/PLT ratio: 0.051±0.006 vs. 0.027±0.001, all P < 0.05], PLT was significantly lower than that in the non-critically ill group (×10⁹/L: 260.50±18.89 vs. 337.70±11.90, P < 0.01), and FIB was slightly lower than that in the non-critically ill group (g/L: 3.28±0.19 vs. 3.67±0.14, P > 0.05). Correlation analysis showed that pSIC score was significantly positively correlated with MPV/PLT ratio and coagulation indicators including PT, APTT and INR in pediatric sepsis (r value was 0.583, 0.571, 0.296 and 0.518, respectively, all P < 0.01), and MPV/PLT ratio was also significantly positively correlated with PT, APTT and INR (r value was 0.300, 0.203 and 0.307, respectively, all P < 0.05). Multivariate Logistic regression analysis showed that pSIC score and MPV/PLT ratio were independent risk factors for pediatric sepsis and critical pediatric sepsis [pediatric sepsis: odds ratio (OR) and 95% confidence interval (95%CI) for pSIC score was 14.117 (4.190-47.555), and the OR value and 95%CI for MPV/PLT ratio was 1.128 (1.059-1.202), both P < 0.01; critical pediatric sepsis: the OR value and 95%CI for pSIC score was 8.142 (3.672-18.050), and the OR value and 95%CI for MPV/PLT ratio was 1.068 (1.028-1.109), all P < 0.01]. ROC curve analysis showed that pSIC score and MPV/PLT ratio had certain application value in the diagnosis of pediatric sepsis [area under the ROC curve (AUC) and 95%CI was 0.754 (0.700-0.808) and 0.720 (0.643-0.798), respectively] and the determination of critical pediatric sepsis [AUC and 95%CI was 0.849 (0.778-0.919) and 0.731 (0.632-0.830)], and the combined AUC of the two indictors was 0.815 (95%CI was 0.751-0.879) and 0.872 (95%CI was 0.806-0.938), respectively. CONCLUSIONS pSIC score and MPV/PLT ratio have potential application value in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis, and the combined application of both is more valuable.
Humans ; Sepsis/complications* ; Platelet Count ; Mean Platelet Volume ; Retrospective Studies ; Child ; Blood Coagulation Disorders/diagnosis* ; Intensive Care Units, Pediatric ; Male ; Female ; Partial Thromboplastin Time ; Child, Preschool ; Blood Coagulation ; International Normalized Ratio ; Infant

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To investigate the status and influencing factors of type 2 diabetes mellitus(T2DM)patients' fear of complications,and to provide a reference for formulating targeted intervention measures.Methods From April to November 2024,370 patients with T2DM in 2 tertiary general hospitals in Daqing City were selected by convenience sampling method.General data questionnaire,Fear of Complications Questionnaire,Self-Perceived Burden Scale,Psychological Capital Questionnaire,Mishel Uncertainty in Illness Scale and Family Apgar Index Questionnaire were used for investigation.Univariate analysis and binary Logistic regression were performed to analyze the influencing factors.Results A total of 364 valid questionnaires were collected,with an effective recovery rate of 98.38％.The score of Fear of Complications Questionnaire was(23.47±7.47),and the incidence of fear of complications was 22.25％.Logistic regression analysis showed that medical payment methods,the number of complications,positive psychological capital and family care were the influencing factors of FoC in T2DM patients.Conclusion The fear of complications in T2DM patients is at a moderate level.Nursing staff should pay attention to the early assessment of patients' fear of complications,promptly identify and take effective measures to reduce the level of patients' fear of complications,improve their quality of life.

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 investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To investigate the status and influencing factors of type 2 diabetes mellitus(T2DM)patients' fear of complications,and to provide a reference for formulating targeted intervention measures.Methods From April to November 2024,370 patients with T2DM in 2 tertiary general hospitals in Daqing City were selected by convenience sampling method.General data questionnaire,Fear of Complications Questionnaire,Self-Perceived Burden Scale,Psychological Capital Questionnaire,Mishel Uncertainty in Illness Scale and Family Apgar Index Questionnaire were used for investigation.Univariate analysis and binary Logistic regression were performed to analyze the influencing factors.Results A total of 364 valid questionnaires were collected,with an effective recovery rate of 98.38％.The score of Fear of Complications Questionnaire was(23.47±7.47),and the incidence of fear of complications was 22.25％.Logistic regression analysis showed that medical payment methods,the number of complications,positive psychological capital and family care were the influencing factors of FoC in T2DM patients.Conclusion The fear of complications in T2DM patients is at a moderate level.Nursing staff should pay attention to the early assessment of patients' fear of complications,promptly identify and take effective measures to reduce the level of patients' fear of complications,improve their quality of life.

Objective:To investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.