Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Autoimmune pancreatitis is a special type of chronic pancreatitis that can lead to abnormal pancreatic exocrine function in patients. Autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency has a complex pathogenesis， and there is limited research on this topic， leading to the lack of understanding of such patients in clinical practice. This article introduces the epidemiology of autoimmune pancreatitis， briefly describes the pathogenesis of pancreatic exocrine insufficiency caused by autoimmune pancreatitis， and summarizes the various detection methods for pancreatic exocrine function， nutritional assessments， lifestyle management， and drug therapy， in order to strengthen the understanding of autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency and improve the clinical diagnosis and treatment of pancreatic exocrine insufficiency.

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Peroxisome proliferator activated receptor-α (PPAR-α) is significantly expressed in various tissues such as the liver, kidney, myocardium, and skeletal muscle, which plays a central role in the development of various diseases by regulating key physiological processes such as energy homeostasis, redox balance, inflammatory response, and ferroptosis. As an important metabolic and excretory organ of the body, renal dysfunction can lead to water and electrolyte imbalance, toxin accumulation, and multiple system complications. The causes of kidney injury are complex and diverse, including acute injury factors (such as ischemia/reperfusion, nephrotoxic drugs, septic shock, and immune glomerulopathy), as well as chronic progressive causes [such as metabolic disease-related nephropathy, hypertensive nephropathy (HN)], and risk factors such as alcohol abuse, obesity, and aging. This review briefly describes the structure, function, and activity regulation mechanism of PPAR-α, systematically elucidates the molecular regulatory network of PPAR-α in the pathological process of kidney injury including acute kidney injury (AKI) such as renal ischemia/reperfusion injury (IRI), drug-induced AKI, sepsis-associated acute kidney injury (SA-AKI), glomerulonephritis, chronic kidney disease (CKD) such as diabetic nephropathy (DN), HN, and other kidney injury, and summarizes the mechanisms related to PPAR-α regulation of kidney injury, including regulation of metabolism, antioxidation, anti-inflammation, anti-fibrosis, and anti-ferroptosis. This review also evaluates PPAR-α's medical value as a novel therapeutic target, and aims to provide theoretical basis for the development of kidney protection strategies based on PPAR-α targeted intervention.
Humans ; PPAR alpha/metabolism* ; Acute Kidney Injury/therapy* ; Animals ; Kidney/metabolism*

OBJECTIVES: To evaluate the application value of genetic newborn screening (gNBS) in the Yunnan region. METHODS: A prospective study was conducted with a random selection of 3 001 newborns born in the Yunnan region from February to December 2021. Traditional newborn screening (tNBS) was used to test biochemical indicators, and targeted next-generation sequencing was employed to screen 159 genes related to 156 diseases. Positive-screened newborns underwent validation and confirmation tests, and confirmed cases received standardized treatment and long-term follow-up. RESULTS: Among the 3 001 newborns, 166 (5.53%) were initially positive for genetic screening, and 1 435 (47.82%) were genetic carriers. The top ten genes with the highest variation frequency were GJB2 (21.29%), DUOX2 (7.27%), HBA (6.14%), GALC (3.63%), SLC12A3 (3.33%), HBB (3.03%), G6PD (2.94%), SLC25A13 (2.90%), PAH (2.73%), and UNC13D (2.68%). Among the initially positive newborns from tNBS and gNBS, 33 (1.10%) and 47 (1.57%) cases were confirmed, respectively. A total of 48 (1.60%) cases were confirmed using gNBS+tNBS. The receiver operating characteristic curve analysis demonstrated that the areas under the curve for tNBS, gNBS, and gNBS+tNBS in diagnosing diseases were 0.866, 0.982, and 0.968, respectively (P<0.05). DeLong's test showed that the area under the curve for gNBS and gNBS+tNBS was higher than that for tNBS (P<0.05). CONCLUSIONS gNBS can expand the range of disease detection, and its combined use with tNBS can significantly shorten diagnosis time, enabling early intervention and treatment.
Humans ; Infant, Newborn ; Neonatal Screening ; Genetic Testing ; Female ; Male ; Follow-Up Studies ; Prospective Studies ; China

The p21-activated kinase 4 (PAK4), a key regulator of malignancy, is negatively correlated with immune infiltration and has become an emergent drug target of cancer therapy. Given the lack of high efficacy PAK4 inhibitors, we herein reported the identification of a novel inhibitor 13 bearing a tetrahydrobenzofuro[2,3-c]pyridine tricyclic core and possessing high potency against MIA PaCa-2 and Pan02 cell lines with IC₅₀ values of 0.38 and 0.50 μmol/L, respectively. This compound directly binds to PAK4 in a non-ATP competitive manner. In the mouse Pan02 model, compound 13 exhibited significant tumor growth inhibition at a dose of 100 mg/kg, accompanied by reduced levels of PAK4 and its phosphorylation together with immune infiltration in mice tumor tissue. Overall, compound 13 is a novel allosteric PAK4 inhibitor with a unique tricyclic structural feature and high potency both in vitro and in vivo, thus making it worthy of further exploration.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

Objective:To investigate the effect and safety of pancreatic extracorporeal shock wave lithotripsy (P-ESWL) in treating the chronic pancreatitis (CP) patients with main pancreatic duct (MPD) stones and to analyze the influencing factors of success of lithotripsy.Methods:Clinical data of 132 patients with CP complicated with MPD stones treated with P-ESWL in the Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2022 to December 2023 were retrospectively analyzed, including 103 males and 29 females, aged (50.3±16.9) years. The times of P-ESWL, the success rate of stone fragmentation, the necessity of combining endoscopic retrograde cholangiopancreatograhy (ERCP), clearance rate of MPD stones, and incidence of post-P-ESWL complications (acute pancreatitis, abdominal hematoma, infection, steinstrasse, perforation, etc.) were evaluated. The factors influencing the success rate of lithotripsy were analyzed using univariate and multivariate logistic regression.Results:All patients underwent P-ESWL treatment, with (2.23±0.82) times of P-ESWL per person. The success rate of stone fragmentation was 87.1%(115/132). Of 107 CP patients (81.1%, 107/132) were treated with their first P-ESWL. Of 12 patients (9.1%, 12/132) underwent single P-ESWL, and 120 (90.9%, 120/132) underwent P-ESWL combined with ERCP. There were 95 cases (72.0%, 95/132) with effective removal of stones, and 62 (47.0%, 62/132) with complete removal of stones. Post-P-ESWL complications included eight cases (6.1%, 8/132) of acute pancreatitis, two (7.6%, 2/132) of steinstrasse complicated with acute pancreatitis and one (0.8%, 1/132) of abdominal hematoma. No infection or perforation occurred. Multivariate logistic regression analysis showed that the higher CT value of stones ( OR=1.239, 95% CI: 1.040-1.477, P=0.017) was associated with the lower success rate of stone fragmentation. Conclusion:P-ESWL is safe and effective in treating patients with CP complicated with MPD stones. The CT value of stones is a risk factor for the success rate of P-ESWL.

Objective To explore the associations of dietary patterns with bone mineral density(BMD)and osteoporosis(OP)in patients with knee osteoarthritis(KOA)in south China,identify the dietary patterns that could reduce the risk of OP,and provide a reference for prevention of OP.Methods Data from 600 KOA patients who were enrolled in the Pearl River Osteoarthritis Cohort(PROC)between April 2020 and April 2024 were extracted,including general characteristics,dietary questionnaire and BMD test.Factor analysis was employed to identify dietary patterns.Scores of dietary pattern were treated as continuous or categorical variables(quartile-based groups).Multivariable linear regression was used to assess associations between scores of dietary pattern and BMD,while the relationships with OP were analysed by binary logistic regression.Results The prevalence of OP in KOA patients was 19.8%.Factor analysis extracted eight dietary patterns:seafood-nuts,livestock-poultry meats,coarse grains-fruits,wheat-eggs,fish-dairy-vegetables,legumes-fruits,rice-wheat flour,and soy-dairy.After adjusting for confounders,it was found that the pattern of coarse grains-fruits was positively correlated with the BMD of left femoral neck and lumbar spine(P<0.01)and the fish-dairy-vegetables pattern was positively correlated with the BMD of lumbar spine(P<0.01).When pattern scores were discretized into quartiles,the legumes-fruits pattern(Q3 vs.Q1)was found in association with a 68.2%reduction in the risk of OP(P<0.05).Conclusion This study has identified that the pattern of legumes-fruits may lower a risk of OP,while the patterns of coarse grains-fruits and fish-dairy-vegetables correlate with higher BMD.The diet patterns are potentially beneficial to prevention of OP.For bone health,a balanced diet rich in legumes,fruits,vegetables,coarse grains,fish,nuts and dairy is recommended.