OBJECTIVE: To investigate the genetic etiology of a fetus diagnosed with Mandibulofacial dysostosis with microcephaly (MFDM). METHODS: A fetus that underwent prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, on May 19, 2025 was selected for analysis. Results of fetal ultrasound findings, chromosomal karyotyping, copy number variation sequencing (CNV-seq), and whole-exome sequencing (WES) were collected. Sanger sequencing was performed for familial validation of the pathogenic variant. The Human Protein Atlas (HPA), STRING, and Simple ClinVar databases were queried to characterize the biological features of the candidate gene. Three-dimensional structures of the wild-type and variant proteins were modeled and analyzed, and the evolutionary conservation of the affected amino acid was assessed using UGENE. Prenatal phenotypes associated with EFTUD2 variants were summarized through a review of the literature. This study was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Ethics No.: 2025-KY-029-01). RESULTS: At 23⁺² weeks of gestation, ultrasound examination revealed bilateral microtia with low-set ears, mild micrognathia with a reduced mandibular-facial angle, a single umbilical artery, a slightly narrow aortic diameter, and trivial mitral regurgitation. Amniotic fluid karyotyping and CNV-seq showed no abnormalities. WES identified a de novo, previously unreported EFTUD2 variant, c.698dupA (p.V235Gfs*27), in the fetus. This frameshift variant is predicted to alter the structural integrity of the EFTUD2 protein. Literature review indicated that micrognathia and microtia or low-set ears are the most common sonographic features in fetuses with EFTUD2 variants, while secondary findings may include abnormal stomach bubble, cleft palate, single umbilical artery, gastrointestinal atresia, polyhydramnios, and reduced aortic diameter. CONCLUSION The EFTUD2: c.698dupA (p.V235Gfs*27) variant is likely the genetic cause underlying MFDM in this fetus.
Humans ; Mandibulofacial Dysostosis/diagnostic imaging* ; Microcephaly/diagnostic imaging* ; Female ; Pregnancy ; Ribonucleoprotein, U5 Small Nuclear/chemistry* ; Peptide Elongation Factors/chemistry* ; Fetus ; DNA Copy Number Variations/genetics* ; Adult ; Ultrasonography, Prenatal

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective:To clarify the genetic associations between obesity, diabetes, smoking, non-alcoholic fatty liver disease, acute and chronic pancreatitis, and pancreatic cancer risk.Methods:Summary data from genome-wide association studies (GWAS) of individuals of European descent were used. Obesity, alcohol consumption, diabetes, and acute and chronic pancreatitis data for the UK population were obtained from the GWAS catalog, while alcohol consumption, non-alcoholic fatty liver disease, occasional smoking, and regular smoking data were obtained from the UK biobank. Pancreatic cancer-related data for the Finnish population were sourced from the latest R11 version of the Finnish database. Two-sample Mendelian randomization (MR) analysis was conducted on the associations between the aforementioned risk factors and pancreatic cancer using five MR methods, primarily inverse variance weighting. The robustness of the results was assessed through Q heterogeneity tests, pleiotropy tests, MR-PRESSO analysis, and reverse MR analysis.Results:Obesity showed a significant positive association with pancreatic cancer risk ( OR=1.407, 95% CI: 1.100-1.714, P=0.030), and the results were robust based on Q heterogeneity tests, pleiotropy tests, MR-PRESSO, and reverse MR analysis (all P>0.05). However, no significant associations were found between pancreatic cancer risk and alcohol consumption ( P=0.330), heavy drinking ( P=0.382), type 1 diabetes ( P=0.674), type 2 diabetes ( P=0.825), occasional smoking ( P=0.607), regular smoking ( P=0.758), non-alcoholic fatty liver disease ( P=0.287), acute pancreatitis ( P=0.336), or chronic pancreatitis ( P=0.545). Conclusion:This study further confirms the strong genetic association between obesity and increased pancreatic cancer risk.

To summarize the clinical experience of Professor LIU Shangyi in treating pruritus vulvae. It is believed that women have the physiological characteristics of liver and kidney as the root， and their pubic area is easily attacked by wind-dampness pathogenic qi， so the core mechanism of pruritus vulvae is proposed as wind-dampness accumulation and deficiency of liver and kidney. The core treatment method is to dispel wind-dampness and nourish the liver and kidneys， and modify the Danggui Decoction （当归饮子） to form a self-prescribed Yinyang Formula （阴痒方） as the basic prescription to treat pruritus vulvaen.

OBJECTIVE: To determine the prevalence of lumbar spondylolysis (LS) and the proportion of spondylolytic spondylolisthesis (SS) in China, and to evaluate the musculoskeletal status of patients with LS and SS. METHODS: Spine Computed Tomography (CT) images were collected from community populations aged 40 and above in a nationwide multi-center project. LS was diagnosed, and SS was graded by an experienced radiologist. Bone mineral density (BMD) and paraspinal muscle parameters were quantified based on CT images. RESULTS: One hundred and seventeen patients of a total of 3,317 individuals were diagnosed with LS, corresponding to a prevalence rate of 3.53%. 63 of the 1,214 males (5.18%) and 54 of the 2,103 females (2.57%) were diagnosed with LS. SS occurred in 64/121 vertebrae (52.89%). BMD was not associated with LS ( P = 0.341). The L5 extensor paraspinal muscle density was higher in the LS group than in the non-LS group. In the LS group, patients with SS had a smaller L5 paraspinal extensor muscle cross-sectional area than those without SS ( P = 0.003). CONCLUSION The prevalence of LS in Chinese adults was 3.53%, with prevalence rates of 5.18% in males and 2.57% in females. Patients with LS have higher muscle density, whereas those with SS have smaller muscle cross-sectional areas at the L5 level.
Humans ; Male ; Female ; Middle Aged ; China/epidemiology* ; Prevalence ; Adult ; Lumbar Vertebrae/diagnostic imaging* ; Spondylolysis/diagnostic imaging* ; Aged ; Bone Density ; Tomography, X-Ray Computed ; Aged, 80 and over ; Spondylolisthesis/epidemiology* ; East Asian People