ObjectiveTo investigate the feasibility and safety of endoscopic retrograde cholangiopancreatography （ERCP） combined with oral cholangiopancreatography in the treatment of major duodenal papilla gallbladder polyps. MethodsA retrospective analysis was performed for the clinical data of eight patients with choledocholithiasis and gallbladder polyps who underwent ERCP and combined with oral cholangiopancreatography for major duodenal papilla cholecystectomy in Center of Digestive Endoscopy， Jilin People’s Hospital， from May 2022 to June 2024， and related data were collected， including the success rate of surgery， the technical success rate of gallbladder polyp removal， the superselective method of cystic duct， the time of operation， the time of gallbladder polyp removal， and surgical complications. ResultsBoth the success rate of surgery and the technical success rate of gallbladder polyp removal reached 100%， and of all eight patients， three patients used guide wire to enter the gallbladder under direct view， while five patients received oral cholangiopancreatography to directly enter the gallbladder. The time of operation was 51.88±12.34 minutes， and the time of gallbladder polyp removal was 23.13±10.94 minutes. The diameter of gallbladder polyp was 2‍ ‍—‍ ‍8 mm， and pathological examination showed inflammatory polyps in three patients， adenomatous polyps in one patient， and cholesterol polyps in four patients. There were no complications during or after surgery. The patients were followed up for 2‍ ‍—‍ ‍27 months after surgery， and no recurrence of gallbladder polyp was observed. ConclusionOral cholangiopancreatography is technically safe and feasible in endoscopic major duodenal papilla cholecystectomy.

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

Varicocele (VC) is a common cause of male infertility, yet there is a lack of molecular information for VC-associated male infertility. This study investigated alterations in the seminal plasma metabolomic and lipidomic profiles of infertile male VC patients. Twenty infertile males with VC and twenty-three age-matched healthy controls (HCs) were recruited from Peking Union Medical College Hospital (Beijing, China) between October 2019 and April 2021. Untargeted metabolite and lipid profiles from seminal plasma were analyzed using mass spectrometry. Four hundred and seventy-six metabolites and seventeen lipids were significantly different in infertile male VC patients compared to HCs. The top enriched pathways among these significantly different metabolites are protein digestion and absorption, aminoacyl-transfer RNA (tRNA) biosynthesis, and biosynthesis of amino acids. Different key lipid species, including triglyceride (TG), diacylglycerol (DG), ceramides (Cer), and phosphatidylserine (PS), varied between VC and HC groups. The distinct metabolites and lipids were moderately correlated. DL-3-phenyllactic acid is a potential diagnostic biomarker for VC-related male infertility (area under the curve [AUC] = 0.893), positively correlating with sperm count, concentration, and motility. Furthermore, DL-3-phenyllactic acid is the only metabolite shared by all four comparisons (VC vs HC, VC-induced oligoasthenospermia [OAS] vs VC-induced asthenospermia [AS], OAS vs HC, and AS vs HC). DL-3-phenyllactic acid significantly decreased in OAS than AS. Metabolite-targeting gene analysis revealed carbonic anhydrase 9 (CA9) might be the strongest candidate associated with the onset and severity of VC. The seminal plasma metabolite and lipid profiles of infertile males with VC differ significantly from those of HCs. DL-3-phenyllactic acid could be a promising biomarker.
Humans ; Male ; Varicocele/complications* ; Infertility, Male/etiology* ; Semen/metabolism* ; Lipidomics ; Adult ; Metabolomics ; Case-Control Studies ; Biomarkers/metabolism*

OBJECTIVE: To analyze the distribution of thalassemia (referred to as "thalassemia") gene variant types in the population of the Wuhan area, aiming to provide a genetic basis for the precise prevention and control as well as clinical diagnosis of thalassemia in the Wuhan region. METHODS: In this study, 2 133 suspected thalassemia patients and individuals undergoing prenatal screening who visited the Department of Hematology, Obstetrics and Gynecology, Reproductive Medicine, Pediatrics, and Neurology at Wuhan First Hospital from October 2022 to October 2024 were selected as the research subjects. Peripheral blood samples were collected from the patients. The common 27 thalassemia genotypes of α- and β-thalassemia were initially screened using fluorescence PCR melting curve analysis technology. For samples where the fluorescence PCR melting curve results indicated unknown variants or where the clinical phenotype was inconsistent with the common genotypes, Sanger sequencing technology was used for review and verification. RESULTS: Among the 2 133 specimens analyzed, common thalassemia gene variants were detected in 210 cases (9.85%, 210/2 133). A total of 156 cases (8.05%, 156/1 938) of thalassemia gene variants were detected in females and 54 cases (27.69%, 54/195) in males. A total of 94 cases (4.41%, 94/2 133) of α-thalassemia were detected, including 46 cases (2.16%, 46/2 133) of silent α-thalassemia, 47 cases (2.20%, 47/2 133) of mild α-thalassemia, and 1 case (0.05%, 1/2 133) of intermediate α-thalassemia. Additionally, 111 cases of β-thalassemia were identified (5.20%, 111/2 133), including 51 cases of β/β⁺ thalassemia (2.39%, 51/2 133), 59 cases of β/β⁰ thalassemia (2.77%, 59/2 133), and 1 case of β⁺/HbE thalassemia (0.05%, 1/2 133). αβ-composite thalassemia gene variants were detected in 5 cases (0.23%, 5/2 133), including 1 complex variant with a genotype of --^SEA/αα combined with CD41-42 (-TTCT) and 29(A>G), representing a heterozygous variant of three genotypes. Rare globin gene variants were detected in 3 cases, including HBB:c.60C>T, HBB:c.-146G>T, and HBA2:c.*12G>A. CONCLUSION The Wuhan region exhibits a relatively high prevalence of thalassemia genes with notable gender disparities. While maintaining focus on thalassemia screening for females, enhanced males screening efforts and genetic counseling should be implemented in future prevention programs.
Humans ; Female ; Male ; Genotype ; beta-Thalassemia/genetics* ; China ; Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Genetic Variation

[This corrects the article DOI: 10.1016/j.apsb.2022.03.002.].

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

The Guidelines for Occupational Hazard Assessment and Control of Active Pharmaceutical Ingredient in the Pharmaceutical Industry (T/WSJD 60—2024) is the first guiding standard in the field of health in China that focuses on occupational health protection for active pharmaceutical ingredient (API). It covers the general principles, work procedures, assessment methods, and control strategies for API occupational hazard assessment, providing practical guidance and recommendations for pharmaceutical enterprises to eliminate or reduce occupational health risks associated with API, improve working environment, and enhance refined management practices. This article interpreted and analyzed the background of standard establishment, formulation process, fundamental basis, and main content, to provide scientific and comprehensive technical support for occupational health managers in the pharmaceutical industry to better apply this standard.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.