Objective To investigate the effect of sal-like gene 2(Sall2)gene overexpression on the progression of disease in human superoxide dismutase 1(hSOD1)-G93A mutant amyotrophic lateral sclerosis(ALS)transgenic mice,with the aim of identifying potential therapeutic targets for ALS gene therapy.Methods Differential Sall2 gene were screened through bioinformatics analysis.Forty-eight ALS transgenic mice were selected for this study.AAV-PHP.eB-Sall2 adeno-associated virus with a neuron-specific promoter,human synapsin I(hSyn),was constructed and administered via tail vein injection to six-week-old mice.In parallel,the same litter of ALS mice received an injection of AAV-PHP.eB-GFP.The staining of Sall2 and neuron-specific nuclear protein(NeuN)/GFAP in the spinal cord and cerebral cortex of mice were detected through immunofluorescent double-label staining technology.The survival period,weight changes,exercise ability,and electromyographic changes of the gastrocnemius muscle were detected.The morphological changes in the spinal cord anterior horn neurons were detected through Nissl staining.The effect of Sall2 gene overexpression on the expression of the cell cycle protein E1(cyclin E1)was investigated through Western blotting.Results Bioinformatics analysis showed out that Sall2 was differentially expressed in ALS mice.Compared with ALS mice in the control group,the Sall2 protein expression of ALS mice in the overexpressing Sall2 gene group increased in both the spinal cord and cerebral cortex,and the Sall2 integral absorbance values of Sall2+/NeuN+double-positive cells were higher.The survival time of ALS mice in the Sall2 gene overexpressing group was prolonged,the rate of weight loss was slowed down,the performance in the rotarod and inverted grid tests was improved with longer times,and the positive sharp waves and fibrillation potentials in the gastrocnemius electromyography were reduced.The number of Nissl bodies labeled neurons increased in the spinal cord anterior horn of the Sall2 gene overexpressing mice,and the condition of neuronal damage was improved.Overexpression of the Sall2 gene also reduced the expression of cyclin E1 in both the spinal cord and cerebral cortex of ALS transgenic mice.Conclusion Overexpression of the Sall2 gene can delay disease progression and improve motor performance in ALS transgenic mice,affecting the expression of cyclin E1,thus exerting a therapeutic effect on these mice.

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

ObjectiveTo improve the quality standard of Yuanhu Zhitong oral liquid in order to strengthen the quality control of this oral liquid. MethodThin layer chromatography（TLC） was used for the qualitative identification of Corydalis Rhizoma and Angelicae Dahuricae Radix in Yuanhu Zhitong oral liquid by taking tetrahydropalmatine， corydaline reference substances and Corydalis Rhizoma reference medicinal materials as reference， and cyclohexane-trichloromethane-methanol（5∶3∶0.5） as developing solvent， Corydalis Rhizoma was identified using GF254 glass thin layer plate under ultraviolet light（365 nm）. And taking petroleum ether（60-90 ℃） -ether-formic acid（10∶10∶1） as developing solvent， Angelicae Dahuricae Radix was identified using a silica gel G TLC plate under ultraviolet light（305 nm）. High performance liquid chromatography（HPLC） was performed on a Waters XSelect HSS T3 column（4.6 mm×250 mm， 5 μm） with acetonitrile（A）-0.1% glacial acetic acid solution（adjusted pH to 6.1 by triethylamine）（B） as the mobile phase for gradient elution（0-10 min， 20%-30%A； 10-25 min， 30%-40%A； 25-40 min， 40%-50%A； 40-60 min， 50%-60%A）， the detection wavelength was set at 280 nm， then the fingerprint of Yuanhu Zhitong oral liquid was established， and the contents of tetrahydropalmatine and corydaline were determined. ResultIn the thin layer chromatograms， the corresponding spots of Yuanhu Zhitong oral liquid， the reference substances and reference medicinal materials were clear， with good separation and strong specificity. A total of 12 common peaks were identified in 10 batches of Yuanhu Zhitong oral liquid samples， and the peaks of berberine hydrochloride， dehydrocorydaline， glaucine， tetrahydropalmatine and corydaline. The similarities between the 10 batches of samples and the control fingerprint were all >0.90. The results of determination showed that the concentrations of corydaline and tetrahydropalmatine had good linearity with paek area in the range of 0.038 6-0.193 0， 0.034 0-0.170 0 g·L^-1， respectively. The methodological investigation was qualified， and the contents of corydaline and tetrahydropalmatine in 10 batches of Yuanhu Zhitong oral liquid samples were 0.077 5-0.142 9、0.126 1-0.178 2 g·L^-1， respectively. ConclusionThe established TLC， fingerprint and determination are simple， specific and reproducible， which can be used to improve the quality control standard of Yuanhu Zhitong oral liquid.

Currently, the gut-organ axis has become a hot research topic. As increasing attention has been paid to the role of gut microbiota in the health of organs, the complex and integrated dialogue mechanism between the gastrointestinal tract and the associated microbiota has been demonstrated in more and more studies. Skin as the largest organ in the human body serves as the primary barrier protecting the human body from damage. The proposal of the gut-skin axis has established a bidirectional link between the gut and the skin. The disturbance of gut microbiota can lead to the occurrence of skin diseases, the mechanism of which is complex and may involve multiple pathways in immunity, metabolism, and internal secretion. According to the theory of traditional Chinese medicine(TCM), the connection between the intestine and the skin can be established through the lung, and the interior disorders will definitely cause symptoms on the exterior. This paper reviews the research progress in the gut-skin axis and its correlation with TCM theory and provides ideas and a basis for cli-nical treatment and drug development of skin and intestinal diseases.
Humans ; Medicine, Chinese Traditional ; Gastrointestinal Tract ; Skin Diseases/drug therapy* ; Gastrointestinal Microbiome

Humans ; White Matter/pathology* ; Central Nervous System Neoplasms/pathology* ; Lymphoma, Large B-Cell, Diffuse/pathology*

Objective:To construct and validate a prediction model for the risk of intermediate cesarean delivery for primiparous women with failed vaginal trial of labor.Methods:Clinical data of 6 128 pregnant women who gave birth in the Affiliated Hospital of Jining Medical College from January 2019 to December 2020 were collected. The puerpera was randomly divided into train set ( n=4 290) and validation set ( n=1 838). The factors influencing the conversion to cesarean section in primiparous women with failed vaginal trial of labor were analyzed with univariate and binary multivariate logistic regression, and a risk prediction model was established based on the influencing factors. The predictive power of the model was assessed by receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness-of-fit test in train set and validation set. Results:Among 6 128 pregnant women 1 042 cases failed in vaginal trial of labor and were transferred to cesarean section. Univariate analysis showed age, occupation, gestational weight gain, days of gestation, body temperature before delivery, fetal heart condition at delivery, fetal abdominal circumference, Bishop score, premature rupture of membranes, gestational illness, mode of induction of labor, labor analgesia, and fetal orientation were significantly associated with converting to cesarean delivery (all P<0.05). The multivariate binary logistic regression analysis showed that the age, gestational weight gain, body temperature, gestational co-morbidities, days of gestation, premature rupture of membranes, amniotic fluid contamination, induction of labor, and abnormal occipital position were independent risk factors for intermediate cesarean delivery ( OR=1.03-8.06, all P<0.05); while height, occupation, Bishop score, and labor analgesia were protective factors for intermediate cesarean delivery ( OR=0.17-0.96, all P<0.05). A risk prediction model was constructed based on the risk factors and protective factors. In train set, the area under the ROC curve(AUC) of the model was 0.902 (95% CI: 0.89-0.92, P<0.001), with the best cutoff value of 0.138, the sensitivity and specificity were 0.837 and 0.825, respectively; and the Hosmer-Lemeshow goodness-of-fit test showed P=0.192. In validation set the AUC of the model was 0.917 (95% CI: 0.90-0.93, P<0.001), and the sensitivity and specificity were 0.826 and 0.851, respectively; the total correct rate of the model was 87.21% (1 603/1 838). Conclusion:The risk prediction model of failed vaginal trial of labor in primiparous women for intermediate cesarean delivery constructed in this study has good clinical prediction efficacy and high correctness rate.