Objective:To evaluate the impact of self-crosslinked hyaluronic acid (SCH) gel on endometrium recovery after artificial abortion.Methods:A multicenter, prospective randomized controlled trial was conducted across 18 hospitals from December 2021 to February 2023, involving 382 women who underwent artificial abortion. Participants were randomly allocated to receive either treatment with SCH gel (SCH group) or no treatment (control group) in a 1∶1 ratio. The primary outcome was endometrium thickness in 14 to 18 days after the first postoperative menstruation. Secondary outcomes included changes in menstrual volume during the first postoperative menstruation, menstruation resumption within 6 postoperative weeks, time to menstruation resumption, duration of the first postoperative menstruation, and incidence of dysmenorrhea.Results:Baseline characteristics of participants were comparable between the two groups (all P>0.05), with 95.3% (182/191) in SCH group and 92.7% (177/191) in the control group completed the study. The postoperative endometrial thickness in SCH group was significantly greater than that in the control group [(9.78±3.15) vs (8.95±2.32) mm; P=0.005]. SCH group also had significantly fewer participants with reduced menstrual volume [23 cases (12.6%, 23/182) vs 31 cases (17.5%, 31/177); P=0.038]. Although SCH group experienced less dysmenorrhea during the first postoperative menstrual period, this difference was not statistically significant [28.5% (51/179) vs 37.1% (65/175); P=0.083]. Outcomes were similar between SCH group and the control group regarding the proportion of participants who resumed menstruation within 6 weeks postoperatively, time to menstruation resumption, and duration of the first postoperative menstruation ( P=0.792, 0.485, and 0.254, respectively). No serious adverse events were observed during the study period, and no adverse events were attributed to SCH gel treatment. Conclusion:The application of SCH gel after artificial abortion is safe and might aid in the recovery of the endometrium.

Colorectal cancer (CRC) represents a significant global health challenge as a common malignancy of the digestive tract. The involvement of B vitamins—specifically folic acid (B9), riboflavin (B2), pyridoxine (B6), and cobalamin (B12)—is crucial in metabolic processes by mediating the transfer of one-carbon (1C) units, which plays a fundamental role in cellular functions and tumor growth. 1C metabolism is involved in synthesis of proteins, lipids, nucleic acids, and other cofactors. 1C metabolism, intertwined with the metabolism of other nutrients, forms complex pathways where B vitamins act as precursors or coenzymes, influencing the production of various intermediates. These vitamins, as essential nutrients, are implicated to varying the pathogenesis and progression of colorectal cancer such as epigenetics. Furthermore, 1C metabolism affects tumor cell fate through multiple aspects including nucleotide synthesis, redox homeostasis, and the interaction with gut microbiota. Given these roles, understanding and monitoring B vitamin levels and their metabolic pathways are essential for colorectal cancer prevention and management. This approach not only helps in reducing tumor-related mortality but also opens new avenues for research into CRC mechanisms and potential therapeutic strategies.

Colorectal cancer (CRC) represents a significant global health challenge as a common malignancy of the digestive tract. The involvement of B vitamins—specifically folic acid (B9), riboflavin (B2), pyridoxine (B6), and cobalamin (B12)—is crucial in metabolic processes by mediating the transfer of one-carbon (1C) units, which plays a fundamental role in cellular functions and tumor growth. 1C metabolism is involved in synthesis of proteins, lipids, nucleic acids, and other cofactors. 1C metabolism, intertwined with the metabolism of other nutrients, forms complex pathways where B vitamins act as precursors or coenzymes, influencing the production of various intermediates. These vitamins, as essential nutrients, are implicated to varying the pathogenesis and progression of colorectal cancer such as epigenetics. Furthermore, 1C metabolism affects tumor cell fate through multiple aspects including nucleotide synthesis, redox homeostasis, and the interaction with gut microbiota. Given these roles, understanding and monitoring B vitamin levels and their metabolic pathways are essential for colorectal cancer prevention and management. This approach not only helps in reducing tumor-related mortality but also opens new avenues for research into CRC mechanisms and potential therapeutic strategies.

Objective:To develop a prototype artificial intelligence immunofluorescence image recognition system for classification of antinuclear antibodies in order to meet the growing clinical requirements for an automatic readout and classification of immunof luorescence patterns for antinuclear antibody (ANA) images.Methods:Immunofluorescence images with positive results of ANA in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 2020 to December 2021 were collected. Three senior technicians independently and in parallel interpreted the Immunofluorescence images to determine the ANA results. Then the images were labeled according to the ANA International Consensus on Fluorescence Patterns (ICAP) classification criteria. There were 7 labeled groups: Fine speckled, Coarse speckled, Homogeneous, nucleolar, Centromere, Nuclear dots and Nuclear envelope. Each group was randomly divided into training dataset and validation dataset at a ratio of 9∶1 by using random number table. On the deep learning framework PyTORCH 1.7, the convolutional neural network (CNN) training platform was constructed based on ResNet-34 image classification network, and the automatic ANA recognition system was established. After the model was established, the test set was set up separately, the judgment results of the model were output by ranking the prediction probability, with the results of the 2 senior technicians was taken as "golden standard". Parameters such as accuracy, precision, recall and F1-score were used as indicators to evaluate the performance of the model.Results:A total of 23138 immunofluorescence images were obtained after segmentation and annotation. A total of 7 models were trained, and the effects of different algorithms, image processing and enhancement methods on the model were compared. The ResNet-34 model with the highest accuracy andswas selected as the final model, with the classification accuracy of 93.31%, precision rate of 91%, and recall rate of 90% and F1-score of 91% in the test set. The overall coincidence rate between the model and manual interpretation was 90.05%, and the accuracy of recognition of nucleolus was the highest, with the coincidence rate reaching 100% in the test set.Conclusion:The current AI system developed based on deep learning of the ANA immunofluorescence images in the present study showed the ability to recognize ANA pattern, especially in the common, typical, simple pattern.

In recent years, machine learning has become a hot spot in various research fields. Using machine learning can realize the transformation from data driven to knowledge discovery, which is an important development direction of laboratory intelligence in the future. The application of machine learning in laboratory medicine has shown great potential, but t it also has many challenges and difficulties. The direction of our joint efforts is to promote the clinical transformation of machine learning technology, realize the practicality and industrialization in medical laboratories, and achieve the goal of assisting clinical decision-making as soon as possible.

Antinuclear antibodies (ANA) testing is essential for the diagnosis, classification, and disease activity monitoring of systemic autoimmune rheumatic diseases. In recent years, with the enhancement of computing power and the innovation of algorithms, the newly hip branch, deep learning (DL), practically delivered all of the most stunning achievements and breakthroughs in artificial intelligence (AI) so far. The application of DL to visual tasks, known as computer vision, has revealed significant power within the medical image recognition. Indirect immunofluorescence on HEp-2 cells is the reference method for ANA testing, the results is interpreted manually by specialized physicians. ANA fluorescent pattern classification is based on image recognition, which has a broad prospect of combining with DL to realize automatic interpretation system. This paper reviews the recent research progress and challenges of DL in the field of ANA detection in order to provide references for the standardization of ANA testing in the future.

Tumor markers (TM) detection is of great significance in tumor screening, monitoring and treatment intervention, which puts forward higher requirements for its detection quality. TM traceability is very important in the process of reagent production and clinical laboratory testing, which can help improving the reliability and comparability of TM testing. Based on the current principles and classification system of metrology traceability in the world, this paper reviews the quality requirements of reference materials and reference measurement methods related to protein and nucleic acid of TM, as well as the problems existing in the international convention reference measurement procedure and traceability system of TM, so as to provide a new idea for the quality assurance work of TM detection.

Tumor markers (TM) detection is of great significance in tumor screening, monitoring and treatment intervention, which puts forward higher requirements for its detection quality. TM traceability is very important in the process of reagent production and clinical laboratory testing, which can help improving the reliability and comparability of TM testing. Based on the current principles and classification system of metrology traceability in the world, this paper reviews the quality requirements of reference materials and reference measurement methods related to protein and nucleic acid of TM, as well as the problems existing in the international convention reference measurement procedure and traceability system of TM, so as to provide a new idea for the quality assurance work of TM detection.

Laboratory testing is of great value in the management of autoimmune disease. The results can help confirm a diagnosis, estimate disease severity, aid in assessing treatment effect. But the current autoimmunity laboratory system, including testing standards, quality control and supervision, does not match the national conditions well. As a result, the test reports are not mutual-recognized among laboratories. In the current background of precision medicine, with the advances of technology and the application of deep learning and artificial intelligence in the clinical laboratory field, the autoimmune laboratory has ushered in a new development trend of integration, automation and intelligence.

Objective To analyze clinicopathological characteristics and the potential risk-related factors of female breast hyperplasia in different age groups.Method From Jan 2015 to Dec 2016,patients diagnosed with breast hyperplasia in 12 hospitals were evaluated.All patients completed the self-designed questionnaires on women'health,including basic demographic information,clinic examination information,radiologic information and pathologic results.The patients were divided into a young group (＜ 45 years old) and an elderly group (from 45 to 75 years old).Results There were 3 684 cases of breast hyperplasia,including 2 291 cases in young group and 1 393 cases in elder group,respectively Clinically breast pain type were most commonly observed in both young and older groups (50.3％ vs.42.7％,P ＜ 0.001).While pathological research based on biopsy showed that breast adenopathy were the most common changes in both groups (67.9％ vs.61.7％,P ＜0.001).More breast cancer cases were identified in elder group than that in young group,especially in clinically lump type patients (9.4％ vs.4.2％,P ＜ 0.001).Compared with elder group,patients in young group have different distribution characteristics regarding to fertility factors,lifestyle factors and psychology scale including anxiety and depression.Conclusion Distributions of clinicopathological characteristics and risk factors of female breast hyperplasia differ across different age groups.