OBJECTIVE To extract the-SH active fractions(SHF)from Bubali Cornu(water buffalo horn)and evaluate its an-tipyretic activity.METHODS SHF was extracted from Bubali Cornu by SDS-DTT,and the content of native thiols(-SH)was deter-mined by Ellman reagent method.SHF was identified based on nano LC-MS/MS technology.Evaluation of antipyretic activity of SHF was based on LPS-induced fever rat model.The levels of PGE2,IL-1β,and TNF-α in plasma as well as the levels of cAMP,PGE2,and TNF-α in the hypothalamus were measured by ELISA kits.An untargeted metabolomics approach was used to further investigate the intervention of SHF on plasma metabolites in febrile rats.RESULTS SDS-DTT could effectively extract SHF from Bubali Cornu,in which the main components were type Ⅰ,type Ⅱ keratins and keratin-associated proteins,which were rich in Cys,and the ratio of-SH to protein in SHF was increased about 20 times more than that of traditional decoction.SHF could significantly decrease(P＜0.01)the body temperature which lasted for 4.5 hours.SHF could also significantly decrease the levels of PGE2,IL-1β,TNF-α and cAMP in plasma and hypothalamic.A total of 137 potentially differential metabolites were identified from plasma samples of the control and model groups,of which 31 metabolites could be dialed back after SHF administration,including lysophosphatidic acid,phosphatidyli-nositol,phosphatidic acid,triglycerides,phosphatidylcholine and so on,which were mainly involved in the glycerophospholipid meta-bolic pathway.CONCLUSION SHF has precise antipyretic effect,and the dosage of 1/10 of the aqueous extract can show its com-parable antipyretic effect,which provides the direction and basis for the basic research on the antipyretic efficacy of Bubali Cornu.

The concept,connotation and extension,goals and tasks of the discipline of Chinese medicinal materials resource chem-istry have been proposed and developed for 20 years.Looking back at the 20-year construction and development process,continuous exploration and innovative practice have been carried out around the scientific production and effective utilization of traditional Chinese medicinal materials.The theoretical connotation has been further enriched,the research mode has been further improved,and the tech-nical system has been further expanded.A series of research results have been formed and promoted for application,serving the high-quality development of the traditional Chinese medicinal materials industry,and contributing to the improvement of quality,efficiency,and green development of the entire industry chain of Chinese medicinal resources.However,with the rapid growth of Chinese medici-nal materials industry and the continuous expansion and extension of the industry chain,the waste and by-products generated in the production process of Chinese medicinal agriculture and industry are increasing day by day,causing resource waste and environmental pollution,which has become a new major problem facing the development of the industry.This article focuses on the establishment and case analysis of a model for the full industry chain recycling and low-carbon green development of Chinese medicinal materials,as well as the creation of an ecological industry demonstration park for the recycling of Chinese medicinal materials.It showcases the phased a-chievements made in recent years,aiming to provide demonstration and reference for the low-carbon and green transformation of the Chinese medicinal materials industry from a linear economy model to a circular economy model.It provides reference for improving the efficiency of Chinese medicinal materials utilization and creating new quality productivity,and helps promote low-carbon and green de-velopment in the field of Chinese medicinal materials industry.

Objective:To study the technological roadmap of integrated development of digestive tract endoscopy and artificial intelligence precision medicine,and to provide research directions and feasible technological paths for the"overtaking on a curve"of domestic gastrointestinal endoscopy.Methods:The Delphi method was used to investigate the needs and research directions for the refinement of gastrointestinal endoscopy from the perspective of medical professionals.An analysis of development directions of artificial intelligence precision medical technology based on technical documents on artificial intelligence precision medical technology was conducted.The application scenarios and technology roadmap of early gastric cancer and inflammatory bowel disease patients were designed from four main service directions of precise diagnosis,precise treatment,precise medication,and precise health management of artificial intelligence precision medicine.Results:Two refined application scenarios were designed for precise diagnosis of early gastric cancer and precise health management of inflammatory bowel disease patients,the layout direction and feasible path were planned for the development of the new gastrointestinal endoscopy industry,and a technology roadmap for the development of intelligent gastrointestinal endoscopy industrywas formed.Conclusion:The technology roadmap for the integrated development of gastrointestinal endoscopy with artificial intelligence precision medicine provides a sustainable development path for addressing the patent blockade of foreign gastrointestinal endoscopy companies on domestic products,uneven distribution of medical resources in the field of gastroenterology,and early diagnosis and treatment of digestive system diseases.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Objective To propose a method for classifying small bowel capsule endoscopy images by combining the Swin Transformer network with an improved Adapt-RandAugment data augmentation approach,aiming to enhance the accuracy and efficiency of small bowel lesion classification and recognition.Methods An Adapt-RandAugment data augmentation approach was formulated based on the RandAugment data enhancement sub-strategy and the principles of no feature loss and no distortion when enhancing small bowel capsule endoscopy images.In the publicly available Kvasir-Capsule dataset of small bowel capsule endoscopic images,the Adapt-RandAugment data augmentation approach was trained based on the Swin Transformer network,and the convolutional neural networks ResNet152 and DenseNet161 were used as the benchmarks to validate the combined Swin Transformer network and Adapt-RandAugment data augmentation approach for small bowel capsule endoscopy image classification.Results The proposed algorithm gained advantages over ResNet152 and DenseNet161 networks in the indicators,which had the macro average precision(MAC-PRE),macro average recall(MAC-REC),macro average F1 score(MAC-Fi-S)being 0.383 2,0.314 8 and 0.290 5 respectively,the micro average precision(MIC-PRE),micro average recall(MIC-REC)and micro average F1 score(MIC-Fi-S)all being 0.755 3,and the Matthews correlation coe-fficient(MCC)being 0.452 3.Conclusion The proposed small bowel capsule endoscopy image classification method based on Swin Transformer network and Adapt-RandAugment data augmentation approach behaves well in classified recognition efficiency and accuracy.[Chinese Medical Equipment Journal,2024,45(6):9-16]

Objective This study used resting-state functional magnetic resonance imaging(rs-fMRI)to investigate the changes of local brain regional homogeneity in patients with depression and childhood maltreatment,and we calculated the relationship between altered ReHo values and the severity of childhood maltreatment.Methods 25 patients with depression and childhood maltreatment,25 patients with depression without childhood maltreatment,and 25 age,gender,and education-matched healthy controls were prospectively enrolled.All subjects underwent resting-state functional magnetic resonance imaging and ReHo analysis.One-way analysis of variance was used to compare the group differences,along with multiple comparison correction.Pearson correlation analysis was used to explore the relationship between region ReHo values with clinical scales.Results Compared with the group of depression without childhood maltreatment,the abnormal brain regions of depression with childhood maltreatment are mainly located in the dorsolateral prefrontal cortex,cerebellum,parietal gyrus,and precentral gyrus.The ReHo values of depression with childhood maltreatment in the left cerebellum and the left dorsolateral prefrontal cortex are correlated with the severity of childhood maltreatment.Conclusions Depression with childhood mal-treatment is associated with changes in local spontaneous brain activity,which are correlated with the severity of childhood maltreatment.The brain changes in the dorsolateral prefrontal cortex and cerebellum may explain the neurobiological mechanisms of depression with childhood maltreatment.

Malocclusion,identified by the World Health Organization(WHO)as one of three major oral diseases,profoundly impacts the dental-maxillofacial functions,facial esthetics,and long-term development of～260 million children in China.Beyond its physical manifestations,malocclusion also significantly influences the psycho-social well-being of these children.Timely intervention in malocclusion can foster an environment conducive to dental-maxillofacial development and substantially decrease the incidence of malocclusion or reduce the severity and complexity of malocclusion in the permanent dentition,by mitigating the negative impact of abnormal environmental influences on the growth.Early orthodontic treatment encompasses accurate identification and treatment of dental and maxillofacial morphological and functional abnormalities during various stages of dental-maxillofacial development,ranging from fetal stages to the early permanent dentition phase.From an economic and societal standpoint,the urgency for effective early orthodontic treatments for malocclusions in childhood cannot be overstated,underlining its profound practical and social importance.This consensus paper discusses the characteristics and the detrimental effects of malocclusion in children,emphasizing critical need for early treatment.It elaborates on corresponding core principles and fundamental approaches in early orthodontics,proposing comprehensive guidance for preventive and interceptive orthodontic treatment,serving as a reference for clinicians engaged in early orthodontic treatment.