Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Depression is a major cause of disability and has adverse effects.Despite the many types of anti-depressants,clinical treatments of depression remain poor.Therefore,novel anti-depressant mechanisms need to be explored.The beneficial effects of irisin on the nervous system are gradually being elucidated,and studies have found that irisin has an anti-depressant effect,which may become a new treatment for depression.This study explored the mechanism of irisin and its upstream and downstream anti-depressants by reviewing the existing studies explaining the link between irisin and depression,and proposes that SIRT1/PGC-1α may mediate FNDC5/irisin to regulate BDNF to promote neurogenesis and improve depression,which provides a new idea to study irisin and its upstream and downstream anti-depressants.

In recent years,deep learning,a pivotal subset of artificial intelligence machine learning,has achieved noteworthy advancements in the medical domain.It facilitates precise detection,diagnosis and prognostic assessment of various diseases through the analysis of medical images.Within ophthalmology,deep learning techniques have found wide-spread application in the diagnosis and prediction of thyroid-related eye diseases,orbital blowout fracture,melanoma,bas-al cell carcinoma,orbital abscess,lymphoma,retinoblastoma and other diseases.Leveraging images from computed tomo-graphy,magnetic resonance imaging and even pathological sections,this technology demonstrates a capacity to diagnose,differentiate and stage orbital diseases and ocular tumors with a high level of accuracy comparable to that of expert clini-cians.The promising prospects of this technology are expected to enhance the diagnosis and treatment of related diseases,concurrently reducing the time and cost associated with clinical practices.This review consolidates the latest research pro-gress on the application of artificial intelligence deep learning in orbital diseases and ocular tumors,aiming to furnish clini-cians with up-to-date information and developmental trends in this field,thereby furthering the clinical application and widespread adoption of this technology.

T cell receptors(TCR)immune repertoire is sum of all TCR in body,and diversity of TCR immune repertoire is closely related to HIV recognition and immune escape.High-throughput immune repertoire sequencing technology is able to detect and analyze all sequences of TCR immune repertoire,truly reflect genetic information of all TCR,and comprehensively reveal complexity and diversity of TCR immune repertoire.Analysis of changes in TCR immune group database of HIV patients through high-throughput sequencing technology can monitor their diagnosis and treatment,disease progression and prognosis,and can also contribute to vaccine development,latent reservoir clearance and clinical efficacy evaluation,so as to provide a theoretical basis for enriching prevention and treatment ideas and targets of HIV/AIDS.