Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

ObjectiveTo investigate the changing trend of the disease burden of hepatitis B and its related liver cirrhosis in China， to identify related influencing factors， and to provide a basis for optimizing prevention and treatment strategies. MethodsBased on the data from Global Burden of Disease Study in 2021， the Joinpoint regression model was used to calculate the average annual percentage change of the age-standardized incidence rate， prevalence rate， mortality rate， and disability-adjusted life year （DALY） rate of chronic hepatitis B and its related liver cirrhosis from 1992 to 2021. An age-period-cohort model was established to assess the risk of disease onset， and the ARIMA model was used to predict the trend of disease burden from 2022 to 2031. ResultsFrom 1992 to 2021， there was a tendency of reduction in the overall age-standardized incidence rate， prevalence rate， mortality rate， and DALY rate of hepatitis B and its related liver cirrhosis in China， with an average annual decline of 4.52% （95% confidence interval ［CI］： -4.63% to -4.44%， P<0.05）， 2.73% （95%CI： -2.80% to -2.66%， P<0.05）， 3.41% （95%CI： -3.50% to -3.33%， P<0.05）， and 3.55% （95%CI： -3.65% to -3.48%， P<0.05）， respectively. Compared with female individuals， male individuals had significantly higher age-standardized incidence rate， prevalence rate， mortality rate， and DALY rate. From 1992 to 2021， the risk of hepatitis B and its related liver cirrhosis in China first decreased， then increased， and decreased again with age， and it showed an tendency of reduction with time， while it first increased and then decreased with birth cohort. The predictive model showed that there would be a tendency of reduction in the age-standardized incidence rate， prevalence rate， mortality rate， and DALY rate of hepatitis B and its related liver cirrhosis in China from 2022 to 2031. ConclusionFrom 1992 to 2021， there was a tendency of reduction in the disease burden of hepatitis B and its related liver cirrhosis in China， and it would maintain a downward trend in the next decade. There are sex and age differences in the risk of hepatitis B and its related liver cirrhosis.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Background::Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods::This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Na?ve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.Results::The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions::Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Atrial fibrillation(AF)is the most common cardiac arrhythmia.Many medical conditions,including hypertension,diabetes,obesity,sleep apnea,and heart failure(HF),increase the risk for AF.Car-diomyocytes have unique metabolic characteristics to maintain adenosine triphosphate production.Significant changes occur in myocardial metabolism in AF.Glucagon-like peptide-1 receptor agonists(GLP-1 RAs)have been used to control blood glucose fluctuations and weight in the treatment of type 2 diabetes mellitus(T2DM)and obesity.GLP-1 RAs have also been shown to reduce oxidative stress,inflammation,autonomic nervous system modulation,and mitochondrial function.This article reviews the changes in metabolic characteristics in cardiomyocytes in AF.Although the clinical trial outcomes are unsatisfactory,the findings demonstrate that GLP-1 RAs can improve myocardial metabolism in the presence of various risk factors,lowering the incidence of AF.

Objective:This paper summarized the practical experience in the construction of a scientific research platform in a large comprehensive hospital and discusses the innovative management model of the scientific research platform.Methods:This paper introduced the development history and construction status of a scientific research platform in a large comprehensive hospital, and analyzes the contradictions and difficulties existing in equipment sharing, resource allocation and other aspects during the development of the scientific research platform and its countermeasures.Results:Building a strong scientific research platform was of great significance to the hospital's discipline development, talent cultivation, and improvement of academic influence and core competitiveness. The hospital should continue to increase investment in the construction of scientific research platforms and strengthen system layout, cooperation, exchanges, and resource sharing.Conclusions:Large comprehensive hospitals need to build a highly open and efficient scientific research platform system to empower technological innovation.

Objective To explore the effects of different phenotypes macrophages(Mφs)on the proliferation,mi-gration and osteogenic differentiation of periodontal ligament stem cells(PDLSCs).Methods PDLSCs were isola-ted and cultured by tissue block method.Tohoku Hospital Pediatrics-1(THP-1)cell line was stimulated to activate into unpolarized Mφs(M0),then induced to polarize into type Ⅰ Mφs(M1)and type Ⅱ Mφs(M2).Quantitative real-time PCR(qPCR)detected the inflammatory factors tumor necrosis factor α(TNF-α),interleukin(IL)-1 β,IL-6,IL-10 and transforming growth factor-β(TGF-β)mRNA expression level.After collecting culture superna-tants with different phenotypes,PDLSCs were stimulated,native control(NC)group did not receive the culture su-pernatant of Mφs.The effects of PDLSCs proliferation were assessed via Methylthiazolyldiphenyl-tetrazolium bro-mide(MTT)assay,while scratch assays were employed to evaluate their migration.Western blot was utilized to analyze the protein expression of Runt-related transcription factor 2(RUNX2)and alkaline phosphatase(ALP).Additionally,Alizarin Red staining was performed to investigate the deposition of calcified nodules in PDLSCs.Re-sults qPCR showed the relative expression of TNF-α,IL-1 β and IL-6 in M1 Mφs were higher than those in M0 and M2 Mφs(P＜0.05),and the relative expression of IL-10 and TGF-β in M2 Mφs were higher than those in M0 and M1 Mφs(P＜0.05);Western blot showed the expression of RUNX2 and ALP proteins in PDLSCs in M0 and M2 groups was higher than those in the NC group(P＜0.05),Alizarin Red staining showed increased calcified nodule deposition in PDLSCs in M0,M1 and M2 groups compared to the NC group;MTT assay showed the prolifer-ation of PDLSCs in the M0 and M1 groups was suppressed compared to the NC group(P＜0.05);and scratch ex-periment showed the migratory capacity of PDLSCs in the M1 and M2 groups was stronger than that in the NC group.Conclusion M0 and M1 Mφs inhibit PDLSCs proliferation,M1 and M2 Mφs promote PDLSCs migration,and all types of Mφs promote osteogenic differentiation of PDLSCs.