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.

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: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.

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.

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.

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 investigate the prevalence and related influencing factors of thyroid nodules in different occupational groups in Shijiazhuang City.Methods:Different occupational groups who underwent physical examination at the Second Hospital of Shijiazhuang City from May to December 2015 and had lived there for more than 10 years were selected as the investigation subjects, including workers, farmers, science and technology education personnel, medical and health personnel, civil servants and individual businessmen. Questionnaire survey and thyroid examination were conducted, blood lipid, blood glucose and thyroid hormone levels were tested, and related influencing factors of thyroid nodules were analyzed.Results:A total of 1 440 different occupational subjects were surveyed, and the prevalence of thyroid nodules was 20.35% (293/1 440). The prevalence of thyroid nodules in females (22.59%, 159/704) was significantly higher than that in males (18.21%, 134/736), and the difference was statistically significant (χ 2 = 4.26, P = 0.039). There was a statistically significant difference in the prevalence of thyroid nodules in different age groups (χ 2 = 73.87, P < 0.001), with the highest prevalence in the 70 - 80 age group (43.75%, 21/48). There was a statistically significant difference in the prevalence of thyroid nodules among different occupational groups (χ 2 = 36.56, P < 0.001), with the highest prevalence in individual businessmen (31.84%, 78/245). By univariate analysis, the prevalence of thyroid nodules in people with good economic status [27.45% (42/153) vs 19.48% (248/1 273)], radiation exposure history [26.39% (109/413) vs 17.92% (184/1 027)], and diabetes history [33.71% (30/89) vs 19.47% (263/1 351)] was higher (χ 2 = 5.35, 13.06, 10.45, P < 0.05). Conclusions:The prevalence of thyroid nodules in occupational group in Shijiazhuang City is relatively high, and the prevalence of thyroid nodules in females is significantly higher than that in males. Economic status, radiation exposure history and diabetes history are the main influencing factors.

Purpose To observe the CT imaging features of dermatomyosistis patients with anti-melanoma differentiation related genes 5(MDA-5),and to analyze the relationship between CT imaging indicators and short-term prognosis of patients.Materials and Methods Sixty-seven MDA-5-positive dermatomyosistis patients admitted to China-Japan Friendship Hospital from January 2017 to December 2018 were retrospectively included.Prognosis of included patients in one year were followed-up.Analysis of the relationship between chest CT imaging and short-term poor prognosis.Results Among 67 patients,nine patients died in one year.There was a statistically significant difference between the two groups(the death group and the survivor group)in the interstitial lung disease(ILD)imaging types(χ2=0.198,P=0.025)and pulmonary/aortic diameter ratios(U=103.0,P=0.004).CT imaging of dermatomyosistis-ILD with anti-MDA5 antibody positive patients showed organizing pneumonia.Mortality of diffuse alveolar damage type was significantly higher than that of other types.Logistic regression analysis showed that pulmonary/aortic diameter ratio(OR:4.208,P=0.002)was a strong independent risk factor for the death of patients with anti-MDA5 antibody.Conclusion Most patients with anti-MDA5 antibody positive dermatomyositis show ILD,with the main feature of organic pneumonia.Patients with poor prognosis within 1 year have different types of ILD,while the pulmonary/aortic diameter ratio is a strong independent risk factor for the death of dermatomyosistis-ILD with anti-MDA5 antibody positive patients.

The significant increase in clinical diagnosis research of Parkinson′s disease (PD) using artificial intelligence technology not only reflects its potential application in the field of medical engineering, but also highlights its important value in improving diagnostic efficiency and accuracy of PD. This review aims to systematically elaborate the current research status of artificial intelligence in the clinical diagnosis of PD through comprehensive analysis of existing literature, and deeply explore its latest research progress and existing problems in tremor detection, facial expression recognition, speech recognition, gait analysis, and radiomics analysis, providing reference and suggestions for the future clinical practice.

In recent years, a number of studies have found that the dysregulation of intestinal microbes and their metabolites plays an important role in the occurrence and development of Parkinson′s disease (PD), and is closely related to the severity of PD clinical symptoms. Short-chain fatty acids are the main metabolites of intestinal microorganisms and may be involved in the pathogenesis of PD by regulating the inflammatory response, neuronal autophagy and apoptosis and the integrity of the blood-brain barrier and intestinal barrier. In this paper, the research progresses on the role of short-chain fatty acids in the pathogenesis of PD are reviewed, in order to provide new ideas for the treatment of PD.