Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

This paper aimed to explore the effects of Duzhong Decoction(DZD)-containing serum on the proliferation and osteoblast differentiation of MC3T3-E1 cells through L-type voltage-gated calcium channels(L-VGCCs). L-VGCCs inhibitors, nifedipine and verapamil, were used to block L-VGCCs in osteoblasts. MC3T3-E1 cells were divided into a control group, a low-dose DZD-containing serum(L-DZD) group, a medium-dose DZD-containing serum(M-DZD) group, a high-dose DZD-containing serum(H-DZD) group, a nifedipine group, a H-DZD + nifedipine group, verapamil group, and a H-DZD + verapamil group. The CCK-8 method was used for cell proliferation analysis, alkaline phosphatase(ALP) assay kits for intracellular ALP activity measurement, Western blot for protein expression level in cells, real-time fluorescence quantitative PCR technology for intracellular mRNA expression level determination, fluorescence spectrophotometer for free Ca~(2+) concentration determination in osteoblasts, and alizarin red staining(ARS) for mineralized nodule formation in osteoblasts. The experimental results show that compared to the control group, DZD groups can promote MC3T3-E1 cell proliferation, ALP activity, and mineralized nodule formation, increase intracellular Ca~(2+) concentrations, and upregulate the protein expression of bone morphogenetic protein 2(BMP2), collagen Ⅰ(COL1), α2 subunit protein of L-VGCCs(L-VGCCα2), and the mRNA expression of Runt-related transcription factor 2(RUNX2), and BMP2. After blocking L-VGCCs with nifedipine and verapamil, the intervention effects of DZD-containing serum were inhibited to varying degrees. Both nifedipine and verapamil could inhibit ALP activity, reduce mineralized nodule areas, and downregulate the expression of bone formation-related proteins. Moreover, the effects of DZD-containing serum on increasing MC3T3-E1 cell proliferation, osteoblast differentiation, and Ca~(2+) concentrations, upregulating the mRNA expression of osteoprotegerin(OPG) and protein expression of phosphorylated protein kinase B(p-Akt) and phosphorylated forkhead box protein O1(p-FOXO1), and upregulating phosphatase and tensin homolog(PTEN) expression were reversed by nifedipine. The results indicate that DZD-containing serum can increase the Ca~(2+) concentration in MC3T3-E1 cells to promote bone formation, which may be mediated by L-VGCCs and the PTEN/Akt/FoxO1 signaling pathway, providing a new perspective on the mechanism of DZD in treating osteoporosis.
Animals ; Osteoblasts/metabolism* ; Cell Proliferation/drug effects* ; Cell Differentiation/drug effects* ; Mice ; Drugs, Chinese Herbal/pharmacology* ; Calcium Channels, L-Type/genetics* ; Alkaline Phosphatase/genetics* ; Serum/chemistry* ; Cell Line ; Osteogenesis/drug effects* ; Bone Morphogenetic Protein 2/genetics*

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Background::Ainuovirine (ANV) is a new generation of non-nucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus (HIV) type 1 infection. This study aimed to evaluate the population pharmacokinetic (PopPK) profile and exposure-response relationship of ANV among people living with HIV.Methods::Plasma concentration-time data from phase 1 and phase 3 clinical trials of ANV were pooled for developing the PopPK model. Exposure estimates obtained from the final model were used in exposure-response analysis for virologic responses and safety responses.Results::ANV exhibited a nonlinear pharmacokinetic profile, which was best described by a two-compartment model with first-order elimination. There were no significant covariates correlated to the pharmacokinetic parameters of ANV. The PopPK parameter estimate (relative standard error [%]) for clearance adjusted for bioavailability (CL/F) was 6.46 (15.00) L/h, and the clearance of ANV increased after multiple doses. The exposure-response model revealed no significant correlation between the virologic response (HIV-RNA <50 copies/mL) at 48 weeks and the exposure, but the incidence of adverse events increased with the increasing exposure ( P value of steady-state trough concentration and area under the steady-state curve were 0.0177 and 0.0141, respectively). Conclusions::Our PopPK model supported ANV 150 mg once daily as the recommended dose for people living with HIV, requiring no dose adjustment for the studied factors. Optimization of ANV dose may be warranted in clinical practice due to an increasing trend in adverse reactions with increasing exposure.Trial registration::Chinese Clinical Trial Registry https://www.chictr.org.cn (Nos. ChiCTR1800018022 and ChiCTR1800019041).

Objective To investigate the efficacy and safety of ultrafiltration therapy in elderly patients with congestive heart failure(CHF)and frailty.Methods A total of 88 hospitalized elderly patients with CHF and frailty were randomly assigned to the ultrafiltration group(n=44)and the control group(n=44).The control group treated with standard drug therapy.The ultrafiltration group treated with ultrafiltration,however,diuretics were not used during ultrafiltration treatment.Efficacy assessment was compared between the two groups,including patient body weight,N-terminal pro-brain natriuretic peptide(NT-proBNP)levels at 48 hours after treatment,dyspnea severity scores at 48 hours and 1 week after treatment,hospitalization duration and readmission rate within 3 months.Safety assessment parameters included serum creatinine,urea nitrogen,Na+and K+concentration at 48 hours after treatment and creatinine level 1 week after treatment.Results Efficacy assessment indicated that at 48 hours after treatment,both groups showed a significant reduction in patient body weight and NT-proBNP levels compared to pre-treatment levels(P＜0.05).However,there were no significant difference in body weight and NT-proBNP levels before and after treatment between the two groups(P＞0.05).Dyspnea severity scores for both groups increased at 48 hours after treatment,then decreased at 1 week after treatment.The ultrafiltration group exhibited higher dyspnea severity scores than that of the control group at 48 hours after treatment(P＜0.05).The length of hospital stay and the rate of re-hospitalization within 3 months were lower in the ultrafiltration group compared to those of the control group(P＜0.05).Safety assessment revealed that there were no significant differences in serum urea nitrogen and Na+levels before and 48 hours after treatment between the two groups(P＞0.05).However,serum K+levels were higher after 48-hours treatment in the ultrafiltration group than those of the control group(P＜0.05).There were no significant changes in creatinine levels before and after treatment in the control group(P＞0.05),while creatinine levels were lower 1 week after treatment in the ultrafiltration group compared to those of pre-treatment and 48 hours after treatment,and were lower than those of the control group(P＜0.05).Conclusion Ultrafiltration is a safe and effective method for elderly patients with CHF and frailty.

Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.