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.

ObjectiveThe nucleolar protein PES1 (Pescadillo homolog 1) plays critical roles in ribosome biogenesis and cell cycle regulation, yet its involvement in cellular senescence remains poorly understood. This study aimed to comprehensively investigate the functional consequences of PES1 suppression in cellular senescence and elucidate the molecular mechanisms underlying its regulatory role. MethodsInitially, we assessed PES1 expression patterns in two distinct senescence models: replicative senescent mouse embryonic fibroblasts (MEFs) and doxorubicin-induced senescent human hepatocellular carcinoma HepG2 cells. Subsequently, PES1 expression was specifically downregulated using siRNA-mediated knockdown in these cell lines as well as additional relevant cell types. Cellular proliferation and senescence were assessed by EdU incorporation and SA-β-gal staining assays, respectively. The expression of senescence-associated proteins (p53, p21, and Rb) and SASP factors (IL-6, IL-1β, and IL-8) were analyzed by Western blot or qPCR. Furthermore, Northern blot and immunofluorescence were employed to evaluate pre-rRNA processing and nucleolar morphology. ResultsPES1 expression was significantly downregulated in senescent MEFs and HepG2 cells. PES1 knockdown resulted in decreased EdU-positive cells and increased SA‑β‑gal-positive cells, indicating proliferation inhibition and senescence induction. Mechanistically, PES1 suppression activated the p53-p21 pathway without affecting Rb expression, while upregulating IL-6, IL-1β, and IL-8 production. Notably, PES1 depletion impaired pre-rRNA maturation and induced nucleolar stress, as evidenced by aberrant nucleolar morphology. ConclusionOur findings demonstrate that PES1 deficiency triggers nucleolar stress and promotes p53-dependent (but Rb-independent) cellular senescence, highlighting its crucial role in maintaining nucleolar homeostasis and regulating senescence-associated pathways.

OBJECTIVE To provide reference for the improvement of the price system of innovative drugs in China. METHODS Based on the current situation of innovative drug healthcare management in China， innovation recognition and price management of innovative drugs in typical countries or regions were compared and analyzed， and the suggestions were put forward to improve the price system of innovative drugs in China. RESULTS & CONCLUSIONS Taiwan of China， Japan， Australia， Germany and the United Kingdom have different practices in recognition and grading of drug innovation， differentiated pricing and subsequent price management. However， the mainstream practice is to identify and grade drugs mostly based on clinical value， differentiate pricing based on the grading， and carry out risk-sharing agreement or active price adjustment to manage the price. Based on the comparative analysis， it is suggested that China should further clarify the connotation and classification of innovation in the management of innovative drugs， apply a variety of pricing methods to promote differentiated management of innovative drugs， explore the introduction of risk-sharing agreements， and further build an active drug price adjustment mechanism to improve the price system of innovative drugs in China.

Objective To investigate the clinical efficacy and safety of Yiqi Huatan Tongluo Prescription(mainly composed of Fici Simplicissimae Radix,Notoginseng Radix et Rhizoma,Pinelliae Rhizoma Praeparatum,Poria,Nelumbinis Folium,and Glycyrrhizae Radix et Rhizoma,etc.)combined with drug-coated balloon(DCB)in the treatment of coronary heart disease(CHD)and to observe its effect on low-shear related serological indicators.Methods A total of 106 patients with CHD of qi deficiency and phlegm stasis obstructing collateral type who were scheduled to undergo percutaneous coronary intervention were randomly divided into a treatment group and a control group,with 53 cases in each group.The control group was treated with drug-eluting stent implantation,and the treatment group was treated with DCB.After the operation,the control group was given conventional antiplatelet aggregation drugs,and the treatment group was given oral administration of Yiqi Huatan Tongluo Prescription.The medication for the two groups lasted for 12 weeks.The changes in the serum levels of monocyte chemoattractant protein 1(MCP-1),interleukin 1 β(IL-1β)and vascular endothelial growth factor(VEGF)in the two groups were observed before and after treatment.Moreover,the traditional Chinese medicine(TCM)syndrome efficacy after treatment and the incidence of adverse events one year after operation were compared between the two groups.Results(1)After 12 weeks of treatment,the total effective rate for TCM syndrome efficacy of the treatment group was 88.68％(47/53),and that of the control group was 75.47％(40/53).The intergroup comparison(tested by chi-square test)showed that the TCM syndrome efficacy in the treatment group was significantly superior to that in the control group(P＜0.05).(2)The analysis of indicators related to endothelial dysfunction in the blood flow with low shear stress showed that after treatment,the levels of serum MCP-1,IL-1βand VEGF in the control group presented no obvious changes(P＞0.05),but the serum levels of MCP-1 and IL-1β in the treatment group were significantly lowered compared with those before treatment(P＜0.05).The intergroup comparison showed that the decrease of serum MCP-1,IL-1β and VEGF levels in the treatment group was significantly superior to that in the control group(P＜0.05).(3)The one-year follow-up after the operation showed that the total incidence of adverse events in the treatment group was 18.87％(10/53),and that in the control group was 20.75％(11/53).There was no significant difference between the two groups(P＞0.05).Conclusion Yiqi Huatan Tongluo Prescription combined with DCB has definite action on the targets related to endothelial dysfunction in coronary blood flow with low shear stress,which is conducive to reducing inflammatory response,improving the symptoms of angina pectoris and enhancing clinical efficacy.The incidence of adverse events did not increase one year after operation,indicating good safety and effectiveness.

An electrochemical chemical oxygen demand(COD)sensor was proposed based on a FTO/TiO2/PbO2 electrode and a thin-layer electrochemical cell.The FTO/TiO2/PbO2 electrode was characterized by X-ray photoelectronic spectroscopy(XPS),X-ray diffraction(XRD)spectroscopy and electrochemical technique,and the results indicated that the rapid decrease in the output signals of the electrochemical COD sensor could be attributed to oxidation of PbSO4 occurring on the surface of FTO/TiO2/PbO2 electrode.The PbO2 deposition time and concentration of Na2SO4 were further optimized and then the electrochemical COD sensor was challenged by real samples including laker water sample,river water sample and wastewater sample.The evolution trend of signals of the electrochemical COD sensor in response to lake and river water samples was identical with that obtained with the standard method(HJ/T399-2007,Water quality-determination of the chemical oxygen demand-fast digestion-spectrophotometric method).The electrochemical COD sensor exhibited significant increase in the signal intensity after the samples were switched from lake water to wastewater sample,and a mean value of 32.5 mg/L with relative standard deviation(RSD)of 6.8%were obtained after measuring 45 times the wastewater with COD value of 30 mg/L under a sampling interval of 400 s.The as-prepared electrochemical COD sensor possessed good promise in regular monitoring of COD,discharge of wastewater and industrial process control,with advantages such as a small sampling interval,mild reaction conditions and no requirement of toxic and harmful chemical reagents.

Objective Studies on the relationship between iodine,vitamin A(VA),and vitamin D(VD)and thyroid function are limited.This study aimed to analyze iodine and thyroid-stimulating hormone(TSH)status and their possible relationships with VA,VD,and other factors in postpartum women. Methods A total of 1,311 mothers(896 lactating and 415 non-lactating)from Hebei,Zhejiang,and Guangxi provinces were included in this study.The urinary iodine concentration(UIC),TSH,VA,and VD were measured. Results The median UIC of total and lactating participants were 142.00 μg/L and 139.95 μg/L,respectively.The median TSH,VA,and VD levels in all the participants were 1.89 mIU/L,0.44 μg/mL,and 24.04 ng/mL,respectively.No differences in the UIC were found between lactating and non-lactating mothers.UIC and TSH levels were significantly different among the three provinces.The rural UIC was higher than the urban UIC.Obese mothers had a higher UIC and a higher prevalence of excessive TSH.Higher UICs and TSHs levels were observed in both the VD deficiency and insufficiency groups than in the VD-sufficient group.After adjustment,no linear correlation was observed between UIC and VA/VD.No interaction was found between vitamins A/D and UIC on TSH levels. Conclusion The mothers in the present study had no iodine deficiency.Region,area type,BMI,and VD may be related to the iodine status or TSH levels.

Objective This study aimed to understand the epidemic status and phylogenetic relationships of rotavirus group A(RVA)in the Pearl River Delta region of Guangdong Province,China. Methods This study included individuals aged 28 days-85 years.A total of 706 stool samples from patients with acute gastroenteritis collected between January 2019 and January 2020 were analyzed for 17 causative pathogens,including RVA,using a Gastrointestinal Pathogen Panel,followed by genotyping,virus isolation,and complete sequencing to assess the genetic diversity of RVA. Results The overall RVA infection rate was 14.59%(103/706),with an irregular epidemiological pattern.The proportion of co-infection with RVA and other pathogens was 39.81%(41/103).Acute gastroenteritis is highly prevalent in young children aged 0-1 year,and RVA is the key pathogen circulating in patients 6-10 months of age with diarrhea.G9P[8](58.25%,60/103)was found to be the predominant genotype in the RVA strains,and the 41 RVA-positive strains that were successfully sequenced belonged to three different RVA genotypes in the phylogenetic analysis.Recombination analysis showed that gene reassortment events,selection pressure,codon usage bias,gene polymorphism,and post-translational modifications(PTMs)occurred in the G9P[8]and G3P[8]strains. Conclusion This study provides molecular evidence of RVA prevalence in the Pearl River Delta region of China,further enriching the existing information on its genetics and evolutionary characteristics and suggesting the emergence of genetic diversity.Strengthening the surveillance of genotypic changes and gene reassortment in RVA strains is essential for further research and a better understanding of strain variations for further vaccine development.