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.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Three-dimensional graphene hydrogel(3DGH)was successfully prepared through a hydrothermal method,followed by its composition with gold nanoparticles(AuNPs)to construct a highly sensitive Au/3DGH graphene electrochemical transistor(GECT)dopamine(DA)sensor.AuNPs are efficient electrocatalytic materials.However,their tendency to aggregate during electrodeposition hinds the practical application.The porous and interconnected network structure of 3DGH provided abundant attachment sites,effectively preventing AuNPs aggregation.By modifying the sensor's gate with Au/3DGH,the excellent electrocatalytic performance of Au/3DGH towards DA and the high sensitivity of GECT were utilized to achieve highly sensitive detection of DA.The sensor exhibited a low detection limit of 20 nmol/L and a linear range of 20 nmol/L to 2.5 mmol/L.Remarkably,the sensor showed high sensitivity,excellent selectivity and strong stability,and hold great potnetial in highly sensitive portable detection of DA in disease prevention and clinical monitoring.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Objective To explore the effectiveness and safety of percutaneous coronary artery shock wave balloon angioplasty(IVL)under the guidance of intravascular ultrasound(IVUS)for the treatment of severe calcification lesions in the left main artery(LM).Methods A total of 26 patients with severe LM(mouth,body,bifurcation)calcification admitted to Jiangnan University Affiliated Hospital from October 2022 to April 2024 were included,with an average age of 72.0(61.8,75.4)years.Under the guidance of IVUS,IVL was used for pre-treatment of calcified lesions,followed by percutaneous coronary intervention(PCI)with stent/drug balloon implantation.All patients were evaluated using IVUS before and after the use of IVL and after PCI.And compare the IVUS intracavity related data before and after treatment[plaque burden(PB)、minimum lumen area(MLA)、minimum lumen diameter(MLD)]and calcification fracture number,minimum stent area(MSA),stent expansion coefficient(expansion,EXP),etc.Results There were 26 patients(2 with opening lesions,7 with body lesions,and 17 with bifurcation lesions at the end of the main trunk),including 7 with stable angina pectoris(SAP),10 with unstable angina(UA),4 with acute ST-segment elevation myocardial infarction(STEMI),and 5 with non ST-segment elevation myocardial infarction(NSTEMI).The PB at the most severe site of calcification decreased by 79.50(76.00,83.75)％compared to 80.00(76.00,83.75)％after IVL(P=0.001),MLA increased by 3.39(3.14,3.68)mm2 compared to 3.38(3.14,3.67)mm2 after IVL(P=0.039),MLD increased by 3.21(3.07,3.30)mm compared to 3.20(3.07,3.30)mm after IVL(P=0.024),and there was 100％calcification rupture(1/2 cases,2/9 cases,≥3/15 cases).The stent/drug ball was successfully implanted 100％,with EXP of(89.15±4.42)％and an MSA of 7.20(6.46,7.45)mm2.No adverse events such as death,angina or recurrent myocardial infarction occurred during the 3 months follow-up after surgery.Conclusions After evaluation by IVUS and pre-treatment with IVL,PCI was successfully completed for severe calcification lesions in LM,and IVL can be used as an option for the treatment of severe calcification in LM.

AIM To study the phenylpropanoids from Brandisia hancei Hook.f.and their antioxidant activities.METHODS The extract from B.hancei was isolated and purified by Rp-C18,MCI,semi-preparative HPLC,silica gel and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The cytotoxicities was determined by MTT method,and the antioxidant activities were determined by DPPH and ABTS+free radical scavenging methods.RESULTS Fifteen phenylpropanoids were isolated and identified as(+)-pinonesinol(1),(-)-medioresinol(2),(-)-syringaresinol(3),buddlenol D(4),(7R,7'R,7″S,8S,8'S,8″S)-4',5″-dihydroxy-3,5,3',4″-tetramethoxy-7,9':7',9-diepoxy-4,8″-oxy-8,8'-sesquineo-lignan-7″,9″-diol(5),(-)-(7R,7'R,7″R,8S,8'S,8″S)-4',4″-dihydroxy-3,3',3″,5-tetramethoxy-7,9':7',9-diepoxy-4,8″-oxy-8,8'-sesquineolignan-7″,9″-diol(6),hedyotol A(7),dracunculifoside R(8),acteoside(9),isoacteoside(10),arenarioside(11),isomartynoside(12),curcasinlignan B(13),erythro-2,3-bis(4-hydroxy-3-methoxyphenyl)-3-ethoxypropan-l-ol(14),citrusin C(15).Compounds 1-4 and 9-10 had no obvious cytotoxicity to HepG2 hepatoma cells.Compounds 1,3,9,10 and 12 had strong scavenging activities against DPPH radicals.Compounds 1-3,9-10,12 and 14 showed strong scavenging activities against ABTS+radical.CONCLUSION Compounds 1-8 and 12-15 are isolated from genus Brandisia for the first time.The phenylpropanoids from B.hancei show strong antioxidant activities.

Purpose: This study aimed to evaluate the impact of facilitating target delineation of continuous positive airway pressure (CPAP) in patients undergoing stereotactic ablative radiation therapy (SABR) for lung tumors by lung expansion and respiratory motion management. Materials and Methods: We performed a prospective single-institutional trial of patients who were diagnosed with either primary lung cancer or lung metastases and received SABR with a dose of 40 to 60 Gy in 4 fractions. Four-dimensional computed tomography simulations were conducted for each patient: once without CPAP and again with CPAP. Results: Thirty-two patients with 39 tumors were analyzed, after the withdrawal of five patients due to discomfort. For 26 tumors separated from the diaphragm, CPAP significantly increased the superoinferior distance between the tumor and the diaphragm (5.96 cm vs. 8.06 cm; p < 0.001). For 13 tumors located adjacent to the diaphragm, CPAP decreased the overlap of planning target volume (PTV) with the diaphragm significantly (6.32 cm3 vs. 4.09 cm3; p = 0.002). PTV showed a significant reduction with CPAP (25.06 cm3 vs. 22.52 cm3, p = 0.017). In dosimetric analyses, CPAP expanded lung volume by 58.4% with a significant reduction in mean dose and V5 to V40. No more than grade 2 adverse events were reported. Conclusion This trial demonstrated significant improvement of CPAP in target delineation uncertainties for lung SABR, with dosimetric benefits, a favorable safety profile and tolerability. Further investigation is warranted to explore the role of CPAP as a novel strategy for respiratory motion management.