Objective: To classify life stages for companion dogs and cats by identifying clusters in age-specific disease proportions derived from medical records, providing a data-driven foundation for health examination programs. Methods: We collected 505,667 medical records from 82 veterinary facilities in South Korea between 2020 and 2023. Diagnoses were standardized using GPT-4o and S-BioBERT. Following preprocessing, data from 27 facilities yielded 222,706 canine and 39,910 feline records for the final analysis. Principal component analysis and K-means clustering (K = 4) were applied to age-specific disease proportions to identify life stages.The 10 most highest-proportion diagnoses diseases were determined for each cluster. Results: Canine life stages were classified as ≤ 1 year, 2–5 years, 6–10 years, and 11–15+ years.Feline life stages were 1–2 years, 3–8 years, 9–12 years, and 13–15+ years. In dogs, developmental diseases were common in the youngest age group, while chronic diseases were more prevalent in older groups. In cats, oral and urinary diseases were high-ranking, conjunctivitis was most common in the early stage, and chronic diseases increased with age. Conclusions and Relevance: Age-specific diagnosis patterns support four practical life stages for dogs and cats in South Korea. These boundaries can inform evidence-based preventive examination schedules, animal health policy, and pet insurance product design.

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.

A seventeen-year-old Shih Tzu presented with severe abdominal distension and labored respiration. Radiographic examination revealed severe ascites and pleural effusion. Bilateral malignant ovarian tumor, which was non-resectable due to distant metastasis, was diagnosed through computed tomography and cytology. Chemotherapy with carboplatin significantly delayed fluid accumulation, improving quality of life for 6 months. Necropsy later confirmed metastatic malignant bilateral granulosa cell tumor involving lymph nodes, liver, and thoracic cavity. This case demonstrates the feasibility of chemotherapy and symptomatic management as alternatives to ovariohysterectomy in metastatic ovarian tumors, highlighting its potential to extend survival and maintain quality of life when surgery is contraindicated.

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.

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.

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.

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.