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.

Purpose: This study evaluates the viability of a new method that employs transperineal targeted biopsy with frozen section analysis immediately followed by robot-assisted radical prostatectomy (RARP), bypassing the traditional systematic biopsy deemed essential by current guidelines. Materials and Methods: Patient selection was based on the following inclusion criteria: those who underwent magnetic resonance imaging (MRI)-ultrasound fusion-targeted biopsy with frozen section analysis and concurrent RARP. Eligibility also required features indicative of Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesion on multiparametric MRI, along with one of these additional criteria: (1) MRI evidence suggesting extracapsular extension (ECE) with a prostate-specific antigen (PSA) level >10 ng/mL, or (2) a PSA level > 20 ng/mL. Results: Twelve patients were enrolled in this study according to inclusion criteria. The median age (interquartile range) was 73.5 (69.5–75.3) years and PSA was 22.9 (17.0–29.9) ng/mL. Three patients had PI-RADS 5 lesions, and 9 had PI-RADS 5 lesions with findings of ECE. In all cases, the diagnosis based on frozen sections confirmed adenocarcinoma. Following confirmation, each patient underwent immediate RARP. According to the final pathology report, 2 patients had International Society of Urological Pathology (ISUP) grade 2 disease, 7 patients had ISUP grade 3, 1 had ISUP grade 4, and 2 had ISUP grade 5 disease. Four patients had stage pT2 disease, 3 had stage pT3a, and 5 had stage pT3b. The median immediate reporting time of the target biopsy frozen section was 20 (19.3–24.5) minutes. No perioperative complications related to target biopsy were observed. Conclusion For patients with a strong suspicion of prostate cancer and a PI-RADS 5 lesion, integrating simultaneous prostate frozen target biopsy with radical prostatectomy may offer a feasible treatment alternative, obviating the necessity for a preoperative systematic biopsy.

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.

Purpose: Erectile dysfunction (ED) is considered a microvascular disorder and serves as an indicator for the potential development of cardiovascular disease (CVD). Although left ventricular diastolic dysfunction (LVDD) reflects early myocardial damage caused by microvascular disorders, the association between ED and LVDD remains poorly elucidated. Materials and Methods: A cross-sectional study was conducted on 123 patients with ED. They underwent RigiScan, and conventional echocardiography, and attempted International Index of Erectile Function (IIEF) questionnaire. ED severity was evaluated by measuring changes in the penile base circumference and duration of penile rigidity (≥70%) during erection. The early diastolic velocity of mitral inflow (E) and early diastolic velocity of the mitral annulus (e′) were measured using echocardiography. The patients were grouped based on the presence of CVD. Results: Among 123 patients, 29 had CVD and 94 did not. Patients with CVD exhibited more pronounced ED and more severe LVDD. Associations between increased penile circumference with echocardiographic parameters were more prominent in patients with CVD than in those without CVD (ΔTtop and e′ wave, r=0.508 and r=0.282, respectively, p for interaction=0.033; ΔTbase and E/e′ ratio, r=-0.338 and r=-0.293, respectively, p for interaction <0.001). In the multivariate linear regression, the increase of penile base circumference was an independent risk factor for LVDD (e′, B=0.503; E/e′ ratio, B=-1.416, respectively, p<0.001). Conclusions ED severity correlated well with LV diastolic dysfunction, particularly in the presence of CVD. This study highlighted the potential role of ED assessment as early indicator of CVD development.

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.