Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Four soluble factors—putrescine, glucosamine, nicotinamide, and signal transducer and activator of transcription 3 (STAT3) inhibitor BP-1-102—were shown to differentiate bone marrow mononucleated cells (BMNCs) into functional insulin-producing cells (IPCs) in vitro. Transplantation of these IPCs improved hyperglycemia in diabetic mice. However, the role of endogenous BMNC regeneration in this effect was unclear. This study aimed to evaluate the effect of these factors on in vivo BMNC differentiation into IPCs in diabetic mice. Mice were orally administered the factors for 5 days, twice at 2-week intervals, and monitored for 45–55 days. Glucose tolerance, glucose-stimulated insulin secretion, and pancreatic insulin content were measured. Chimeric mice harboring BMNCs from insulin promoter luciferase/green fluorescent protein (GFP) transgenic mice were used to track endogenous BMNC fate. These factors lowered blood glucose levels, improved glucose tolerance, and enhanced insulin secretion. Immunostaining confirmed IPCs in the pancreas, showing the potential of these factors to induce β-cell regeneration and improve diabetes treatment.

OBJECTIVES: Low handgrip strength (HGS) in children and adolescents might be associated with the risk of metabolic syndrome (MetS) and insulin resistance. This study prospectively evaluated the association between HGS in childhood and MetS in adolescence. METHODS: Based on data from the Ewha Birth and Growth Study, this study analyzed HGS at ages 7 to 9 and metabolic indices at ages 13 to 15. In total, 219 participants were analyzed. The risk of MetS was evaluated using the continuous metabolic syndrome score (cMetS), and insulin resistance was assessed using fasting blood insulin and homeostasis model assessment of insulin resistance (HOMA-IR). Relative HGS in childhood was determined by dividing HGS by body weight and categorized as sex-specific quartiles. RESULTS: This study found an inverse association between relative HGS levels in childhood and MetS and insulin resistance in adolescence. For each 1-group increase in relative HGS quartiles, cMetS (standarard [Std] β=-0.64, p<0.01), HOMA-IR (Std β=-0.21, p<0.01), and fasting blood insulin (Std β=-0.21, p<0.01) all decreased on average. These associations remained significant even after adjusting for confounding factors. CONCLUSIONS Our study showed a prospective association between HGS in childhood and the risk of MetS and insulin resistance in adolescence. It provides significant epidemiological evidence, emphasizing the importance of efforts to increase muscle strength from a young age to mitigate the risk of MetS and insulin resistance in adolescence.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Purpose: The Korean Cancer Study Group (KCSG) is a nationwide cancer clinical trial group dedicated to advancing investigator-initiated trials (IITs) by conducting and supporting clinical trials. This study aims to review IITs conducted by KCSG and quantitatively evaluate the survival and financial benefits of IITs for patients. Materials and Methods: We reviewed IITs conducted by KCSG from 1998 to 2023, analyzing progression-free survival (PFS) and overall survival (OS) gains for participants. PFS and OS benefits were calculated as the difference in median survival times between the intervention and control groups, multiplied by the number of patients in the intervention group. Financial benefits were assessed based on the cost of investigational products provided. Results: From 1998 to 2023, KCSG conducted 310 IITs, with 133 completed and published. Of these, 21 were included in the survival analysis. The analysis revealed that 1,951 patients in the intervention groups gained a total of 2,558.4 months (213.2 years) of PFS and 2,501.6 months (208.5 years) of OS, with median gains of 1.31 months in PFS and 1.58 months in OS per patient. When analyzing only statistically significant results, PFS and OS gain per patients was 1.69 months and 3.02 months, respectively. Investigational drug cost analysis from six available IITs indicated that investigational products provided to 252 patients were valued at 10,400,077,294 won (approximately 8,046,481 US dollars), averaging about 41,270,148 won (approximately 31,930 US dollars) per patient. Conclusion Our findings, based on analysis of published research, suggest that IITs conducted by KCSG led to survival benefits for participants and, in some studies, may have provided financial benefits by providing investment drugs.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.