Background: Traditionally, dental implants require a healing period of 4 to 9 months for osseointegration, with longer recovery times considered when bone grafting is needed. This retrospective study evaluates the clinical efficacy of demineralized dentin matrix (DDM) combined with recombinant human bone morphogenetic protein-2 (rhBMP-2) during dental implant placement to expedite the osseointegration period for early loading. Methods: Thirty patients (17 male, 13 female; mean age 55.0 ± 8.8 years) requiring bone grafts due to implant fixture exposure (more than four threads; ≥ 3.2 mm) were included, with a total of 96 implants placed. Implants were inserted using a two-stage protocol with DDM/rhBMP-2 grafts. Early loading was initiated at two months postoperatively in the mandible and three months in the maxilla. Clinical outcomes evaluated included primary and secondary stability (implant stability quotient values), healing period, bone width, and marginal bone level assessed via cone-beam computed tomography. Results: All implants successfully supported final prosthetics with a torque of 50Ncm, without any osseointegration failures. The average healing period was 69.6 days in the mandible and 90.5 days in the maxilla, with significantly higher secondary stability in the mandible (80.7 ± 6.7) compared to the maxilla (73.0 ± 9.2, p < 0.001). Histological analysis confirmed new bone formation and vascularization. Conclusion DDM/rhBMP-2 grafting appears to significantly reduce the healing period, enabling early loading with stable and favorable clinical outcomes.

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.

Background: Traditionally, dental implants require a healing period of 4 to 9 months for osseointegration, with longer recovery times considered when bone grafting is needed. This retrospective study evaluates the clinical efficacy of demineralized dentin matrix (DDM) combined with recombinant human bone morphogenetic protein-2 (rhBMP-2) during dental implant placement to expedite the osseointegration period for early loading. Methods: Thirty patients (17 male, 13 female; mean age 55.0 ± 8.8 years) requiring bone grafts due to implant fixture exposure (more than four threads; ≥ 3.2 mm) were included, with a total of 96 implants placed. Implants were inserted using a two-stage protocol with DDM/rhBMP-2 grafts. Early loading was initiated at two months postoperatively in the mandible and three months in the maxilla. Clinical outcomes evaluated included primary and secondary stability (implant stability quotient values), healing period, bone width, and marginal bone level assessed via cone-beam computed tomography. Results: All implants successfully supported final prosthetics with a torque of 50Ncm, without any osseointegration failures. The average healing period was 69.6 days in the mandible and 90.5 days in the maxilla, with significantly higher secondary stability in the mandible (80.7 ± 6.7) compared to the maxilla (73.0 ± 9.2, p < 0.001). Histological analysis confirmed new bone formation and vascularization. Conclusion DDM/rhBMP-2 grafting appears to significantly reduce the healing period, enabling early loading with stable and favorable clinical outcomes.

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.

Objective: As the demand for community mental health services continues to grow, the need for well-equipped and organized services has become apparent. This study aimed to optimize the roles and infrastructure of mental health services, by establishing, among other initiatives, standardized operating models. Methods: The study was conducted in multiple phases from May 12, 2021, to December 29, 2021. Stakeholders within South Korea and metropolitan mental health welfare centers were targeted, but addiction management support centers, including officials, patients, and their families, were integrated as well. A literature review and survey, focus group interviews, a Delphi survey, and expert consultation contributed to comprehensive revisions and improvements of the mental health service model. Results: The proposed model for community mental health welfare centers emphasizes the expansion of personnel and infrastructure, with a focus on severe mental illnesses and suicide prevention. The model for metropolitan mental health welfare centers delineates essential tasks in areas such as project planning and establishment, community research, and education about severe mental illnesses. The establishment of a 24-hour emergency intervention center was a crucial feature. In the integrated addiction support center model, the need to promote addiction management is defined as an essential task and the establishment of national governance for addiction policies is recommended. Conclusion This study proposed standard operating models for three types of mental health service centers. To meet the increasing need for community care, robust mental health service delivery systems are of primary importance.

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.

Radiation-induced cavernous malformations (RICMs) are rare but significant late complications of highdose radiation therapy, particularly in young survivors of brain tumors. This report presents two cases of RICMs following aggressive multimodal treatment, including surgery, chemotherapy, and radiation therapy. Case 1 was a 22-year-old male patient with medulloblastoma treated with craniospinal irradiation, tumor bed boost, and tandem autologous peripheral blood stem cell transplantation. Approximately 8 years after treatment completion, routine follow-up imaging revealed a small focal hemorrhage in the right cerebellum, consistent with an RICM. The lesion was asymptomatic and managed conservatively with regular imaging, showing spontaneous resolution over time, with a significant size reduction noted 9 years post-treatment. Case 2 describes a 32-year-old male with an intracranial germinoma treated with whole-ventricular irradiation. Three years after treatment, the patient developed a symptomatic hemorrhagic RICM near a pre-existing developmental venous anomaly. Surgical resection and Gamma Knife Surgery stabilized the lesion; however, residual symptoms, including tremors and gait disturbances, persisted, affecting the patient’s daily activities. These cases illustrate the diverse clinical courses of RICMs, ranging from spontaneous resolution to the necessity of surgical intervention, and emphasize the importance of long-term surveillance and tailored management strategies for late-onset complications.

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.