Recent accumulating epidemiological evidence underlines the important role of environmental exposures on kidney diseases. Among environmental exposures, this study addresses “Green space,” which has been recognized as one of the major environmental exposures at the population level. We review a total of seven epidemiological studies currently published on greenness and kidney disease. We also discuss knowledge gaps in the epidemiological evidence in relation to study design, greenness exposure index, emerging kidney outcomes, and inequalities. With an increase in public attention regarding environmental risks and climate change, an improved understanding of the beneficial effects of green space can play an important role in promoting kidney health.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Background: The risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission during the endemic phase may vary from that during the previous pandemic phase. We evaluated the risk of infection in a general population with laboratory-confirmed coronavirus disease 2019 (COVID-19) in a community setting in Korea. Materials and Methods: This study included 1,286 individuals who had been in contact with an index COVID-19 case between January 24, 2020, and June 30, 2022. Variables such as age, sex, nationality, place of contact, level of contact, the status of exposed cases, period, and level of mask-wearing were assessed. Results: Among 1,286 participants, 132 (10.30%) were confirmed to have COVID-19. With increasing age, the risk of the exposed persons contracting COVID-19 from index cases tended to increase (P <0.001), especially for people in their 70s (odds ratio, 1.24; 95% confidence interval, 1.11–1.40; P <0.001). We found an increasing trend in the risk of a COVID-19 exposed case becoming a secondary infection case (P <0.001) in long-term care facilities where the attack rate was high. Conclusion The risk of COVID-19 transmission is high in long-term care facilities where many older adults reside. Intensive management of facilities at risk of infection and strict mask-wearing of confirmed COVID-19 cases are necessary to prevent the risk of COVID-19 infection.

Purpose: Concomitant ankle injuries associated with tibial shaft fractures can affect postoperative ankle joint pain and various postoperative ankle complications. This study compared the clinical outcomes between surgical treatment and conservative treatment of concomitant ankle injuries associated with tibial shaft fractures. Materials and Methods: From January 2015 to June 2020, a retrospective study was conducted on 118 tibia shaft fractures at the orthopedics department of the hospital. Associated ankle injuries were analyzed using plain radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and intraoperative stress exams. The clinical outcomes were compared using the pain visual analog scale (pain VAS), American Orthopaedic Foot and Ankle Society Ankle-Hindfoot score (AOFAS score), and Karlsson–Peterson ankle score (KP score). Results: Seventy-two (61.02%) of the 118 cases were diagnosed with associated ankle injuries. Fifty-six cases underwent surgery for the ankle injury, and 16 cases underwent conservative treatment. The clinical results (according to the pain VAS score, AOFAS score, the KP score) were 1.79±1.26, 94.48±4.03, and 94.57±3.60, respectively, in the surgical treatment group, and 3.00±1.03, 91.06±3.02, and 91.25±3.31, respectively, in the conservative treatment group. Conclusion Surgical treatment showed better clinical outcomes than conservative treatment in concomitant ankle injury in tibia fractures. Therefore, surgical treatment produces better clinical outcomes than conservative treatment in concomitant ankle injuries in tibia fractures. Hence to improve the clinical outcomes, more attention is needed on ankle joint injury in tibial shaft fractures for selecting suitable surgical treatments for those patients.

Dulaglutide, a weekly injectable glucagon-like peptide-1 receptor agonist, has demonstrated effectiveness when combined with basal insulin. We examined whether the efficacy of dulaglutide is comparable to that of prandial insulin in kidney transplant (KT) recipients with type 2 diabetes mellitus (T2DM) undergoing multiple daily insulin injection (MDI) therapy. Thirty-seven patients, who switched from MDI therapy to basal insulin and dulaglutide, were retrospectively analyzed. Changes in glycosylated hemoglobin (HbA1c) and fasting plasma glucose (FPG) levels, body weight, and basal insulin dose were evaluated over 6 months. Dulaglutide was comparable to three injections of prandial insulin in terms of glycemic control (HbA1c 7.1% vs. 7.0%; 95% confidence interval [CI], –0.53 to 0.28; P=0.53). The basal insulin and dulaglutide combination resulted in a reduction in FPG levels by 9.7 mg/dL (95% CI, 2.09 to 41.54; P=0.03), in body weight by 4.9 kg (95% CI, 2.87 to 6.98; P<0.001), and in basal insulin dose by 9.52 IU (95% CI, 5.80 to 3.23; P<0.001). Once-weekly dulaglutide may be an effective alternative for thrice-daily prandial insulin in KT recipients with T2DM currently receiving MDI therapy.