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.

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.

Promyelocytic leukemia (PML) gene, through alternative splicing of its Cterminal region, generates several PML isoforms that interact with specific partners and perform distinct functions. The PML protein is a tumor suppressor that plays an important role by interacting with various proteins. Herein, we investigated the effect of the PML isoforms on oncostatin M (OSM)-induced signal transducer and activator of transcription-3 (STAT-3) transcriptional activity. PML influenced OSMinduced STAT-3 activity in a cell type-specific manner, which was dependent on the p53 status of the cells but regardless of PML isoform. Interestingly, overexpression of PML exerted opposite effects on OSM-induced STAT-3 activity in p53 wild-type and mutant cells. Specifically, overexpression of PML in the cell lines bearing wild-type p53 (NIH3T3 and U87-MG cells) decreased OSM-induced STAT-3 transcriptional activity, whereas overexpression of PML increased OSM-induced STAT-3 transcriptional activity in mutant p53-bearing cell lines (HEK293T and U251-MG cells). When wild-type p53 cells were co-transfected with PML-IV and R273H-p53 mutant, OSM-mediated STAT-3 transcriptional activity was significantly enhanced, compared to that of cells which were transfected with PML-IV alone; however, when cells bearing mutant p53 were co-transfected with PML-IV and wild-type p53, OSM-induced STAT-3 transcriptional activity was significantly decreased, compared to that of transfected cells with PML-IV alone. In conclusion, PML acts together with wild-type or mutant p53 and influences OSM-mediated STAT-3 activity in a negative or positive manner, resulting in the aberrant activation of STAT-3 in cancer cells bearing mutant p53 probably might occur through the interaction of mutant p53 with PML.

Promyelocytic leukemia (PML) gene, through alternative splicing of its Cterminal region, generates several PML isoforms that interact with specific partners and perform distinct functions. The PML protein is a tumor suppressor that plays an important role by interacting with various proteins. Herein, we investigated the effect of the PML isoforms on oncostatin M (OSM)-induced signal transducer and activator of transcription-3 (STAT-3) transcriptional activity. PML influenced OSMinduced STAT-3 activity in a cell type-specific manner, which was dependent on the p53 status of the cells but regardless of PML isoform. Interestingly, overexpression of PML exerted opposite effects on OSM-induced STAT-3 activity in p53 wild-type and mutant cells. Specifically, overexpression of PML in the cell lines bearing wild-type p53 (NIH3T3 and U87-MG cells) decreased OSM-induced STAT-3 transcriptional activity, whereas overexpression of PML increased OSM-induced STAT-3 transcriptional activity in mutant p53-bearing cell lines (HEK293T and U251-MG cells). When wild-type p53 cells were co-transfected with PML-IV and R273H-p53 mutant, OSM-mediated STAT-3 transcriptional activity was significantly enhanced, compared to that of cells which were transfected with PML-IV alone; however, when cells bearing mutant p53 were co-transfected with PML-IV and wild-type p53, OSM-induced STAT-3 transcriptional activity was significantly decreased, compared to that of transfected cells with PML-IV alone. In conclusion, PML acts together with wild-type or mutant p53 and influences OSM-mediated STAT-3 activity in a negative or positive manner, resulting in the aberrant activation of STAT-3 in cancer cells bearing mutant p53 probably might occur through the interaction of mutant p53 with PML.