Chia‐Tien Hsu

spp. It is a pro-insecticide and acts after metabolic transformation to its active metabolite tralopyril. Tralopyril is an uncoupler of oxidative phosphorylation in the mitochondria of the target insects and of experiment animals, leading to the disruption of adenosine triphosphate synthesis and death. Several fatal human poisonings had been reported and no blood chlorfenapyr or tralopyril measurements were available. The treatment remains supportive. A 32-year-old healthy man ingested 200 mL of 10% chlorfenapyr as a suicide attempt. Unfortunately, he succumbed at 157 h post-ingestion, shortly after having fever and seizures. His serum level of chlorfenapyr at 4 h post-exposure was 77.4 ng/mL, and was undetectable at 113 and 156 h, respectively. The serum levels of tralopyril were 723.6, 14,179, and 9654.2 ng/mL at 4, 113, and 156 h post-ingestion, respectively. The delay in the rise of serum tralopyril levels was noticeable, which seems to correlate with the patient's signs and symptoms. The information may have therapeutic implications in the management of this deadly poisoning.

BACKGROUND AND PURPOSE: The fungal product (+)-antroquinonol activates AMP kinase (AMPK) activity in cancer cell lines. The present study was conducted to examine whether chemically synthesized (+)-antroquinonol exhibited beneficial metabolic effects in insulin-resistant states by activating AMPK and inhibiting dipeptidyl peptidase IV (DPP IV) activity. EXPERIMENTAL APPROACH: Effects of (+)-antroquinonol on DPP IV activity were measured with a DPPIV Assay Kit and effects on GLP-1-induced PKA were measured in AR42J cells. Translocation of the glucose transporter 4, GLUT4, induced either by insulin-dependent PI3K/AKT signalling or by insulin-independent AMPK activation, was assayed in differentiated myotubes. Glucose uptake and GLUT4 translocation were assayed in L6 myocytes. Mice with diet-induced obesity were used to assess effects of acute and chronic treatment with (+)-antroquinonol on glycaemic control in vivo. KEY RESULTS: The results showed that of (+)-antroquinonol (100 μM ) inhibited the DPP IV activity as effectively as the clinically used inhibitor, sitagliptin. The phosphorylation of AMPK Thr(172) in differentiated myotubes was significantly increased by (+)-antroquinonol. In cells simultaneously treated with S961 (insulin receptor antagonist), insulin and (+)-antroquinonol, the combination of (+)-antroquinonol plus insulin still increased both GLUT4 translocation and glucose uptake. Further, (+)-antroquinonol and sitagliptin reduced blood glucose, when given acutely or chronically to DIO mice. CONCLUSIONS AND IMPLICATIONS: Chemically synthesized (+)-antroquinonol exhibits dual effects to ameliorate insulin resistance, by increasing AMPK activity and GLUT4 translocation, along with inhibiting DPP IV activity.

Tc-99m MDP bone scans were used to evaluate the heel pain (talalgia) in 38 patients with Reiter's disease, and compared with clinical examination and radiologic findings. In our work, 58% (22/38) patients presented talalgia with a total of 35 lesions. Only two lesions of clinical talalgia were missed by the bone scan. The diagnostic sensitivity was as high as 94% (33/35). However, the diagnostic sensitivity of radiography was only 69% (11/16) when the disease duration was more than one year; furthermore, it declined to 33% (4/12) when the disease duration was less than one year. Based on the bone scans, the correlation between positive scintigraphic findings and clinical talalgia was extremely good. Clinical talalgia occurred in all the 33 lesions demonstrated by bone scan. However, three lesions demonstrated by radiography were not consistent with clinical talalgia and not visualized by radioscintigraphy. Our data show that the radionuclide scan is a more sensitive indicator and has better correlation with clinical talalgia than radiography. We consider that bone scintigraphy is superior to radiography in the evaluation of heel pain in Reiter's disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), and it increases the risk of osteoporosis and fractures. This study aimed to develop predictive machine-learning (ML) models to identify osteoporosis risk in patients with CKD stages 3-5 and ESKD. We retrospectively analyzed a de-identified osteoporosis database from a Taiwanese hospital, including 6614 patients with CKD stages 3-5 and ESKD who underwent bone mineral density (BMD) scans between January 2011 and June 2022. Nine ML algorithms were applied to predict osteoporosis: logistic regression, XGBoost, LightGBM, CatBoost, SVM, decision tree, random forest, k-nearest neighbors, and an artificial neural network (ANN). The ANN model achieved the highest predictive performance, with an area under the curve (AUC) of 0.940 on the validation and 0.930 on the test datasets. The receiver operating characteristic curve, confusion matrix, and predictive probability histogram revealed that the ANN model performed well in terms of discrimination. Calibration and decision curve analyses further demonstrated the reliability and applicability of the ANN model. The ANN model demonstrated the potential for clinical implementation in screening high-risk patients for osteoporosis.

Early detection of rapidly progressive kidney disease is key to improving the renal outcome and reducing complications in adult patients with type 2 diabetes mellitus (T2DM). We aimed to construct a 6-month machine learning (ML) predictive model for the risk of rapidly progressive kidney disease and the need for nephrology referral in adult patients with T2DM and an initial estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2. We extracted patients and medical features from the electronic medical records (EMR), and the cohort was divided into a training/validation and testing data set to develop and validate the models on the basis of three algorithms: logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). We also applied an ensemble approach using soft voting classifier to classify the referral group. We used the area under the receiver operating characteristic curve (AUROC), precision, recall, and accuracy as the metrics to evaluate the performance. Shapley additive explanations (SHAP) values were used to evaluate the feature importance. The XGB model had higher accuracy and relatively higher precision in the referral group as compared with the LR and RF models, but LR and RF models had higher recall in the referral group. In general, the ensemble voting classifier had relatively higher accuracy, higher AUROC, and higher recall in the referral group as compared with the other three models. In addition, we found a more specific definition of the target improved the model performance in our study. In conclusion, we built a 6-month ML predictive model for the risk of rapidly progressive kidney disease. Early detection and then nephrology referral may facilitate appropriate management.