The increasing global prevalence of obesity and its associated disorders point to an urgent need for the development of novel and effective therapeutic strategies that induce healthy weight loss. Obesity is characterized by hyperleptinemia and central leptin resistance. In an attempt to identify compounds that could reverse leptin resistance and thus promote weight loss, we analyzed a library of small molecules with mRNA expression profiles similar to that of celastrol, a naturally-occurring compound we previously identified as a leptin sensitizer. By this process we identified another natural compound, withaferin A, that also acts as a leptin sensitizer. We found that withaferin A treatment of diet-induced obese mice resulted in a 20-25% reduction of body weight, while also decreasing obesity-associated abnormalities including hepatic steatosis. Withaferin A marginally affects the body weight of ob/ob and db/db mice, which are both deficient in leptin signaling. In addition, withaferin A, unlike celastrol, has beneficial effects on glucose metabolism independently from its leptin-sensitizing effect. Our results show that the metabolic abnormalities of diet-induced obesity can be mitigated by sensitizing animals to endogenous leptin, and indicate that withaferin A is a potential leptin sensitizer with additional anti-diabetic actions.

Celastrol, a pentacyclic triterpene is the most potent anti-obesity agent that has been reported to date 1. The mechanism of celastrol's leptin sensitizing and anti-obesity effects has not yet been elucidated. In this study, we identified interleukin 1 receptor 1 (IL1R1) as a mediator of celastrol action by using temporally-resolved analysis of the hypothalamic transcriptome in celastrol-treated DIO, lean and db/db mice. We demonstrate that IL1R1-deficient mice are completely resistant to celastrol's leptin sensitization, anti-obesity, anti-diabetic and anti-NASH effects. Thus, we conclude that IL1R1 is a gate-keeper for celastrol's metabolic actions. Increased ER stress in the hypothalamus plays a central role in the development of leptin resistance, and thus obesity 2-5. Given these findings, we undertook in silico screens utilizing systems biology approaches to identify new chemical chaperones that would serve as stronger leptin sensitizers. These efforts yielded celastrol, a pentacyclic triterpene as a potentially efficacious chemical chaperone and leptin sensitizer 1. Celastrol reduces the body weight of diet-induced obese (DIO) mice by 45-50% and further ameliorates insulin resistance/type-2 diabetes, nonalcoholic steatohepatitis (NASH), hypercholesterolemia, and liver damage in DIO mice 1. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Objectives This study aimed to develop a dual-input convolutional neural network (CNN)–based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance. Materials and Methods To develop the deep-learning model, 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution were split into a training set (1012 pairs, 79.9%) and a validation set (254 pairs, 20.1%). We performed external tests using 2 types of distinct datasets: one temporally and the other geographically separated from the model development. We used 258 pairs of radiographs examined in 2018 at the same institution as a temporal test set and 95 examined between January 2016 and December 2018 at another hospital as a geographic test set. Images underwent preprocessing, including cropping and histogram equalization, and were input into a dual-input neural network constructed by merging 2 ResNet models. An observer study was performed by radiologists on the geographic test set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model and human readers were calculated and compared. Results Our trained model showed an AUC of 0.976 in the validation set, 0.985 in the temporal test set, and 0.992 in the geographic test set. In AUC comparison, the model showed comparable results to the human readers in the geographic test set; the AUCs of human readers were in the range of 0.977 to 0.997 (P's > 0.05). The model had a sensitivity of 93.9%, a specificity of 92.2%, a PPV of 80.5%, and an NPV of 97.8% in the temporal test set, and a sensitivity of 100%, a specificity of 86.1%, a PPV of 69.7%, and an NPV of 100% in the geographic test set. Compared with the developed deep-learning model, all 3 human readers showed a significant difference (P's < 0.05) using the McNemar test, with lower specificity and PPV in the model. On the other hand, there was no significant difference (P's > 0.05) in sensitivity and NPV between all 3 human readers and the proposed model. Conclusions The proposed dual-input deep-learning model that interprets both AP and lateral elbow radiographs provided an accurate diagnosis of pediatric supracondylar fracture comparable to radiologists.