0008); (3) BFM was positively correlated to DBP (R2 = 0.1232, P = 0.02) and partially correlated to urine protein (R2 = 0.047, P = 0.12) and FBG (R2 = 0.07, P = 0.06); (4) overweight young adults had higher urinary mRNA levels of renin, angiotensinogen, IL-18 and CTGF. These suggest that BMI directly affects BP, kidney injury markers, and the activation of the intra-tubular RAS even in normotensive young adults. Given that BMI measurements and urine analyses are non-invasive, our findings may pave the way to developing a new and simple method of screening for the risk of chronic kidney disease in adults.Pupillometry has proven effective for the monitoring of intraoperative analgesia in non-cardiac surgery. We performed a prospective randomized study to evaluate the impact of an analgesia-guided pupillometry algorithm on the consumption of sufentanyl during cardiac surgery.  https://www.selleckchem.com/products/kribb11.html  Fifty patients were included prior to surgery. General anesthesia was standardized with propofol and target-controlled infusions of sufentanyl. The standard group consisted of sufentanyl target infusion left to the discretion of the anesthesiologist. The intervention group consisted of sufentanyl target infusion based on the pupillary pain index. The primary outcome was the total intraoperative sufentanyl dose. The total dose of sufentanyl was lower in the intervention group than in the control group and (55.8 µg [39.7-95.2] vs 83.9 µg [64.1-107.0], p = 0.04). During the postoperative course, the cumulative doses of morphine (mg) were not significantly different between groups (23 mg [15-53] vs 24 mg [17-46]; p = 0.95). We found no significant differences in chronic pain at 3 months between the 2 groups (0 (0%) vs 2 (9.5%) p = 0.49). Overall, the algorithm based on the pupillometry pain index decreased the dose of sufentanyl infused during cardiac surgery.Clinical trial number NCT03864016.Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test-retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic optic disc photographs (ODPs). A total of 1200 image pairs (ODPs and SAP results) for 563 eyes of 327 participants were enrolled. A DL model was built by combining a pre-trained DL network and subsequently trained fully connected layers. The correlation coefficient and mean absolute error (MAE) between the predicted and measured MDs were calculated. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the detection ability for glaucomatous visual field (VF) loss. The data were split into training/validation (1000 images) and testing (200 images) sets to evaluate the performance of the algorithm. The predicted MD showed a strong correlation and good agreement with the actual MD (correlation coefficient = 0.755; R2 = 57.0%; MAE = 1.94 dB). The model also accurately predicted the presence of glaucomatous VF loss (AUC 0.953). The DL algorithm showed great feasibility for prediction of MD and detection of glaucomatous functional loss from ODPs.Development of deep-learning models for intermolecular noncovalent (NC) interactions between proteins and ligands has great potential in the chemical and pharmaceutical tasks, including structure-activity relationship and drug design. It still remains an open question how to convert the three-dimensional, structural information of a protein-ligand complex into a graph representation in the graph neural networks (GNNs). It is also difficult to know whether a trained GNN model learns the NC interactions properly. Herein, we propose a GNN architecture that learns two distinct graphs-one for the intramolecular covalent bonds in a protein and a ligand, and the other for the intermolecular NC interactions between the protein and the ligand-separately by the corresponding covalent and NC convolutional layers. The graph separation has some advantages, such as independent evaluation on the contribution of each convolutional step to the prediction of dissociation constants, and facile analysis of graph-building strategies for the NC interactions. In addition to its prediction performance that is comparable to that of a state-of-the art model, the analysis with an explainability strategy of layer-wise relevance propagation shows that our model successfully predicts the important characteristics of the NC interactions, especially in the aspect of hydrogen bonding, in the chemical interpretation of protein-ligand binding.Patients with cancer frequently experience malnutrition, which is associated with higher rates of morbidity and mortality. Therefore, the implementation of strategies for its early detection and for intervention should improve the evolution of these patients. Our study aim is to design and implement a protocol for outpatients starting chemotherapy, by means of which any malnutrition can be identified and treated at an early stage. Before starting chemotherapy for patients with cancer, a complete assessment was made of their nutritional status, using the Nutriscore screening tool. When nutritional risk was detected, an interventional protocol was applied. Of 234 patients included in the study group, 84 (36%) required an individualised nutritional approach 27 (32.1%) presented high nutritional risk, 12 had a Nutriscore result ≥ 5 and 45 experienced weight loss during chemotherapy. Among this population, the mean weight loss (with respect to normal weight) on inclusion in the study was - 3.6% ± 8.2. By the end of the chemotherapy, the mean weight gain was 0% ± 7.3 (p  less then  0.001) and 71.0% of the patients had experienced weight gain or maintenance, with respect to the initial weight. More than a third of cancer patients who start chemotherapy are candidates for early nutritional intervention. This finding highlights the importance of early identification of patients at risk in order to improve the efficacy of nutritional interventions, regardless of the stage of the disease.