Developing a nomogram for predicting acute complicated course in pediatric acute hematogenous osteomyelitis.

Background: The objective of this study was to develop and validate a nomogram for predicting the risk of an acute complicated course in pediatric patients with Acute Hematogenous Osteomyelitis (AHO).

Methods: A predictive model was developed based on a dataset of 82 pediatric AHO patients. Clinical data, imaging findings, and laboratory results were systematically collected for all patients.

Predictive value of PAR and PNI for the acute complicated course of pediatric acute hematogenous osteomyelitis.

Objective: Platelet to albumin ratio (PAR) and prognostic nutritional index (PNI) are potential indicators for evaluating nutritional and inflammatory status. This study aimed to examine the relationship between PAR and PNI and the acute complicated course of acute hematogenous osteomyelitis (AHO).

Methods: AHO patients were divided into the simple course group and the acute complicated course group.

MoS Quantum Dot-Optimized Conductive Channels for a Conjugated Polymer-Based Synaptic Memristor.

Donor-acceptor-type conjugated polymers are widely used in memristors due to their unique push-pull electron structures and charge transfer mechanisms. However, the inherently inhomogeneous microstructure of polymer films and their low crystallinity produce randomness that destabilizes formed conductive channels, giving polymer-based memristors unstable switching behavior. In this contribution, we prepared a synaptic device based on PM6-MoS QD (molybdenum disulfide quantum dot) nanocomposites.

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction.