Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research.

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.

Acute stress and PTSD among trauma-exposed children and adolescents: Computational prediction and interpretation.

BACKGROUND: Youth receiving medical care for injury are at risk of PTSD. Therefore, accurate prediction of chronic PTSD at an early stage is needed. Machine learning (ML) offers a promising approach to precise prediction and interpretation. AIMS: The study proposes a clinically useful predictive model for PTSD 6-12 months after injury, analyzing the relationship among predictors, and between predictors and outcomes. METHODS: A ML approach was utilized to train models based on 1167 children and adolescents of nine perspective studies. Demographics, trauma characteristics and acute traumatic stress (ASD) symptoms were used as initial predictors. PTSD diagnosis at six months was derived using DSM-IV PTSD diagnostic criteria. Models were validated on external datasets. Shapley value and partial dependency plot (PDP) were applied to interpret the final model. RESULTS: A random forest model with 13 predictors (age, ethnicity, trauma type, intrusive memories, nightmares, reliving, distress, dissociation, cognitive avoidance, sleep, irritability, hypervigilance and startle) yielded F-scores of.973,0.902 and.961 with training and two external datasets. Shapley values were calculated for individual and grouped predictors. A cumulative effect for intrusion symptoms was observed. PDP showed a non-linear relationship between age and PTSD, and between ASD symptom severity and PTSD. A 43 % difference in the risk between non-minority and minority ethnic groups was detected. CONCLUSIONS: A ML model demonstrated excellent classification performance and good potential for clinical utility, using a few easily obtainable variables. Model interpretation gave a comprehensive quantitative analysis on the operations among predictors, in particular ASD symptoms.

Solid-state characterization of sertraline base-ß-cyclodextrin inclusion complex.

Sertraline is one of the serotonin-specific reuptake inhibitors that is effective in treating several disorders such as major depression, obsessive-compulsive disorder, panic disorder, and social phobia. It is marketed in the form of its hydrochloride salt, which exhibits better solubility in water than its free base form. However, the absorption of sertraline through biological membranes could be improved by enhancing the solubility of its base because it is more hydrophobic than sertraline hydrochloride. To clarify the mechanism for the interaction of sertraline base with ß-CD, it is important to study the basic interaction between the ß-CD ring and sertraline base. Therefore, in this study, the currently used hydrochloride salt form was converted into the free base and ß-CD was used as a model for ß-CD derivatives to evaluate the interaction between ß-CD and the sertraline base. The solid-state physicochemical characteristics of the sertraline-ß-CD complex were investigated by the phase solubility method, differential scanning calorimetry, Fourier transform IR spectroscopy, FT-Raman spectroscopy, powder X-ray diffraction, and (13)C cross-polarization magic-angle spinning NMR measurements. The results showed that sertraline base and ß-CD form an inclusion complex, and the stoichiometric ratio of the solid-state sertraline base-ß-CD complex is 1:1, which was estimated by the (1)H NMR measurements of the complex dissolved in DMSO-d6.