Predicting slight freezing of gait in Parkinson's disease with anticipatory postural adjustments and limits of stability.

INTRODUCTION: Gait initiation (GI) includes automatic and voluntary movements. However, research on their impact on the first step in patients with Parkinson's disease (PD) and their relationship to freezing of gait (FOG) is lacking. We examined the effects of automatic movements (anticipatory postural adjustments [APAs]) and voluntary movements (limits of stability [LOS]) on the first step (first-step duration and first-step range of motion), along with their early recognition and prediction of slight FOG. METHODS: Twenty-three patients with PD and slight freezing (PD + FOG) and 25 non-freezing patients with PD (PD-FOG) were tested while off medications and compared with 24 healthy controls (HC). All participants completed a 7-m Stand and Walk Test (7 m SAW) and wore inertial sensors to quantify the APAs and first step. LOS was quantified by dynamic posturography in different directions using a pressure platform. We compared differences among all three groups, analysed correlations, and evaluated their predictive value for slight FOG. RESULTS: In PD + FOG, APAs and LOS were worse than those in the PD-FOG and HC groups (p < 0.001), and the first step was worse than that in HC (p < 0.001). APAs were correlated mainly with the first-step duration. APAs and LOS were correlated with the first-step range of motion. APAs have been recognized as independent predictors of FOG, and their combination with LOS enhances predictive sensitivity. CONCLUSION: APAs and LOS in patients with PD directly affect the first step during GI. In addition, the combination of APAs and LOS helped predict slight FOG.

Enhanced Photo-excitation and Angular-Momentum Imprint of Gray Excitons in WSe2 Monolayers by Spin-Orbit-Coupled Vector Vortex Beams.

A light beam can be spatially structured in the complex amplitude to possess orbital angular momentum (OAM), which introduces an extra degree of freedom alongside the intrinsic spin angular momentum (SAM) associated with circular polarization. Furthermore, superimposing two such twisted light (TL) beams with distinct SAM and OAM produces a vector vortex beam (VVB) in nonseparable states where not only complex amplitude but also polarization is spatially structured and entangled with each other. In addition to the nonseparability, the SAM and OAM in a VVB are intrinsically coupled by the optical spin-orbit interaction and constitute the profound spin-orbit physics in photonics. In this work, we present a comprehensive theoretical investigation, implemented on the first-principles base, of the intriguing light-matter interaction between VVBs and WSe2 monolayers (WSe2-MLs), one of the best-known and promising two-dimensional (2D) materials in optoelectronics dictated by excitons, encompassing bright exciton (BX) as well as various dark excitons (DXs). One of the key findings of our study is that a substantial enhancement of the photoexcitation of gray excitons (GXs), a type of spin-forbidden DX, in a WSe2-ML can be achieved through the utilization of a 3D-structured TL with the optical spin-orbit interaction. Moreover, we show that a spin-orbit-coupled VVB surprisingly allows for the imprinting of the carried optical information onto GXs in 2D materials, which is robust against the decoherence mechanisms in the materials. This suggests a promising method for deciphering the transferred angular momentum from structured light to excitons.

Modulation of lipid profile by secretory phospholipase A2 group IIA: Verification with a transgenic mouse model.

We previously demonstrated a positive relation of secretory phospholipase A2 group IIA (sPLA2-IIA) with circulating high-density lipoprotein cholesterol (HDL-C) in patients with coronary artery disease, and sPLA2-IIA increased cholesterol efflux in THP-1 cells through peroxisome proliferator-activated receptor-γ (PPAR-γ)/liver X receptor α/ATP-binding cassette transporter A1 (ABCA1) signaling pathway. The aim of the present study was to examine the role of sPLA2-IIA over-expression on lipid profile in a transgenic mouse model. Fifteen apoE-/- and C57BL/7 female mice received bone marrow transplantation from transgenic SPLA2-IIA mice, and treated with specific PPAR-γ inhibitor GW9662. High fat diet was given after one week of bone marrow transplantation, and animals were sacrificed after twelve weeks. Immunohistochemical staining showed over-expression of sPLA2-IIA protein in the lung and spleen. The circulating level of HDL-C, but not that of low-density lipoprotein cholesterol (LDL-C), total cholesterol, or total triglyceride, was increased by sPLA2-IIA over-expression, and was subsequently reversed by GW9662 treatment. Over-expression of sPLA2-IIA resulted in augmented expression of cholesterol transporter ABCA1 at mRNA level in the aortas, and at protein level in macrophages, co-localized with macrophage specific antigen CD68. GW9662 exerted potent inhibitory effects on sPLA2-IIA-induced ABCA1 expression. Conclusively, we demonstrated the effects of sPLA2-IIA on circulating HDL-C level and the expression of ABCA1, possibly through regulation of PPAR-γ signaling in transgenic mouse model, that is in concert with the conditions in patients with coronary artery disease.

Effect of S-Ketamine on Postoperative Nausea and Vomiting in Patients Undergoing Video-Assisted Thoracic Surgery: A Randomized Controlled Trial.

Purpose: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy. Patients and Methods: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine and an intraoperative continuous infusion of S-ketamine at a rate of 0.25 mg/kg/h. The control group received an equivalent volume of saline. All patients were equipped with patient-controlled intravenous analgesia (PCIA), with a continuous infusion rate of 0.03 mg/kg/h S-ketamine in the S-ketamine group or 0.03 µg/kg/h sufentanil in the control group. The primary outcome was the incidence of PONV. Secondary outcomes included perioperative opioid consumption, hemodynamics, postoperative pain, and adverse events. Results: The incidence of PONV in the S-ketamine group (9.7%) was significantly lower than in the control group (30.5%). Analysis of perioperative opioid usage revealed that remifentanil usage was 40.0% lower in the S-ketamine group compared to the control group (1414.8 µg vs 2358.2 µg), while sufentanil consumption was 75.2% lower (33.1 µg vs 133.6 µg). The S-ketamine group demonstrated better maintenance of hemodynamic stability. Additionally, the visual analogue scale (VAS) scores on postoperative day 1 (POD-1) and postoperative day 3 (POD-3) were significantly lower in the S-ketamine group. Finally, no statistically significant difference in other postoperative adverse reactions was observed between the two groups. Conclusion: The results of this trial indicate that perioperative S-ketamine infusion can effectively reduce the incidence of PONV in patients undergoing VATS lobectomy.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes.

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

FluPMT: Prediction of Predominant Strains of Influenza A Viruses Via Multi-task Learning.

Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vaccine strain is generally based on the assessment of the current predominant strains. Nevertheless, the process of vaccine production and distribution is very time-consuming, leaving a window for the emergence of new variants that could decrease vaccine effectiveness, so predictions of influenza A virus evolution can inform vaccine evaluation and selection. Hence, we present FluPMT, a novel sequence prediction model that applies an encoder-decoder architecture to predict the hemagglutinin (HA) protein sequence of the upcoming season's predominant strain by capturing the patterns of evolution of influenza A viruses. Specifically, we employ time series to model the evolution of influenza A viruses, and utilize attention mechanisms to explore dependencies among residues of sequences. Additionally, antigenic distance prediction based on graph network representation learning is incorporated into the sequence prediction as an auxiliary task through a multi-task learning framework. Experimental results on two influenza datasets highlight the exceptional predictive performance of FluPMT, offering valuable insights into virus evolutionary dynamics, as well as vaccine evaluation and production.

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC)  of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.

Manipulating the processing window of directed self-assembly in contact hole shrinking with binary block copolymer/homopolymer blending.

Directed self-assembly (DSA) lithography has demonstrated significant potential in fabricating integrated circuits. However, DSA encounters limited processing windows due to the requirement for precise matching between the period of block copolymers (BCPs) and graphoepitaxy templates. We propose a binary BCP/homopolymer blending strategy to manipulate the self-assembly behavior and the processing window of graphoepitaxy DSA in contact hole shrinking. By carefully tailoring the blending rates of poly(methyl methacrylate) (PMMA) with different molecular weights in cylindrical polystyrene-b-poly(methyl methacrylate) (PS-b-PMMA), we manipulate the period and morphology of BCP/homopolymer self-assembly. Specifically, we employ BCP/homopolymer blending to fine-tune the critical dimension (CD) of contact holes with PS-affined topographical templates. Subsequent pattern transferring is achieved by selectively etching defect-free shrinkable cylinders as hard masks. Furthermore, self-consistent field theory (SCFT) simulation was employed to explore the self-assembly of BCP/homopolymer blending in confined cylindrical space and the results were in good consistency with the experimental results.

Modeling and analysis of the effect of optimal virus control on the spread of HFMD.

A within-host and between-host hand, foot and mouth disease (HFMD) mathematical model is established and the affect of optimal control in its within-host part on HFMD transmission is studied. Through define two basic reproduction numbers, by using the fast-slow system analysis method of time scale, the global stabilities of the between-host (slow) system and within-host (fast) system are researched, respectively. An optimal control problem with drug-treatment control on coupled within-host and between-host HFMD model is formulated and analysed theoretically. Finally, the purposed optimal control measures are applied to the actual HFMD epidemic analysis in Zhejiang Province, China from April 1, 2021 to June 30, 2021. The numerical results show that the drug control strategies can reduce the virus load per capita and can effectively prevent large-scale outbreaks of HFMD.

Effects of Shixiao Huoxue Decoction on pain, tumor necrosis factor-α, and interleukin-8 in patients with adenomyosis.

OBJECTIVE: To explore the effect of Shixiao Huoxue Decoction on pain and tumor necrosis factor (TNF)-α and interleukin (IL)-8 levels in patients with adenomyosis. METHODS: A total of 65 patients with adenomyosis admitted to South District of Guang'anmen Hospital from January 2020 to December 2021 were divided into two groups according to the treatment methods. The control group was treated with pregnatrienone, and the study group was treated with additional Shixiaohuoxue decoction. The incidence of complications, treatment efficacy, levels of inflammatory factors, Traditional Chinese Medicine symptom score, dysmenorrhea score, menstrual volume score, dysmenorrhea symptom score, changes in uterine volume, level of insulin-like growth factor 1 (IGF-1), and changes in the level of carbohydrate antigen (CA125) were observed before and after treatment in both two groups. Univariate Logistic analysis showed that uterine volume, IGF-1, CA125, serum IL-8 and TNF-α were correlated with the short-term efficacy of Meixiaohuoxue Decoction in the treatment of uterine adenomyosis (P<0.05). RESULTS: The levels of IL-8 and TNF-α in the study group were significantly lower than those in the control group after treatment (P<0.05). The scores of dyspareunia and non-menstrual pelvic pain in the study group were significantly lower than those in the control group (P<0.05). The overall response rate in the study group (93.75%) was significantly higher than that in the control group (66.66%) (P<0.05). The scores of Traditional Chinese Medicine symptoms, dysmenorrhea, menstrual volume, and dysmenorrhea symptoms in the study group were significantly lower than those in the control group after treatment (P<0.05). The IGF-1 and CA125 levels in the study group were significantly lower than those in the control group after treatment (P<0.05). However, no significant difference in uterine volume was found between the two groups after treatment (P>0.05). CONCLUSION: Xiaoxiao Huoxue Decoction demonstrated a better treatment efficacy in patients with adenomyosis through improving dysmenorrhea and Traditional Chinese Medicine symptoms, as well as reducing the levels of body inflammatory factors, non-menstrual pelvic pain, and dyspareunia, thus contributing to early recovery of patients. Therefore, Xiaoxiao Huoxue Decoction is worthy of promotion in clinical treatment of adenomyosis.

Ba3SnGa10-xInxO20 (0 ≤ x ≤ 2): site-selective doping, band structure engineering and photocatalytic overall water splitting.

Developing new photocatalysts and deciphering the structure-property relationship are always the central topics in photocatalysis. In this study, a new photocatalyst Ba3SnGa10O20 containing two d10 metal cations was prepared by a high temperature solid state reaction, and its crystal structure was investigated by Rietveld refinements of monochromatic X-ray powder diffraction data for the first time. There are 2 Ba, 4 metal cations and 6 O independent atoms in a unit cell. Sn4+ and Ga3+ co-occupy the octahedral cavities named M1 and M2 sites, and the other two metal sites are fully occupied by Ga3+. Rational In3+-to-Ga3+ substitution was performed to reduce the potential of the conduction band minimum and enhance the light absorption ability, which was indeed confirmed using UV-vis diffuse reflectance spectra and Mott-Schottky plots for Ba3SnGa10-xInxO20 (0 ≤ x ≤ 2). Interestingly, In3+ exhibits site selective doping at M1 and M2 sites exclusively. With the light absorption ability enhanced, the photocatalytic overall water splitting activity was also improved, i.e. the photocatalytic H2 generation rate was 1.7(1) µmol h-1 for Ba3SnGa10O20, and the optimal catalyst Ba3SnGa8.5In1.5O20 loaded with 1.0 wt% Pd exhibited the H2 generation rate of 27.5(4) µmol h-1 and the apparent quantum yield at 254 nm was estimated to be 2.28% in pure water.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

Compliant-Control-Based Assisted Walking with Mobile Manipulator.

In this paper, a new approach involving the use of a mobile manipulator to assist humans with mobility impairments to walk is proposed. First, in order to achieve flexible interaction between humans and mobile manipulators, we propose a variable admittance controller that can adaptively regulate the virtual mass and damping parameters based on the interaction forces and the human motion intention predicted using the fuzzy theory. Moreover, a feedforward velocity compensator based on a designed state observer is proposed to decrease the inertia resistance of the manipulator, effectively enhancing the compliance of the human-robot interaction. Then, the configuration of the mobile manipulator is optimized based on a null-space approach by considering the singularity, force capacity, and deformation induced by gravity. Finally, the proposed assisted walking approach for the mobile manipulator is implemented using the human-robot interaction controller and the null-space controller. The validity of the proposed controllers and the feasibility of assisted human walking are verified by conducting a set of tests involving different human volunteers.

Teleoperated Grasping Using Data Gloves Based on Fuzzy Logic Controller.

Teleoperated robots have attracted significant interest in recent years, and data gloves are one of the commonly used devices for their operation. However, existing solutions still encounter two challenges: the ways in which data gloves capture human operational intentions and achieve accurate mapping. In order to address these challenges, we propose a novel teleoperation method using data gloves based on fuzzy logic controller. Firstly, the data are collected and normalized from the flex sensors on data gloves to identify human manipulation intentions. Then, a fuzzy logic controller is designed to convert finger flexion information into motion control commands for robot arms. Finally, experiments are conducted to demonstrate the effectiveness and precision of the proposed method.

UCFNNet: Ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating.

BACKGROUND AND OBJECTIVE: Ulcerative colitis (UC) is a chronic disease characterized by recurrent symptoms and significant morbidity. The exact cause of the disease remains unknown. The selection of current treatment options for ulcerative colitis depends on the severity and location of the disease in each patient. Therefore, developing a fully automated endoscopic images for evaluating UC is crucial for guiding treatment plans and facilitating early prevention efforts. METHODS: We propose a network called ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating (UCFNNet). UCFNNet contains three novel modules. Firstly, a fine-grained lesion feature learner (FG-LF Learner) is proposed by integrating local features and a Softmax category prediction (SCP) module to improve the feature accuracy in small lesion areas. Subsequently, a graph convolutional feature combiner (GCFC) is developed to connect features across adjacent convolutional layers and to incorporate short connections between input and output, thereby mitigating feature loss during transmission. Thereafter, a noise suppression gating (NS gating) technique is designed by implementing a grid attention mechanism and a feature gating (FG) module to prioritize significant lesion features and suppress irrelevant and noisy regions in the input feature map. RESULTS: We evaluate the performance of the proposed network on both privately-collected and publicly-available datasets. The evaluation of UC achieves excellent results on privately-collected dataset, with an accuracy (ACC) of 89.57 %, Matthews correlation coefficient (MCC) of 85.52 %, precision of 89.26 %, recall of 89.48 %, and F1-score of 89.78 %. The results are also impressive on publicly-available dataset, with ACC of 85.47 %, MCC of 80.42 %, precision of 85.62 %, recall of 84.00 %, and F1-score of 84.53 %, surpassing the performance of state-of-the-art techniques. CONCLUSION: Our proposed model introduces three innovative algorithm modules, which outperform the current state-of-the-art methods and achieve high ACC and F1-score. This indicates that our method has superior performance compared to traditional machine learning and existing deep methods, which means that our method has good application prospects. Meanwhile, it has been verified that the proposed model demonstrates good interpretability. The source code is available at github.com/YinLeRenNB/UCFNNet.

Micro-biological degradation and transformation of dissolved organic matter following continuous cropping of tobacco.

In recent years, the problems associated with continuous cropping (CC) that cause soil degradation have become increasingly serious. As a key soil quality property, dissolved organic matter (DOM) affects the circulation of carbon and nutrients and the composition of bacterial communities in soil. However, research on the changes in the molecular composition of DOM after CC is limited. In this study, the soil chemical properties, DOM chemical diversity, bacterial community structure, and their interactions are explored in the soil samples from different CC years (CC1Y, CC3Y, CC5Y, and CC7Y) of tobacco. With increasing CC year of tobacco, most of the soil chemical properties, such as total carbon, total nitrogen and organic matter, decreased significantly, while dissolved organic carbon first decreased and then increased. Likewise, the trends of DOM composition differed with changing duration of CC, such as the tannin compounds decreased from 18.13 to 13.95%, aliphatic/proteins increased from 2.73 to 8.85%. After 7 years of CC, the soil preferentially produced compounds with either high H/C ratios (H/C > 1.5), including carbohydrates, lipids, and aliphatic/proteins, or low O/C ratios (O/C < 0.1), such as unsaturated hydrocarbons. Furthermore, core microorganisms, including Nocardioides, wb1-P19, Aquabacterium, Methylobacter, and Thiobacillus, were identified. Network analysis further indicated that in response to CC, Methylobacter and Thiobacillus were correlated with the microbial degradation and transformation of DOM. These findings will improve our understanding of the interactions between microbial community and DOM in continuous cropping soil.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Microscopic Origins of the Distinct Mechanical Response of ABA and ABC Block Copolymer Nanostructures.

It has been commonly believed that the ordered thermoplastic elastomers formed by the ABC triblock copolymer should have better mechanical performance than that by the ABA counterpart due to the higher bridging fraction. However, the thermoplastic elastomer of ABA was often observed to perform better than that of ABC. To compare the performance of two kinds of thermoplastic elastomers and unveil the underlying microscopic mechanism, we have calculated their stress-strain curves using coarse-grained molecular dynamics simulations in conjunction with self-consistent field theory. It is revealed that the stretching degree of the bridging blocks and the network connectivity play important roles in determining the mechanical properties in addition to the bridging fraction. The higher degree in the stretching of bridging blocks and network connectivity of the structure formed by the ABA triblock copolymer enables its superior mechanical performance over the ABC block copolymer.

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.