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Background: Cerebral arteriovenous malformation (AVM) is a cerebrovascular disorder posing a risk for intracranial hemorrhage. However, there are few reliable quantitative indices to predict hemorrhage risk accurately. This study aimed to identify potential biomarkers for hemorrhage risk by quantitatively analyzing the hemodynamic and morphological features within the AVM nidus., Methods: This study included three datasets comprising consecutive patients with untreated AVMs between January 2008 to December 2023. Training and test datasets were used to train and evaluate the model. An independent validation dataset of patients receiving conservative treatment was used to evaluate the model performance in predicting subsequent hemorrhage during follow-up. Hemodynamic and morphological features were quantitatively extracted based on digital subtraction angiography (DSA). Individual models using various machine learning algorithms and an ensemble model were constructed on the training dataset. Model performance was assessed using the confusion matrix-related metrics., Results: This study included 844 patients with AVMs, distributed across the training (n=597), test (n=149), and validation (n=98) datasets. Five hemodynamic and 14 morphological features were quantitatively extracted for each patient. The ensemble model, constructed based on five individual machine-learning models, achieved an area under the curve of 0.880 (0.824-0.937) on the test dataset and 0.864 (0.769-0.959) on the independent validation dataset., Conclusion: Quantitative hemodynamic and morphological features extracted from DSA data serve as potential indicators for assessing the rupture risk of AVM. The ensemble model effectively integrated multidimensional features, demonstrating favorable performance in predicting subsequent rupture of AVM., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods., Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms., Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur., Conclusions: To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Wang, Fang, Li, Ding, Song, He, Rao, Diao, Zhu and Yang.)

Background: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage., Methods: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets., Results: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively., Conclusions: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations., Competing Interests: Disclosures None.

The aim of the study was to develop soybean polysaccharide (SSPS) -carboxylated nanocellulose (CNC) blending films with enhanced mechanical and barrier properties to be used as a tear-free and edible packaging materials. The films were formed by casting method, with CNC as the strengthening unit and glycerol as the plasticizer. The effect of CNC on structural and physical performances of the SSPS-CNC films were studied. SEM indicated that the film will stratify with excess CNC (10 %), but the film remains intact and compact. Incorporation of CNC into SSPS films did not change peak position in the XRD pattern significantly. Hydrogen bonds among SSPS, glycerol and CNC were indicated by the FTIR spectra. The compounding of CNC greatly lessened the light transmittance and hydrophilicity (CA increased from 55.42° to 70.67°), but perfected the barrier (WVP decreased from 3.595 × 10 -10 to 2.593 × 10 -10  g m -1  s -1  Pa -1 ) and mechanical properties (TS improved from 0.806 to 1.317 MPa). The results of packaging dehydrated vegetable indicated that the SSPS-8CNC film can effectively inhibit the packaged cabbage absorption water vapor. As a consequence, SSPS film perfected by CNC is hopeful to pack dehydrated vegetables in instant foods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Bone marrow-derived endothelial progenitor cells (EPCs) play a dynamic role in maintaining the structure and function of blood vessels. But how these cells maintain their growth and angiogenic capacity under bone marrow hypoxic niche is still unclear. This study aims to explore the mechanisms from a perspective of cellular metabolism., Methods: XFe96 Extracellular Flux Analyzer was used to analyze the metabolic status of EPCs. Gas Chromatography-Mass Spectrometry (GC-MS) was used to trace the carbon movement of 13 C-labeled glucose and glutamine under 1 % O 2 (hypoxia) and ∼20 % O 2 (normoxia). Moreover, RNA interference, targeting isocitrate dehydrogenase-1 (IDH1) and IDH2, was used to inhibit the reverse tricarboxylic acid (TCA) cycle and analyze metabolic changes via isotope tracing as well as changes in cell growth and angiogenic potential under hypoxia. The therapeutic potential of EPCs under hypoxia was investigated in the ischemic hindlimb model., Results: Compared with normoxic cells, hypoxic cells showed increased glycolysis and decreased mitochondrial respiration. Isotope metabolic tracing revealed that under hypoxia, the forward TCA cycle was decreased and the reverse TCA cycle was enhanced, mediating the conversion of α-ketoglutarate (α-KG) into isocitrate/citrate, and de novo lipid synthesis was promoted. Downregulation of IDH1 or IDH2 under hypoxia suppressed the reverse TCA cycle, attenuated de novo lipid synthesis (DNL), elevated α-KG levels, and decreased the expression of hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor A (VEGFA), eventually inhibiting the growth and angiogenic capacity of EPCs. Importantly, the transplantation of hypoxia-cultured EPCs in a mouse model of limb ischemia promoted new blood vessel regeneration and blood supply recovery in the ischemic area better than the transplantation of normoxia-cultured EPCs., Conclusions: Under hypoxia, the IDH1- and IDH2-mediated reverse TCA cycle promotes glutamine-derived de novo lipogenesis and stabilizes the expression of α-KG and HIF-1α, thereby enhancing the growth and angiogenic capacity of EPCs., Competing Interests: Declaration of competing interests The authors declare no potential conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Artificial light harvesting, a process that involves converting sunlight into chemical potential energy, is considered to be a promising part of the overall solution to address urgent global energy challenges. Conjugated polyelectrolyte complexes (CPECs) are particularly attractive for this purpose due to their extended electronic states, tunable assembly thermodynamics, and sensitivity to their local environment. Importantly, ionically assembled complexes of conjugated polyelectrolytes can act as efficient donor-acceptor pairs for electronic energy transfer (EET). However, to be of use in material applications, we must understand how modifying the chemical structure of the CPE backbone alters the EET rate beyond spectral overlap considerations. In this report we investigate the dependence of the EET efficiency and rate on the electronic structure and excitonic wave function of the CPE backbone. To do so, we synthesized a series of alternating copolymers where the electronic states are systematically altered by introducing comonomers with electron withdrawing and electron-rich character while keeping the linear ionic charge density nearly fixed. We find evidence that the excitonic coupling may be significantly affected by the exciton delocalization radius, in accordance with analytical models based on the line-dipole approximation and quantum chemistry calculations. Our results imply that care should be taken when selecting CPE components for optimal CPEC EET. These results have implications for using CPECs as key components in water-based light-harvesting materials, either as standalone assemblies or as adsorbates on nanoparticles and thin films.

Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer., (© 2023. Springer Nature Switzerland AG.)

Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first-principles free energy calculations to predict the ground-state crystal structure of a given material composition. These ab initio algorithms are currently too slow for predicting complex material structures. Inspired by the AlphaFold algorithm for protein structure prediction, herein, we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model for predicting the atomic contact map of a target material followed by three-dimensional (3D) structure reconstruction using genetic algorithms. Extensive experiments on 20 benchmark structures showed that our AlphaCrystal algorithm can predict structures close to the ground truth structures, which can significantly speed up the crystal structure prediction and handle relatively large systems., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Background: Cognitive behavioral therapy (CBT) is the recommended treatment for depression in patients with epilepsy (PWE). However, there are no studies that calculate the effect size of CBT on depression and quality of life (QoL) in PWE., Methods: We searched seven electronic databases (PubMed, Web of Science, Embase, Cochrane Library, Clinical Trials, Ovid Medline, and PsycINFO). We included 13 studies examining CBT for depression in PWE and calculated its effect size., Results: A total of 13 studies met the criteria. After treatment, CBT improves depression in PWE (g = 0.36, 95%CI: 0.18 to 0.54, I 2  = 50%), and the efficacy maintains during follow-up (g = 0.47, 95%CI: 0.04 to 0.89, I 2  = 80%). Subgroup analysis has shown that individual CBT (g = 0.47, 95%CI: 0.20 to 0.73, I 2  = 0%) had a greater effect size than group CBT (g = 0.30, 95%CI: 0.07 to 0.53, I 2  = 62%) in the treatment of depression. Likewise, CBT has a positive effect on the QoL improvement of PWE (g = 0.34, 95%CI: 0.11 to 0.57, I 2  = 64%). In controlling seizures, CBT did not differ from the control group (g = -0.06, 95%CI: -0.32 to 0.19, I 2  = 0%)., Conclusions: Cognitive behavioral therapy interventions were effective in improving depression and QoL in PWE, but not effective in controlling seizures. The efficacy of CBT interventions targeting seizure control seems to be uncertain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Molecularly imprinted monolithic column embedded with silver nanoparticles (MIMC@Ag) was synthesized by in-situ polymerization with template and porogen inside capillary tube followed by silver precursor reduction and template/porogen removal for realizing Raman detection of cortisol. Dense silver nanoparticles generated within the monolith makes this kind of column suitable for surface enhanced Raman scattering (SERS) detection, designated as SERS-MIMC. Scanning electron microscopy and BET profiler confirmed larger pore structure in the column after template removal. The corresponding increased mass transfer/binding rate, selective adsorption and adsorptive mechanism of the MIMC were well studied with a series of adsorption experiments. The minimum Raman detectable concentration of cortisol is 1 × 10 -7  mol L -1  by using MIMC@Ag with a good linear relationship in the concentration range from 1 × 10 -3 to 1 × 10 -7  mol L -1 . SERS sigmal of cortisol can be clearly distinguished from its analogs (estradiol, cholesterol and dexamethasone), proving the selective recognition of cortisol for SERS detection by MIMC@Ag. This ease-to-prepare SERS-MIMC sensor also shows good stability and reusability. The SERS-MIMC has been successfully applied for the easy, sensitive and selective detection of cortisol in dog saliva., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This meta-analysis mainly examined the effect size of exposure and response prevention (ERP) for obsessive-compulsive disorder (OCD) when compared with different control conditions, and the difference in the efficacy of different variants of ERP in the treatment of OCD. Thirty studies were included, involving 39 randomized controlled trials with 1793 participants, from 30 studies up to January 18, 2022. Hedge's g was calculated using random-effects models. The results showed that ERP had a definite effect on OCD (g = 0.37), and its effect was significant when the control condition was placebo (g = 0.97) or drug (g = 0.59). However, ERP did not show statistical differences with other therapies in improving OCD (g = -0.07). In addition, we found that both therapist and self-controlled exposure (at the same time as the therapist controls, self-control is exercised after the therapy session) and total response prevention can better improve OCD symptoms. In addition, compared with the control group, ERP reduced depression (g = 0.15) and anxiety symptoms (g = 0.23) in patients with OCD. Meta-regression results showed that the longer the length of sessions, the better the treatment effect (t = 2.41, p = 0.022)., Competing Interests: Declaration of Competing Interest The authors declare that there is no cqonflict of interest relevant to the content of the article., (Copyright © 2022. Published by Elsevier B.V.)