Theoretical prediction of diffusive ionic current through nanopores under salt gradients

In charged nanopores, ionic diffusion current reflects the ionic selectivity and ionic permeability of nanopores which determines the performance of osmotic energy conversion, i.e. the output power and efficiency. Here, theoretical predictions of the diffusive currents through cation-selective nanopores have been developed based on the investigation of diffusive ionic transport under salt gradients with simulations. The ionic diffusion current I satisfies a reciprocal relationship with the pore length I correlates with a/L (a is a constant) in long nanopores. a is determined by the cross-sectional areas of diffusion paths for anions and cations inside nanopores which can be described with a quadratic power of the diameter, and the superposition of a quadratic power and a first power of the diameter, respectively. By using effective concentration gradients instead of nominal ones, the deviation caused by the concentration polarization can be effectively avoided in the prediction of ionic diffusion current. With developed equations of effective concentration difference and ionic diffusion current, the diffusion current across nanopores can be well predicted in cases of nanopores longer than 100 nm and without overlapping of electric double layers. Our results can provide a convenient way for the quantitative prediction of ionic diffusion currents under salt gradients

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Electroencephalographic (EEG) signals are acquired non-invasively from electrodes placed on the scalp. Experts in the field can use EEG signals to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects using classification models. However, the training of deep learning or machine learning models requires a large number of trials. Datasets related to Alzheimer’s disease are typically small in size due to the lack of AD patient samples. The lack of data samples required for the training process limits the use of deep learning techniques for further development in clinical settings. We propose to increase the number of trials in the training set by means of a decomposition–recombination system consisting of three steps. Firstly, the original signals from the training set are decomposed into multiple intrinsic mode functions via multivariate empirical mode decomposition. Next, these intrinsic mode functions are randomly recombined across trials. Finally, the recombined intrinsic mode functions are added together as artificial trials, which are used for training the models. We evaluated the decomposition–recombination system on a small dataset using each subject’s functional connectivity matrices as inputs. Three different neural networks, including ResNet, BrainNet CNN, and EEGNet, were used. Overall, the system helped improve ResNet training in both the mild AD dataset, with an increase of 5.24%, and in the mild cognitive impairment dataset, with an increase of 4.50%. The evaluation of the proposed data augmentation system shows that the performance of neural networks can be improved by enhancing the training set with data augmentation. This work shows the need for data augmentation on the training of neural networks in the case of small-size AD datasets.Fil: Jia, Hao. Universitat de Vic; España. Nankai University; ChinaFil: Huang, Zihao. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Duan, Feng. Nankai University; ChinaFil: Zhang, Yu. Lehigh University; Estados UnidosFil: Sun, Zhe. Juntendo University; ChinaFil: Solé Casals, Jordi. Universitat de Vic; Españ

Dyschromatopsia: a comprehensive analysis of mechanisms and cutting-edge treatments for color vision deficiency

Color blindness is a retinal disease that mainly manifests as a color vision disorder, characterized by achromatopsia, red-green color blindness, and blue-yellow color blindness. With the development of technology and progress in theory, extensive research has been conducted on the genetic basis of color blindness, and various approaches have been explored for its treatment. This article aims to provide a comprehensive review of recent advances in understanding the pathological mechanism, clinical symptoms, and treatment options for color blindness. Additionally, we discuss the various treatment approaches that have been developed to address color blindness, including gene therapy, pharmacological interventions, and visual aids. Furthermore, we highlight the promising results from clinical trials of these treatments, as well as the ongoing challenges that must be addressed to achieve effective and long-lasting therapeutic outcomes. Overall, this review provides valuable insights into the current state of research on color blindness, with the intention of informing further investigation and development of effective treatments for this disease

A new integrated framework for the identification of potential virus–drug associations

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes

Association between healthy eating index-2015 and abdominal aortic calcification among US Adults

AimsTo evaluate the relationship of the healthy eating index-2015 (HEI-2015) with abdominal aortic calcification (AAC) in US adults.MethodsWe conducted a cross-sectional study with data extracted from the National Health and Nutrition Examination Survey (NHANES). AAC score was measured using the scoring system of Kauppila (AAC-24) and Schousboe (AAC-8). HEI-2015, which was used for evaluating compliance with Dietary Guidelines for Americans (DGA), was calculated through two rounds of 24-h recall interviews. HEI-2015 was categorized as inadequate (<50), average (50~70), and optimal (≥70) groups for analysis, while the AAC-24 score was grouped by whether the score was >0. Weighted multiple regression analyses were conducted to estimate the association of HEI-2015 with AAC score and the presence of AAC. Moreover, smooth curve fittings, based on a generalized additive model (GAM), were applied to evaluate a possible non-linear relationship. Sensitivity analysis and subgroup analysis were performed to provide more supporting information.ResultsA total of 2,704 participants were included in the study (mean age, 57.61 ± 11.40 years; 51.78% were women). The mean score of HEI-2015 was 56.09 ± 13.40 (41.33 ± 6.28, 59.44 ± 5.54, and 76.90 ± 5.37 for inadequate, average, and optimal groups, respectively). After adjusting for covariates, higher HEI-2015 was associated with decreased AAC score (AAC-24: β = −0.121, 95% CI: −0.214, −0.028, P = 0.010; AAC-8: β= −0.054, 95% CI: −0.088, −0.019, P = 0.003) and lower risk of AAC (OR = 0.921, 95% CI: 0.855, 0.993, P = 0.031). Among the components of HEI-2015, a higher intake of fruits, greens, and beans was associated with a lower AAC score. Subgroup analysis showed that an inverse association of HEI-2015 with AAC score existed among different groups.ConclusionThe study presented that higher HEI-2015 was related to a lower AAC score and decreased risk of having AAC, indicating that greater compliance with 2015–2020 DGA, assessed by HEI-2015, might be beneficial for preventing vascular calcification and CVD among US adults

Gas foaming of electrospun poly(L-lactide-co-caprolactone)/silk fibroin nanofiber scaffolds to promote cellular infiltration and tissue regeneration

Electrospun nanofibers emulate extracellular matrix (ECM) morphology and architecture; however, small pore size and tightly-packed fibers impede their translation in tissue engineering. Here we exploited in situ gas foaming to afford three-dimensional (3D) poly(L-lactide-co-ε-caprolactone)/silk fibroin (PLCL/SF) scaffolds, which exhibited nanotopographic cues and a multilayered structure. The addition of SF improved the hydro philicity and biocompatibility of 3D PLCL scaffolds. Three-dimensional scaffolds exhibited larger pore size (38.75 ± 9.78 μm2 ) and high porosity (87.1% ± 1.5%) than that of their 2D counterparts. 3D scaffolds also improved the deposition of ECM components and neo-vessel regeneration as well as exhibited more numbers of CD163+/ CCR7+ cells after 2 weeks implantation in a subcutaneous model. Collectively, 3D PLCL/SF scaffolds have broad implications for regenerative medicine and tissue engineering applications.info:eu-repo/semantics/publishedVersio

Positive impact of a tower inlet cover on natural draft dry cooling towers under crosswind conditions

This study proposes a tower inlet cover to improve the performance of the small natural draft dry cooling tower (NDDCT) under crosswind conditions. CFD analyses are performed on a small NDDCT with tower inlet covers of different lengths, and the CFD model is validated against experimental results. The air temperature, air pressure, air flow and heat flux fields are presented, and the thermal performance for each heat exchanger and the NDDCT are obtained using CFD simulations. The CFD simulation results show that the high-pressure zone around the tower side wall, formed by the crosswind, causes the decrease in air flow through the tower and the deterioration in tower performance with a crosswind. The tower inlet cover can improve the tower performance in crosswinds by increasing the air flow of the heat exchangers. Tower inlet covers with lengths of 1.5 m, 3 m and 4.5 m improve the tower heat load by 40–65%, 70–130% and 85–230%, respectively, when the crosswind increases from 4 m/s to 12 m/s

Functional and postoperative outcomes after high-intensity interval training in lung cancer patients: A systematic review and meta-analysis

ObjectiveThe study evaluated the effects of high-intensity interval training (HIIT) on postoperative complications and lung function in patients with lung cancer compared to usual care.MethodsWe searched electronic databases in April 2022, including PubMed, Embase, the Cochrane Library, Web of Science, and the China National Knowledge Infrastructure (CNKI). Two authors independently applied the Cochrane Risk of Bias tool to assess the quality of RCTs. The postoperative complications, length of hospitalization, and cardiopulmonary functions from the studies were pooled for statistical analysis.ResultsA total of 12 randomized controlled trials were eligible for inclusion and were conducted in the meta-analysis. HIIT significantly increased VO2peak (MD = 2.65; 95% CI = 1.70 to 3.60; I2 = 40%; P <0.001) and FEV1 (MD = 0.12; 95% CI = 0.04 to 0.20; I2 = 51%; P = 0.003) compared with usual care. A subgroup analysis of studies that applied HIIT perioperatively showed significant improvement of HIIT on FEV1 (MD = 0.14; 95% CI = 0.08 to 0.20; I2 = 36%; P <0.0001). HIIT significantly reduced the incidence of postoperative atelectasis in lung cancer patients compared with usual care (RD = −0.16; 95% CI = −0.24 to −0.08; I2 = 24%; P <0.0001). There was no statistically significant effect of HIIT on postoperative arrhythmias (RD = −0.05; 95% CI = −0.13 to 0.03; I2 = 40%; P = 0.22), length of hospitalization (MD = −1.64; 95% CI = −3.29 to 0.01; P = 0.05), and the six-minute walk test (MD = 19.77; 95% CI = −15.25 to 54.80; P = 0.27) compared to usual care.ConclusionHIIT may enhance VO2peak and FEV1 in lung cancer patients and reduce the incidence of postoperative atelectasis. However, HIIT may not reduce the incidence of postoperative arrhythmia, shorten the length of hospitalization, or improve the exercise performance of patients with lung cancer.Systematic review registrationPROSPERO, CRD4202233544