Browse Articles

Investigating blood–brain barrier penetration and neurotoxicity of natural products for central nervous system drug development

Abstract Natural Products (NPs) are increasingly utilized worldwide for their potential therapeutic benefits, including central nervous system (CNS) disorders. Studies have shown açai berries mitigating Parkinson’s disease progression through dopaminergic neuroprotection via Nrf-2 HO-1 pathways. Ashwagandha, an evergreen shrub, has shown potential as a therapeutic for neurodegenerative disorders via axonal regeneration in Aβ25-35-treated cortical neurons in vitro. In most cases, promising NPs are tested using in vitro assays or simpler systems during the early stages of drug discovery. However, a critical challenge lies in the lack of data on blood-brain barrier (BBB) penetration, which is a significant determinant for the successful development of CNS drugs. Our first goal was to test our in-house NP constituent library via the Parallel Artificial Membrane Permeability Assay (PAMPA-BBB), with the aim of understanding their BBB-penetration potential. Of the constituents tested, 255 were found to have moderate to high BBB permeability. Our next goal was to understand if these compounds could exhibit CNS toxicity. Neuronal viability and neurite outgrowth assays were performed with this subset to identify compounds with neurotoxicity potential. Around 35% of compounds tested showed neurite outgrowth inhibition. The habitual and widespread consumption of NPs underscores the importance of subjecting this subset of compounds to additional testing and validation in vivo to ascertain their potential detrimental effects. Understanding BBB permeability and assessing neurotoxicity mechanisms of NPs will significantly benefit the CNS drug discovery community.

Design and dynamics analysis of three-degree-of-freedom kinematic mechanism for helicopter attitude simulation

Semaphorin 3F inhibits breast cancer metastasis by regulating the Akt-mTOR and TGFβ signaling pathways via neuropilin-2

Single-nucleus RNA sequencing uncovers metabolic dysregulation in the prefrontal cortex of major depressive disorder patients

High performance fake review detection using pretrained DeBERTa optimized with Monarch Butterfly paradigm

Abstract In this era of internet, e-commerce has grown tremendously and the customers are increasingly relying on reviews for product information. As these reviews influence the purchasing ability of the future customer, it can give a positive or negative impact on the businesses. The effectiveness of online reviews is compromised by fake reviews that provide false information about the product. Fake reviews can not only impact the reputation of the businesses but also involve financial losses. Thus, detection of fake reviews is essential to solve the problem for maintaining the integrity of online reviews. Existing Machine learning models often struggle with deep contextual understanding. Scaling machine learning models while maintaining accuracy and efficiency becomes increasingly challenging as the volume of online reviews continues to grow. Hence, this research work introduces a novel MBO-DeBERTa, a deep neural network with Monarch Butterfly Optimizer. The proposed model improves the capacity to differentiate between overlapping characteristics of fake and authentic reviews. MBO-DeBERTa attained a classification accuracy of 98% for detecting the fake reviews. The proposed framework is tested on three different datasets such as Amazon, Fake Review and Deceptive Opinion Spam containing 21000,40000 and 1600 reviews respectively which are publicly available in Kaggle. The proposed model also detects adversarial attacks using the Fast Gradient Sign Method (FGSM) and thereby evaluating its resistance to such attacks and noise. The proposed model was also tested on the unseen data of Myntra and Amazon verified customer reviews and our model works efficiently for real world data. Thus the results show that the suggested model outperforms the current models showing increased accuracy, precision, recall, F1 score and reduced loss rate.

Explainable handcrafted features for mitotic event detection and classification

Evaluating the comprehensive water resources utilization level in China: Dynamic distribution analysis and spatial convergence insights

This research provides an overview of the comprehensive water resources utilization level (CWRULE) in China, highlighting its significance in national water management. The study aims to evaluate performance and trends in CWRULE across various regions. Employing methods such as the Dagum Gini coefficient, spatial kernel density estimation, and spatial convergence models, the analysis explores regional disparities, distribution dynamics, and convergence trends. Key findings indicate that while national water resources management has improved annually, significant disparities persist between the coastal eastern and central regions versus the western and northeastern regions, where CWRULE indicators remain relatively low. Notably, the convergence speed in the central, western, and northeastern regions increases significantly after controlling for variables, showcasing the beneficial impacts of policy support, economic development, and technological advancements. In contrast, the eastern region exhibits weak convergence, underscoring the necessity for targeted strategies to enhance water resources management and efficiency.

Identifying KLF14 as a potential regulatory factor in liver regeneration trough transcriptomic and metabolomic

A voxel-based approach for simulating microbial decomposition in soil: Comparison with LBM and improvement of morphological models

This paper deals with the computational modeling of biological dynamics in soil using an exact micro-scale pore space description from 3D Computed Tomography (CT) images. Within this context, computational costs and storage requirements constitute critical factors for running simulations on large datasets over extended periods. In this research, we represent the pore space by a graph of voxels (Voxel Graph-Based Approach, VGA) and model transport in fully saturated conditions (two-phase system) using Fick’s law and coupled diffusion with biodegradation processes to simulate microbial decomposition in soil. To significantly decrease the computational time of our approach, the diffusion model is solved by means of Euler discretization schemes, along with parallelization strategies. We also tested several numerical strategies, including implicit, explicit, synchronous, and asynchronous schemes. To validate our VGA, we compare it with LBioS, a 3D model that integrates diffusion (via the Lattice Boltzmann method) with biodegradation, and Mosaic, a Pore Network Geometrical Modelling (PNGM) which represents the pore space using geometrical primitives. Our method yields result similar to those of LBioS in a quarter of the computing time. While slower than Mosaic, it is more accurate and requires no calibration. Additionally, we show that our approach can improve PNGM-based simulations by using a machine-learning approach to approximate diffusional conductance coefficients.

Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment

Abstract A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients with psoriasis taken in a clinical setting. A latent w-space vector was identified that allowed the degree of severity of the psoriasis in the generated images to be modified. A second latent w-space vector was identified that allowed the size of the psoriasis plaque to be modified and this was used to show the potential to alleviate bias in the training data. With appropriate training data, such an approach could see a future application in a clinical setting where a patient is able to observe a prediction for the appearance of their skin and associated skin condition under a range of treatments and after different time periods, hence allowing an informed and data-driven decision on optimal treatment to be determined.

Economic evaluation of diagnostic tests for Thai patients with tuberculosis: A dynamic transmission model approach

Conventional tuberculosis (TB) diagnosis is time-consuming, while newer molecular assays such as Xpert MTB/RIF and loop-mediated amplification test for TB (TB-LAMP) provide faster results but at a higher cost compared to sputum smear microscopy (SSM) with culture and drug susceptibility testing (DST) in Thailand. This study assessed the cost-utility of TB diagnostic algorithms as either initial or add-on tests from a societal perspective for TB diagnosis in the general Thai population. A dynamic transmission model was employed to evaluate five TB diagnostic algorithms over a 15-year period. Costs were calculated in 2023 Thai Baht, with results presented as incremental cost-effectiveness ratios (ICERs) compared to SSM with culture and DST. One-way and probability sensitivity analyses were conducted to assess parameter uncertainty. Compared to SSM with culture and DST, the ICER values (Baht per QALY gained) of TB-LAMP Add-On (3,563), Xpert MTB/RIF Add-On (3,670), and TB-LAMP Initial (6,429) indicated that these algorithms were cost-effective, while Xpert MTB/RIF Initial emerged as a cost-saving option. One-way sensitivity analysis results revealed that the utility of the first-line treatment exhibited the highest variability in ICERs, followed by the unit cost of Xpert MTB/RIF. The results supported the adoption of Xpert MTB/RIF as an initial test for the general Thai population. These findings provide evidence for policymakers to integrate molecular testing into Thailand’s Universal Coverage Scheme benefit package, aligning with national TB strategies to reduce TB incidence and mortality.

Ascertaining sustainability for affordable energy generation with non-renewable sources using computational intelligence algorithm

The putative forkhead transcription factor FhpA is necessary for development, aflatoxin production, and stress response in Aspergillus flavus

Forkhead transcription factors regulate several important biological processes in many eukaryotic species including fungi. Bioinformatic analysis of the Aspergillus flavus genome revealed four putative forkhead transcription factor genes. Genetic disruption of (AFLA_005634), a homolog of the Aspergillus nidulans fhpA/fkhA gene (AN4521), revealed that the fhpA gene is a negative regulator of both asexual spore production and aflatoxin B1 production in A. flavus. Furthermore, disruption of the fhpA gene caused a complete loss of sclerotial formation. Overexpression of the fhpA gene caused A. flavus to become more sensitive to sodium chloride whereas disruption of the fhpA gene did not change the ability of A. flavus to respond to any osmotic stress agent tested. Interestingly, both disruption and overexpression of the fhpA gene led to increases in sensitivity to the oxidative stress agent menadione. Overall, these results suggest that fhpA is an important regulator of morphological and chemical development in addition to stress response in A. flavus.

Efficacy of azathioprine in reducing recurrence in idiopathic granulomatous mastitis

Understanding the early molecular changes associated with radiation therapy—A preliminary bulk RNA sequencing study

Introduction Cancer is the second leading cause of death in the United States, with breast cancer being the most commonly diagnosed new cancer in women. Radiation therapy provides well-documented survival and recurrence benefits; however, it can lead to significant adverse effects, such as radiation-induced fibrosis (RIF), which can cause pain and result in poor aesthetic outcomes. The biological mechanisms underlying RIF are not entirely understood and require further investigation to identify potential intervention avenues. In this study, we investigated the biological response to radiation therapy by analyzing non-irradiated and irradiated tissues from breast cancer patients. Materials and methods We collected tissue from breast cancer patients who underwent unilateral radiation and bilateral breast reconstruction. At the time of final reconstruction (post-radiation), samples were collected from both non-irradiated and irradiated reconstruction sites. These samples were analyzed using bulk RNA sequencing, histology, and immunohistochemistry (IHC). Results In fibrous tissue capsules, CLCA2, COL4A5, and COL6A6 were differentially expressed and may be related to reduced micro-vascularization. CXCL9 and PTCHD4 were upregulated within the skin, possibly conferring an increased immune response, while multiple keratin-related genes (KRT6B, KRT17, KRT25, KRT28, and KRT75) were downregulated. In irradiated muscle tissue, there was increased expression of CXCL10 and downregulation of DCD. These results were confirmed using IHC. Conclusions This study highlights the utility of bulk RNA sequencing studies in conjunction with IHC to identify target genes and biological processes responsible for RIF in tissues at final breast reconstruction. Due to the sample size limitation, further research is warranted to understand the role of keratin and collagen genes in regulating epidermal changes, vascularity, and fibrosis.

Cerebrospinal fluid metabolomics, lipidomics and serine pathway dysfunction in myalgic encephalomyelitis/chronic fatigue syndroome (ME/CFS)

Expanding the fluorescent toolkit: Blue fluorescent protein-expressing Plasmodium berghei for enhanced multiplex microscopy

Fluorescent proteins are widely used as markers to differentiate genetically modified cells from their wild-type counterparts. In malaria research, the prevalent fluorescent markers include red fluorescent proteins (RFPs) and their derivatives, such as mCherry, along with green fluorescent proteins (GFPs) and their derivatives. Recognizing the need for additional fluorescent markers to facilitate multiplexed imaging, this study introduced parasite lines expressing blue fluorescent protein (BFP). These lines enable simultaneous microscopy studies of proteins tagged with GFP, RFP, or detected by fluorophore-labeled antibodies, enhancing the analysis of complex biological interactions. Expression of BFP throughout the parasite’s life cycle was driven by the robust Hsp70 promoter, ensuring stable, detectable protein levels suitable for fluorescent light analysis methods, including flow cytometry and fluorescent microscopy. We generated two Plasmodium berghei (P. berghei) lines expressing cytosolic BFP through double crossover homologous recombination targeting the silent 230p locus: eBFP2 (PbeBFP2) and mTagBFP2 (PbmTagBFP2). We compared these transgenic lines to established mCherry-expressing parasites PbmCherryHsp70 (PbmCherry) across their life cycles. The PbmTagBFP2 parasites exhibited fluorescence approximately 4.5 times brighter than the PbeBFP2 parasites in most life cycle stages. Both BFP-expressing lines developed normally through the entire parasite life cycle, offering a valuable expansion to the toolkit for studying Plasmodium biology at the host-pathogen interface.

Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control

Reported severity of psychotic, depressive and anxiety symptoms in relation to bilingual language profile: An exploratory study and the validation of Basque versions of the PQ-B, DASS-42, PHQ-9 and GAD-7

Background Language plays a crucial role in health care and especially in mental health, since the use of the native language helps to make a good diagnosis as several studies have shown. Aim We studied the influence of language on the accurate detection of psychotic and affective symptoms, exploring differences in the severity of reported symptomatology in a bilingual Basque-Spanish population. Methods The study uses the Prodromal Questionnaire-Brief for the detection of psychosis and the Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, and Depression, Anxiety and Stress Scale-42 for the assessment of stress, anxiety and depression. Basque versions of the scales were developed and their psychometric properties were evaluated in a sample of 623 individuals, including 521 from the general population and 102 psychiatric patients. Possible relations between questionnaire scores and four linguistic factors, namely first language (L1), proficiency, age of acquisition and language exposure, were examined. Results The four translated questionnaires showed adequate sensitivity, goodness-of-fit, and reliability indices, thus validating their suitability for general and clinical settings. The results showed that reporting of depressive symptoms seemed to be modulated by linguistic variables, mainly L1, whereas the severity of psychotic symptoms was less reliably associated with the gathered linguistic factors. Conclusions Overall, our results suggest that language of assessment by means of written instruments may have a limited impact on healthcare outcomes in balanced bilingual populations. The study enriches the understanding by considering various linguistic factors beyond L1, and by exploring the effect of these factors on affective symptoms, apart from psychotic ones.

Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity

Abstract Despite few studies focusing on the OAZ2 gene in colorectal cancer, its potential role in colon adenocarcinoma (COAD) prognosis and immune modulation remains underexplored. This study examines the expression and mechanistic involvement of OAZ2 in COAD using data from The Cancer Genome Atlas (TCGA) and additional laboratory experiments. We employed uni- and multivariate Cox hazard regression analyses to evaluate its prognostic significance and gene set enrichment analysis (GSEA) to identify related signaling pathways. Our findings demonstrate significantly lower OAZ2 expression in COAD tissues compared to normal counterparts (P < 0.05) and establish its value as an independent prognostic indicator (P < 0.05). Laboratory experiments further revealed that the protein and mRNA levels of OAZ2 are significantly diminished in COAD compared to adjacent normal tissues, while its antagonist AZIN2 shows elevated expression, suggesting a competitive interaction that may regulate tumor behavior. Overexpression of OAZ2 in RKO colorectal cancer cells significantly reduced their proliferation rate and impaired migration, confirming the functional impact of OAZ2 dysregulation in COAD. Gene Set Enrichment Analysis (GSEA) highlighted the involvement of OAZ2 in cardiac muscle contraction and oxidative phosphorylation pathways. Additionally, OAZ2’s association with immune features such as tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltration underscores its integral role in the tumor microenvironment. These comprehensive findings position OAZ2 as a promising biomarker for COAD prognosis and a potential target for therapeutic intervention, with evidence supporting its regulatory effects on cell dynamics and tumor aggressiveness.