Browse Articles

Diagnosing malignant transformation potential in oral submucous fibrosis with leukoplakia: an approach utilizing deoxyribonucleic acid image cytometry

Establishing a robust genetic sequencing and gene expression data library in cardiovascularly healthy cats

Abstract Cardiovascular disease is a leading cause of increased morbidity and mortality in cats, with hypertrophic cardiomyopathy (HCM) significantly overrepresented. While feline HCM is hereditary, the genetic etiology of disease remains poorly understood. Establishing a cohort of well-phenotyped and -genotyped healthy control cats is essential to fuel future genetic/pharmacogenetic discoveries. We sought to construct a robust genetic sequencing and gene expression library from cardiovascularly healthy cats using whole genome sequencing (WGS) and RNA sequencing (RNA-Seq). Fifty-four client-owned cats ( $$\ge$$  10 years) were screened, of which 18 cats (cohort 1) were prospectively enrolled after being deemed cardiovascularly healthy by clinicopathology, biochemistry, and echocardiography. DNA isolated from blood samples was submitted for paired-end WGS at ~ 30X coverage. Standard pipelines were employed for variant calling across sequenced cats. A second cohort of 15 purpose-bred cats were euthanized for non-cardiac reasons. Flash-frozen left ventricular (LV), interventricular septum (IVS), and left atrium (LA) tissues from 11, 14, and 13 cats, respectively, were submitted for stranded mature RNA-Seq at 50 million reads/sample. Gene variants and expression profiling were catalogued for both meticulously selected cohorts. Transcriptomic and WGS data libraries were generated to serve as an open-access resource in future investigations of feline cardiovascular precision medicine.

Pre-exposure prophylaxis knowledge as a mediator between eligibility and intention to use among voluntary counselling and testing clients, Thailand

Spatially varying polarization of guided fundamental mode in a titanium indiffused periodically poled lithium niobate waveguide

Nesting niche partitioning between two sympatrically breeding Chlidonias Tern species revealed by remote sensing

Abstract Sympatrically breeding avian species may have similar environmental requirements for nesting sites, resulting in interspecific competition. It may be reduced by partitioning resources in space or/and time, allowing relatively stable coexistence in the shared habitat. Here, we investigated nesting niches of sympatrically breeding, Black Terns (BT, Chlidonias niger) and Whiskered Terns (WT, Chlidonias hybrida) in Druzno Lake (Poland) in 2024. We compared nesting site proximity characteristics between both studied species at two different spatial scales using remotely sensed from drone surveys: (contribution of water and vegetation) and indices derived from satellite imagery: Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation density, and Normalized Difference Water Index (NDWI) as a proxy for open water contribution. Both species partitioned location (they bred in separate locations) and resources within common breeding ground. BT’s nests were situated significantly closer to the lake shore (mean ± SD 102.25 ± 62.47 m) and further away from each other (mean ± SD 22.22 ± 14.81 m) compared to WTs (mean ± SD 257.24 ± 109.64 m & 11.24 ± 10.67 m, respectively). BTs, unlike WTs prefer to nest in areas with higher open water contribution and lower vegetation density. The nesting niches of BT were wider than those of WT, suggesting that BT use more diversified habitats for breeding.

A robust resilient-oriented design for the cyber-physical distribution system against sequential typhoons

Trade-offs between agricultural production and ecosystem services under different land management scenarios in the Loess Plateau of China

Artificial sweeteners differentially activate sweet and bitter gustatory neurons in Drosophila

Trophic behavior and parasite communities in kelp gulls from the northern Patagonian coast, Argentina

Multiomics integration prioritizes potential drug targets for multiple sclerosis

Multiple sclerosis (MS) is an immune-mediated disease with no current cure. Drug discovery and repurposing are essential to enhance treatment efficacy and safety. We utilized summary statistics for protein quantitative trait loci (pQTL) of 2,004 plasma and 1,443 brain proteins, a genome-wide association study of MS susceptibility with 14,802 cases and 26,703 controls, both bulk and cell-type specific transcriptome data, and external pQTL data in blood and brain. Our integrative analysis included a proteome-wide association study to identify MS-associated proteins, followed by summary-data-based Mendelian randomization to determine potential causal associations. We used the HEIDI test and Bayesian colocalization analysis to distinguish pleiotropy from linkage. Proteins passing all analyses were prioritized as potential drug targets. We further conducted pathway annotations and protein–protein interaction network analysis (PPI) and verified our findings at mRNA and protein levels. We tested hundreds of MS-associated proteins and confirmed 18 potential causal proteins (nine in plasma and nine in brain). Among these, we found 78 annotated pathways and 16 existing non-MS drugs targeting six proteins. We also identified intricateAQ PPIs among seven potential drug targets and 19 existing MS drug targets, as well as PPIs of four targets across plasma and brain. We identified two targets using bulk mRNA expression data and four targets expressed in MS-related cell types. We finally verified 10 targets using external pQTL data. We prioritized 18 potential drug targets in plasma and brain, elucidating the underlying pathology and providing evidence for potential drug discovery and repurposing in MS.

Human cancer-targeted immunity via transgenic hematopoietic stem cell progeny

Abstract Adoptive transfer of genetically engineered T cells expressing a tumor-antigen-specific transgenic T cell receptor (TCR) can result in clinical responses in a variety of malignancies. However, these responses are frequently short-lived, and patients typically relapse within several months. This phenomenon is largely due to poor persistence of the transgenic T cells, as well as a progressive loss of their functionality and terminal differentiation in vivo. This underscores the need for cell therapy approaches able to sustain the initial antitumor efficacy and lead to long-term antitumor efficacy. Herein, we report the use of tandem cell therapies involving autologous T cells and hematopoietic stem cells engineered to express the NY-ESO-1 TCR for the treatment of solid tumors in a first-in-human phase I clinical trial (NCT03240861). This therapy is shown to be safe, feasible, and leads to initial tumor regression activity. T cell progeny from the HSC progenitors is shown to provide circulating transgenic NY-ESO-1 TCR-T cells, which display tumor-antigen-specific antitumor functionality, without any evidence of anergy or exhaustion. These results demonstrate the utility of transgenic HSCs to generate a self-renewing source of tumor-specific cellular immunotherapy in human participants. Clinicaltrials.gov: NCT NCT03240861

Fast growth and high-titer bioproduction from renewable formate via metal-dependent formate dehydrogenase in Escherichia coli

Abstract Microbial bioproduction using one-carbon (C1) feedstocks has the potential to decarbonize the manufacturing of materials, fuels, and chemicals. Formate is a promising C1 feedstock, and the realization of industrial, formatotrophic platform organisms is a key goal for C1-based bioproduction. So far, a major limitation for synthetic formatotrophy has been slow energy supply due to slow formate dehydrogenase activity. Here, we implement a fast, metal-dependent formate dehydrogenase complex in a synthetic formatotrophic Escherichia coli utilizing the reductive glycine pathway. After a short-term evolution, we demonstrate formatotrophic growth of E. coli with a doubling time of less than 4.5 h, comparable to the fastest natural formatotrophs. To further explore the potential of a formate-based bioeconomy, this strain is engineered to produce mevalonate, as well as the terpenoid and aviation fuel precursor isoprenol, using formate we generate directly from the electrochemical reduction of CO2. This work demonstrates an improvement in bioproduct titer from formate, achieving the production of 3.8 g/L of mevalonate. Additionally, the abundant and recalcitrant polymer lignin is chemically decomposed into a formate-rich mixture of small organic acids and subsequently bioconverted into mevalonate. Overall, the described fast-growing, formatotrophic bioproduction strain demonstrates that a sustainable formate bioeconomy is within reach.

Author Correction: Base-excision repair pathway shapes 5-methylcytosine deamination signatures in pan-cancer genomes

Microbial dynamics in rice ecosystem under supplementation of organic sources of nitrogen with inorganics and their concomitant impact on yield

The BioSUD Biobank as a genomic resource for substance use disorders in Italy

Design and numerical evaluation of a high sensitivity plasmonic biosensor based on MISM nanoring for versatile virus detection

Smartphone addiction and its association with accident risk in Iranian adults

A novel S-scheme heterojunction photocatalyst, Yb6Te5O19.2/g-C3N4: synthesis, characterization, photocatalytic activity, and mechanism

When the crowd gets it wrong – the limits of collective wisdom in machine learning

Abstract This study examines collective decision-making dynamics using a machine learning framework, drawing parallels between a previously established synthetic population model and a newly introduced ensemble machine learning counterpart. Grounded in the “wisdom of crowds” principle, the research explores scenarios where the accuracy of group decisions may unexpectedly decrease as group size increases, particularly when individuals share highly correlated information. By replicating these conditions with machine learning ensembles, such as decision trees and support vector machines, the study identifies circumstances where collective accuracy declines, challenging the assumption that larger groups inherently make better decisions. The findings reveal the limitations of collective models in machine learning and provide valuable insights for data-scarce environments.

Synergistic antimicrobial and probiotic activity of lactic acid bacteria isolated from Tarkhineh against Candida albicans