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At least 271 records · Page 15

Evaluation of DNA Vaccine Candidates against Foot-and-Mouth Disease Virus in Cattle

We evaluated four DNA vaccine candidates for their ability to produce virus-like particles (VLPs) and elicit a protective immune response against Foot-and-mouth disease virus (FMDV) in cattle. Two traditional DNA plasmids and two DNA minicircle constructs were evaluated. Both the pTarget O1P1-3C plasmid and O1P1-3C minicircle encoded a wild-type FMDV 3C protease to process the P1-2A polypeptide, whereas the O1P1-HIV-3CT minicircle used an HIV-1 ribosomal frameshift to down-regulate expression of a mutant 3C protease. A modified pTarget plasmid with a reduced backbone size, mpTarget O1P1-3C LT , used a 3C protease containing two mutations reported to enhance expression. All constructs produced mature FMDV P1 cleavage products in transfected cells, as seen by western blot analysis. Three constructs, O1P1-3C minicircles, pTarget O1P1-3C, and mpTarget O1P1-3C LT plasmids, produced intracellular VLP crystalline arrays detected by electron microscopy. Despite VLP formation in vitro, none of the DNA vaccine candidates elicited protection from clinical disease when administered independently. Administration of pTarget O1P1-3C plasmid enhanced neutralizing antibody titers when used as a priming dose prior to administration of a conditionally licensed adenovirus-vectored FMD vaccine. Further work is needed to develop these DNA plasmid-based constructs into standalone FMD vaccines in cattle.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

The Evolution of Randomized Clinical Trial Designs to Assess Therapeutics in Alzheimer Disease

Importance The success of recent randomized clinical trials (RCTs) for Alzheimer disease (AD), particularly those focusing on anti-amyloid therapies, has been discussed at length. However, the evolution of RCT design features for AD that preceded this success remain underexplored. Objective To describe temporal changes in the features of RCT design for interventions in AD. Evidence Review PubMed, Scopus, and Web of Science databases were searched in January 2025 for phase 2 and 3 AD RCTs published between January 1992 and December 2024. RCTs that investigated an intervention for AD, with a placebo or standard-of-care control group, were included. Four assessors independently reviewed full-text articles to capture study characteristics. Main Outcomes and Measures The number of participants and the duration of RCTs as well as the target population, outcomes, and funding were extracted from published reports. These features were analyzed with respect to time using linear regression and χ 2 analyses. Results The study included 203 RCTs with 79 589 participants testing interventions in AD. From 1992 to 2024, the mean sample size increased by 464% for phase 2 RCTs (from 42 to 237), and 50% for phase 3 RCTs (from 632 to 951), while the mean trial duration increased by 188% (from 16 to 46 weeks) for phase 2, and 256% (from 20 to 71 weeks) for phase 3 RCTs. This longer duration of RCTs may be partially attributed by a greater share of disease-modifying rather than symptomatic treatments. Similarly, more recent trials required AD biomarker evidence for enrollment (from 1 of 36 [2.7%] before 2006 to 40 of 76 [52.6%] since 2019). A substantial difference in the type of therapeutics researched was observed, with anti-amyloid and anti-tau RCTs being more likely to be funded by the pharmaceutical industry compared with neurotransmitter or other RCTs (anti-amyloid or anti-tau, 68 of 71 [95.8%]; neurotransmitter, 52 of 69 [77.6%]; other, 33 of 52 [63.5%]). RCT transparency improved, with more frequent data accessibility statements, registered reports, and better reporting on race and ethnicity. Conclusions and Relevance This methodology research of AD RCTs highlights substantial changes in key features of AD clinical trials from 1992 to 2024. AD RCTs have become larger and longer, such that they are powered to detect smaller clinical differences. The increased sample sizes and duration should enable the detection of smaller and more slowly occurring outcomes, which may lead to successful RCTs of therapies with slower and more subtle efficacy.

General & Internal Medicine↗

Assessment of Demographic, Genetic, and Imaging Variables Associated With Brain Resilience and Cognitive Resilience to Pathological Tau in Patients With Alzheimer Disease

Importance: Better understanding is needed of the degree to which individuals tolerate Alzheimer disease (AD)-like pathological tau with respect to brain structure (brain resilience) and cognition (cognitive resilience). Objective: To examine the demographic (age, sex, and educational level), genetic (APOE-ε4 status), and neuroimaging (white matter hyperintensities and cortical thickness) factors associated with interindividual differences in brain and cognitive resilience to tau positron emission tomography (PET) load and to changes in global cognition over time. Design, setting, an participants: In this cross-sectional, longitudinal study, tau PET was performed from June 1, 2014, to November 30, 2017, and global cognition monitored for a mean [SD] interval of 2.0 [1.8] years at 3 dementia centers in South Korea, Sweden, and the United States. The study included amyloid-β-positive participants with mild cognitive impairment or AD dementia. Data analysis was performed from October 26, 2018, to December 11, 2019. Exposures: Standard dementia screening, cognitive testing, brain magnetic resonance imaging, amyloid-β PET and cerebrospinal fluid analysis, and flortaucipir (tau) labeled with fluor-18 (18F) PET. Main outcomes and measures: Separate linear regression models were performed between whole cortex [18F]flortaucipir uptake and cortical thickness, and standardized residuals were used to obtain a measure of brain resilience. The same procedure was performed for whole cortex [18F]flortaucipir uptake vs Mini-Mental State Examination (MMSE) as a measure of cognitive resilience. Bivariate and multivariable linear regression models were conducted with age, sex, educational level, APOE-ε4 status, white matter hyperintensity volumes, and cortical thickness as independent variables and brain and cognitive resilience measures as dependent variables. Linear mixed models were performed to examine whether changes in MMSE scores over time differed as a function of a combined brain and cognitive resilience variable. Results: A total of 260 participants (145 [55.8%] female; mean [SD] age, 69.2 [9.5] years; mean [SD] MMSE score, 21.9 [5.5]) were included in the study. In multivariable models, women (standardized β = -0.15, P = .02) and young patients (standardized β = -0.20, P = .006) had greater brain resilience to pathological tau. Higher educational level (standardized β = 0.23, P < .001) and global cortical thickness (standardized β = 0.23, P < .001) were associated with greater cognitive resilience to pathological tau. Linear mixed models indicated a significant interaction of brain resilience × cognitive resilience × time on MMSE (β [SE] = -0.235 [0.111], P = .03), with steepest slopes for individuals with both low brain and cognitive resilience. Conclusions and relevance: Results of this study suggest that women and young patients with AD have relative preservation of brain structure when exposed to neocortical pathological tau. Interindividual differences in resilience to pathological tau may be important to disease progression because participants with both low brain and cognitive resilience had the most rapid cognitive decline over time.

60 APPLIED LIFE SCIENCES↗

Plasma GFAP for populational enrichment of clinical trials in preclinical Alzheimer's disease

Abstract INTRODUCTION Cognitively unimpaired (CU) amyloid beta (Aβ)+ individuals with elevated plasma glial fibrillary acidic protein (GFAP) have an increased risk of Alzheimer's disease (AD)‐related progression. We tested the utility of plasma GFAP for population enrichment CU populations in clinical trials. METHODS We estimated longitudinal progression, effect size, and costs of hypothetical clinical trials designed to test an estimated 25% drug effect on reducing tau positron emission tomography (PET) accumulation in the medial temporal lobe (MTL) and temporal neocortical region (NEO‐T). RESULTS CU GFAP+/Aβ+ individuals present an increased annual rate of change and effect size in tau PET MTL and tau PET NEO‐T compared to the other groups. An enrichment strategy selecting CU GFAP+/Aβ+ individuals would require a smaller sample size (≈ 57% reduction) and fewer Aβ PET scans (≈ 74% reduction) than trials enriched with Aβ PET alone, reducing total clinical trial costs by up to 64%. DISCUSSION Our results suggest that clinical trials focusing on preclinical AD recruiting Aβ+ individuals with elevated GFAP levels would improve cost effectiveness. Highlights Cognitively unimpaired (CU) glial fibrillary acidic protein (GFAP)+/amyloid beta (Aβ)+ shows increased changes in tau positron emission tomography (PET) . CU GFAP+/Aβ+ enriched clinical trials require a reduced sample size compared to Aβ+ only. CU GFAP+/Aβ+ enrichment reduces Aβ PET scans required and costs. CU GFAP+/Aβ+ enrichment allows the selection of individuals at early stages of the Alzheimer's disease continuum.

Neurosciences & Neurology↗

Detection of a multi–disease biomarker in saliva with graphene field effect transistors

Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes, and Sjogren’s syndrome. However, the lack of a rapid, cheap, accurate, and easy-to-use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label-free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G-FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltage V D of the G-FETs, the magnitude of which depended on target concentration. These aptameric G-FET biosensors showed the binding affinity (K D ) of ~2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value of K D enabled us to achieve a detection range (10 pg/ml - 100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. In conclusion, the utilization of this label-free biosensor could facilitate the early stage identification of various diseases associated with changes in concentration of CAs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Dietary Sodium Butyrate Supplementation Attenuates the Detrimental Effects of High-Fat Diets on Growth Performance, Liver Health, and Disease Resistance in Grass Carp

Abstract An 8-week experiment was conducted to investigate the effects of sodium butyrate (NaBT) inclusion in high-fat (HF) diets on growth performance, liver health, and disease resistance in Grass Carp Ctenopharyngodon idella. Three diets (Control diet containing crude lipid at 58 g/kg, HF diet with 108-g/kg crude lipid, and NaBT diet with 108-g/kg crude lipid and 1-g/kg NaBT) were randomly assigned to nine tanks with 30 fish (9.50 ± 0.06 g) in each tank. After the feeding trial, disease resistance was assessed by injecting the fish with Aeromonas hydrophila. Compared to the Control diet group, the HF diet group showed lower specific growth rate, feed efficiency, and survival rate (15.7%) after the A. hydrophila challenge; significantly higher activity levels of alanine aminotransferase and aspartate aminotransferase in plasma; higher malondialdehyde content; higher messenger RNA (mRNA) expression of interleukin-8, cysteinyl aspartate specific protease (caspase) 9, and caspase 3; lower activity level of glutathione peroxidase; and lower mRNA expression of nuclear factor erythroid 2-related factor 2 in liver. However, the NaBT diet significantly increased fish growth performance and survival rate (39.7%) after the A. hydrophila challenge and reduced hepatic oxidative stress, inflammation, and apoptosis compared to the HF diet. In conclusion, NaBT can ameliorate the detrimental effects of HF diets on fish growth performance and fish health.

Gao, Shiyang↗

The direct and indirect effects of copper on vector-borne disease dynamics

Metal pollution is a growing concern that affects the health of humans and animals globally. Copper is an essential insect micronutrient required for respiration, pigmentation and oxidative stress protection but can also act as a potentially toxic trace element. While several studies have focused on the negative fitness effects of copper on the aquatic larvae of mosquitoes, the effects of larval copper exposure on adult mosquito fitness (i.e., survival and fecundity) and their ability to transmit parasites (i.e., vector competence) remains unclear. Here, using a well-studied model vector-parasite system, the mosquito Aedes aegypti and parasite Dirofilaria immitis, we show that sublethal copper exposure in larval mosquitoes alters adult female fecundity and vector competence. Specifically, mosquitoes exposed to copper had a hormetic fecundity response and mosquitoes exposed to 600 μg/L of copper had significantly fewer infective parasite larvae than control mosquitoes not exposed to copper. Therefore, exposure of mosquito larvae to copper levels far below EPA-mandated safe drinking water limits (1300 μg/L) can impact vector-borne disease dynamics not only by reducing mosquito abundance (through increased larval mortality), but also by reducing parasite transmission risk. Our results also demonstrated that larval copper is retained through metamorphosis to adulthood in mosquitoes, indicating that these insects could transfer copper from aquatic to terrestrial foodwebs, especially in urban areas where they are abundant. To our knowledge this is the first study to directly link metal exposure with vector competence (i.e., ability to transmit parasites) in any vector-parasite system. Additionally, it also demonstrates unequivocally that mosquitoes can transfer contaminants from aquatic to terrestrial ecosystems. These results have broad implications for public health because they directly linking contaminants and vector-borne disease dynamics, as well as linking mosquitoes and contaminant dynamics.

54 ENVIRONMENTAL SCIENCES↗

Clinician Attitudes to Using Low-Dose Radiation Therapy to Treat COVID-19 Lung Disease

Current treatments for coronavirus disease 2019 (COVID-19) lung disease have limited efficacy. Low-dose radiation therapy (LDRT) has received both interest and criticism as a potential treatment for this condition. In this qualitative study we explored clinicians’ perspectives to identify barriers to testing LDRT in clinical trials and implementing it in clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses, and their accuracy compared to existing diagnostic tools is not well characterized. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5213 previously published case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to seven LLMs, including four generalist models and three LLMs specialized for medical applications. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.

Reese, Justin T. [Lawrence Berkeley National Labor↗

Comprehensive characterization of protein–protein interactions perturbed by disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. Further, we experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanism of allosteric inhibition of human p97/VCP ATPase and its disease mutant by triazole inhibitors

Human p97 ATPase is crucial in various cellular processes, making it a target for inhibitors to treat cancers, neurological, and infectious diseases. Triazole allosteric p97 inhibitors have been demonstrated to match the efficacy of CB-5083, an ATP-competitive inhibitor, in cellular models. However, the mechanism is not well understood. This study systematically investigates the structures of new triazole inhibitors bound to both wild-type and disease mutant forms of p97 and measures their effects on function. These inhibitors bind at the interface of the D1 and D2 domains of each p97 subunit, shifting surrounding helices and altering the loop structures near the C-terminal α2 G helix to modulate domain-domain communications. A key structural moiety of the inhibitor affects the rotameric conformations of interacting side chains, indirectly modulating the N-terminal domain conformation in p97 R155H mutant. The differential effects of inhibitor binding to wild-type and mutant p97 provide insights into drug design with enhanced specificity, particularly for oncology applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Diagnosis of Alzheimer’s disease using plasma biomarkers adjusted to clinical probability

Abstract Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.

Cell Biology↗

Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease

Varicose veins represent a common cause of cardiovascular morbidity, with limited available medical therapies. Although varicose veins are heritable and epidemiologic studies have identified several candidate varicose vein risk factors, the molecular and genetic basis remains uncertain. Here we analyzed the contribution of common genetic variants to varicose veins using data from the Veterans Affairs Million Veteran Program and four other large biobanks. Among 49,765 individuals with varicose veins and 1,334,301 disease-free controls, we identified 139 risk loci. We identified genetic overlap between varicose veins, other vascular diseases and dozens of anthropometric factors. Using Mendelian randomization, we prioritized therapeutic targets via integration of proteomic and transcriptomic data. Finally, topological enrichment analyses confirmed the biologic roles of endothelial shear flow disruption, inflammation, vascular remodeling and angiogenesis. Further, these findings may facilitate future efforts to develop nonsurgical therapies for varicose veins.

59 BASIC BIOLOGICAL SCIENCES↗

Impact of Nutrition on the Gut Microbiota: Implications for Parkinson’s Disease

Abstract Parkinson’s disease (PD) is a multifactorial neurodegenerative disease that is characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta and by the anomalous accumulation of α-synuclein aggregates into Lewy bodies and Lewy neurites. Research suggests 2 distinct subtypes of PD: the brain-first subtype if the pathology arises from the brain and then spreads to the peripheral nervous system (PNS) and the body-first subtype, where the pathological process begins in the PNS and then spreads to the central nervous system. This review primarily focuses on the body-first subtype. The influence of the gut microbiota on the development of PD has been the subject of growing interest among researchers. It has been suggested that gut inflammation may be closely associated with pathogenesis in PD, therefore leading to the hypothesis that gut microbiota modulation could play a significant role in this process. Nutrition can influence gut health and alter the risk and progression of PD by altering inflammatory markers. This review provides an overview of recent research that correlates variations in gut microbiota composition between patients with PD and healthy individuals with the impact of certain nutrients and dietary patterns, including the Mediterranean diet, the Western diet, and the ketogenic diet. It explores how these diets influence gut microbiota composition and, consequently, the risk of PD. Last, it examines fecal transplantation and the use of prebiotics, probiotics, or synbiotics as potential therapeutic strategies to balance the gut microbiome, aiming to reduce the risk or delay the progression of PD.

Sobral, Joana (ORCID:0009000334922337)↗

Genome Assembly and Transcriptome of Colletotrichum sublineola CsGL1, a New Resource to Study Anthracnose Disease in Sorghum

Colletotrichum species are globally distributed and well known as members of a destructive phytopathogenic genus, causing the anthracnose disease in a wide variety of crops and fruits. Colletotrichum sublineola is the causal agent of the anthracnose disease in sorghum, causing losses of up to 50% in yield. Here, we used PacBio sequencing combined with RNA-seq to generate a chromosome-level assembly and annotation of the Colletotrichum sublineola strain CsGL1. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Biochemistry & Molecular Biology↗

A Metabolomics Assay to Diagnose Citrus Huanglongbing Disease and to Aid in Assessment of Treatments to Prevent or Cure Infection

Citrus greening disease, or Huanglongbing (HLB), has devastated citrus crops globally in recent years. The causal bacterium, ‘ Candidatus Liberibacter asiaticus’, presents a sampling issue for qPCR diagnostics and results in a high false negative rate. In this work, we compared six metabolomics assays to identify HLB-infected citrus trees from leaf tissue extracted from 30 control and 30 HLB-infected trees. A liquid chromatography-mass spectrometry-based assay was most accurate. A final partial least squares-discriminant analysis (PLS-DA) model was trained and validated on 690 leaf samples with corresponding qPCR measures from three citrus varieties (Rio Red grapefruit, Hamlin sweet orange, and Valencia sweet orange) from orchards in Florida and Texas. Trees were naturally infected with HLB transmitted by the insect vector Diaphorina citri. In a randomized validation set, the assay was 99.9% accurate to classify diseased from nondiseased samples. This model was applied to samples from trees receiving plant defense-inducer compounds or biological treatments to prevent or cure HLB infection. From two trials, HLB-related metabolite abundances and PLS-DA scores were tracked longitudinally and compared with those of control trees. We demonstrate how our assay can assess tree health and the efficacy of HLB treatments and conclude that no trialed treatment was efficacious.

Plant Sciences↗

Optimizing Individualized Treatment Planning for Parkinson’s Disease Using Deep Reinforcement Learning

More than one million people currently live with Parkinson's Disease (PD) in the U.S. alone. Medications, such as levodopa, can help manage PD symptoms. However, medication treatment planning is generally based on patient history and limited interaction between physicians and patients during office visits. This limits the extent of benefit that may be derived from the treatment as disease/patient characteristics are generally non-stationary. Wearable sensors that provide continuous monitoring of various symptoms, such as bradykinesia and dyskinesia, can enhance symptom management. However, using such data to overhaul the current static medication treatment planning approach and prescribe personalized medication timing and dosage that accounts for patient/care-giver/physician feedback/preferences remains an open question. We develop a model to prescribe timing and dosage of medications, given the motor fluctuation data collected using wearable sensors in real-time. We solve the resulting model using deep reinforcement learning (DRL). The prescribed policy determines the optimal treatment plan that minimizes patient's symptoms. Our results show that the model-prescribed policy outperforms the static a priori treatment plan in improving patients' symptoms, providing a proof-of-concept that DRL can augment medical decision making for treatment planning of chronic disease patients.

Watts, Jeremy↗