Experimental Biology and Medicine最新文献

Pharmacogenomics (PGx) holds the promise of personalizing medical treatments based on individual genetic profiles, thereby enhancing drug efficacy and safety. However, the current landscape of PGx research is hindered by fragmented data sources, time-consuming manual data extraction processes, and the need for comprehensive and up-to-date information. This study aims to address these challenges by evaluating the ability of Large Language Models (LLMs), specifically Llama3.1-70B, to automate and improve the accuracy of PGx information extraction from the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling (FDA PGx Biomarker table), which is well-structured with drug names, biomarkers, therapeutic area, and related labeling texts. Our primary goal was to test the feasibility of LLMs in streamlining PGx data extraction, as an alternative to traditional, labor-intensive approaches. Llama3.1-70B achieved 91.4% accuracy in identifying drug-biomarker pairs from single labeling texts and 82% from mixed texts, with over 85% consistency in aligning extracted PGx categories from FDA PGx Biomarker table and relevant scientific abstracts, demonstrating its effectiveness for PGx data extraction. By integrating data from diverse sources, including scientific abstracts, this approach can support pharmacologists, regulatory bodies, and healthcare researchers in updating PGx resources more efficiently, making critical information more accessible for applications in personalized medicine. In addition, this approach shows potential of discovering novel PGx information, particularly of underrepresented minority ethnic groups. This study highlights the ability of LLMs to enhance the efficiency and completeness of PGx research, thus laying a foundation for advancements in personalized medicine by ensuring that drug therapies are tailored to the genetic profiles of diverse populations.

Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresentation of diverse strabismus types and reliance on single-source data remain prevalent. Emphasizing the need for inclusive AI systems, future research should focus on expanding AI capabilities with large model technologies, integrating multimodal data to bridge existing gaps, and developing integrated management platforms to better accommodate diverse patient demographics and clinical scenarios.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by progressive cognitive decline. Genetic factors have been implicated in disease susceptibility as its etiology remains multifactorial. The CD33 and the HLA-DRB1 genes, involved in immune responses, have emerged as potential candidates influencing AD risk. In this study, 644 Lebanese individuals, including 127 AD patients and 250 controls, were genotyped, by KASP assay, for six SNPs selected from the largest GWAS study in 2021. Logistic regression analysis assessed the association between SNP genotypes and AD risk, adjusting for potential confounders. Among the six SNPs analyzed, rs1846190G>A in HLA-DRB1 and rs1354106T>G in CD33 showed significant associations with AD risk in the Lebanese population (p < 0.05). Carriers of the AG and AA genotypes of rs1846190 in HLA-DRB1 exhibited a protective effect against AD (AG: OR = 0.042, p = 0.026; AA: OR = 0.052, p = 0.031). The GT genotype of rs1354106T>G in CD33 was also associated with reduced risk (OR = 0.173, p = 0.005). Following Bonferroni correction, a significant correlation of rs1354106T > G with AD risk was established. Our results might highlight the complex interplay between genetic and immunological factors contributing to the development of the disease.

Secondary lymphedema is a chronic disease associated with deformity of limbs and dysfunction; however, conventional therapies are not curative. Adipose-derived stem cells (ADSCs) based therapy is a promising way, but a single transplantation of ADSCs has limited efficacy. In this study, ADSCs were engineered in vitro and then transplanted into the site of lymphedema. Yes-associated protein (YAP), a crucial regulator of Hippo pathway, plays an important role in regulating stem cell functions. We examined the YAP expression in a mouse tail lymphedema model, and found that transplanted ADSCs exhibited high expression level of YAP and a large number of YAP positive cells existed in lymphedema environment. In vitro, the downregulation of YAP in ADSCs resulted in higher expression levels of genes related to lymphangiogenesis such as Lyve-1, VEGFR-3 and Prox-1. In vivo, YAP-engineered ADSCs generated abundant VEGFR-3-positive lymphatic vessels and significantly improved subcutaneous fibrosis. These results indicated that the transplantation of pre-engineered ADSCs by manipulating YAP is a promising strategy for lymphatic reconstruction.

The focus of this study was to identify risk factors for severe and critical COVID-19, evaluate local respiratory immune responses to SARS-CoV-2 infection, and develop a prognostic tool for COVID-19 severity using accessible early indicators. Using nasopharyngeal swab samples from hospitalized patients with COVID-19 of varying severity during the first wave of the pandemic from March to May 2020 in Louisiana, we evaluated the association between COVID-19 severity and viral load, respiratory immune mediators, and demographic/clinical factors. We found that the SpO₂/FiO₂ ratio at triage, total comorbidity burden (represented by Charlson Comorbidity Index), and gender were significantly associated with COVID-19 severity. Using these early significant indicators, we developed a prognostic tool for COVID-19 severity that is simple and convenient. Additionally, our study demonstrated that elevated levels of respiratory immune mediators, including IL-10, IL-6, MCP-1, and MCP-3, were significantly associated with COVID-19 severity. We also found that viral load at the time of admission was associated with disease severity. Our findings highlight the feasibility and importance of evaluating the humoral component of local mucosal immune responses and viral load at the infected site using convenient nasopharyngeal swab samples, which could be an effective method to understand the relationship between viral infection and immune responses at the early stages of infection. Our proposed prognostic tool has the potential to be useful for COVID-19 management in clinical settings, as it utilizes accessible and easy-to-collect variables at the time of admission.

Cognitive deficit is a debilitating complication of sickle cell disease (SCD), with a multifactorial etiopathogenesis. Here we show that neuroinflammation and dysregulation in lipidomics and transcriptomics profiles are major underlying mechanisms of social stress-induced cognitive deficit in SCD. Male Townes sickle cell (SS) mice and controls (AA) were exposed to social stress using the repeat social defeat (RSD) paradigm concurrently with or without treatment with minocycline. Mice were tested for cognitive deficit using novel object recognition and fear conditioning tests. SS mice exposed to RSD without treatment had worse performance on cognitive tests compared to SS mice exposed to RSD with treatment or to AA controls, irrespective of their RSD or treatment disposition. Additionally, compared to SS mice exposed to RSD with treatment, SS mice exposed to RSD without treatment had significantly more cellular evidence of neuroinflammation coupled with a significant shift in the differentiation of neural progenitor cells towards astrogliogenesis. Additionally, brain tissue from SS mice exposed to RSD was significantly enriched for genes associated with blood-brain barrier dysfunction, neuron excitotoxicity, inflammation, and significant dysregulation in sphingolipids important to neuronal cell processes. We demonstrate in this study that social stress induces cognitive deficit in SS mice, concurrently with neuroinflammation and lipid dysregulation.

This review outlines some of the many approaches taken over a decade or more to repair damaged hearts. We showcase the recent breakthroughs in organ regeneration elicited by reprogramming factors OCT3/4, SOX2, KLF4, and C-MYC (OKSM). Transient OKSM transgene expression rejuvenated senescent organs in mice. OKSM transgenes also caused murine heart cell regeneration. A triplet alanine mutation of the N-terminus of Serum Response Factor's MADS box SRF153(A3), termed STEMIN, and the YAP mutant, YAP5SA synergized and activated OKSM and NANOG in adult rat cardiac myocytes; thus, causing rapid nuclear proliferation and blocked myocyte differentiation. In addition, ATAC seq showed induced expression of growth factor genes FGFs, BMPs, Notchs, IGFs, JAK, STATs and non-canonical Wnts. Injected STEMIN and YAP5SA synthetic modifying mRNA (mmRNA) into infarcted adult mouse hearts, brought damaged hearts back to near normal contractility without severe fibrosis. Thus, STEMIN and YAP5SA mmRNA may exert additional regenerative potential than OKSM alone for treating heart diseases.

Fructose and lactate are present in high concentrations in uterine luminal fluid, fetal fluids and fetal blood of ungulates and cetaceans, but their roles have been ignored and they have been considered waste products of pregnancy. This review provides evidence for key roles of both fructose and lactate in support of key metabolic pathways required for growth and development of fetal-placental tissues, implantation and placentation. The uterus and placenta of ungulates convert glucose to fructose via the polyol pathway. Fructose is sequestered within the uterus and cannot be transported back into the maternal circulation. Fructose is phosphorylated by ketohexokinase to fructose-1-PO4 (F1P) by that is metabolized via the fructolysis pathway to yield dihydoxyacetone phosphate and glyceraldehyde-3-PO4 that are downstream of phosphofructokinase. Thus, there is no inhibition of the fructolysis pathway by low pH, citrate or ATP which allows F1P to continuously generate substrates for the pentose cycle, hexosamine biosynthesis pathway, one-carbon metabolism and tricarboxylic acid cycle, as well as lactate. Lactate sustains the activity of hypoxia-inducible factor alpha and its downstream targets such as vascular endothelial growth factor to increase utero-placental blood flow critical to growth and development of the fetal-placental tissues and a successful outcome of pregnancy. Pregnancy has been referred to as a controlled cancer and this review addresses similarities regarding metabolic aspects of tumors and the placenta.

Prolonged exposure to volatile anesthetics may raise the risk of developing cognitive impairment by acting on gamma-a Aminobutyric acid A receptors (GABAAR). The dentate gyrus plays an important role in the hippocampus and has a high potential for neural plasticity. However, it is unknown whether prolonged anesthesia induces a change in acute phasic or tonic inhibition in dentate gyrus granule cells (DGGCs) by acting on GABAAR. In order to verify the effects of volatile anesthetics on the current in DGGCs, a whole-cell patch was applied to record acute brain slices, and this study indicated that 4 h but not 2 h of isoflurane (ISO) exposure induced significantly larger tonic currents in DGGCs other than hippocampal CA1 pyramidal and thalamic relay neurons. Furthermore, this study demonstrated that the increased tonic current in DGGCs was dependent on the δ subunit-containing GABAARs by using transgenic δ subunit knockout mice. In conclusion, the δ subunit specific GABAAR is the key element that increased acute tonic inhibition in DGGCs of mice after prolonged ISO exposure, which may be one of the mechanisms of ISO neurotoxicity to the developing brain.

This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status. Validation involved quality and quantitative metrics, comparing translated OCTA (TR-OCTA) with ground truth OCTAs (GT-OCTA) to assess the feasibility for objective disease diagnosis. In our study, TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution and contrast quality, moderate structural similarity compared to GT-OCTAs). Vascular features like tortuosity and vessel perimeter index exhibits more consistent trends compared to density features which are affected by local vascular distortions. For the validation dataset (UIC), the metrics show similar trend with a slightly decreased performance since the model training was blind on UIC data, to evaluate inference performance. Overall, this study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. By making detailed vascular imaging more widely accessible and reducing reliance on expensive OCTA equipment, this research has the potential to significantly enhance the diagnostic process for retinal diseases.