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MIP-3α-antigen fusion DNA vaccine enhances sex differences in tuberculosis model and alters dendritic cell activity early post vaccination
Abstract Tuberculosis disease (TB) caused by Mycobacterium tuberculosis (Mtb) bacteria remains a major cause of global morbidity and mortality. Efforts to control TB are hampered by the lengthy and cumbersome treatment required to eradicate the Mtb infection. Bacterial persistence during exposure to bactericidal antibiotics is at least partially mediated by the bacterial stringent response enzyme, RelMtb. A therapeutic DNA vaccine targeting RelMtb has been shown to increase the efficacy of antitubercular drugs, and fusing macrophage-inflammatory protein 3α (MIP-3α), which interacts with CCR6 on immature dendritic cells (iDCs), to RelMtb further increases the vaccine’s therapeutic efficacy. A secondary analysis of these prior studies elucidated prominent sex-based differences in vaccine therapeutic efficacy, with female mice showing improved microbial outcomes compared to males as a result of the Rel and MIP-3α-Rel vaccine constructs, with a more pronounced sex-associated difference in the MIP-3α-Rel group. In the current study, we addressed the hypothesis that these sex-related differences are at least in part due to differential DC activation/function soon after vaccination. An EαGFP reporter vaccine model was used to track vaccine antigen presentation in the draining node with flow cytometry panels by an antibody Y-Ae which binds the Eα peptide tag in complex with I-Ab MHC-II molecules. Additionally, a qRT-PCR panel assessing DC-related genes compared sexes receiving the MIP-3α-Rel vaccine. MIP-3α-EαGFP groups had more DCs presenting vaccine antigen infiltrating from the periphery, with more abundant Langerhans cells in males and greater CD8+ CD103+ cross-presenting dermal DCs in females. This model also shows there is greater DC activation, as measured by CD80 and MHC II MFI, by MIP-3α compared to EαGFP alone, especially in female mice. The genetic panel showed females more enriched for chemokines and genes related to cell movement and cross-presentation. Our findings are consistent with the sex- and MIP-3α-related differences seen in the therapeutic model and supports the hypothesis that in both sexes MIP-3α enhances vaccine uptake and cell activation by peripheral iDCs. Additionally, female mice showed greater levels of antigen presentation, especially in DCs able to cross-present antigen, likely explaining why they had the best outcomes. Further studies are required to understand underlying mechanisms and to link APC results directly to T-cell responses.
Context aware hierarchical attention for abstractive dialogue summarization
ICKAN: A deep musical instrument classification model incorporating Kolmogorov-Arnold network
Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning
Abstract This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) ultrasound-based malignancy risk stratification systems. A total of 327 eligible patients with FTC and FTA who underwent preoperative thyroid ultrasound examination were retrospectively enrolled between August 2017, and August 2024. Patients were randomly assigned to a training cohort (n = 263) and a test cohort (n = 64) in an 8:2 ratio using stratified sampling. Five CNN models, including VGG16, ResNet101, MobileNetV2, ResNet152, and ResNet50, pre-trained with ImageNet, were developed and tested to distinguish FTC from FTA. The CNN models exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) ranging from 0.64 to 0.77. The ResNet152 model demonstrated the highest AUC (0.77; 95% CI, 0.67–0.87) for distinguishing between FTC and FTA. Decision curve and calibration curve analyses demonstrated the models’ favorable clinical value and calibration. Furthermore, when comparing the performance of the developed models with that of the C-TIRADS and ACR-TIRADS systems, the models developed in this study demonstrated superior performance. This can potentially guide appropriate management of FTC in patients with follicular neoplasms.
Circulating MiRNAs as diagnostic biomarkers of lupus nephritis in patients with systemic lupus erythematosus: a systematic review and meta-analysis
Genetic characteristics of novel extreme alkaline-inducible promoter located in five prime upstream region of peptidyl-prolyl cis/trans isomerase from Vibrio anguillarum
Abstract This study presents the identification and characterization of the promoter region of Vibrio anguillarum NB10, which enhances the expression of FK506-binding protein (FKBP)-type peptidyl-prolyl cis/trans isomerase (PPIase; FklB), capable of binding to the immunosuppressant FK506 under extremely alkaline conditions. Our proteomic analysis of V. anguillarum NB10 revealed that FklB (VaFklB) expression is significantly upregulated under extreme alkaline stress (pH 10). When the putative core promoter regions were coupled with a β-galactosidase reporter gene and introduced into Escherichia coli, we observed β-galactosidase activities of 61.47 ± 2.91 and 95.83 ± 6.76 Miller units (MU) at pH 9 and 10, respectively, after 4 h of stress exposure. These values represent 1.97- and 2.88-fold increases compared to normal conditions (25 °C, pH 7: 31.27 ± 1.15 MU). This alkaline-inducible promoter system has potential for biotechnological applications, including the development of pH-responsive gene expression systems, biomanufacturing processes requiring alkaline environments, and targeted activation of silent biosynthetic gene clusters for novel bioactive compound discovery. Our findings provide a valuable molecular tool for synthetic biology and metabolic engineering, enabling precise genetic control under specific environmental conditions that may revolutionize industrial biotechnology.
TERT PfeRNA regulates telomere length during cellular senescence of normal human bronchial epithelial cells
Effect of oral sodium bicarbonate supplementation on urine TGF-𝜷 in normal serum bicarbonate CKD, a randomized controlled trial
Metagenomic insights into resistance trends related to microbial VB12 synthesis in eutrophic urban lakes
A study on classification based concurrent API calls and optimal model combination for tool augmented LLMs for AI agent
Abstract AI Agents have evolved to not only recommend content but also facilitate information retrieval and task processing. Developing AI Agents using general-purpose LLM models necessitates integration with external tools, leading to tool-augmented LLM studies. Despite the availability of multiple tools for the same purpose, existing research has not fully leveraged this diversity. This study categorizes external tools by type and proposes a method to simultaneously call tools of the same type. This allows for the utilization of diverse external tools in LLM inference, thereby achieving a higher accuracy compared to when only a single tool for one task is used. Experimental results show an accuracy improvement of 4.4–9.3% over existing studies. Furthermore, when utilizing tool-augmented LLM, a multi-step reasoning approach that divides the process into stages such as planning and tool invocation is widely employed. With the rapid advancement of LLMs, enhanced models continue to emerge. Considering the trade-offs between performance and cost in models, it is crucial to find an optimal combination of models in each stage of tool augmented LLM. In this study, we propose a novel method for efficiently utilizing both enhanced LLM models and existing models, which reduces response errors by up to 9%.
Behavior-aware energy management in microgrids using quantum-classical hybrid algorithms under social and demand dynamics
Golgi protein 73 and IL-6 as strong predictors of short-term prognosis in patients with HBV-ACLF
Study on coal drawing parameters of deeply buried hard coal seams based on PFC
Analysis of risk factors for poor wound healing after Nuss procedure for pectus excavatum and establishment of a predictive model
Chaotic and quasi-periodic dynamics in fractional-order nonlinear wave systems within dispersive-dissipative media
Emerging roles of the cancerous inhibitor of protein phosphatase 2A (CIP2A) in ovarian cancer
Abstract Ovarian cancer (OvCa) is the sixth most common gynaecological cancer in the UK, accounting for over 200,000 deaths worldwide. Cancerous Inhibitor of Phosphatase 2 A (CIP2A) is an oncoprotein and an endogenous inhibitor of PP2A. CIP2A is a key regulator for cellular processes (e.g. proliferation, DNA damage) and is involved in the progression of many malignancies. In this study we provide a comprehensive overview of its role in OvCa making use of in silico tools, clinical samples and in vitro models. CIP2A is overexpressed in OvCa patients, with metastatic patients having significantly higher expression when compared to patients with malignant and benign ovarian tumours. High CIP2A expression reduces both overall-and progression-free survival, whereas an R530T mutation is predicted to cause structural destabilisation of the CIP2A dimer. We also provide evidence for microRNA (miRNA) and mRNA target interactions with CIP2A. Finally, we have studied the effects of CIP2A inhibition in an in vitro BRCA2 model compared to BRCA2 wild-type OvCa cells, using RNA-sequencing. Gene enrichment pointed towards changes p53 pathway, protein metabolism, transporter activity, DNA replication, and cell cycle. Our data provide a novel insight into the role of CIP2A in OvCa and the potential of drug repurposing for therapeutic interventions.