Biology subjects

Patel, T. S.

Publications and source records attributed to Patel, T. S..

6 recordsLinked to original sources

bioRxiv2026

Multi-omic characterization of axolotl perilymph-cerebrospinal fluid reveals shifts in composition during limb regeneration

The axolotl salamander can fully regenerate amputated limbs, yet the systemic consequences underlying this process remain largely understudied. Cerebrospinal fluid is an emerging signaling medium capable of communicating with both the central and peripheral nervous systems, but its composition and potential role in salamander limb regeneration have not yet been examined using modern multi-omics techniques. Here, we developed a protocol for extracting mixed perilymph-cerebrospinal fluid (P-CSF) from axolotl and provided the first proteomic and metabolomic characterization of this biological fluid. We identified 2,626 unique proteins and 173 high-confidence metabolites and quantified them across four time points of early limb regeneration. We demonstrated that limb amputation drives progressive shifts in P-CSF proteins, including an elevation of sarcomeric muscle proteins, regeneration-associated factors, and protease/extracellular matrix proteins. We observed shifts in metabolites involved in oxidative stress, polyunsaturated fatty acid oxidation, and histamine metabolism. Injury-comparison experiments revealed that the observed proteomic changes as a result of limb amputation are different than crush injury, denervation, or tail amputation. This study proposes axolotl P-CSF as a reservoir for limb amputation-associated systemic signaling and as a potential conduit of signals involved in limb regeneration.

systems biology↗

bioRxiv2025

Therapeutic Potential of Cyclodextrins Targeting Dengue Virus and SARS-CoV-2 Infection and Pathogenesis

Cyclodextrins (CDs) are cyclic oligosaccharides with promising therapeutic applications, including antiviral activity. During viral infections, pathogenesis arises not only from viral replication but also from viral proteins that act as "toxins", disrupting cellular barriers and inducing endothelial dysfunction, a hallmark of severe diseases such as dengue and COVID-19. Dengue virus (DENV) NS1 and SARS-CoV-2 Spike proteins induce endothelial hyperpermeability, contributing to severe complications. Here we explored the potential of a panel of 18 CDs in mitigating endothelial dysfunction caused by these viral proteins and evaluated the CDs antiviral activity in vitro and in vivo. The effect of CDs on endothelial hyperpermeability was assessed using a trans-endothelial electrical resistance assay with human pulmonary microvascular endothelial cells exposed to DENV NS1 and SARS-CoV-2 Spike proteins. Antiviral efficacy of CDs was evaluated in Vero cells infected with DENV2 and Calu-3 cells infected with SARS-CoV-2, and in vivo protection was assessed in a lethal DENV2 mouse model. CDs effectively inhibited DENV NS1-induced endothelial hyperpermeability in vitro, demonstrating their potential to counteract NS1-mediated barrier disruption. In the murine model, CD1 treatment provided partial protection against DENV-induced morbidity and mortality. Further, CDs significantly reduced SARS-CoV-2 infection in vitro and inhibited Spike-induced endothelial dysfunction. These findings indicate that CDs can prevent endothelial hyperpermeability induced by DENV NS1 and SARS-CoV-2 Spike proteins and exhibit antiviral activity against SARS-CoV-2, positioning them as promising candidates for mitigating endothelial complications associated with viral infections. Further research is needed to explore the clinical relevance of CDs and their mechanisms of action.

microbiology↗

bioRxiv2024

Comparative assessment of machine learning algorithms to predict severity of disease in COVID-19 patients based on eight cofactors

Machine learning is one of the important tools to diagnose and predict the diseased state accurately and effectively. The COVID-19 pandemic caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become one of the most researched healthcare topics worldwide. Machine learning algorithms can find efficient and reliable ways to predict the COVID-19 from vast amounts of existing health care data, allowing faster, effective, and more accurate diagnosis with lower risk based on the symptoms. Based on the countrywide data published by the Israeli Ministry of Health, we propose a system that detects COVID-19 instances using simple variables. The COVID-19 dataset used in the study consisted of 278848 patients samples with five different symptoms, namely cough, fever, sore throat, shortness of breath, and headache, apart from other basic information like age, gender, and test indication excluding confirmed COVID-19 result. The data was analyzed using traditional supervised machine learning algorithms namely, Decision tree, Support vector machine, Random Forest, Logistic regression, k-nearest neighbor, and Naive Bayes based on eight cofactors with high accuracy rate ([≥] 0.9450). Apart from Support vector machine, all other algorithms displayed better performance based on the AUC score calculated using the receiver operator characteristic (ROC) curve. This study also highlights the significant differences between precision, recall and accuracy for each model. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/617884v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@33bb11org.highwire.dtl.DTLVardef@3e93aaorg.highwire.dtl.DTLVardef@508f76org.highwire.dtl.DTLVardef@faa9e2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2024

IRF8-driven reprogramming of the immune microenvironment enhances anti-tumor adaptive immunity and reduces immunosuppression in murine glioblastoma

BackgroundGlioblastoma (GBM) has a highly immunosuppressive tumor immune microenvironment (TIME), largely mediated by myeloid-derived suppressor cells (MDSCs). Here, we utilized a retroviral replicating vector (RRV) to deliver Interferon Regulatory Factor 8 (IRF8), a master regulator of type 1 conventional dendritic cell (cDC1) development, in a syngeneic murine GBM model. We hypothesized that RRV-mediated delivery of IRF8 could "reprogram" intratumoral MDSCs into antigen-presenting cells (APCs) and thereby restore T-cell responses. MethodsEffects of RRV-IRF8 on survival and tumor growth kinetics were examined in the SB28 murine GBM model. Immunophenotype was analyzed by flow cytometry and gene expression assays. We assayed functional immunosuppression and antigen presentation by ex vivo T-cell-myeloid co-culture. ResultsMice with RRV-IRF8 pre-transduced intracerebral tumors had significantly longer survival and slower tumor growth compared to controls. RRV-IRF8 treated tumors exhibited significant enrichment of cDC1s and CD8+ T-cells. Additionally, myeloid cells derived from RRV-IRF8 tumors showed decreased expression of the immunosuppressive markers Arg1 and IDO1 and demonstrated reduced suppression of naive T-cell proliferation in ex vivo co-culture, compared to controls. Furthermore, DCs from RRV-IRF8 tumors showed increased antigen presentation compared to those from control tumors. In vivo treatment with azidothymidine (AZT), a viral replication inhibitor, showed that IRF8 transduction in both tumor and non-tumor cells is necessary for survival benefit, associated with a reprogrammed, cDC1- and CD8 T-cell-enriched TIME. ConclusionsOur results indicate that reprogramming of glioma-infiltrating myeloid cells by in vivo expression of IRF8 may reduce immunosuppression and enhance antigen presentation, achieving improved tumor control. Key pointsO_LIGBM intra-tumoral myeloid cells are proliferative and targets for RRV therapy. C_LIO_LIExpression of IRF8 significantly improves survival and slows tumor growth in murine GBM. C_LIO_LIIRF8 expression in MDSCs reduces immunosuppression and enriches cDC1s in vivo. C_LI Importance of the studyRecent publications have presented conflicting studies regarding the role of IRF8 in GBM. While some studies showed IRF8 as a negative prognostic factor, others demonstrated the conversion of tumor cells into DCs using IRF8. Here, we show that RRV-mediated delivery of IRF8, a clinically relevant modality, allows for transduction of both tumor and immune cells in vivo. We show that a significant survival effect relies heavily on the infection and modulation of both populations, and that even a modest number of reprogrammed intra-tumoral MDSCs can have a substantial impact on the immunological milieu, significantly enriching and activating cytotoxic T-cells. Further, this work reveals intra-tumoral myeloid cells as a target for other RRV-based gene therapies.

immunology↗

bioRxiv2023

Prediction of Heart Disease and Survivability using Support Vector Machine and Naive Bayes Algorithm

PurposeIn the present work, we examined the outcomes and accuracy of the Support vector machine (SVM) and the Naive Bayes algorithms on a dataset, to predict whether the patient has heart disease or not, and the patients survival prediction status. MethodThe machine learning procedures were developed using the clinically validated datasets with sixteen attributes from the University of California, Irvines Centre for Machine Learning, and Intelligent Systems. Confusion matrix was used to visualise the accuracy, recall, precision, and error of the models. Statistical analysis was done to prove the model accuracy using the receiver operating characteristic (ROC) curve and area under the curve (AUC). ResultsThe proposed method of heart disease prediction using Naive Bayes had 87 % accuracy. The accuracy for heart survivability models using SVM and Naive Bayes were 88 % and 93 %. The model efficiency for heart survivability using ROC curve with AUC 0.93 for Naive Bayes and AUC 0.91 for SVM. ConclusionSuch prediction systems can help the medical sector to save energy, cost, and time by providing more efficient techniques to forecast decisions with high accuracy. This study will enable the statisticians and researchers to select more efficient and accurate machine learning algorithms to achieve better prediction of the "cardiovascular disease".

bioinformatics↗

bioRxiv2021

SARS-CoV-2 Spike triggers barrier dysfunction and vascular leak via integrins and TGF-β signaling

Severe COVID-19 is associated with epithelial and endothelial barrier dysfunction within the lung as well as in distal organs. While it is appreciated that an exaggerated inflammatory response is associated with barrier dysfunction, the triggers of this pathology are unclear. Here, we report that cell-intrinsic interactions between the Spike (S) glycoprotein of SARS-CoV-2 and epithelial/endothelial cells are sufficient to trigger barrier dysfunction in vitro and vascular leak in vivo, independently of viral replication and the ACE2 receptor. We identify an S-triggered transcriptional response associated with extracellular matrix reorganization and TGF-{beta} signaling. Using genetic knockouts and specific inhibitors, we demonstrate that glycosaminoglycans, integrins, and the TGF-{beta} signaling axis are required for S-mediated barrier dysfunction. Our findings suggest that S interactions with barrier cells are a contributing factor to COVID-19 disease severity and offer mechanistic insight into SARS-CoV-2 triggered vascular leak, providing a starting point for development of therapies targeting COVID-19 pathogenesis.

microbiology↗