A mathematical model of ascending Henle limb (AHL) epithelium has been fashioned using kinetic representations of Na+-K+-2Cl- cotransporter (NKCC2), KCC4, and type 3 Na+/H+ exchanger (NHE3), with transporter densities selected to yield the reabsorptive Na+ flux expected for rat tubules in vivo. Of necessity, this model predicts fluxes that are higher than those measured in vitro. The kinetics of the NKCC and KCC are such that Na+ reabsorption by the model tubule is responsive to variation in luminal NaCl concentration over the range of 30 to 130 mM, with only minor changes in cell volume. Peritubular KCC accounts for about half the reabsorptive Cl- flux, with the remainder via peritubular Cl- channels. Transcellular Na+ flux is turned off by increasing peritubular KCl, which produces increased cytosolic Cl- and thus inhibits NKCC2 transport. In the presence of physiological concentrations of ammonia, there is a large acid challenge to the cell, due primarily to NH4+ entry via NKCC2, with diffusive NH3 exit to both lumen and peritubular solutions. When NHE3 density is adjusted to compensate this acid challenge, the model predicts luminal membrane proton secretion that is greater than the HCO3(-)-reabsorptive fluxes measured in vitro. The model also predicts luminal membrane ammonia cycling, with uptake via NKCC2 or K+ channel, and secretion either as NH4+ by NHE3 or as diffusive NH3 flux in parallel with a secreted proton. If such luminal ammonia cycling occurs in vivo, it could act in concert with luminal K+ cycling to facilitate AHL Na+ reabsorption via NKCC2. With physiological ammonia, peritubular KCl also blunts NHE3 activity by inhibiting NH4+ uptake on the Na-K-ATPase, and alkalinizing the cell.
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http://dx.doi.org/10.1152/ajprenal.00231.2009 | DOI Listing |
JCI Insight
January 2025
Medical Oncology Department, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands.
Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.
Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.
Brief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129 Shaanxi, China.
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing.
View Article and Find Full Text PDFPulmonology
December 2025
Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China.
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