Biology subjects

Kotanko, P.

Publications and source records attributed to Kotanko, P..

4 recordsLinked to original sources

bioRxiv2023

The protein-bound uremic solute 3-carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF) increases erythrocyte osmotic fragility through interaction with the mechanosensitive channel Piezo1.

Jedi1, a chemical activator of the mechanosensitive cation channel Piezo1, shares structural similarities with the uremic solute 3-carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF). We explored the hypothesis that CMPF at a concentration seen in uremia activates Piezo1 located on red blood cells (RBC). We incubated RBC from five healthy individuals with either Jedi1 or CMPF (both 87 M), with or without the Piezo1 inhibitor GsMTx-4 (2 M), and quantified the cells osmotic fragility. Our results indicate that compared to controls (i.e., RBC incubated with buffer), both Jedi1 and CMPF increase the osmotic fragility of RBC (i.e., reduce their resistance to osmotic stress). Effects of Jedi1 and CMPF were reversed to the control level by GsMTx-4. These results indicate a role of Piezo1 in augmenting RBC osmotic fragility and modulation of this effect by Jedi1 and CMPF. Our findings open the possibility that CMPF may act as an endogenous chemical activator of Piezo1.

cell biology↗

bioRxiv2021

Modeling osteoporosis to design and optimize pharmacologic therapies comprising multiple drug types

For the treatment of postmenopausal osteoporosis, several drug classes with different mechanisms of action are available. Since only a limited set of dosing regimens and drug combinations can be tested in clinical trials, it is currently unclear whether common medication strategies achieve optimal bone mineral density gains or are outperformed by alternative dosing schemes and combination therapies that have not been explored so far. Here we develop a mathematical framework of drug interventions for postmenopausal osteoporosis that unifies fundamental mechanisms of bone remodeling and the mechanisms of action of four drug classes: bisphosphonates, parathyroid hormone (PTH) analogs, sclerostin inhibitors and receptor activator of NF-{kappa}B ligand (RANKL) inhibitors. Using data from several clinical trials, we calibrate and validate the model, demonstrating its predictive capacity for complex medication scenarios including sequential and parallel drug combinations. Via simulations, we reveal that there is a large potential to improve gains in bone mineral density by exploiting synergistic interactions between different drug classes, without increasing the total amount of drug administered.

pharmacology and toxicology↗

bioRxiv2018

Comparison of Approaches to the identification of Symptom Burden in Hemodialysis Patients Utilizing Electronic Health Records

BackgroundIdentification of symptoms is challenging with surveys, which are time-intensive and low-throughput. Natural language processing (NLP) could be utilized to identify symptoms from narrative documentation in the electronic health record (EHR).\n\nMethodsWe utilized NLP to parse notes for maintenance hemodialysis (HD) patients from two EHR databases (BioMe and MIMIC-III) to identify fatigue, nausea/vomiting, anxiety, depression, cramping, itching, and pain. We compared NLP performance with International Classification of Diseases (ICD) codes and validated the performance of both NLP and codes against manual chart review in a representative subset.\n\nResultsWe identified 1034 and 929 HD patients from BioMe and MIMIC-III respectively. The most frequently identified symptoms by NLP from both cohorts were fatigue, pain, and nausea and/or vomiting. NLP was significantly more sensitive than ICD codes for nearly all symptoms. In the BioMe dataset, sensitivity for NLP ranged from 0.85-0.99 vs. 0.09-0.59 for ICD codes. In the MIMIC-III dataset, NLP sensitivity was 0.8-0.98 vs. 0.02-0.53 for ICD. ICD codes were significantly more specific for nausea and/or vomiting (NLP 0.57 vs. ICD 0.97, P=0.03) in BioMe and for depression (NLP 0.67 vs. ICD 0.99, P=0.002) in MIMIC-III. A majority of patients in both cohorts had ?4 symptoms. The more encounters available for a patient the more likely NLP was to identify a symptom.\n\nConclusionsNLP out performed ICD codes for identification of symptoms on several tests parameters including sensitivity for a majority of symptoms. NLP may be useful for the high-throughput identification of patient centered outcomes from EHR.\n\nSignificance StatementPatients on maintenance hemodialysis experience a high frequency of symptoms. However, symptoms have been measured utilizing time-intensive surveys. This paper compares natural language processing (NLP) to administrative codes for the identification of seven key symptoms from two cohorts with electronic health records and validation through manual chart review. NLP identified high rates of symptoms; the most common were fatigue, pain, and nausea and/or vomiting. A majority of patients had [≥]4 symptoms. NLP was significantly more sensitive at identifying symptoms compared to administrative codes for nearly all symptoms but specificity was not significantly different compared to codes. This paper demonstrates utility of a high throughput method of identifying symptoms from EHR which may advance the field of patient centered research in nephrology.

epidemiology↗

bioRxiv2017

Meta-analysis and Commentary: Preemptive Correction of Arteriovenous Access Stenosis

BackgroundA recent meta-analysis by Ravani and colleagues (Ravani, P., et al., Am J Kidney Dis, 2016. 67(3): p. 446-60.) studied the effect of pre-emptive correction of arterio-venous dialysis vascular access versus deferred care, based on data from 11 trials. The authors reported a non-significant protective treatment effect of pre-emptive correction on access loss, while showing a significant protective effect on thrombosis rates conferred by pre-emptive correction. We revisit this analysis, including data extraction and effects of a heterogenous study population.\n\nMethodsWe repeated data extraction from all referenced publications in the meta-analysis by Ravani et al. and corrected event counts where applicable. We repeated the meta-analyses with access loss as the outcome for studies that recruited patients with arterio-venous fistulae (AVF) and grafts (AVG), respectively, using a random effects model with relative risk (RR) and risk difference (RD) of access loss as the outcomes of interest. We repeated data extraction from all referenced publications, and corrected event counts where applicable.\n\nResultsOur conclusions differ from the original findings in two ways. First, after some amendment of the event counts extracted from Mayer et al. (Vascular and Endovascular Surgery 1993), we find a significant overall positive effect of pre-emptive correction on arterio-venous access loss in the overall study population [RR 0.80 (95% CI 0.64 to 0.99), RD -0.07 (95% CI -0.12 to -0.02); Figure 1]. Secondly, we highlight the impact of heterogeneous study populations on the meta-analysis. Whereas the data do not conclusively show a benefit of pre-emptive correction for arteriovenous grafts (AVG; RR = 0.87, 95% CI: 0.69 - 1.11), they show a strong protective effect for arteriovenous fistulae (AVF; RR = 0.5, 95% CI: 0.29 to 0.86).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC=\"FIGDIR/small/179580_fig1.gif\" ALT=\"Figure 1\">\nView larger version (41K):\norg.highwire.dtl.DTLVardef@af3c13org.highwire.dtl.DTLVardef@911a50org.highwire.dtl.DTLVardef@568cf1org.highwire.dtl.DTLVardef@3f3c08_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Meta-analysis of access loss, overall and by access type using risk ratio (RR) as the measure of association.\n\nC_FIG\n\nDiscussion and ConclusionThese findings corroborate clinical arguments such as superior long-term patency of AVF and the nature of AVG failure that often involve infectious causes. The available data indicate mild or no benefit of pre-emptive correction for AVG, but strongly support tight monitoring of dialysis accesses and preemptive intervention and correction upon the slightest suspicion of access stenosis for AVF.

epidemiology↗