Category: Nevin Manimala

Protein Sci. 2021 Oct 31. doi: 10.1002/pro.4215. Online ahead of print.

ABSTRACT

The interactions of proteins with surfaces are important in both biological processes and biotechnologies. In contrast to decades of study regarding the biophysics of proteins in bulk solution, however, our mechanistic understanding of the biophysics of proteins interacting with surfaces remains largely qualitative. In response, we have set to explore quantitatively the thermodynamics of protein-surface interactions. In this work we explore systematically the role of electrostatics in modulating the interaction between proteins and charged surfaces. In particular, we use electrochemistry to explore the extent to which a macroscopic, hydroxyl-coated surface held at a slightly negative potential affects the folding thermodynamics of surface-attached protein variants with different composition of charged amino acids. Doing so, we find that attachment to the surface generally leads to a net stabilization, presumably due to excluded volume effects that reduce the entropy of the unfolded state. The magnitude of this stabilization, however, is strongly correlated with the charged-residue content of the protein. In particular, we find statistically significant correlations with both the net charge of the protein, with greater negative charge leading to less stabilization by the surface, and with the number of arginines, with more arginines leading to greater stabilization. Such findings refine our understanding of protein-surface interactions, providing in turn a guiding rationale to achieve the functional deposition of proteins on artificial surfaces for implementation in, for example, protein-based biotechnologies. This article is protected by copyright. All rights reserved.

PMID:34719069 | DOI:10.1002/pro.4215

Eur J Neurol. 2021 Oct 31. doi: 10.1111/ene.15162. Online ahead of print.

ABSTRACT

BACKGROUND: To investigate smartphone keystroke dynamics (KD), derived from regular typing, on sensitivity to relevant change in disease activity, fatigue, and clinical disability in multiple sclerosis (MS).

METHODS: Preplanned interim analysis of a cohort study with 102 MS patients assessed at baseline and 3-month follow-up for gadolinium-enhancing lesions on MRI, relapses, fatigue and clinical disability outcomes. Keyboard interactions were unobtrusively collected during typing using the Neurokeys App. From these interactions 15 keystroke features were derived and aggregated using 16 summary and time series statistics. Responsiveness of KD to clinical anchor-based change was assessed by calculating the area under the receiver operating characteristic curve (AUC). The optimal cut-point was used to determine the minimal clinically important difference (MCID) and compared to the smallest real change (SRC). Commonly used clinical measures were analyzed for comparison.

RESULTS: 94 patients completed the follow-up. The five best performing keystroke features had AUC-values ranging from 0.72 to 0.78 for change in gadolinium-enhancing lesions, 0.67-0.70 for the Checklist Individual Strength Fatigue subscale, 0.66-0.79 for the Expanded Disability Status Scale, 0.69-0.73 for the Ambulation Functional System, and 0.72-0.75 for Arm function in MS Questionnaire. The MCID of these features exceeded the SRC on group level. KD had higher AUC-values than comparative clinical measures for the study outcomes, aside from ambulatory function.

CONCLUSIONS: KD demonstrated good responsiveness to changes in disease activity, fatigue, and clinical disability in MS, and detected important change beyond measurement error on group level. Responsiveness of KD was better than commonly used clinical measures.

PMID:34719076 | DOI:10.1111/ene.15162

Ecology. 2021 Oct 30:e03578. doi: 10.1002/ecy.3578. Online ahead of print.

ABSTRACT

Recently, Nakagawa et al. (2021) provided a timely and insightful comment to our paper on statistical methods for non-independent data in ecological meta-analyses (Song et al. 2020). Their comment highlighted the value of using hierarchical models in meta-analysis to address non-independence, and offered two assertions.

PMID:34719023 | DOI:10.1002/ecy.3578

Equine Vet J. 2021 Oct 30. doi: 10.1111/evj.13532. Online ahead of print.

ABSTRACT

BACKGROUND: For medication control in several jurisdictions, withdrawal time is the period of refrain from racing after drug administration. It is set by adding a safety period to an experimental detection time. However, there are no reports of statistical analyses of detection time for the determination of withdrawal time in flunixin meglumine-treated horses.

OBJECTIVE: To analyse the population pharmacokinetics of flunixin in horses through the generation of a dataset for detection time statistical analysis and predictions via Monte Carlo simulation.

STUDY DESIGN: Experimental study.

METHODS: Drug plasma and urine concentrations following single intravenous (i.v.) administration of flunixin 1.1 mg/kg bodyweight (BW) in 10 horses and multiple administration of q 24 h for 5 days in 10 horses were measured using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Data were modelled using a nonlinear mixed effect model followed by Monte Carlo simulation. Irrelevant plasma concentration (IPC) and irrelevant urine concentration (IUC) were calculated using the Toutain approach. Detection times were obtained considering the time after the last administration for selected quantiles of 5000 hypothetical horses under the International Screening Limit (ISL) proposed by the International Federation of Horseracing Authorities (plasma: 1 ng/ml, urine; 100 ng/ml) RESULTS: For a regimen of 1.1 mg/kg BW q 24 h, the IPC and IUC values were 2.0 and 73.0 ng/ml, respectively. Detection times in plasma above the ISL for 90% of simulated horses were estimated as 74 h after a single 1.1 mg/kg dose administration, 149 h and 199 h after multiple doses over 5 days at either 24- or 12-h intervals, respectively. Corresponding detection times in urine were 46 h, 68 h and 104 h, respectively.

MAIN LIMITATION: Only female horses were investigated.

CONCLUSIONS: Statistical detection times for different flunixin meglumine regimens indicated a delay of detection time in plasma after multiple administrations under ISL.

PMID:34719043 | DOI:10.1111/evj.13532

Daru. 2021 Oct 31. doi: 10.1007/s40199-021-00424-6. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Fibrocystic disease (FCD) of the breast as a very common health problem in women has estrogen-dependent and proliferative features. No effective management strategy has been validated for this disorder, so far. The anti-hyperglycemic agent metformin has both anti-proliferative and estrogen-suppressing effects. Thus, we investigated metformin as a management strategy for FCD.

METHODS: The study was a double-blind placebo-controlled randomized clinical trial. Premenopausal women with FCD according to history, physical exam and ultrasound, who had measurable microcyst clusters on ultrasound (US) were entered the study. Oral placebo and metformin tablets (500 mg) were used twice daily by participants in the intervention and control groups. Size and number of microcyst clusters on US and the subjective pain score were recorded before and after the intervention.

RESULTS: 154 participants were randomly allocated into two groups of 77 interventions and 77 controls. The decrease in size of the largest microcyst cluster in each patient and the mean decrease in number of microcyst clusters were not statistically significant (P = 0.310 and P = 0.637, respectively). However, those microcyst clusters which were ≥ 14 mm became significantly smaller after metformin use (P = 0.006). Additionally, in the subset of participants with pain at baseline, a larger proportion in the intervention group experienced at least 50% reduction in pain score (63.8% (30/47) in the intervention vs. 44.2% (19/43) in the placebo groups, P = 0.031).

CONCLUSION: Our study showed that metformin might be effective in the management of FCD. Further studies are proposed for confirmation of this subject.

PMID:34719004 | DOI:10.1007/s40199-021-00424-6

Thromb Haemost. 2021 Oct 31. doi: 10.1055/s-0041-1736617. Online ahead of print.

ABSTRACT

BACKGROUND: Hip fracture surgeries are associated with considerable blood loss, while the perioperative coagulopathy is associated with the bleeding risk of these patients. We aimed to evaluate the ability of rotational thromboelastometry (ROTEM) to detect patients at high risk for excessive bleeding and increased transfusion requirements.

METHODS: We conducted a prospective observational study of 221 patients who underwent hip fracture surgeries. ROTEM analysis was performed preoperatively and immediately postoperatively. Blood loss parameters including blood loss volume, number of transfused red blood cell (RBC) units, and drop in hemoglobin levels were recorded. ROTEM parameters were compared between patients with and without excessive bleeding, and between patients with and without increased transfusion requirements (i.e., ≥2 RBC units).

RESULTS: The postoperative FIBTEM MCF value ≤15 mm had 66.6% (95% confidence interval [CI]: 59.7-74.1%) sensitivity and 92.0% (95% CI: 80.7-97.7%) specificity to prognose excessive bleeding, and preoperative FIBTEM MCF value ≤15 mm had 80.4% (95% CI: 73.5-86.2%) sensitivity and 91.2% (95% CI: 80.7-97.0%) specificity to prognose increased transfusion requirements. Preoperative FIBTEM MCF ≤11 mm and postoperative FIBTEM MCF ≤15 mm were associated with considerably increased risks of excessive bleeding (odds ratio [OR]: 44.8, 95% CI: 16.5-121.3, p < 0.001; and OR: 23.0, 95% CI: 7.8-67.0, p < 0.001, respectively).

CONCLUSION: ROTEM parameters demonstrated high prognostic accuracy for excessive bleeding and increased transfusion requirements. This can enable implementation of blood sparing strategies in high-risk patients, while blood banks could be better prepared to ensure adequate blood supply.

PMID:34719014 | DOI:10.1055/s-0041-1736617

Adv Ther. 2021 Oct 31. doi: 10.1007/s12325-021-01960-y. Online ahead of print.

ABSTRACT

INTRODUCTION: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality in Italy, accounting for 22% of total deaths. Lowering low-density lipoprotein cholesterol (LDL-C) levels reduces the risk of cardiovascular (CV) events; thus, lipid-lowering therapy (LLT) is the first-line treatment for patients with ASCVD and hypercholesterolaemia. However, many patients with ASCVD fail to reach LDL-C treatment thresholds, leaving them at greater risk of CV events. Inpatient care accounts for 51% of total expenditure on cardiovascular disease in the European Union, but healthcare resource utilization (HCRU) data for ASCVD in Italy is limited.

METHODS: The study analysed healthcare claims data for 17,881 patients with acute coronary syndrome, ischemic stroke or peripheral artery disease from the Umbria 2 and Marche regions of Italy. LLT treatment patterns and CV event rates were collected and HCRU estimated in the year before and after the index event.

RESULTS: High-intensity LLTs were prescribed to 44.3% of patients and 49.6% received moderate-/low-intensity LLTs during the 6 months after the index event. The first year CV event rate was 18.0/100 patient-years for patients receiving high-intensity LLTs and 17.2/100 patient-years for those on moderate-/low-intensity LLTs. Higher costs were associated with patients untreated with LLT 6 months post-index event (€8323) than patients prescribed high-intensity (€6278) or moderate-/low-intensity LLTs (€6270). Hospitalization accounted for most of the total costs.

CONCLUSIONS: This study found that CV events in secondary prevention Italian patients are associated with substantial HCRU and costs. More intensive LDL-C lowering can prevent CV events, easing the financial burden on the healthcare system.

PMID:34718949 | DOI:10.1007/s12325-021-01960-y

Environ Sci Pollut Res Int. 2021 Oct 31. doi: 10.1007/s11356-021-17201-2. Online ahead of print.

ABSTRACT

The risk of the waterborne toxicity caused by herbicides threatens the aquatic environment. In this study, propolis nanoparticles were shown to relieve the impacts of glyphosate-induced oxidative stress and immunosuppression in Nile tilapia. The control group was fed a basal diet and maintained in a glyphosate-free water (control). Simultaneously, the other three groups were exposed to sublethal concentrations of glyphosate (0.6 mg/L) and fed diets containing 0 and 10 g propolis and 10 g propolis nanoparticles for 4 weeks. Nile tilapia exposed to glyphosate for 2 and 4 weeks exhibited a significant increase in serum alanine aminotransferase, aspartate aminotransferase, urea, and creatinine values compared to the control. After 2 and 4 weeks, fish exposed to glyphosate who were not fed propolis and propolis nanoparticles showed a significant reduction in total protein, albumin, and globulin levels, lysozyme activity, and total immunoglobulin levels. Nile tilapia exposed to glyphosate displayed a significant increase in blood glucose and cortisol concentrations after 2 and 4 weeks. Furthermore, liver and gill tissues from fish exposed to glyphosate exhibited a significant increase in malondialdehyde (MDA) concentrations. Conversely, a statistically significant decrease was observed in the liver and gill MDA levels and AChE activity of the groups treated with propolis and propolis nanoparticles compared to the groups exposed to glyphosate and fed the basal diet. Fish exposed to glyphosate for 2 and 4 weeks showed a significant decrease (p < 0.05) in hepatic and gill glutathione (GSH) concentration and white blood cell and red blood cell counts compared to the control group. Meanwhile, these parameters in groups fed propolis and propolis nanoparticles were markedly ameliorated compared to exposed fish fed the basal diet. Dietary supplementation of propolis nanoparticles is superior to supplementation of propolis in the normal form for protecting Nile tilapia from glyphosate toxicity.

PMID:34718976 | DOI:10.1007/s11356-021-17201-2

J Appl Genet. 2021 Oct 31. doi: 10.1007/s13353-021-00667-8. Online ahead of print.

ABSTRACT

A total of 96 different genotypes of upland cotton (Gossypium hirsutum) were selected from the breeding material and germplasm available at CCS HAU, India, to find the novel marker-trait associations for morphological traits used for registration of variety in upland cotton. Twenty-three morphological traits of the selected genotypes were recorded in field trials conducted in two replication of randomized block design during Kharif 2018 and 2019. A total of 11 traits showed sufficient variations in the screened germplasm and the same were further used for association mapping. A total of 168 SSRs were used for genotyping, of which 97 SSRs showed polymorphism amplifying 293 different alleles with an average of 3.02 alleles per SSR. Clustering, principal component analysis, and population structure analysis advocated that the current germplasm panel has enough diversity to be considered for association mapping. A total of 20 significant marker-trait associations were identified by the mixed linear model (MLM) and compressed mixed linear model (CMLM), of which 15 were common to both models, hence considered as promising associations. To the best of our knowledge, it is a first attempt to identify the linked markers in relation to morphological traits for the cotton crop. Results of the present study will be highly useful in speeding up variety registration programmes of upland cotton complementing to Distinctiveness, Uniformity, and Stability (DUS) testing.

PMID:34718944 | DOI:10.1007/s13353-021-00667-8

Med Biol Eng Comput. 2021 Oct 30. doi: 10.1007/s11517-021-02461-4. Online ahead of print.

ABSTRACT

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.

PMID:34718933 | DOI:10.1007/s11517-021-02461-4