Essentials
Perioperative blood loss and inflammatory response can significantly affect recovery after surgery.
We studied the effects of multiple‐dose oral tranexamic acid on blood loss and ...inflammatory response.
A postoperative four‐dose regimen brought about maximum reduction in postoperative blood loss.
A postoperative four‐dose regimen reduced inflammatory response and promoted early rehabilitation.
Summary
Background
Tranexamic acid (TXA) can reduce blood loss and the inflammatory response at multiple doses in total knee arthroplasty patients. However, the optimal regimen has not been determined.
Objectives
To identify the most effective regimen for achieving maximum reductions in blood loss and the inflammatory response.
Patients/Methods
Two hundred and seventy‐five patients were randomized to receive a placebo (group A), a single 2‐g oral dose of TXA 2 h preoperatively followed by 1 g of oral TXA 3 h postoperatively (group B), a single dose followed by 1 g of oral TXA 3 h and 7 h postoperatively (group C), a single dose followed by 1 g of oral TXA 3 h, 7 h and 11 h postoperatively (group D), or a single dose followed by 1 g of oral TXA 3 h, 7 h, 11 h and 15 h postoperatively (group E). The primary outcome was total blood loss on postoperative day (POD) 3. Secondary outcomes included a decrease in the hemoglobin level, coagulation parameters, inflammatory marker levels, and thromboembolic complications.
Results
Groups D and E had significantly lower blood loss and smaller decreases in hemoglobin level than groups A, B, and C, with no significant difference on POD 3 between groups D and E. Significantly enhanced coagulation was identified for the four multiple‐dose regimens; however, all thromboelastographic parameters remained within normal ranges. Group E had the lowest inflammatory marker levels and pain, and the greatest range of motion. No thromboembolic complications were identified.
Conclusion
The four‐dose regimen yielded the maximum reductions in blood loss and inflammatory response, improved analgesia, and promoted early rehabilitation. Further studies are required to ensure that these findings are reproducible.
Abstract
Motivation
Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic ...peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning-based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high-quality generic prediction and informative physicochemical properties (IPPs) identification simultaneously.
Results
In this work, Physicochemical Property-based Therapeutic Peptide Predictor (PPTPP), a Random Forest-based prediction method was presented to address this issue. A novel feature encoding and learning scheme were initiated to produce and rank physicochemical property-related features. Besides being capable of predicting multiple therapeutics peptides with high comparability to established predictors, the presented method is also able to identify peptides’ informative IPP. Results presented in this work not only illustrated the soundness of its working capacity but also demonstrated its potential for investigating other therapeutic peptides.
Availability and implementation
https://github.com/YPZ858/PPTPP.
Supplementary information
Supplementary data are available at Bioinformatics online.
In this work, we compare the resolution of V2-V3 and V3-V4 16S rRNA regions for the purposes of estimating microbial community diversity using paired-end Illumina MiSeq reads, and show that the ...fragment, including V2 and V3 regions, has higher resolution for lower-rank taxa (genera and species). It allows for a more precise distance-based clustering of reads into species-level OTUs. Statistically convergent estimates of the diversity of major species (defined as those that together are covered by 95% of reads) can be achieved at the sample sizes of 10000 to 15000 reads. The relative error of the Shannon index estimate for this condition is lower than 4%.
Illuminating gravitational waves Kasliwal, M. M.; Nakar, E.; Singer, L. P. ...
Science,
12/2017, Letnik:
358, Številka:
6370
Journal Article
Recenzirano
Odprti dostop
Merging neutron stars offer an excellent laboratory for simultaneously studying strong-field gravity and matter in extreme environments. We establish the physical association of an electromagnetic ...counterpart (EM170817) with gravitational waves (GW170817) detected from merging neutron stars. By synthesizing a panchromatic data set, we demonstrate that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis. The weak gamma rays seen in EM170817 are dissimilar to classical short gamma-ray bursts with ultrarelativistic jets. Instead, we suggest that breakout of a wide-angle, mildly relativistic cocoon engulfing the jet explains the low-luminosity gamma rays, the high-luminosity ultraviolet-optical-infrared, and the delayed radio and x-ray emission. We posit that all neutron star mergers may lead to a wide-angle cocoon breakout, sometimes accompanied by a successful jet and sometimes by a choked jet.
Heat processing has been used to improve protein utilization and availability of animal nutrition. However, to date, few studies exist on heat-induced protein molecular structure changes on a ...molecular basis. The aims of this study were to use molecular spectroscopy as a novel approach to determine heat-induced protein molecular structure changes affected by moist and dry heating and quantify protein molecular structures and nutritive value in the rumen and intestine in dairy cattle. In this study, soybean was used as a model for feed protein and was autoclaved at 120°C for 1h (moist heating) and dry heated at 120°C for 1h. The parameters assessed in this study included protein structure α-helix and β-sheet and their ratio, protein subfractions associated with protein degradation behaviors, intestinal protein availability, and energy values. The results show that heat treatments changed the protein molecular structure. Both dry and moist heating increased the amide I-to-amide II ratio. However, for the protein α-helix-to-β-sheet ratio, moist heating decreased but dry heating increased the ratio. Compared with dry heating, moist heating dramatically changed the chemical and nutrient profiles of soybean seed. It greatly decreased soluble crude protein, nonprotein nitrogen, and increased neutral detergent insoluble protein. Both dry and moist heating treatments did not alter digestible nutrients and energy values. Heating tended to decrease the nonprotein nitrogen fraction (soluble and rapidly degradable protein fraction) and true protein 1 fraction (fast-degradable protein fraction). Conversely, the true protein 3 fraction (slowly degradable fraction) significantly increased. The in situ rumen study showed that moist heating decreased protein rumen degradability and increased intestinal digestibility of rumen-undegradable protein. Compared with the raw soybeans, dry heating did not affect rumen degradability and intestinal digestibility. In conclusion, compared with dry heating, moist heating dramatically affected the nutrient profile, protein subfractions, rumen degradability, intestinal digestibility, and protein molecular structure (amide I-to-II ratio; α-helix-to-β-sheet ratio). The sensitivity of soybean seed to moist heating was much higher than that to dry heating in terms of the structure and nutrient profile changes.
ABSTRACT We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their ...likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.
PDF
