Blood flow changes in pelvic vessels associated with the application of an abdominal compression belt in healthy postpartum women

Introduction: Postpartum haemorrhage (PPH) accounts for a high proportion of maternal mortality and morbidity throughout the world. A uterine compression belt which has been developed recently represents a very low tech, low cost solution in managing postpartum haemorrhage. Objectives: To evaluate the blood flow changes in pelvic vessels following application of the postpartum haemorrhage compression belt (Laerdal Global Health, Stavanger, Norway). Methods: The sample included healthy postpartum women within 6 hours of vaginal delivery. The study was performed at Teaching Hospital, Ragama, Sri Lanka. PPH compression belt was applied on the lower abdomen in a supine position with a slight lateral tilt. Patient’s pulse, blood pressure and Doppler indices (RI, PI and PFV) of the uterine, internal iliac and femoral arteries were measured using transabdominal Doppler ultrasonography. Lower limb oxygen saturation was also measured. Measurements were obtained by connecting the subjects to a multimonitor throughout the study period of 20 minutes. Median RI, PI and PFV was calculated and comparisons were made between the baseline and after belt application at 10 and 20 minutes. Results: A total of 20 healthy women were included and the mean time from delivery to study inclusion was 2.5 (range 0.5–5.0) hours. There were no adverse outcomes or altered vital signs noted among participants. Overall there were no significant changes in the internal iliac, uterine and femoral artery blood flow after application of the compression belt. Conclusions: There were no significant changes in the internal iliac, uterine and femoral artery blood flow after application of the compression belt. This preliminary study only shows that the application of the PPH compression belt has no apparent adverse changes in the iliac, uterine and femoral artery blood flow in postpartum mothers

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

Effects of glucose availability in <i>Lactobacillus sakei</i>; metabolic change and regulation of the proteome and transcriptome

<div><p>Effects of glucose availability were investigated in <i>Lactobacillus sakei</i> strains 23K and LS25 cultivated in anaerobic, glucose-limited chemostats set at high (<i>D</i> = 0.357 h^-1) and low (<i>D</i> = 0.045 h^-1) dilution rates. We observed for both strains a shift from homolactic towards more mixed acid fermentation when comparing high to low growth rates. However, this change was more pronounced for LS25 than for 23K, where dominating products were lactate>formate>acetate≥ethanol at both conditions. A multivariate approach was used for analyzing proteome and transcriptome data from the bacterial cultures, where the predictive power of the omics data was used for identifying features that can explain the differences in the end-product profiles. We show that the different degree of response to the same energy restriction revealed interesting strain specific regulation. An elevated formate production level during slow growth, more for LS25 than for 23K, was clearly reflected in correlating pyruvate formate lyase expression. With stronger effect for LS25, differential expression of the Rex transcriptional regulator and NADH oxidase, a target of Rex, indicated that maintainance of the cell redox balance, in terms of the NADH/NAD⁺ ratio, may be a key process during the metabolic change. The results provide a better understanding of different strategies that cells may deploy in response to changes in substrate availability.</p></div

Metabolite production, carbon flux and carbon flux/growth rate in <i>L</i>. <i>sakei</i> during continuous cultivation in glucose-limited CDM-LAB medium.

<p>Metabolite production, carbon flux and carbon flux/growth rate in <i>L</i>. <i>sakei</i> during continuous cultivation in glucose-limited CDM-LAB medium.</p

PCA of proteins and gene transcripts after variable selection towards the phenome.

<p>Proteins and gene transcripts selected by elastic net (left), where <i>L</i>. <i>sakei</i> strains 23K and LS25 are shown in blue and red, respectively. Squares indicate high growth rate and high glucose availability. Open triangles indicate low growth rate and restricted glucose availability. The selected proteins and transcripts were assigned to 4 groups with similar pattern (right), indicated in green, orange, black and red for groups 1–4, respectively.</p

PCA on all features of the proteome and transcriptome.

<p>PCA of the proteome (all 643 variables) and transcriptome (all 1632 variables) mean centered and standardized to unit variance. Score plots (left) and loading plots (right) on PC1 (x-axis) vs PC2 (y-axis). <i>L</i>. <i>sakei</i> strains 23K and LS25 are shown in blue and red, respectively. Squares indicate high growth rate and high glucose availability. Open triangles indicate low growth rate and restricted glucose availability.</p