Nocturnal Heart Rate Variability Might Help in Predicting Severe Obstructive Sleep-Disordered Breathing

Obstructive sleep apnea (OSA) can have long-term cardiovascular and metabolic effects. The identification of OSA-related impairments would provide diagnostic and prognostic value. Heart rate variability (HRV) as a measure of cardiac autonomic regulation is a promising candidate marker of OSA and OSA-related conditions. We took advantage of the Physionet Apnea-ECG database for two purposes. First, we performed time- and frequency-domain analysis of nocturnal HRV on each recording of this database to evaluate the cardiac autonomic regulation in patients with nighttime sleep breathing disorders. Second, we conducted a logistic regression analysis (backward stepwise) to identify the HRV indices able to predict the apnea-hypopnea index (AHI) categories (i.e., "Severe OSA", AHI ≥ 30; "Moderate-Mild OSA", 5 ≥ AHI < 30; and "Normal", AHI < 5). Compared to the "Normal", the "Severe OSA" group showed lower high-frequency power in normalized units (HFnu) and higher low-frequency power in normalized units (LFnu). The standard deviation of normal R-R intervals (SDNN) and the root mean square of successive R-R interval differences (RMSSD) were independently associated with sleep-disordered breathing. Our findings suggest altered cardiac autonomic regulation with a reduced parasympathetic component in OSA patients and suggest a role of nighttime HRV in the characterization and identification of sleep breathing disorders

Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

: Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills

Exploring Coagulase-Negative Staphylococci Diversity from Artisanal Llama Sausages: Assessment of Technological and Safety Traits

Llama sausage is still produced following artisanal procedures, with the autochthonous microbiota being mainly responsible for the fermentation process. In this work, the taxonomical identification and technological-safety criteria of coagulase-negative staphylococci (CNS) isolated from two dierent productions of llama sausages (P: pilot and A: artisanal) were investigated. Staphylococcus (S) equorum and S. saprophyticus were the species most frequently found in P production, followed by S. succinis and S. warneri; a wider species variability was observed in A factory being S. equorum, S. capitis, S. xylosus, S. pasteuri, S. epidermidis and S. saprophyticus as the main identified species. The technological characterization of 28 CNS strains showed their ability to hydrolyze gelatin and tributyrin together with a relevant nitrate reductase activity. Phenotypic and genotypic approaches were conducted to investigate the main safety traits. Llama\u2019s CNS strains exhibited weak decarboxylase and hemolytic activity and low biofilm production; additionally, no enterotoxin genes were detected. Correlation analysis between phenotypic and genotypic data showed low values for the biofilm parameters, while high correlation was observed for oxacillin, ampicillin, tetracycline and aminoglycosides resistance and their genetic determinants. Data obtained may contribute to broaden knowledge about the autochthonous strains of this poorly studied fermented product, thus helping to select an appropriate combination of potential starter cultures to improve llama sausage safety and quality

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol

Infant Early Gut Colonization by Lachnospiraceae: High Frequency of Ruminococcus gnavus

Lachnospiraceae is a bacterial family usually isolated from human and mammalian intestinal microbiota. However, its presence and role in the infant microbiota is not fully elucidated. This may be due to the strictly anaerobic behavior of its members that hampers the possibility of culture-dependent enumeration. Here, we report on the presence of this bacterial group, using biomolecular techniques, in stool samples from 25 babies aged between 1 and 24\u2009months. Denaturing gradient gel electrophoresis (DGGE) was used as a first detection step, and data were confirmed by quantitative PCR (qPCR). The DGGE showed the presence of Lachnospiraceae in infant fecal specimens and indicated the prevalence of Ruminococcus gnavus (R. gnavus). The qPCR confirmed the presence of the Clostridium XVIa group, Blautia genus, and R. gnavus, which are the main members of this family. We detected R. gnavus in 22 of 25 (88%) samples with a qPCR probe assay. Despite the difficulties associated with their detection and enumeration, Lachnospiraceae, and in particular R. gnavus, should be included in future studies on the infant microbiota composition

Cardiac kinematic parameters computed from video of in situ beating heart

Mechanical function of the heart during open-chest cardiac surgery is exclusively monitored by echocardiographic techniques. However, little is known about local kinematics, particularly for the reperfused regions after ischemic events. We report a novel imaging modality, which extracts local and global kinematic parameters from videos of in situ beating hearts, displaying live video cardiograms of the contraction events. A custom algorithm tracked the movement of a video marker positioned ad hoc onto a selected area and analyzed, during the entire recording, the contraction trajectory, displacement, velocity, acceleration, kinetic energy and force. Moreover, global epicardial velocity and vorticity were analyzed by means of Particle Image Velocimetry tool. We validated our new technique by i) computational modeling of cardiac ischemia, ii) video recordings of ischemic/reperfused rat hearts, iii) videos of beating human hearts before and after coronary artery bypass graft, and iv) local Frank-Starling effect. In rats, we observed a decrement of kinematic parameters during acute ischemia and a significant increment in the same region after reperfusion. We detected similar behavior in operated patients. This modality adds important functional values on cardiac outcomes and supports the intervention in a contact-free and non-invasive mode. Moreover, it does not require particular operator-dependent skills

Synthetic recovery of impulse propagation in myocardial infarction via silicon carbide semiconductive nanowires

: Myocardial infarction causes 7.3 million deaths worldwide, mostly for fibrillation that electrically originates from the damaged areas of the left ventricle. Conventional cardiac bypass graft and percutaneous coronary interventions allow reperfusion of the downstream tissue but do not counteract the bioelectrical alteration originated from the infarct area. Genetic, cellular, and tissue engineering therapies are promising avenues but require days/months for permitting proper functional tissue regeneration. Here we engineered biocompatible silicon carbide semiconductive nanowires that synthetically couple, via membrane nanobridge formations, isolated beating cardiomyocytes over distance, restoring physiological cell-cell conductance, thereby permitting the synchronization of bioelectrical activity in otherwise uncoupled cells. Local in-situ multiple injections of nanowires in the left ventricular infarcted regions allow rapid reinstatement of impulse propagation across damaged areas and recover electrogram parameters and conduction velocity. Here we propose this nanomedical intervention as a strategy for reducing ventricular arrhythmia after acute myocardial infarction

Differential effects of coconut versus soy oil on gut microbiota composition and predicted metabolic function in adult mice

Background: Animal studies show that high fat (HF) diet-induced gut microbiota contributes to the development of obesity. Oil composition of high-fat diet affects metabolic inflammation differently with deleterious effects by saturated fat. The aim of the present study was to examine the diversity and metabolic capacity of the cecal bacterial community in C57BL/6 N mice administered two different diets, enriched respectively with coconut oil (HFC, high in saturated fat) or soy oil (HFS, high in polyunsaturated fat). The relative impact of each hypercaloric diet was evaluated after 2 and 8 weeks of feeding, and compared with that of a low-fat, control diet (LF). Results: The HFC diet induced the same body weight gain and fat storage as the HFS diet, but produced higher plasma cholesterol levels after 8 weeks of treatment. At the same time point, the cecal microbiota of HFC diet-fed mice was characterized by an increased relative abundance of Allobaculum, Anaerofustis, F16, Lactobacillus reuteri and Deltaproteobacteria, and a decreased relative abundance of Akkermansia muciniphila compared to HFS mice. Comparison of cecal microbiota of high-fat fed mice versus control mice indicated major changes that were shared between the HFC and the HFS diet, including the increase in Lactobacillus plantarum, Lutispora, and Syntrophomonas, while some other shifts were specifically associated to either coconut or soy oil. Prediction of bacterial gene functions showed that the cecal microbiota of HFC mice was depleted of pathways involved in fatty acid metabolism, amino acid metabolism, xenobiotic degradation and metabolism of terpenoids and polyketides compared to mice on HFS diet. Correlation analysis revealed remarkable relationships between compositional changes in the cecal microbiota and alterations in the metabolic and transcriptomic phenotypes of high-fat fed mice. Conclusions: The study highlights significant differences in cecal microbiota composition and predictive functions of mice consuming a diet enriched in coconut vs soy oil. The correlations established between specific bacterial taxa and various traits linked to host lipid metabolism and energy storage give insights into the role and functioning of the gut microbiota that may contribute to diet-induced metabolic disorders