Effects of consumption of coconut oil or coconut on glycemic control and insulin sensitivity: A systematic review and meta-analysis of interventional trials.

BACKGROUND AND AIMS: The often purported claim that coconut fat is beneficial for cardiovascular health and was disputed in several recent meta-analyses. However, the evidence on the effects of coconut fat intake on glycemic control remains equivocal. We conducted a systematic review and meta-analysis in accordance with the PRISMA guidelines to determine the effects of dietary coconut fats on markers of acute and long-term glycemic control. METHODS AND RESULTS: PubMed, Scopus, ProQuest, and Web-of-Science databases were searched and the records were screened by three independent reviewers to identify interventional studies examining acute and long-term (i.e., >10 days) effects of coconut fat on glycemic control. DerSimonian-Laird random-effects meta-analyses were performed using the meta-package in R (4.0.2). Seven interventional studies on acute effects and 11 interventional studies on long-term effects of coconut fat were included. Meals with coconut fat acutely increased the incremental area under the curve (AUC) of glucose (p = 0.046) and decreased the incremental AUC of insulin (p = 0.037) vs. control meals. Long-term coconut fat intake increased HOMA-IR (p = 0.049), but did not significantly affect fasting glucose, insulin, or HOMA-ß vs. control meals. CONCLUSIONS: Coconut fat in meals seems to be associated with a diminished postprandial insulin response, resulting in a subtle increase in the postprandial glycemic response. Long-term intake of coconut fat seems to increase insulin resistance, yet does not seem to be beneficial for long-term glycemic control. Thus, our results disprove the popular claim that coconut fat improves glycemic control. REGISTRATION: PROSPERO registry (CRD42020183450).

Diagnostic dissonance and negotiations of biomedicalisation: mental health practitioners' resistance to the DSM technology and diagnostic standardisation.

Previous research demonstrates that biomedicalisation and diagnostic processes are intertwined in American mental health care, but few studies examine practitioners' negotiations. This study examines how Mental Health Practitioners (MHPs) negotiate the Diagnostic and Statistical Manual (DSM), diagnosis, standardisation and biomedicalisation-in-practice. Feminist grounded theory analysis of 42 semi-structured interviews with licensed adolescent MHPs reveals accounts of discursive, everyday resistance to the DSM technology and standardisation, which I regard as key aspects of biomedicalisation. Findings demonstrate MHPs seemingly practice what I term diagnostic dissonance: a deep conflict between their professional theoretical orientations and the biomedical model legitimated in the DSM technology and insurers' diagnostic standardisation. MHPs enact dissonance by undermining the DSM, working around standardisation and by coding the social. Coding the social refers to the employment of V-codes - illegitimate secondary diagnoses - which convey social and relational conditions of mental distress. MHPs' contestations of the DSM and standardisation are responses to a healthcare infrastructure that decontextualises mental health. Practitioner resistance to biomedicalisation-in-diagnosis is important because the biomedicalisation of mental health takes focus away from the social and relational conditions and solutions to individual and community health and illness.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Tryptanthrin derivatives as Toxoplasma gondii inhibitors--structure-activity-relationship of the 6-position.

A panel of derivatives of the natural product tryptanthrin was synthesized and screened for its in vitro activity against the intracellular parasite Toxoplasma gondii. We concentrated on the modification of the 6-keto group of tryptanthrin and prepared a series of oximes, hydrazones and alcohols based on tryptanthrin. We evaluated parasite growth inhibition and host cell cytotoxicity. Our results indicate that in particular alcohol analogs are promising candidates for further investigation.

Is the 2,3-carbon-carbon bond of indole really inert to oxidative cleavage by Oxone?--synthesis of isatoic anhydrides from indoles.

A recent report has indicated that the oxidizing agent Oxone does not possess the ability to cleave the 2,3-carbon-carbon bond of indole. Work in our laboratory shows that this is not the case. Indole and a variety of aryl ring substituted derivatives readily react to form synthetically important isatoic anhydrides.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

Cancellous bone fast and slow waves obtained with Bayesian probability theory correlate with porosity from computed tomography.

A Bayesian probability theory approach for separating overlapping ultrasonic fast and slow waves in cancellous bone has been previously introduced. The goals of this study were to investigate whether the fast and slow waves obtained from Bayesian separation of an apparently single mode signal individually correlate with porosity and to isolate the fast and slow waves from medial-lateral insonification of the calcaneus. The Bayesian technique was applied to trabecular bone data from eight human calcanei insonified in the medial-lateral direction. The phase velocity, slope of attenuation (nBUA), and amplitude were determined for both the fast and slow waves. The porosity was assessed by micro-computed tomography (microCT) and ranged from 78.7% to 94.1%. The method successfully separated the fast and slow waves from medial-lateral insonification of the calcaneus. The phase velocity for both the fast and slow wave modes showed an inverse correlation with porosity (R(2) = 0.73 and R(2) = 0.86, respectively). The slope of attenuation for both wave modes also had a negative correlation with porosity (fast wave: R(2) = 0.73, slow wave: R(2) = 0.53). The fast wave amplitude decreased with increasing porosity (R(2) = 0.66). Conversely, the slow wave amplitude modestly increased with increasing porosity (R(2) = 0.39).

Determining attenuation properties of interfering fast and slow ultrasonic waves in cancellous bone.

Previous studies have shown that interference between fast waves and slow waves can lead to observed negative dispersion in cancellous bone. In this study, the effects of overlapping fast and slow waves on measurements of the apparent attenuation as a function of propagation distance are investigated along with methods of analysis used to determine the attenuation properties. Two methods are applied to simulated data that were generated based on experimentally acquired signals taken from a bovine specimen. The first method uses a time-domain approach that was dictated by constraints imposed by the partial overlap of fast and slow waves. The second method uses a frequency-domain log-spectral subtraction technique on the separated fast and slow waves. Applying the time-domain analysis to the broadband data yields apparent attenuation behavior that is larger in the early stages of propagation and decreases as the wave travels deeper. In contrast, performing frequency-domain analysis on the separated fast waves and slow waves results in attenuation coefficients that are independent of propagation distance. Results suggest that features arising from the analysis of overlapping two-mode data may represent an alternate explanation for the previously reported apparent dependence on propagation distance of the attenuation coefficient of cancellous bone.