Project description:Importance:Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke. A readily accessible means to continuously monitor for AF could prevent large numbers of strokes and death. Objective:To develop and validate a deep neural network to detect AF using smartwatch data. Design, Setting, and Participants:In this multinational cardiovascular remote cohort study coordinated at the University of California, San Francisco, smartwatches were used to obtain heart rate and step count data for algorithm development. A total of 9750 participants enrolled in the Health eHeart Study and 51 patients undergoing cardioversion at the University of California, San Francisco, were enrolled between February 2016 and March 2017. A deep neural network was trained using a method called heuristic pretraining in which the network approximated representations of the R-R interval (ie, time between heartbeats) without manual labeling of training data. Validation was performed against the reference standard 12-lead electrocardiography (ECG) in a separate cohort of patients undergoing cardioversion. A second exploratory validation was performed using smartwatch data from ambulatory individuals against the reference standard of self-reported history of persistent AF. Data were analyzed from March 2017 to September 2017. Main Outcomes and Measures:The sensitivity, specificity, and receiver operating characteristic C statistic for the algorithm to detect AF were generated based on the reference standard of 12-lead ECG-diagnosed AF. Results:Of the 9750 participants enrolled in the remote cohort, including 347 participants with AF, 6143 (63.0%) were male, and the mean (SD) age was 42 (12) years. There were more than 139 million heart rate measurements on which the deep neural network was trained. The deep neural network exhibited a C statistic of 0.97 (95% CI, 0.94-1.00; P < .001) to detect AF against the reference standard 12-lead ECG-diagnosed AF in the external validation cohort of 51 patients undergoing cardioversion; sensitivity was 98.0% and specificity was 90.2%. In an exploratory analysis relying on self-report of persistent AF in ambulatory participants, the C statistic was 0.72 (95% CI, 0.64-0.78); sensitivity was 67.7% and specificity was 67.6%. Conclusions and Relevance:This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help identify the optimal role for smartwatch-guided rhythm assessment.
Project description:High consumer demand for cannabidiol (CBD) has made high-CBD hemp (Cannabis sativa) an extremely high-value crop. However, high demand has resulted in the industry developing faster than the research, resulting in the sale of many hemp accessions with inconsistent performance and chemical profiles. These inconsistencies cause significant economic and legal problems for growers interested in producing high-CBD hemp. To determine the genetic and phenotypic consistency in available high-CBD hemp varieties, we obtained seed or clones from 22 different named accessions meant for commercial production. Genotypes (∼48,000 SNPs) and chemical profiles (% CBD and THC by dry weight) were determined for up to 8 plants per accession. Many accessions-including several with the same name-showed little consistency either genetically or chemically. Most seed-grown accessions also deviated significantly from their purported levels of CBD and THC based on the supplied certificates of analysis. Several also showed evidence of an active tetrahydrocannabinolic acid (THCa) synthase gene, leading to unacceptably high levels of THC in female flowers. We conclude that the current market for high-CBD hemp varieties is highly unreliable, making many purchases risky for growers. We suggest options for addressing these issues, such using unique names and developing seed and plant certification programs to ensure the availability of high-quality, verified planting materials.
Project description:Plant small RNAs are a diverse and complex set of molecules, ranging in length from 21 to 24 nt, involved in a wide range of essential biological processes. Nowadays, high-throughput sequencing is the most commonly used method for the discovery and quantification of small RNAs. However, it is known that several biases can occur during the preparation of small RNA libraries, especially using low input RNA. We used two types of plant biological samples to evaluate the performance of seven commercially available methods for small RNA library construction, using different RNA input amounts. We show that when working with plant material, library construction methods have differing capabilities to capture small RNAs, and that different library construction methods provide better results when applied to the detection of microRNAs, phased small RNAs, or tRNA-derived fragments.
Project description:Since several years there has been a demand for food products free of palm oil, noticeable in the Western European market. Alternatives based on liquid oils, fully hydrogenated fats, and exotic fats like shea and sal etc., have been developed by the research groups of several specialty oils and fats suppliers. This article describes the advantages and disadvantages of those products and compares them to similar products based on palm oil. It is also discussed how reasonable the replacement of palm products would be, since sustainable and 3-MCPD/glycidolester-reduced palm based specialty oils are also available on the market.
Project description:Replacing in vivo with in vitro studies can increase sustainability in the development of medicines. This principle has already been applied in the biowaiver approach based on the biopharmaceutical classification system, BCS. A biowaiver is a regulatory process in which a drug is approved based on evidence of in vitro equivalence, i.e., a dissolution test, rather than on in vivo bioequivalence. Currently biowaivers can only be granted for highly water-soluble drugs, i.e., BCS class I/III drugs. When evaluating poorly soluble drugs, i.e., BCS class II/IV drugs, in vitro dissolution testing has proved to be inadequate for predicting in vivo drug performance due to the lack of permeability interpretation. The aim of this review was to provide solid proofs that at least two commercially available cell-free in vitro assays, namely, the parallel artificial membrane permeability assay, PAMPA, and the PermeaPad® assay, PermeaPad, in different formats and set-ups, have the potential to reduce and replace in vivo testing to some extent, thus increasing sustainability in drug development. Based on the literature review presented here, we suggest that these assays should be implemented as alternatives to (1) more energy-intense in vitro methods, e.g., refining/replacing cell-based permeability assays, and (2) in vivo studies, e.g., reducing the number of pharmacokinetic studies conducted on animals and humans. For this to happen, a new and modern legislative framework for drug approval is required.
Project description:We compared the performance of six commercial kits (QIAseq, SMARTer, CATS, CleanTag, srLp, TailorMix) capable of handling <100ng total RNA input for miRNA detection sensitivity, reliability, titration response and ability to detect differentially expressed miRNAs at different amounts of synthetic miRNAs. We observed differences in detection sensitivity between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs.
Project description:ObjectivesMouthwashes, a cornerstone of oral and dental hygiene, play a pivotal role in combating the formation of dental plaque, a leading cause of periodontal disease and dental caries. This study aimed to review the composition of mouthwashes found on retail shelves in Turkey and evaluate their prevalence and side effects, if any.MethodsThe mouthwashes examined were sourced from the 5 largest chain stores in each district of Istanbul. A comprehensive list of the constituents was meticulously recorded. The research was supported by an extensive compilation of references from scholarly databases such as Google Scholar, PubMed, and ScienceDirect. Through rigorous analysis, the relative proportions of mouthwash ingredients and components were determined.ResultsA total of 45 distinctive variations of mouthwashes, representing 17 prominent brands, were identified. Amongst the 116 ingredients discovered, 70 were evaluated for potential adverse effects and undesirable side effects. The aroma of the mouthwash (n = 45; 100%), as welll as their sodium fluoride (n = 28; 62.22%), sodium saccharin (n = 29; 64.44%), sorbitol (n = 21; 46.6%), and propylene glycol (n = 28; 62.22%) content were the main undesireable features.ConclusionsThe limited array of mouthwashes found on store shelves poses a concern for both oral and public health. Furthermore, the intricate composition of these products, consisting of numerous ingredients with the potential for adverse effects, warrants serious attention. Both clinicians and patients should acknowledge the importance and unwarranted side effects of the compnents of the mouthwashes.
Project description:Although automated external defibrillators (AEDs) have contributed to a better survival of out-of-hospital cardiac arrests, there have been reports of their malfunctioning. We investigated the diagnostic accuracy of commercially available AEDs using surface ECGs of ventricular fibrillation (VF), ventricular tachycardia (VT), and supraventricular tachycardia (SVT).ECGs(VF 31, VT 48, SVT 97) were stored during electrophysiological studies and transmitted to 4 AEDs, the LifePak CR Plus (CR Plus), HeartStart FR3 (FR3), and CardioLife AED-2150 (CL2150) and -9231 (CL9231), through the pad electrode cables. For VF, the CL2150 and CL9231 advised shocks in all cases, and the CR Plus and FR3 advised shocks in all but one VF case. For VTs faster than 180 bpm, the ratios for advising shocks were 79%, 36%, 89%, and 96% for the CR Plus, FR3, CL2150, and CL9231, respectively. The FR3 and CR Plus did not advise shocks for narrow QRS SVTs, whereas the CL9231 tended to treat high-rate tachycardias faster than 180 bpm even with narrow QRS complexes. The characteristics of the shock advice for the FR3 differed from that for the CL9231 (kappa coefficient [κ]=0.479, P<0.001), and the CR Plus and CL2150 had characteristics somewhere between the 2 former AEDs (κ=0.818, P<0.001).Commercially available AEDs diagnosed VF almost always correctly. For VT and SVT diagnoses, a discrepancy was evident among the 4 investigated AEDs. The differences in the arrhythmia diagnosis algorithms for differentiating SVT from VT were thought to account for these differences.
Project description:Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers' health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of [Formula: see text] (activity recognition) and [Formula: see text] (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human-robot interaction.