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Virtual representations of personnel for Teleconsultation: Effects of The movie avatar Embodiment Tactics in Individual Perception in 3 dimensional Uneven Telepresence.
The experimental results showed that the proposed C-GCNN model achieves the best classification performance in CIN grading compared with hand-crafted feature extraction methods and classic deep learning methods. The results showed an accuracy of 96.87 %, a sensitivity of 95.68 %, and a specificity of 98.72 %.

A multistate colposcopy image dataset (MSCI) is proposed. A CIN grading model (C-GCNN) based on the MSCI dataset is established, which provides a potential method for automated cervical cancer screening.
A multistate colposcopy image dataset (MSCI) is proposed. A CIN grading model (C-GCNN) based on the MSCI dataset is established, which provides a potential method for automated cervical cancer screening.
Determining the key sets of competencies necessary for a Health Informatics (HI) professional to practice effectively either solo or as a member of a multidisciplinary team has been challenging for the regulator and registration body responsible for the healthcare workforce in Saudi Arabia, which is the Saudi Commission for Health Specialties (SCFHS).

The aim of this study was to develop a HI competency framework to guide SCFHS to introduce a HI certification program that meets local healthcare needs and is aligned with the national digital health transformation strategy.

A two-phase mixed methods approach was used in this study. For phase 1, a scoping review was conducted to identify HI competencies that have been published in the relevant literature. Out of a total 116 articles found relevant, 20 were included for further analysis. For phase 2, Saudi HI stakeholders (N = 24) that included HI professionals, administrators, academics, and healthcare professionals were identified and participated in an oties. We anticipate that this work will be expanded and adopted by relative professional and scientific bodies in the Gulf Cooperation Council (GCC) region.Crude glycerol, a by-product of biodiesel industry, has been used for production of biodiesel and polyhydroxy-alkanoates. But question is which product is economically favorable using crude glycerol as substrate? In this study, energy balance and economic assessment has been carried out for crude glycerol valorization for B10 biodiesel and polyhydroxy-butyrate (PHB) production. For same quantity of crude glycerol utilized, energy ratio for B10 production was higher than PHB production while unit production cost for B10 was lower than that of PHB. For 50 million L plant capacity of biodiesel, unit production cost was 0.77 $/L B10 while for 2 million kg plant capacity of PHB, unit production cost was 4.88 $/kg PHB. Thus, in present scenario production of biodiesel seems economically better than production of PHA with crude glycerol as raw material. This study is useful for researchers, environmental scientists and industries in identifying effective route for crude glycerol valorization.Fermentative production of sodium lactate generally is a low efficient process because of the high Na+ osmatic stress on lactic acid bacterium cells. In this study, the homogeneous genes encoding Na+/H+ antiporters were screened and overexpressed in Pediococcus acidilactici for the enhancement of Na+ transmembrane transportation. The function of the gene RS02775 was identified and its overexpressing in P. acidilactici resulted in the significantly improved sodium lactate production. The recombinant not only accelerated the sugar consumption, but also achieved the record high titer of sodium lactate by 121.1 g/L using pure sugars and 132.4 g/L using wheat straw. The transcription analysis shows that the overexpression of Na+/H+ antiporter significantly upregulated the transcription of the sugar phosphorylation genes of P. acidilactici under high Na+ stress. This study provides an effective method for high titer production of sodium lactate using both pure sugars and lignocellulose feedstocks.Enzyme cascades are efficient tools to perform multi-step synthesis in one-pot in a green and sustainable manner, enabling non-natural synthesis of valuable chemicals from easily available substrates by artificially combining two or more enzymes. Bioproduction of many high-value chemicals such as chiral and highly functionalised molecules have been achieved by developing new enzyme cascades. This review summarizes recent advances on engineering and application of enzyme cascades to produce high-value chemicals (alcohols, aldehydes, ketones, amines, carboxylic acids, etc) from simple starting materials. While 2-step enzyme cascades are developed for versatile enantioselective synthesis, multi-step enzyme cascades are engineered to functionalise basic chemicals, such as styrenes, cyclic alkanes, and aromatic compounds. New cascade reactions have also been developed for producing valuable chemicals from bio-based substrates, such as ʟ-phenylalanine, and renewable feedstocks such as glucose and glycerol. The challenges in current process and future outlooks in the development of enzyme cascades are also addressed.Malfunction and amyloid formation of the Islet Amyloid Polypeptide (IAPP) are factors contributing to Type 2 diabetes. Unravelling the mechanism of IAPP aggregate formation may forward our understanding of this process and its effect on pancreatic β-islet cell. Such mechanistic studies require access to sequence homogeneous and highly pure IAPP. Here we present a new facile protocol for the production of pure recombinant human IAPP at relatively high yield. The protocol uses a His-tagged version of the Npro mutant EDDIE, which drives expression to inclusion bodies, from which the peptide is purified using sonication, refolding and auto-cleavage, removal of EDDIE using Ni-NTA chromatography and reverse-phase HPLC. The purified material is used at multiple concentrations in aggregation kinetics measurements monitored by thioflavin-T fluorescence. Global analysis of the data implies a double nucleation aggregation mechanism including both primary and secondary nucleation.The present study investigated the antioxidant and invitro antidiabetic capacities of Justicia carnea aqueous leaf extract (JCAE) using α-amylase inhibition model. α-Amylase binding-interaction with JCAE was also investigated using fluorescence spectroscopy and molecular docking. Phytochemical screening and Gas Chromatography-Mass Spectrometry (GC-MS) analysis indicated presence of bioactive compounds. Phenolic (132 mg GAE/g) and flavonoid contents (31.08 mg CE/g) were high. Cerivastatin sodium cost JCAE exhibited high antioxidant capacity and effectively inhibited α-amylase activity (IC50, 671.43 ± 1.88 μg/mL), though lesser than acarbose effect (IC50, 108.91 ± 0.61 μg/mL). α-Amylase intrinsic fluorescence was quenched in the presence of JCAE. Ultraviolet-visible and FT-IR spectroscopies affirmed mild changes in α-amylase conformation. Synchronous fluorescence analysis indicated alterations in the microenvironments of tryptophan residues near α-amylase active site. Molecular docking affirmed non-polar interactions of compounds 6 and 7 in JCAE with Asp-197 and Trp-58 residues of α-amylase, respectively. Overall, JCAE indicated potential to prevent postprandial hyperglycemia by slowing down carbohydrate hydrolysis.
Asymmetric dimethylarginine (ADMA) is a cardiac biomarker in humans, symmetric dimethylarginine (SDMA) a renal biomarker in humans, cats, and dogs. The purpose of this prospective study was to investigate if measuring serum ADMA and SDMA concentrations via ELISA allows detection of cardiac disease in horses in a routine laboratory setting. In this context, reference values in horses were established.

Seventy-eight horses with no known medical history were compared to 23 horses with confirmed structural cardiac disease with/or without arrhythmias. link2 Horses underwent physical examination, electrocardiography, echocardiography and venous blood sampling and were staged based on the severity of cardiac disease from 0 to II. Asymmetric dimethylarginine and SDMA were measured via ELISA and crosschecked using liquid chromatograph triple quadrupole mass spectrometry. Reference intervals with 90th percent confidence intervals were evaluated and standard software was used to test for significant differences in ADMA, SDMA, and the l-arginine/ADMA ratio between groups.

The reference ranges were 1.7-3.8μmol/L and 0.3-0.8μmol/L for ADMA and SDMA, respectively. Serum ADMA was higher in horses with heart disease compared to healthy horses (p<0.01) and highest in horses with stage II heart disease (p=0.02). The l-Arginine/ADMA ratio was significantly higher in healthy animals than those with cardiac disease (p=0.001).

Reference values for serum ADMA and SDMA using ELISA methods are presented in horses. This study confirms the association between heart disease and increased serum ADMA concentration as well as a decreased l-Arginine/ADMA ratio in horses.
Reference values for serum ADMA and SDMA using ELISA methods are presented in horses. link3 This study confirms the association between heart disease and increased serum ADMA concentration as well as a decreased l-Arginine/ADMA ratio in horses.
Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications.

Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Aiogram quality is good or bad for heart rate analysis. Furthermore, removing bad quality segments decreases errors in heart rate calculation.
According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provided that a standard Lead I or Lead II is available. Such models accurately establish whether the electrocardiogram quality is good or bad for heart rate analysis. Furthermore, removing bad quality segments decreases errors in heart rate calculation.
Multiscale feature fusion is a feasible method to improve tumor segmentation accuracy. However, current multiscale networks have two common problems 1. Some networks only allow feature fusion between encoders and decoders of the same scale. It is obvious that such feature fusion is not sufficient. 2. Some networks have too many dense skip connections and too much nesting between the coding layer and the decoding layer, which causes some features to be lost and means that not enough information will be learned from multiple scales. To overcome these two problems, we propose a multiscale double-channel convolution U-Net (MDCC-Net) framework for colorectal tumor segmentation.

In the coding layer, we designed a dual-channel separation and convolution module and then added residual connections to perform multiscale feature fusion on the input image and the feature map after dual-channel separation and convolution. By fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.
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