Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS)

Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases.Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data.Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69.Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy

An improved algorithm for the series step-up method based on a linear three-ports network

Capacitive leakage and adjacent interference are the main influence sources of the measuring error in the traditional series step-up method. To solve the two problems, a new algorithm was proposed in this study based on a three-ports network. Considering the two influences, it has been proved that response of this three-ports network still has characteristics of linear superposition with this new algorithm. In this threeport network, the auxiliary series voltage transformers use a two-stage structure that can further decrease measurement uncertainty. The measurement uncertainty of this proposed method at 500/√3 kV is 6.8 ppm for ratio error and 7 μrad for phase displacement ( k = 2). This new method has also been verified by comparing its results with measurement results of the PTB in Germany over the same 110/√3 kV standard voltage transformer. According to test results, the error between the two methods was less than 2.7 ppm for ratio error and 2.9 μrad for phase displacement

Inhibition of Ocular Neovascularization by Co-Inhibition of VEGF-A and PLGF

Background/Aims: Age-related macular degeneration (AMD) appears to be a disease with increasing incidence in Western countries and may develop into acquired blindness. Choroidal neovascularization (CNV) is the most frequent cause for AMD, and is commonly induced by regional inflammation. Past studies have highlighted vascular endothelial growth factor A (VEGF-A) as a major trigger for CNV. However, studies on the associated angiogenic factors other than VEGF-A are lacking. Methods: Here, we used a well-established laser burn (LB)-induced experimental CNV mouse model to study the molecular mechanisms underlying the development of CNV after ocular injury. We analyzed vessel density by lectin labeling. We isolated macrophages, endothelial cells and other cell types by flow cytometry, and analyzed levels of different angiogenic factors in these populations. We used antisera against VEGF-A (aVEGF) and/or antisera against placental growth factor (PLGF; aPLGF) to antagonize CNV. We used an antibody-driven toxin to selectively eliminate macrophages to evaluate the role of macrophages in CNV. We also examined expression of PLGF in macrophage subtypes. Results: The choroidal vessel density increased significantly 7 days after LB. LB increased significantly the levels of VEGF-A and PLGF in mouse eyes. Treatment with aVEGF significantly blunted increases in vessel density by LB. Treatment with aPLGF alone did not significantly reduce increases in vessel density. However, aPLGF significantly increased the inhibitory effects of aVEGF on vessel density increases. While VEGF-A was produced by endothelial cells, macrophages and other types at similar levels, PLGF seemed to be predominantly produced by macrophages. Selective macrophage depletion significantly reduced CNV. M2, but M1 macrophages produced high levels of PLGF. Conclusions: Together, our data suggest a previously unappreciated role of PLGF in coordination with VEGF-A to regulate CNV during ocular injury. Our study highlights macrophages and their production of PLGF as novel targets for CNV therapy

Rheumatic Symptoms Following Coronavirus Disease 2019 (COVID-19): A Chronic Post–COVID-19 Condition

Background!#!Detailed characteristics of rheumatic symptoms of coronavirus disease 2019 (COVID-19) were still unknown. We aim to investigate the proportions, characteristics, and risk factors of this condition.!##!Methods!#!In this prospective, longitudinal cohort study, discharged patients with COVID-19 were interviewed face-to-face at 12 months after symptom onset. Rheumatic symptoms following COVID-19 included newly occurring joint pain and/or joint swelling. The risk factors of developing rheumatic symptoms were identified by multivariable logistic regression analysis.!##!Results!#!In total, 1296 of 2469 discharged patients with COVID-19 were enrolled in this study. Among them, 160 (12.3% [95% confidence interval {CI}, 10.6%-14.3%]) suffered from rheumatic symptoms following COVID-19 at 12-month follow-up. The most frequently involved joints were the knee joints (38%), followed by hand (25%) and shoulder (19%). Rheumatic symptoms were independent of the severity of illness and corticosteroid treatment during the acute phase, while elderly age (odds ratio [OR], 1.22 [95% CI, 1.06-1.40]) and female sex (OR, 1.58 [95% CI, 1.12-2.23]) were identified as the risk factors for this condition.!##!Conclusions!#!Our investigation showed a considerable proportion of rheumatic symptoms following COVID-19 in discharged patients, which highlights the need for continuing attention. Notably, rheumatic symptoms following COVID-19 were independent of the severity of illness and corticosteroid treatment during the acute phase

Inheritance and QTL mapping of cucumber mosaic virus resistance in cucumber (<i>Cucumis Sativus</i> L.)

<div><p>The commercial yield of cucurbit crops infected with Cucumber mosaic virus (CMV) severely decreases. Chemical treatments against CMV are not effective; therefore, genetic resistance is considered the primary line of defense. Here, we studied resistance to CMV in cucumber inbred line ‘02245’ using a recombinant inbred line (RIL) population generated from a cross between ‘65G’ and ‘02245’ as susceptible and resistant parents, respectively. Genetic analysis revealed that CMV resistance in cucumber is quantitatively inherited. Analysis of the RIL population revealed that a quantitative trait locus (QTL) was found on chromosome 6; named <i>cmv6</i>.<i>1</i>, this QTL was delimited by SSR9-56 and SSR11-177 and explained 31.7% of the phenotypic variation in 2016 and 28.2% in 2017. The marker SSR11-1, which is close to the locus, was tested on 78 different cucumber accessions and found to have an accuracy of 94% in resistant and moderately resistant lines but only 67% in susceptible lines. The mapped QTL was delimited within a region of 1,624.0 kb, and nine genes related to disease resistance were identified. Cloning and alignment of the genomic sequences of these nine genes between ‘65G’ and ‘02245’ revealed that Csa6M133680 had four single-base substitutions within the coding sequences (CDSs) and two single-base substitutions in its 3’-untranslated region, and the other eight genes showed 100% nucleotide sequence identity in their exons. Expression pattern analyses of Csa6M133680 in ‘65G’ and ‘02245’ revealed that the expression levels of Csa6M133680 significantly differed between ‘65G’ and ‘02245’ at 80 h after inoculation with CMV and that the expression in ‘02245’ was 4.4 times greater than that in ‘65G’. The above results provide insights into the fine mapping and marker-assisted selection in cucumber breeding for CMV resistance.</p></div

ELISA results for CMV and the distribution of CMV DIs.

<p>a Symptoms of the susceptible parental line ‘65G’, the resistant line ‘02245’ and their F₁ hybrid progeny after they were inoculated with CMV. b DAS-ELISA results for CMV in the leaves of P1 (‘65G’), P2 (‘02245’) and F1 plants as well as some plants within the RIL population; the result is consistent with the phenotypic identification. c Frequency distribution of the DI from CMV among the ‘65G’ב02245’ RIL population. The frequency distribution in June 2016 and June 2017 each presented a normal distribution ranging from resistant to susceptible phenotypes.</p

Mutation of the Csa6M133680 gene.

<p><b>a Distribution of 4 exons and 3 introns within the coding region of Csa6M133680.</b> b A single-nucleotide substitution (A / C) in exon 1, three single-nucleotide substitutions (C / T, C / T, T / A) in exon 4 and two single-nucleotide substitutions (T / A, C / G) in the 3’-untranslated region. c The four single-nucleotide substitution caused four amino acid substitutions (Lys / Thr, Ser / Leu, Pro / Leu, Asp / Glu). Note: The orange box indicates the 5’-untranslated region, the black box indicates coding regions, the black line indicates an intron, and the blue box indicates the 5’-untranslated region.</p