Complete mitochondrial genome of Dahlica (Dahlica) ochrostigma Roh and Byun, 2018 (Lepidoptera: Psychidae)

We, herein, report the complete mitochondrial genome of Dahlica (Dahlica) ochrostigma. This species’ genome has a total length of 15,429 bp (GenBank accession number: MK890245), consisting of 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and an A + T rich control region. The nucleotide composition is 39.1% T, 42.8% A, 11.1% C, and 7.0% G. This is the first report of a complete mitochondrial genome of the subfamily Naryciinae, and this mitogenomic sequence can be used as a reference for phylogenetic studies on the family Psychidae

Undetected lung cancer at posteroanterior chest radiography: Potential role of a deep learning–based detection algorithm

Purpose: To evaluate the performance of a deep learning–based algorithm in detecting lung cancers not reported on posteroanterior chest radiographs during routine practice. Materials and Methods: The retrospective test dataset included 168 posteroanterior chest radiographs acquired between March 2017 and December 2018 (168 patients; mean age, 71.9 years ± 9.5 [standard deviation]; age range, 42–91 years) with 187 lung cancers (mean size, 2.3 cm ± 1.2) undetected during initial clinical evaluation, and 50 normal chest radiographs. CT served as the reference standard for ground truth. Four thoracic radiologists independently reevaluated the chest radiographs for lung nodules both without and with the aid of the algorithm. The performances of the algorithm and the radiologists were evaluated and compared on a per–chest radiograph basis and a per-lesion basis, according to the area under the receiver operating characteristic curve (AUROC) and area under the jackknife free-response ROC curve (AUFROC). Results: The algorithm showed excellent diagnostic performances both in terms of per-chest radiograph classification (AUROC, 0.899) and per-lesion localization (AUFROC, 0.744); both of these values were significantly higher than those of the radiologists (AUROC, 0.634–0.663; AUFROC, 0.619–0.651; P < .001 for all). The algorithm also demonstrated higher sensitivity (69.6% [117 of 168] vs 47.0% [316 of 672]; P < .001) and specificity (94.0% [47 of 50] vs 78.0% [156 of 200]; P = .01). When assisted by the algorithm, the radiologists AUROC (0.634–0.663 vs 0.685–0.724; P < 0.01 for all) and pooled AUFROC (0.636 vs 0.688; P = .03) substan-tially improved. The false-positive rate of the algorithm, that is, the total number of false-positive nodules divided by the total number of chest radiographs, was similar to that of pooled radiologists (21.1% [46 of 218] vs 19.0% [166 of 872]; P > .05). Conclusion: A deep learning–based nodule detection algorithm showed excellent detection performance of lung cancers that were not reported on chest radiographs during routine practice and significantly reduced reading errors when used as a second reader.Y

Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction

Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. Methods One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 +/- 1.53 mGy; abdominal CT, 7.10 +/- 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. Results The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 +/- 2.81, 14.8 +/- 2.56, and 12.3 +/- 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 +/- 0.23 vs. 2.87 +/- 0.26; p < .001), while DLIR showed better spatial resolution (2.60 +/- 0.34 vs. 2.44 +/- 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. Conclusion With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers.N

N-chloro hydantoin functionalized polyurethane fibers toward protective cloth against chemical warfare agents

Polyurethane nanofibers functionalized by high amount of N-chloro hydantoin were prepared for the decontamination of chemical warfare agents. Azido-polyurethane was firstly synthesized using azidopolydiol with 4,40-methylenebis(phenylisocyanate) and 1,4-butanediol via step-addition polymerization. Hydantoin was introduced into the polyurethane via click reaction, followed by electrospinning and chlorination to obtain the decontaminable fibers. This N-chlorinated hydantoin-polyurethane fiber is an active decontaminable species for 2-chloroethyl ethyl sulfide and demeton-S-methyl, the simulant of chemical warfare agent. The decontamination efficiency of each exhibits 69% and 16% for 2-chloroethyl ethyl sulfide and demeton-S-methyl, respectively, with molar ratio of 1/1 for 2 h at ambient condition. This N-chlorinated hydantoin-polyurethane fiber exhibited considerable potential as the decontaminable material against toxic chemical warfare agents. (C) 2018 Elsevier Ltd. All rights reserved

Prospective evaluation of thoracic diseases using a compact flat-panel detector spiral computed tomographic scanner

Objective: To prospectively evaluate the image quality and diagnostic performance of a compact flat-panel detector (FD) scanner for thoracic diseases compared to a clinical CT scanner.Materials and methods: The institutional review board approved this single-center prospective study, and all participants provided informed consent. From December 2020 to May 2021, 30 patients (mean age, 67.1 +/- 8.3 years) underwent two same-day low-dose chest CT scans using clinical state-of-art and compact FDCT scanners. Image quality was assessed visually and quantitatively. Two readers evaluated the diagnostic performance for nodules, parenchymal opacifications, bronchiectasis, linear opacities, and pleural abnormalities in 40 paired CT scans. The other 20 paired CT scans were used to examine the agreement of semi-quantitative CT scoring regarding bronchiectasis, bronchiolitis, nodules, airspace consolidations, and cavities.Results: FDCT images had significantly lower visual image quality than clinical CT images (all p < 0.001). The two CT image sets showed no significant differences in signal-to-noise and contrast-to-noise ratios (56.8 +/- 12.5 vs. 57.3 +/- 15.2; p = 0.985 and 62.9 +/- 11.7 vs. 60.7 +/- 16.9; p= 0.615). The pooled sensitivity was comparable for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities (p = 0.065-0.625), whereas the sensitivity was significantly lower in FDCT images than in clinical CT images for micronodules (p = 0.007) and bronchiectasis (p= 0.004). The specificity was mostly 1.0. Semi-quantitative CT scores were similar between the CT image sets (p > 0.05), and intraclass correlation coefficients were around 0.950 or higher, except for bronchiectasis (0.869).Conclusion: Compact FDCT images provided lower image quality but comparable diagnostic performance to clinical CT images for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities.Y

Nodule detection sensitivity of Lung VCAR.

PurposeTo prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).MethodsThe institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool.ResultsDLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P 50 (P = .01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65).ConclusionDLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.</div

Metabolic Adaptation in Obesity and Type II Diabetes: Myokines, Adipokines and Hepatokines

Obesity and type II diabetes are characterized by insulin resistance in peripheral tissues. A high caloric intake combined with a sedentary lifestyle is the leading cause of these conditions. Whole-body insulin resistance and its improvement are the result of the combined actions of each insulin-sensitive organ. Among the fundamental molecular mechanisms by which each organ is able to communicate and engage in cross-talk are cytokines or peptides which stem from secretory organs. Recently, it was reported that several cytokines or peptides are secreted from muscle (myokines), adipose tissue (adipokines) and liver (hepatokines) in response to certain nutrition and/or physical activity conditions. Cytokines exert autocrine, paracrine or endocrine effects for the maintenance of energy homeostasis. The present review is focused on the relationship and cross-talk amongst muscle, adipose tissue and the liver as secretory organs in metabolic diseases

Scoring system of subjective image quality.

PurposeTo prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).MethodsThe institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool.ResultsDLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P 50 (P = .01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65).ConclusionDLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.</div

Flowchart of participants and lung nodules included for the nodule detectability and Lung-RADS category evaluation.

Flowchart of participants and lung nodules included for the nodule detectability and Lung-RADS category evaluation.</p