Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the constant region of interest (ROI) and only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to investigate the nodule presence outside the given VOI and also include the redundant structures into VOI, which may lead to inaccurate nodule segmentation. In this work, a novel semi-automated approach for 3-D segmentation of nodule in volumetric computerized tomography (CT) lung scans has been proposed. The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy. The adaptive ROI algorithm enables the solution to dynamically select the ROI for the surrounding slices to investigate the presence of nodule using deep residual U-Net architecture. The first stage provides the initial estimation of nodule which is further utilized to extract the VOI. At the second stage, the extracted VOI is further investigated along the coronal and sagittal axis with two different networks and finally, all the estimated masks are fed into the consensus module to produce the final volumetric segmentation of nodule. The proposed approach has been rigorously evaluated on the LIDC dataset, which is the largest publicly available dataset. The result suggests that the approach is significantly robust and accurate as compared to the previous state of the art techniques.Comment: The manuscript is currently under review and copyright shall be transferred to the publisher upon acceptanc

MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively

The relationship between changes in grit, taekwondo ability, and academic achievement of university students majoring in science and engineering and participating in taekwondo class

GRIT, which was conceptualized by the American psychologist Duckworth, was designed by grouping growth (G), resilience (R), intrinsic motivation (I), and tenacity (T), which means con-tinuing to be patient and put in effort to achieve goals without being frustrated by adversity or failures experienced in the process of striving toward one’s goals. The purpose of this study was to determine GRIT changes caused by participation of students majoring in science and engineering in taekwondo class. Effects of taekwondo ability on GRIT and academic achievement were also examined to determine structural relationships among taekwondo ability, GRIT, and academic achievement. We selected a total of 305 students (204 participants and 101 non-participants) as research subjects and conducted a GRIT (preliminary) measurement. After one-year of taekwondo class, we collected and statistically processed the data of GRIT (post) measurement, taekwondo ability, and academic achievement of the participants. Reliability analysis, technical statistics, paired sample t-test, correlation analysis, and path analysis were performed. Changes in the GRIT values of the participants were found to be greater than those of non-participants. It was also found that taekwondo ability, GRIT, and academic achievement had significant correlations with each other. Finally, it was found that the higher the taekwondo ability, the higher the academic achievement and the higher the GRIT. Moreover, the higher the GRIT, the higher the academic achievement. Taekwondo training increased the GRIT values of participants. In addition, the taekwondo ability had positive effects on GRIT and academic achievement. GRIT also had a positive effect on academic achievement. Thus, there were structural relationships among taekwondo ability, GRIT, and academic achievement. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Yoga Training Improves Metabolic Parameters in Obese Boys

Yoga has been known to have stimulatory or inhibitory effects on the metabolic parameters and to be uncomplicated therapy for obesity. The purpose of the present study was to test the effect of an 8-week of yoga-asana training on body composition, lipid profile, and insulin resistance (IR) in obese adolescent boys. Twenty volunteers with body mass index (BMI) greater than the 95th percentile were randomly assigned to yoga (age 14.7±0.5 years, n=10) and control groups (age 14.6±1.0 years, n=10). The yoga group performed exercises three times per week at 40~60% of heart-rate reserve (HRR) for 8 weeks. IR was determined with the homeostasis model assessment of insulin resistance (HOMA-IR). After yoga training, body weight, BMI, fat mass (FM), and body fat % (BF %) were significantly decreased, and fat-free mass and basal metabolic rate were significantly increased than baseline values. FM and BF % were significantly improved in the yoga group compared with the control group (p\u3c0.05). Total cholesterol (TC) was significantly decreased in the yoga group (p\u3c0.01). HDL-cholesterol was decreased in both groups (p\u3c0.05). No significant changes were observed between or within groups for triglycerides, LDL-cholesterol, glucose, insulin, and HOMA-IR. Our findings show that an 8-week of yoga training improves body composition and TC levels in obese adolescent boys, suggesting that yoga training may be effective in controlling some metabolic syndrome factors in obese adolescent boys

Bacillus subtilis spores as adjuvants against avian influenza H9N2 induce antigen-specific antibody and T cell responses in White Leghorn chickens

Low-pathogenicity avian influenza H9N2 remains an endemic disease worldwide despite continuous vaccination, indicating the need for an improved vaccine strategy. Bacillus subtilis (B. subtilis), a gram-positive and endospore-forming bacterium, is a non-pathogenic species that has been used in probiotic formulations for both animals and humans. The objective of the present study was to elucidate the effect of B. subtilis spores as adjuvants in chickens administered inactivated avian influenza virus H9N2. Herein, the adjuvanticity of B. subtilis spores in chickens was demonstrated by enhancement of H9N2 virus-specific IgG responses. B. subtilis spores enhanced the proportion of B cells and the innate cell population in splenocytes from chickens administered both inactivated H9N2 and B. subtilis spores (Spore + H9N2). Furthermore, the H9N2 and spore administration induced significantly increased expression of the pro-inflammatory cytokines IL-1β and IL-6 compared to that in the H9N2 only group. Additionally, total splenocytes from chickens immunized with inactivated H9N2 in the presence or absence of B. subtilis spores were re-stimulated with inactivated H9N2. The subsequent results showed that the extent of antigen-specific CD4+ and CD8+ T cell proliferation was higher in the Spore + H9N2 group than in the group administered only H9N2. Taken together, these data demonstrate that B. subtilis spores, as adjuvants, enhance not only H9N2 virus-specific IgG but also CD4+ and CD8+ T cell responses, with an increase in pro-inflammatory cytokine production. This approach to vaccination with inactivated H9N2 together with a B. subtilis spore adjuvant in chickens produces a significant effect on antigen-specific antibody and T cell responses against avian influenza virus.This study and medical writing support were funded by Sanofi Genzyme and Regeneron Pharmaceuticals, Inc

Positive Association between Aspirin-Intolerant Asthma and Genetic Polymorphisms of FSIP1: a Case-Case Study

<p>Abstract</p> <p>Background</p> <p>Aspirin-intolerant asthma (AIA), which is caused by non-steroidal anti-inflammatory drugs (NSAIDs) such as aspirin, causes lung inflammation and reversal bronchi reduction, leading to difficulty in breathing. Aspirin is known to affect various parts inside human body, ranging from lung to spermatogenesis. <it>FSIP1</it>, also known as <it>HDS10</it>, is a recently discovered gene that encodes fibrous sheath interacting protein 1, and is regulated by amyloid beta precursor protein (APP). Recently, it has been reported that a peptide derived from APP is cleaved by α disintegrin and metalloproteinase 33 (<it>ADAM33</it>), which is an asthma susceptibility gene. It has also been known that the <it>FSIP1 </it>gene is expressed in airway epithelium.</p> <p>Objectives</p> <p>Aim of this study is to find out whether <it>FSIP1 </it>polymorphisms affect the onset of AIA in Korean population, since it is known that AIA is genetically affected by various genes.</p> <p>Methods</p> <p>We conducted association study between 66 single nucleotide polymorphisms (SNPs) of the <it>FSIP1 </it>gene and AIA in total of 592 Korean subjects including 163 AIA and 429 aspirin-tolerant asthma (ATA) patients. Associations between polymorphisms of <it>FSIP1 </it>and AIA were analyzed with sex, smoking status, atopy, and body mass index (BMI) as covariates.</p> <p>Results</p> <p>Initially, 18 SNPs and 4 haplotypes showed associations with AIA. However, after correcting the data for multiple testing, only one SNP showed an association with AIA (corrected <it>P</it>-value = 0.03, OR = 1.63, 95% CI = 1.23-2.16), showing increased susceptibility to AIA compared with that of ATA cases. Our findings suggest that <it>FSIP1 </it>gene might be a susceptibility gene for aspirin intolerance in asthmatics.</p> <p>Conclusion</p> <p>Although our findings did not suggest that SNPs of <it>FSIP1 </it>had an effect on the reversibility of lung function abnormalities in AIA patients, they did show significant evidence of association between the variants in <it>FSIP1 </it>and AIA occurrence among asthmatics in a Korean population.</p

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

Automated assessment of pelvic radiographs using deep learning: A reliable diagnostic tool for pelvic malalignment

Pelvic malalignment leads to general imbalance and adversely affects leg length. Timely and accurate diagnosis of pelvic alignment in patients is crucial to prevent additional complications arising from delayed treatment. Currently, doctors typically assess pelvic alignment either manually or through radiography. This study aimed to develop and assess the validity of a deep learning-based system for automatically measuring 10 radiographic parameters necessary for diagnosing pelvic displacement using standing anteroposterior pelvic X-rays. Between March 2016 and June 2021, pelvic radiographs from 1215 patients were collected. After applying specific selection criteria, 550 pelvic radiographs were chosen for analysis. These data were utilized to develop a deep learning-based system capable of automatically measuring radiographic parameters relevant to pelvic displacement diagnosis. The system's diagnostic accuracy was evaluated by comparing automatically measured values with those assessed by a clinician using 200 radiographs selected from the initial 550. The results indicated that the system exhibited high reliability, accuracy, and reproducibility, with a Pearson correlation coefficient of ≥0.9, an intra-class correlation coefficient of ≥0.9, a mean absolute error of ≤1 cm, mean square error of ≤1 cm, and root mean square error of ≤1 cm. Moreover, the system's measurement time for a single radiograph was found to be 18 to 20 times faster than that required by a clinician for manual inspection. In conclusion, our proposed deep learning-based system effectively utilizes standing anteroposterior pelvic radiographs to precisely and consistently measure radiographic parameters essential for diagnosing pelvic displacement