Improving Automatic Melanoma Diagnosis using Deep Learning-Based Segmentation of Irregular Networks

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a Ground-Truth Irregular Pigment Network Dataset. We Trained Multiple Transfer Learned Segmentation Models to Detect Irregular Networks in This Training Set. a Separate, Mutually Exclusive Subset of the International Skin Imaging Collaboration (ISIC) 2019 Dataset with 500 Melanomas and 500 Benign Lesions Was Used for Training and Testing Deep Learning Models for the Binary Classification of Melanoma Versus Benign. the Best Segmentation Model, U-Net++, Generated Irregular Network Masks on the 1000-Image Dataset. Other Classical Color, Texture, and Shape Features Were Calculated for the Irregular Network Areas. We Achieved an Increase in the Recall of Melanoma Versus Benign of 11% and in Accuracy of 2% over DL-Only Models using Conventional Classifiers in a Sequential Pipeline based on the Cascade Generalization Framework, with the Highest Increase in Recall Accompanying the Use of the Random Forest Algorithm. the Proposed Approach Facilitates Leveraging the Strengths of Both Deep Learning and Conventional Image Processing Techniques to Improve the Accuracy of Melanoma Diagnosis. Further Research Combining Deep Learning with Conventional Image Processing on Automatically Detected Dermoscopic Features is Warranted

Paediatric reference values for total psoas muscle area

Background: Sarcopenia, the unintentional loss of skeletal muscle mass, is associated with poor outcomes in adult patient populations. In adults, sarcopenia is often ascertained by cross-sectional imaging of the psoas muscle area (PMA). Although children with chronic medical illnesses may be at increased risk for muscle loss because of nutritional deficiencies, physical deconditioning, endocrine anomalies, and systemic inflammation, consistent quantitative definitions for sarcopenia in children are lacking. We aimed to generate paediatric reference values for PMA at two intervertebral lumbar levels, L3–4 and L4–5. Methods: In this cross-sectional study, we analysed abdominal computed tomography scans of consecutive children presenting to the emergency department. Participants were children 1–16 years who required abdominal cross-sectional imaging after paediatric trauma between January 1, 2005 and December 31, 2015 in a large Canadian quaternary care centre. Children with a documented chronic medical illness or an acute spinal trauma at presentation were excluded. Total PMA (tPMA) at levels L3–4 and L4–5 were measured in square millimetres (mm2) as the sum of left and right PMA. Age-specific and sex-specific tPMA percentile curves were modelled using quantile regression. Results: Computed tomography images from 779 children were included. Values of tPMA at L4–5 were significantly larger than at L3–4 at all ages, but their correlation was high for both girls (r = 0.95) and boys (r = 0.98). Amongst girls, tPMA 50th percentile values ranged from 365 to 2336 mm2 at L3–4 and from 447 to 2704 mm2 for L4–5. Amongst boys, 50th percentile values for tPMA ranged between 394 and 3050 mm2 at L3–4 and from 498 to 3513 mm2 at L4–5. Intraclass correlation coefficients were excellent at L3–4 (0.97, 95% CI 0.94 to 0.981) and L4–5 (0.99, 95% CI 0.986 to 0.995). Weight and tPMA were correlated, stratified by sex for boys (L3–4 r = 0.90; L4–5 r = 0.90) and for girls (L3–4 r = 0.87; L4–5 r = 0.87). An online application was subsequently developed to easily calculate age-specific and sex-specific z-scores and percentiles. Conclusions: We provide novel paediatric age-specific and sex-specific growth curves for tPMA at intervertebral L3–4 and L4–5 levels for children between the ages of 1-16 years. Together with an online tool (https://ahrc-apps.shinyapps.io/sarcopenia/), these tPMA curves should serve as a reference enabling earlier identification and targeted intervention of sarcopenia in children with chronic medical conditions

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Building an Analog Clock with Complex Numbers

In this article, we describe a lesson that enabled 10th and 11th grade students to create an analog clock using GeoGebra. This self-directed, exploratory lesson is built on students’ prior knowledge of complex numbers and vector transformation and relies on the technological features of GeoGebra

Selective ethylene oligomerization with nickel oxime complexes

Two nickel-oxime complexes, Ni (2-pyridine aldoxime)2 dichloride (6) and Ni (methyl 2-pyridyl ketone oxime)2 dichloride (7), were prepared and characterized by X-ray crystallography. Both complexes are six-coordinate and have 2-pyridylimines as ligand backbones, where the two nitrogen atoms from each of the oxime ligands coordinate to the metal, but the oxygen atom remains protonated and does not participate in bonding to the metal center. The complexes were found to be selective ethylene dimerization catalysts in the presence of cocatalyst such as methylaluminoxane (MAO) or diethyl aluminum chloride (DEAC). With DEAC, the productivity is considerably higher than with MAO. Under optimum conditions the productivity values are as high as 200 kg/mol catalyst/h/bar (for 6) to 335 kg/mol catalyst/h/bar (for 7). The selectivities toward dimerization under these conditions are 84% and 77%, respectively, with the terminal isomer 1-butene being the only (≥99.5%) C4 product

Study of sexual behavior and prevalence of STIs/RTIs and HIV among female workers of textile industries in Surat city, Gujarat, India

Background: Surat city is vulnerable to transmission of sexually transmitted infections (STIs)/HIV due to its huge migratory population in diamond and textile industries. Females working in textile industries were not receiving focused intervention although they were at high risk of acquiring STIs/HIV. Objective: The present study was conducted to know the prevalence of various STIs and HIV among the group of female textile workers in Surat city. The findings of the study will be helpful for policy decision makers to address the issues of a specific vulnerable group. Materials and Methods: A total 257 female workers in various textile markets were enrolled in the present study. Data were collected by the help of a pre-tested questionnaire and analysis was done by using Microsoft Excel and the EPI Info software. Result: Overall prevalence of various STIs/RTIs (reproductive tract infections) was 16.73%, whereas HIV positivity was 1.17%. Bacterial vaginosis and candidiasis were the most common infections. Conclusion: Groups such as female textile workers need to be taken care of especially to enhance the HIV prevention and control activities in Surat city, which would help in breaking the chain of transmission

Detection of Faulty Sensors in Wireless Sensor Networks and Avoiding the Path Failure Nodes

Abstract — For variety of applications, Wireless Sensor Networks (WSNs) have become a new information collection and a monitoring solution. Faults occurring due to sensor nodes are common due low-cost sensors used in WSNs, deployed in large quantities and prone to failure. The goal of this paper is to detect faulty sensors in WSNs and avoiding the path failure nodes. Fault detection is based on the local pairwise verification between the sensors monitoring the same physical system. Specifically, a linear relationship is shown between the output of any pair of sensors, when the input of a system comes from a common source. Using this relationship, faulty sensors may be detected by using forecasting model based on the parameter (i.e., temperature) and it also identifies which sensor is normal or abnormal. After the fault nodes are detected, first of all disable all the faulty nodes so that network is not affected by erroneous reading and send the information to the base station. Due to the nature of proposed algorithm, it can be scaled to large sensor networks and also saves energy from reduced wireless communication compared to the centralized approaches