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

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate

Prognostics, Health Assessment, and Modelling of Material Removal Rate by EDM for Al 6061 and AISI 304 via Cockroach Swarm and Fruit Fly Optimization Approaches

Micro-electric discharge machining (Micro-EDM) is deployed for machining hard-to-machine materials, such as various grades of titanium alloys, heat-treated alloy steels, composites, tungsten carbides, and so forth. Mild steel is known for its easy machinability. However, conventional machining of mild steel can often lead to the built-up edge formation on the tool. There is a minimal focus on machining ductile materials using nonconventional machining processes. This is due to the rapid work hardening in cold forming conditions. In the present study, the aluminium alloy 6061 and mild steel AISI 304 were taken as a work piece. Input pulse on factors considered as three levels and orthogonal array utilized to optimize the EDM parameters. Numerical results confirm the influence of input parameters in the response. The highest MRR is obtained at Ton = 40  μs and Toff = 4 μs, and the least MRR is acquired at Ton = 20 μs and Toff = 3 μs. The fruit fly algorithm and the cockroach swarm algorithm were used to predict the optimal minimized MRR value. The experimental results show that the cockroach swarm algorithm was performing better than the fruit fly algorithm in the MRR minimization process

Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review

Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington’s disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention

Automatic image caption generation is an intricate task of describing an image in natural language by gaining insights present in an image. Featuring facial expressions in the conventional image captioning system brings out new prospects to generate pertinent descriptions, revealing the emotional aspects of the image. The proposed work encapsulates the facial emotional features to produce more expressive captions similar to human-annotated ones with the help of Cross Stage Partial Dense Network (CSPDenseNet) and Self-attentive Bidirectional Long Short-Term Memory (BiLSTM) network. The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. Then, the extracted image feature vectors and word vectors are fused to form an encoding vector representing the rich image content. The decoding unit employs a self-attention mechanism encompassed with BiLSTM to create more descriptive and relevant captions in natural language. The Flickr11k dataset, a subset of the Flickr30k dataset is used to train, test, and evaluate the present model based on five benchmark image captioning metrics. They are BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Consensus-based Image Description Evaluation (CIDEr), and Semantic Propositional Image Caption Evaluation (SPICE). The experimental analysis indicates that the proposed model enhances the quality of captions with 0.6012(BLEU-1), 0.3992(BLEU-2), 0.2703(BLEU-3), 0.1921(BLEU-4), 0.1932(METEOR), 0.2617(CIDEr), 0.4793(ROUGE-L), and 0.1260(SPICE) scores, respectively, using additive emotional characteristics and behavioral components of the objects present in the image

Powder Bed Fusion via Machine Learning-Enabled Approaches

Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same

Effect of Antiplatelet Therapy on Survival and Organ Support–Free Days in Critically Ill Patients With COVID-19

International audienc

Long-term (180-Day) outcomes in critically Ill patients with COVID-19 in the REMAP-CAP randomized clinical trial

Importance The longer-term effects of therapies for the treatment of critically ill patients with COVID-19 are unknown. Objective To determine the effect of multiple interventions for critically ill adults with COVID-19 on longer-term outcomes. Design, Setting, and Participants Prespecified secondary analysis of an ongoing adaptive platform trial (REMAP-CAP) testing interventions within multiple therapeutic domains in which 4869 critically ill adult patients with COVID-19 were enrolled between March 9, 2020, and June 22, 2021, from 197 sites in 14 countries. The final 180-day follow-up was completed on March 2, 2022. Interventions Patients were randomized to receive 1 or more interventions within 6 treatment domains: immune modulators (n = 2274), convalescent plasma (n = 2011), antiplatelet therapy (n = 1557), anticoagulation (n = 1033), antivirals (n = 726), and corticosteroids (n = 401). Main Outcomes and Measures The main outcome was survival through day 180, analyzed using a bayesian piecewise exponential model. A hazard ratio (HR) less than 1 represented improved survival (superiority), while an HR greater than 1 represented worsened survival (harm); futility was represented by a relative improvement less than 20% in outcome, shown by an HR greater than 0.83. Results Among 4869 randomized patients (mean age, 59.3 years; 1537 [32.1%] women), 4107 (84.3%) had known vital status and 2590 (63.1%) were alive at day 180. IL-6 receptor antagonists had a greater than 99.9% probability of improving 6-month survival (adjusted HR, 0.74 [95% credible interval {CrI}, 0.61-0.90]) and antiplatelet agents had a 95% probability of improving 6-month survival (adjusted HR, 0.85 [95% CrI, 0.71-1.03]) compared with the control, while the probability of trial-defined statistical futility (HR >0.83) was high for therapeutic anticoagulation (99.9%; HR, 1.13 [95% CrI, 0.93-1.42]), convalescent plasma (99.2%; HR, 0.99 [95% CrI, 0.86-1.14]), and lopinavir-ritonavir (96.6%; HR, 1.06 [95% CrI, 0.82-1.38]) and the probabilities of harm from hydroxychloroquine (96.9%; HR, 1.51 [95% CrI, 0.98-2.29]) and the combination of lopinavir-ritonavir and hydroxychloroquine (96.8%; HR, 1.61 [95% CrI, 0.97-2.67]) were high. The corticosteroid domain was stopped early prior to reaching a predefined statistical trigger; there was a 57.1% to 61.6% probability of improving 6-month survival across varying hydrocortisone dosing strategies. Conclusions and Relevance Among critically ill patients with COVID-19 randomized to receive 1 or more therapeutic interventions, treatment with an IL-6 receptor antagonist had a greater than 99.9% probability of improved 180-day mortality compared with patients randomized to the control, and treatment with an antiplatelet had a 95.0% probability of improved 180-day mortality compared with patients randomized to the control. Overall, when considered with previously reported short-term results, the findings indicate that initial in-hospital treatment effects were consistent for most therapies through 6 months