Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Mortality of emergency abdominal surgery in high-, middle- and low-income countries

Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low- or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI). Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression. Results: Data were obtained for 10 745 patients from 357 centres in 58 countries; 6538 were from high-, 2889 from middle- and 1318 from low-HDI settings. The overall mortality rate was 1⋅6 per cent at 24 h (high 1⋅1 per cent, middle 1⋅9 per cent, low 3⋅4 per cent; P < 0⋅001), increasing to 5⋅4 per cent by 30 days (high 4⋅5 per cent, middle 6⋅0 per cent, low 8⋅6 per cent; P < 0⋅001). Of the 578 patients who died, 404 (69⋅9 per cent) did so between 24 h and 30 days following surgery (high 74⋅2 per cent, middle 68⋅8 per cent, low 60⋅5 per cent). After adjustment, 30-day mortality remained higher in middle-income (odds ratio (OR) 2⋅78, 95 per cent c.i. 1⋅84 to 4⋅20) and low-income (OR 2⋅97, 1⋅84 to 4⋅81) countries. Surgical safety checklist use was less frequent in low- and middle-income countries, but when used was associated with reduced mortality at 30 days. Conclusion: Mortality is three times higher in low- compared with high-HDI countries even when adjusted for prognostic factors. Patient safety factors may have an important role. Registration number: NCT02179112 (http://www.clinicaltrials.gov)

AN ENSEMBLE ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK WITH BAYESIAN EXPONENTIAL ADAPTIVE GRADIENTS FOR SOUND CLASSIFICATION

With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks

Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4% in detecting cyberbullying on Twitter dataset and 90.97% on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance

An Ensemble One Dimensional Convolutional Neural Network with Bayesian Optimization for Environmental Sound Classification

With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks. This paper introduces an end-to-end method for environmental sound classification based on a one-dimensional convolution neural network with Bayesian optimization and ensemble learning, which directly learns features representation from the audio signal. Several convolutional layers were used to capture the signal and learn various filters relevant to the classification problem. Our proposed method can deal with any audio signal length, as a sliding window divides the signal into overlapped frames. Bayesian optimization accomplished hyperparameter selection and model evaluation with cross-validation. Multiple models with different settings have been developed based on Bayesian optimization to ensure network convergence in both convex and non-convex optimization. An UrbanSound8K dataset was evaluated for the performance of the proposed end-to-end model. The experimental results achieved a classification accuracy of 94.46%, which is 5% higher than existing end-to-end approaches with fewer trainable parameters. Four measurement indices, namely: sensitivity, specificity, accuracy, precision, recall, F-measure, area under ROC curve, and the area under the precision-recall curve were used to measure the model performance. The proposed approach outperformed state-of-the-art end-to-end approaches that use hand-crafted features as input in selected measurement indices and time complexity

RNN-LSTM: From applications to modeling techniques and beyond—Systematic review

Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequential data with long-term dependencies. Despite its popularity, the challenge of effectively initializing and optimizing RNN-LSTM models persists, often hindering their performance and accuracy. This study presents a systematic literature review (SLR) using an in-depth four-step approach based on the PRISMA methodology, incorporating peer-reviewed articles spanning 2018–2023. It aims to address how weight initialization and optimization techniques can bolster RNN-LSTM performance. This SLR offers a detailed overview across various applications and domains, and stands out by comprehensively analyzing modeling techniques, datasets, evaluation metrics, and programming languages associated with these networks. The findings of this SLR provide a roadmap for researchers and practitioners to enhance RNN-LSTM networks and achieve superior results

Deep deterministic policy gradient algorithm: A systematic review

Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published in the last five years (2018-2023). We provide a comprehensive overview of the key concepts and components of DDPG, including its formulation, implementation, and training. Then, we highlight the various applications and domains of DDPG, including Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. Additionally, we provide an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, highlighting its strengths and weaknesses. We believe that this review will be an essential resource for researchers, offering them valuable insights into the methods and techniques utilized in the field of DRL and DDPG

A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)

A systematic Review of YOLO for medical Object Detection (2018 - 2023)</p

New Alpha-Amylase Inhibitory Metabolites from Pericarps of <i>Garcinia mangostana</i>

Two new benzophenones: garcimangophenones A (6) and B (7) and five formerly reported metabolites were purified from the pericarps EtOAc fraction of Garcinia mangostana ((GM) Clusiaceae). Their structures were characterized by various spectral techniques and by comparing with the literature. The α-amylase inhibitory (AAI) potential of the isolated metabolites was assessed. Compounds 7 and 6 had significant AAI activity (IC50 9.3 and 12.2 µM, respectively) compared with acarbose (IC50 6.4 µM, reference α-amylase inhibitor). On the other hand, 5 had a moderate activity. Additionally, their activity towards the α-amylase was assessed utilizing docking studies and molecular dynamics (MD) simulations. The docking and predictive binding energy estimations were accomplished using reported crystal structure of the α-amylase (PDB ID: 5TD4). Compounds 7 and 6 possessed highly negative docking scores of −11.3 and −8.2 kcal/mol, when complexed with 5TD4, respectively while acarbose had a docking score of −16.1 kcal/mol, when complexed with 5TD4. By using molecular dynamics simulations, the compounds stability in the complexes with the α-amylase was analyzed, and it was found to be stable over the course of 50 ns. The results suggested that the benzophenone derivative 7 may be potential α-amylase inhibitors. However, further investigations to support these findings are required