Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack.

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.

Lower versus higher dose of enteral caloric intake in adult critically ill patients: a systematic review and meta-analysis.

BACKGROUND: There is conflicting evidence about the relationship between the dose of enteral caloric intake and survival in critically ill patients. The objective of this systematic review and meta-analysis is to compare the effect of lower versus higher dose of enteral caloric intake in adult critically ill patients on outcome. METHODS: We reviewed MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from inception through November 2015. We included randomized and quasi-randomized studies in which there was a significant difference in the caloric intake in adult critically ill patients, including trials in which caloric restriction was the primary intervention (caloric restriction trials) and those with other interventions (non-caloric restriction trials). Two reviewers independently extracted data on study characteristics, caloric intake, and outcomes with hospital mortality being the primary outcome. RESULTS: Twenty-one trials mostly with moderate bias risk were included (2365 patients in the lower caloric intake group and 2352 patients in the higher caloric group). Lower compared with higher caloric intake was not associated with difference in hospital mortality (risk ratio (RR) 0.953; 95 % confidence interval (CI) 0.838-1.083), ICU mortality (RR 0.885; 95 % CI 0.751-1.042), total nosocomial infections (RR 0.982; 95 % CI 0.878-1.077), mechanical ventilation duration, or length of ICU or hospital stay. Blood stream infections (11 trials; RR 0.718; 95 % CI 0.519-0.994) and incident renal replacement therapy (five trials; RR 0.711; 95 % CI 0.545-0.928) were lower with lower caloric intake. The associations between lower compared with higher caloric intake and primary and secondary outcomes, including pneumonia, were not different between caloric restriction and non-caloric restriction trials, except for the hospital stay which was longer with lower caloric intake in the caloric restriction trials. CONCLUSIONS: We found no association between the dose of caloric intake in adult critically ill patients and hospital mortality. Lower caloric intake was associated with lower risk of blood stream infections and incident renal replacement therapy (five trials only). The heterogeneity in the design, feeding route and timing and caloric dose among the included trials could limit our interpretation. Further studies are needed to clarify our findings.

Safety of Non-steroidal Anti-inflammatory Drugs as Part of Enhanced Recovery After Laparoscopic Sleeve Gastrectomy-A Systematic Review and Meta-Analysis.

Laparoscopic sleeve gastrectomy (LSG) is an effective bariatric surgery option for managing extreme obesity in most patients. While non-steroidal anti-inflammatory drugs (NSAIDs) promise postoperative pain management after bariatric surgeries, their safety in LSG remains unexplored. In this systematic review, we studied the safety of NSAIDs following LSG reported by six studies involving 588 patients. Our study demonstrated that NSAIDs effectively alleviated the postoperative pain after LSG without major safety concerns. Most reported (>20% incidence) adverse events included postoperative nausea and vomiting (PONV, 21%). For patients undergoing LSG, NSAIDs offer a valuable option for pain management and improved care, potentially reducing opioid consumption. However, additional research is required to optimize NSAID usage and ensure safety, especially concerning renal and gastrointestinal issues.

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System.

Medical data, such as electronic health records, are a repository for a patient's medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data.

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.

An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model.

Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA.

Awareness of healthcare providers on environment-friendly practices in operating rooms in selected hospitals in Riyadh, Saudi Arabia.

Climate change will have a great impact on humanity in upcoming years and will affect the health of all living creatures. Hospitals play a significant role in climate change due to their substantial waste production and they are considered a profound pollution source, with the Operating Theater as a main contributor. This study was aimed to examine the level of knowledge among healthcare professionals in Saudi Arabia concerning the proper implementation of operating room (OR) environmental procedures and efficient management of hospital waste. This is a cross sectional study performed across 3 hospitals in Riyadh, Saudi Arabia. The hospitals included are Prince Sultan military hospital, National guard hospital and King Salman hospital. The study included all the staff and health workers in OR (operating room), excluding all staff and health workers not in OR. The study took place between September 1 and November 1, 2022. None of the study participants mentioned that their institute or hospital fully engaged in Greenhealth Greening the OR initiative. Almost 1 to 3rd of the study participants (38.1%) mentioned that endorsement and participation in the practice of Greenhealth Greening the OR initiative was not implemented at all, and 45% of the participants were completely unaware of such an initiative. The study's findings suggest that healthcare providers in Saudi Arabia are not fully aware of environmentally friendly practices. Further, the current initiatives undertaken by the hospital administration fall short in attaining environmentally sustainable benchmarks.

Partial dehiscence of an intraoperative staple line during laparoscopic sleeve gastrectomy: A case report.

INTRODUCTION: Leakage along a staple line during sleeve gastrectomy is a serious complication. Mechanical causes are uncommon; however, they should be considered as sources of acute postoperative leaks. The presented case discusses an important intraoperative complication with an avoidable cause that could benefit practicing surgeons as well as residents in training programs. PRESENTATION OF CASE: This case describes the mechanical failure of a stapler that was identified intraoperatively. The staple line was oversewn using a 3-0 V Lok suture. The methylene blue test was negative, and the patient had an uneventful postoperative recovery. DISCUSSION: While most leaks were attributed to ischemia of the upper third of the stomach, leaks occurring within the first three postoperative days have a different pathophysiology. This may involve mechanical complications (stapler failure), direct gastric tissue trauma from aggressive handling, or thermal injuries. In our case, the likely cause of the partial dehiscence was the proximity of the stapler to the bougie and an unnoticed small fold at the antrum. CONCLUSION: Surgeons should avoid placing the stapler too close to the bougie. Furthermore, surgeons should ensure that the stomach is flat and there are no gastric folds that could lead to stapling failure.

Glycemic Control for Type 2 Diabetes Mellitus Patients: A Systematic Review.

Diabetes mellitus is a chronic metabolic disorder resulting in hyperglycemia and microvascular and macrovascular complications in individuals globally. Type 2 diabetes mellitus (T2DM) is highly prevalent and accounts for 90% of patients. Maintaining blood glucose concentration is essential to avoid severe complications. Glycemic control is the optimal serum glucose concentration in diabetic patients. It is necessary to identify factors affecting the glycemic control of patients to prevent control and complications. We conducted this systematic review to assess the factors affecting glycemic control among type 2 diabetes mellitus patients. Published literature between the years 2020 to 2022 was retrieved from PubMed, Science Direct, and Google Scholar using different combinations of keywords: T2DM, Glycemic control, Poor, Good, Adequate, Inadequate, Factors, Association, and Determinants. All original articles written in the English language with full-text available and the value of glycemic control defined were included. A total of 1866 studies were retrieved. After the title, abstract, screening, and full-text screening, 12 studies were eligible. The prevalence of poor glycemic control was high, and it ranged between 45.2% and 93% among the studies. The factors associated with glycemic control were stratified into four categories: personal or body-related, clinical, medication-related, and behavioral factors. There was a high prevalence of poor glycemic control in all included studies. The glycemic control was associated with various factors; some were related to the patient or medical conditions while others were related to the behavior of the patients or the medication administrated.

Effect of epidural analgesia on postoperative opioid requirements following elective laparotomies performed at Vancouver General Hospital.

BACKGROUND: Enhanced recovery pathways aim to reduce postoperative opioid use and opioid-related complications. These pathways often include epidural analgesia (EA). This study examines postoperative opioid use after elective laparotomy with and without EA. METHODS: Retrospective chart review of elective laparotomies performed by General Surgery at a tertiary academic center during 2017 was completed. Primary outcome was postoperative opioid usage. Secondary outcomes were time to mobilization, duration of urinary catheterization and postoperative ileus. RESULTS: Among 236 patients, 213 (90%) received EA. There was no significant difference in mean total oral morphine equivalent (OME) usage between EA and non-EA groups. Mean OME use on postoperative day three was higher in the EA group (38.0 vs 22.4 mg, p = 0.02). On multivariate analysis, preoperative opioid use was associated with increased postoperative OME consumption (regression coefficient 147.5, p < 0.001). CONCLUSIONS: In this cohort, epidural analgesia did not reduce postoperative opioid consumption.

Laparoscopic Cholecystectomy for Acute Calcular Cholecystitis in a Patient with Ventriculoperitoneal Shunt: A Case Report and Literature Review.

Management of patients who have ventriculoperitoneal shunt presenting with acute calcular cholecystitis has remained a clinical challenge. In this paper, the hospital course and the follow-up of a patient presenting with acute calcular cholecystitis and ventriculoperitoneal shunt managed with laparoscopic cholecystectomy are presented followed by literature review on the management of acute calcular cholecystitis in patients who have ventriculoperitoneal shunts.