AWARENESS OF SCABIES AMONG SCHOOL STUDENTS IN HAIL CITY AND ITS SURROUNDING VILLAGES, KSA.

Background: scabies resulted from the burrowing effect of a female parasite, Sarcoptesscabiei. Scabies considered to be one of the possible health problems in our area. The aim of this study was toMeasure the level of awareness of Hail school students and its surrounding villages on scabies. Material:The work was cross-sectional study on different males and females schools, forming three groups; primary, middle and secondary, of Hail city and surrounding villages between 2018 January and 2019. Methods: The collected data, from previously designed questionnaires of different groups, were analyzed by computer using statistical SPSS program. Results: The total mean of awareness of the three groups indicated that the students of secondary schools had the highest awareness level followed by the primary level then middle students, Also the awareness level in female students more than males

Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models.

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters

A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Global variation in anastomosis and end colostomy formation following left-sided colorectal resection

Background End colostomy rates following colorectal resection vary across institutions in high-income settings, being influenced by patient, disease, surgeon and system factors. This study aimed to assess global variation in end colostomy rates after left-sided colorectal resection. Methods This study comprised an analysis of GlobalSurg-1 and -2 international, prospective, observational cohort studies (2014, 2016), including consecutive adult patients undergoing elective or emergency left-sided colorectal resection within discrete 2-week windows. Countries were grouped into high-, middle- and low-income tertiles according to the United Nations Human Development Index (HDI). Factors associated with colostomy formation versus primary anastomosis were explored using a multilevel, multivariable logistic regression model. Results In total, 1635 patients from 242 hospitals in 57 countries undergoing left-sided colorectal resection were included: 113 (6·9 per cent) from low-HDI, 254 (15·5 per cent) from middle-HDI and 1268 (77·6 per cent) from high-HDI countries. There was a higher proportion of patients with perforated disease (57·5, 40·9 and 35·4 per cent; P < 0·001) and subsequent use of end colostomy (52·2, 24·8 and 18·9 per cent; P < 0·001) in low- compared with middle- and high-HDI settings. The association with colostomy use in low-HDI settings persisted (odds ratio (OR) 3·20, 95 per cent c.i. 1·35 to 7·57; P = 0·008) after risk adjustment for malignant disease (OR 2·34, 1·65 to 3·32; P < 0·001), emergency surgery (OR 4·08, 2·73 to 6·10; P < 0·001), time to operation at least 48 h (OR 1·99, 1·28 to 3·09; P = 0·002) and disease perforation (OR 4·00, 2·81 to 5·69; P < 0·001). Conclusion Global differences existed in the proportion of patients receiving end stomas after left-sided colorectal resection based on income, which went beyond case mix alone

Secured 6G Communication for Consumer Electronics With Advanced Artificial Intelligence Algorithms

In this paper, advanced features of 6G networks by examining security of consumer electronic products are discussed. With rapid growth of consumer electronic products the network features are updated thus providing fast response to end users but security of transmission remains a major concern. Hence a collaborative framework is formulated in proposed method that solves all uncertainties in consumer electronic products if it is recommended to provide operation using 6G networks. The major significance of proposed method is to identify all problems that occurs in consumer electronic products that operated with advanced technological networks such as 6G and advanced 6G communications. Hence foremost importance is provided to identify all problems by using advanced artificial intelligence algorithm where electronic products can be identified by using natural language processors to convert machine language to identifiable ones thereby expert solutions are achieved. Here, a unique AI model known as Deep Adaptive Neuro Convoluted Chameleon Classifier (DANC3) is used for data classification, which aids in the identification and categorization of consumer data acquired from 6G networks.In each case, the objective functions are optimized with maximized security of data transmission for every consumer electronic product in 6G is reduced below 1%

The journey of rheumatoid arthritis patients: a review of reported lag times from the onset of symptoms

Alaa S Barhamain,1 Rami F Magliah,1 Mohammad H Shaheen,1 Shurooq F Munassar,1 Ayman M Falemban,1 Mohammed M Alshareef,1 Hani M Almoallim1&ndash;3 1Department of Medicine, Faculty of Medicine, 2Alzaidi Chair of Research in Rheumatic Diseases, Umm Alqura University, Makkah, 3Department of Medicine, Dr Soleiman Fakeeh Hospital, Jeddah, Saudi Arabia Background: Even after achieving tremendous advances in diagnosis and treatment of rheumatoid arthritis (RA), many of the patients undergo delays in diagnosis and initiation of treatment, which leads to worsening of the condition and poor prognosis. Objective: The objective of this study was to perform a literature review to quantify the lag times in diagnosis and treatment of RA and study the reported factors associated with it.Methods: The authors searched literature published until September 2016 in electronic full-text and abstract databases and hand-searched the suitable articles. Results: The weighted average of median lag time from symptom onset to therapy was 11.79 months (12 studies, 5,512 patients, range 3.6&ndash;24.0 months). Lag1 was 3.14 months (onset of symptoms to first physician consultant; 12 studies, 6,055 patients, range 0&ndash;5.7 months); lag2 was 2.13 months (physician visit to RA specialist referral; 13 studies, 34,767 patients, range 0.5&ndash;6.6 months); lag3 was 2.91 months (consultation with rheumatologist to diagnosis; 3 studies, 563 patients, range 0&ndash;5 months), lag4 was 2.14 months (diagnosis to initiation of disease-modifying antirheumatic drug therapy; 5 studies, 30,685 patients, range 0&ndash;2.2 months). Numerous patient- and physician-related factors like gender, ethnicity, primary care physician knowledge of the condition, availability of diagnostics, and so on were responsible for the delays.Conclusion: This review estimated the delay times and identified the main factors for delay in RA patients in diagnosis and initiation of treatment. A most plausible solution to this is coordinated effort by the rheumatology and primary care physicians. Keywords: arthritis, rheumatoid, rheumatologists, lag time, delay, diagnosis, disease managemen