Impact of quarantine on sleep quality and psychological status in COVID-19 suspected cases in Riyadh, Saudi Arabia.

Aim: The purpose of this study was to investigate into mental health outcomes such as stress, anxiety, and depression, as well as sleep quality in suspected COVID-19 quarantined cases. Methods: This is a cross-sectional study that used an online survey to target suspected COVID-19 cases in quarantine facilities. Data were collected on several aspects of participants' sociodemographic characteristics, sleep disturbance, and mental health status. Depression, anxiety, stress scale and Pittsburgh Sleep Quality Index were used to measure both the psychological impact and mental health status. Results: Of the 362 people who took the survey, 234 (64.6%) were men, and 148 were between the ages of 26-35. Poor sleep was found to be prevalent in 65% of study participants. The rates of individuals reporting severe depression, anxiety, and stress symptoms were 14.6%, 15%, and 15%, respectively. According to the findings, there were significant gender differences in depression and anxiety (p = 0.001). When compared to the other age groups, participants aged 26-35 reported the highest level of stress (p = 0.001). Being male [OR = -1.23, 95% CI (-2.75-1.95) P = 0.050], single [OR = 0.98, CI (0.15-4.20), P = 0.001], and a healthcare worker [OR = -2.20, 95% CI (-2.59-1.82), P = 0.001] were all associated with poor sleep quality. Conclusion: Approximately-one-third of quarantine COVID-19 patients had poor sleep quality with mild-moderate depression. Both anxiety and stress scales were prevalent in nearly half of the studied samples.

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN.

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader's level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.

A retrospective analysis of substance use among female psychiatric patients in Saudi Arabia.

Objective: This study investigated the prevalence of substance use (SU), and its risk factors, among women attending psychiatric outpatients center in Saudi Arabia. Design: A retrospective cross-sectional design. Materials and methods: We reviewed outpatients' records of 200 female patients with a history of SU from a psychiatric unit in Jeddah, Saudi Arabia from December 2018 to February 2019. The researchers developed the pro forma, and 2 psychiatrists and a family medicine physician validated the form. Results: The most common and widely used were psychoactive substances (58%), followed by central nervous system (CNS) depressants (22%), and finally cannabinols (9.5%). Overall, the highest substance use was the amphetamine-cannabis-nicotine (ACN) representing nearly half of the illicit items (46.6%), followed by heroine-alcohol-benzodiazepine (16.4%), and with the lowest being benzodiazepine-nicotine (1.7%). There was a significant difference between the single substance and multiple substance use in terms of age (p = 0.001), smoking behavior (p = 0.001), patients past history (p = 0.005), and age of the patient at the start of drug use (p = 0.005). Conclusion: Although the prevalence of substance use among women is low in Saudi Arabia, screening of substance use disorders risks and building a rehabilitation program to control drug dependence are needed.

Demographic Characteristics, Comorbidities, and Length of Stay of COVID-19 Patients Admitted Into Intensive Care Units in Saudi Arabia: A Nationwide Retrospective Study.

Background: This study aimed to describe the demographic characteristics and determine the risk factors associated with disease severity and length of hospital and intensive care unit (ICU) stay in a cohort of COVID-19 patients admitted into ICU in Saudi Arabia. Methods: This was a national, multi-center, retrospective cross-sectional study of all COVID-19 cases admitted into different ICUs in Saudi Arabia between March 2020 and September 202l. Demographic, clinical features, comorbidities, and length of stay (LOS) data were retrieved from the national Health Electronic Surveillance Network (HESN) and Taqassi databases at the Saudi Ministry of Health (MOH) for subsequent analyses. We used multiple linear regression models to determine risk factors associated with critical outcomes (including LOS in ICU) among COVID-19 cases. Results: A total of 12,436 COVID-19 patients were included in this study, with a mean age of 59.57 ± 18.30 years and 7,679 (62%) were <65 years old. COVID-19 was more common in males (N = 7,686, 61.9%) and Saudi nationals (N = 8,516, 68.5%). The clinical characteristic findings showed that 36.3% of patients required invasive ventilation whilst 65.4% received tracheostomies for ventilation, and 4% were on dialysis. Our analysis revealed that 2,978 (23.9%) patients had one comorbidity, 4,977 (47.4%) had two or more comorbidities, and diabetes (48.2%) was the most prevalent comorbidity, followed by hypertension (44.2%), and chronic cardiovascular disease (10.5%). Thirteen variables emerged as significant predictors of LOS in ICU using multiple linear regression analyses, with invasive ventilation as the strongest predictor of LOS in the ICU (beta = -0.68, p = 0.001) and hospital admission (beta = -0.65, p = 0.001). Conclusions: COVID-19 continues to affect millions of people around the world, with a mortality rate of about 2-3% of all infected patients. Our analysis revealed that comorbidities such as chronic kidney disease, cardiovascular disease, diabetes, and older age were significant risk factors associated with a poorer prognosis and longer duration of stay in hospitals and ICU.

Impact of ANN in Revealing of Viral Peptides.

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.

Deep Neural Networks for Optimal Selection of Features Related to Flu.

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

Recurrent Neural Networks for Feature Extraction from Dengue Fever.

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.

Dense Convolutional Neural Network for Detection of Cancer from CT Images.

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.

Predictors of Mental Health Status among Quarantined COVID-19 Patients in Saudi Arabia.

BACKGROUND: The negative psychological impact of COVID-19 in the general population has been well documented. Similar studies among those who were infected and who underwent quarantine remain scarce, particularly in the Arab region. The present study aims to fill this gap. METHODS: In this cross-sectional study, suspected/confirmed COVID-19 individuals who were quarantined in the Ministry of Health (MOH) facilities were invited to participate in an online survey. All consenting participants answered a generalized questionnaire that included demographic characteristics, as well as a five-part questionnaire that assessed the symptoms of depression, anxiety, insomnia, and distress. RESULTS: A total of 335 suspected/confirmed COVID-19 individuals (198 males and 137 females) participated. Being female is associated with increased risk of depression (odds ratio OR 1.8 (confidence interval, CI 1.1-3.1; p = 0.03)) as well as being employed by the government (OR 2.8 (CI 1.1-7.0; p = 0.03)). Level of education (OR 2.3 (CI 1.0-5.4; p = 0.049)) and employment in government (OR 3.0 (CI 1.2-7.8; p = 0.02)) were significantly associated with distress. Increasing age (45 years and above) appeared to be protective against distress (OR 0.2 (CI 0.02-0.69; p = 0.008)), as well anxiety and sleep pattern (OR 0.3 p < 0.05). CONCLUSION: Findings of the present study highlight that infected COVID-19 populations are at higher risk for acute and detrimental psychological well-being during quarantine and/or self-isolation. Identification of the coping mechanisms of older adults during periods of distress may prove beneficial in the pandemic preparedness of younger generations.

Gender Difference in Pain Management Among Adult Cancer Patients in Saudi Arabia: A Cross-Sectional Assessment.

OBJECTIVE: To compare gender differences in pain management among adult cancer patients in Saudi Arabia and to explore the predictors associated with attitudinal barriers of cancer patients to pain management. METHODS: A descriptive cross-sectional study was conducted among 325 cancer patients from tertiary hospitals in Saudi Arabia. RESULT: Of the total participants, 67.4% were women (N = 219) and 32.6% were men (N = 106). The overall mean scores of the attitudinal barriers questionnaire were 49.51 ± 13.73 in men and 54.80 ± 22.53 in women. The analysis shows significant differences in scores in subscales of tolerance (men = 7.48 ± 2.37), (women = 8.41 ± 3.01) (p = 0.003) and fear of distraction in the course of treatment (men = 6.55 ± 1.34), and (women = 7.15 ± 2.63) (p = 0.008). Female patients reported a more moderate to severe level of pain than men (worst pain in last week of 7.07 ± 1.50, worst pain in last week of 5.84 ± 2.65, respectively). Splitting by gender, the significant predictor for physiology effect domains in male cancer patients includes age, marital status, employment status, monthly income, cancer type, and presence of comorbid disease (p < 0.050). Age was a significant predictor of the domains of fatalism, communication, and harmful effects (p < 0.050) among female cancer patients. CONCLUSION: The present study revealed significant differences between men and women with attitudinal barriers to cancer pain management. Managing pain requires the involvement of all methods in a comprehensive manner, thus unalleviated pain influences the patient's psychological or cognitive aspect.

Mental Health Outcomes of Healthcare Providers During COVID-19 Pandemic in Saudi Arabia: A Cross-Sectional Study.

Objective: In this descriptive cross-sectional study we aimed, to assess the level of depression, anxiety, insomnia and distress symptoms experienced by healthcare providers during the COVID-19 pandemic in Saudi Arabia. Methods: All healthcare providers currently working in different hospitals were invited to participate in this study. Data gathering started in March 2020 to May 2020. The participants answered a five-part questionnaire which includes demographic data, a 9-item Patient Health Questionnaire, a 7-item Generalized Anxiety Disorder, a 7-item Insomnia Severity Index, and a 22-item Impact of Event Scale-Revised, which assess the level of depression, anxiety, insomnia, and distress. Results: Out of 200 healthcare providers, 40% were males. 52% were aged 31-40 years old, 61% were married. The majority of the participants were Saudi nationals (84%), 74% were nurses, 11% were physicians and 15% were other healthcare providers. More than half of the participants worked as front-liners (57%). Overall, 73, 69, 62, and 83% of all healthcare providers reported symptoms of depression, anxiety, insomnia, and distress, respectively. The analysis showed severe symptoms level of depression for physicians and nurses was 35% and 20% (p < 0.05), respectively. Only three of the independent variables made a unique contribution to the model (gender, profession, and working position) (p < 0.05). Conclusion: COVID-19 pandemic has a significant impact on the mental health of healthcare providers in Saudi Arabia. Female nurses and healthcare providers working in the frontline who were directly treating patients with COVID-19 are at increased risk of severe depression, anxiety and distress.

Association between self-care management practices and glycemic control of patients with type 2 diabetes mellitus in Saud Arabia: A cross -sectional study.

OBJECTIVE: In this cross-sectional study, we aimed to determine the association of self-care management practices and glycemic control of type 2 diabetes mellitus in Saudi Arabia. METHODS: A total of 352 type 2 diabetes mellitus (T2DM) patients from two public tertiary hospitals in Saudi Arabia participated in this study. All T2DM patients were recruited and interviewed by a researcher between January to April 2018 from the outpatient diabetes clinics. All respondents answered a four-part questionnaire which includes demographics data, Diabetes Self-Management Questionnaire (DSMQ). Linear Regression was performed to assess the significance of predictors and compute the coefficient of determination. RESULTS: The mean age of the participants was 51.89 ± 10.94. Of the 352 participants, 52% were obese (BMI: ≥30 kgm2) and 77% of the participants had glycated haemoglobin (HbA1c) over 7%. The analysis showed that subscale of Glucose management was the strongest predictor of Hba1c levels of participants' followed by physical activity. Gender and marital status emerged as significant predictors for their self-care management practices. Female patients had more self-care management practices than male patients (B 0.20; 95CI 0.10- 0.96 (p = 0.015). CONCLUSION: This study provides an evidence on the self-care management of T2DM patients in Saudi Arabia. The high self-care management found in the study highlights that the patients are aware of the severity of and possible complications associated with T2DM.

Predictors of students' academic achievements in allied health professions at King Saud University: a retrospective cohort study.

BACKGROUND: The admissions criteria for colleges of medicine and allied health professions include several cognitive predictors. Little is known of the admissions criteria for the allied health professions and their correlation with students' academic performance. This study investigates predictors for students' academic achievements at allied health colleges at King Saud University. DESIGN: Retrospective cohort study. SETTINGS: College of Applied Medical Sciences, College of Nursing, and Prince Sultan bin Abdulaziz College for Emergency Medical Services, Saudi Arabia. PARTICIPANTS: The sample comprised 1634 students. METHOD: The high school grade average (HSGA), aptitude test (APT) score, achievement test (ACT) score, and current grade point average (GPA) were retrieved. The data were analysed using Pearson's correlation coefficient and regression analysis. RESULTS: HSGA, ACT, and APT were significantly positively associated with students' academic performance in colleges for all allied health professions. Multivariate regression analysis showed that the most predictive variable for all allied healthcare professions was HSGA (ß = 0.347), followed by ACT (ß = 0.270) and APT (ß = 0.053) scores. The regression model indicated that the HSGA, APT, and ACT together predicted 26.5% of the variation in students' cumulative GPAs at the time of graduation. CONCLUSION: The admissions criteria for the allied health colleges at King Saud University predicted only 26.5% of the students' cumulative GPA at the time of graduation. Other noncognitive admission criteria should be taken into consideration to improve the prediction of students' academic potential.

Recommendation of Aspirin-Guide App and Physicians Clinical Decision of Aspirin Use to Prevent CVD Among Diabetic Patients, Is there any Differences?

OBJECTIVE: This study aims to identify the prevalence of aspirin use among type 2 diabetic (T2DM) patients and assess the concordance in aspirin use among these patients as prescribed by physicians and as recommended by the Aspirin-Guide app. METHODS: A total of 301 T2DM patients from King Khalid University Hospital in Riyadh, Saudi Arabia participated in this cross-sectional study. Patient's electronic medical records through eSihi system were reviewed and all data included in the free online and mobile app called Aspirin-Guide were collected in a specially designed data checklist. RESULT: The prevalence of aspirin use was more common in patients who were in the age group of 51 to 59 and male participants' with T2DM. Males were nearly twice more likely to use aspirin compared to females (P = 0.001). Based on recommendations from the Aspirin-Guide app, 26% of the patients who were on aspirin (N = 51) were not eligible for aspirin therapy, while 37.7% (N = 40) of the patients eligible for aspirin therapy had not been put on aspirin by their physicians (P = 0.039). Male sex (P = 0.003), use of statins (P = 0.001), and being advised to use aspirin (P = 0.041), were significantly associated with aspirin use in T2DM patients. CONCLUSION: There was a significant difference in the proportion of patients currently on aspirin as prescribed by their physicians and those eligible for aspirin therapy as per the Aspirin-Guide app. The use of an app to uniformized aspirin use among eligible patients should be based on up-to-date guidelines and account for patient acceptability and willingness to commence treatment.

Assessment of psychological distress among cancer patients undergoing radiotherapy in Saudi Arabia.

BACKGROUND: Cancer patients under psychological distress have reduced compliance with treatment and prolonged hospitalizations. Identifying cancer patients who may be more likely to suffer from psychological distress over the disease trajectory is essential to targeting the proper interventions and providing the best care. In this descriptive cross-sectional study, we aim to assess the levels of emotional and physical distress, depressive symptoms, and social concerns of cancer patients undergoing radiotherapy treatment in Saudi Arabia. METHODS: A total of 148 cancer patients undergoing radiotherapy from five different public tertiary-level hospitals in Saudi Arabia participated in this study. The survey was conducted by a trained researcher from November 2015 through April 2016. The Psycho-Oncology Screening Tool was used to identify levels of distress of cancer patients undergoing radiotherapy. Emotional and physical distress, depressive symptoms, and social concerns in receiving psychosocial services were assessed. RESULTS: The findings show above average levels of physical and emotional distress was (342.07±78.9) and social concerns was (7.27±1.68). Age was a significant predictor of psychological distress and in social concerns (P<0.05). Marital status and employment status emerged as significant predictors of depressive symptoms and social concerns (<0.05), (P<0.001), respectively. The patients' type of cancer was significantly associated with the level of physical distress, emotional distress, and depressive symptoms (P<0.05). CONCLUSION: Cancer patients undergoing radiotherapy displayed above-average levels of distress. This may have significant implications on their adherence for treatment and patient outcomes. Routine screening and support services or psychosocial care for patients are warranted among cancer patients undergoing radiotherapy.

Association of health literacy and self-management practices and psychological factor among patients with type 2 diabetes mellitus in Saudi Arabia.

OBJECTIVES: To determine the association between sociodemographic, clinical, and health literacy and the presence of depressed mood and their relationships  to diabetes self-management among type 2 diabetes mellitus (T2DM) patients in Saudi Arabia. METHODS: A total of 352 T2DM patients from 2 public tertiary hospitals in Saudi Arabia participated in this descriptive cross-sectional study between December 2016 and February 2017. All respondents answered a 4-part questionnaire, which includes demographic data, Diabetes Self-Management Questionnaire (DSMQ), 36-item test based Short Test of Functional Health Literacy in Adults, and a 2-item Patient Health Questionnaire (PHQ-2). The Chi-square test and logistic regression analysis were conducted to determine the relationship and significant predictors for self-management among T2DM patients. Results: The analysis showed that majority of the participants had low to marginal functional health literacy. The overall DSM-16 score was good, indicating more effective self-care, while 20% of the participants had a score of 2 or more in the PHQ-2 indicating presence of depressed mood.  No significant association was found between DSM and health literacy among the study participants while age, gender, educational level, employment status, and level of the depressive symptom were significantly associated with differences in the level of functional literacy of the participants. Conclusion: The study shows that nearly half of the T2DM patients that exhibited low level of functional health literacy had low diabetes self-management.  Our findings also show that gender significantly affects diabetes self-management in which odds are a lot higher among females than males with diabetes self-management. This study underscores the importance of proper counselling and education about diabetes control on both patients and family members.

Health promoting lifestyle of university students in Saudi Arabia: a cross-sectional assessment.

BACKGROUND: College is a critical time where students are more prone to engage in risky health behaviors known to negatively affect well-being, such as physical inactivity, stress, and poor dietary habits. A health promoting lifestyle is an important determinant of health status and is recognized as a major factor for the maintenance and improvement of health. This study was designed to assess the health-promoting lifestyle of students in health colleges and non-health colleges in Saudi Arabia. METHODS: A total of 1656 students participated in this descriptive cross-sectional study. Data gathering was conducted from November 2016 to February 2017 at King Saud University. Participating students completed a self-reported questionnaire that included questions regarding their demographic characteristics and their health-promoting behaviors. RESULTS: The majority of participants were females (70.4%), 20% of the participants were overweight and 11.3%, were obese. The analysis showed that there was a significant difference between health colleges and non-health colleges with regards to the factor of health responsibility. Students at both schools were found to have an inadequate level of adherence to recommendations regarding physical activity and healthy eating habits. The analysis also found that majority of the students in both colleges do not attend educational programs on health care. The model shows that gender, type of college, year in school, and family structure were significant predictors of the health lifestyle of students in Saudi Arabia. CONCLUSION: The results of the current study indicate that university students are leading unhealthy lives, where the majority of them have unhealthy eating habits and poor physical activity level. Universities are ideal settings for implementing health promotion programs. Therefore, planning and implementing programs to motivate students to be more responsible for their own health, to engage more in physical activity, and to practice healthy eating habits and other forms of wellness are of paramount importance.

A Single-Blind Study Evaluating the Efficacy of Gold Nanoparticle Photothermal-Assisted Liposuction in an Ex Vivo Human Tissue Model.

BACKGROUND: Liposuction is one of the most performed cosmetic surgery procedures. In a previously reported study, gold-nanoparticle (GNP) laser-assisted liposuction (NanoLipo) was shown to improve procedure parameters and outcomes in a porcine model. OBJECTIVES: An ex vivo human liposuction model was developed to assess the ease, efficacy, and outcomes of NanoLipo, and to further explore its mechanism of action in facilitating liposuction. METHODS: NanoLipo was compared to a control without GNPs in sets of fresh, nonperfused, anatomically symmetric, matched tissue specimens from 12 patients. A subset of three experiments was performed under single-blinded conditions. Intraoperative assessments included lipoaspirate volume, percentage of free oil, ease of removal, and temperature rise. Specimens were palpated, visualized for evenness, and graded with and without skin. Postoperative assessment included viability staining of the lipoaspirate and remaining tissues. Microcomputed tomography was used to assess the distribution of infused GNPs within the tissues. RESULTS: NanoLipo consistently removed more adipose tissue with more liberated triglycerides compared to control. NanoLipo specimens were smoother, thinner, and had fewer and smaller irregularities. Infused solutions preferentially distributed between fibrous membranes and fat pearls. After NanoLipo, selective structural-tissue disruptions, indicated by loss of metabolic activity, were observed. Thus, NanoLipo likely creates a bimodal mechanism of action whereby fat lobules are dislodged from surrounding fibro-connective tissue, while lipolysis is simultaneously induced. CONCLUSIONS: NanoLipo showed many advantages compared to control under blinded and nonblinded conditions. This technology may be promising in facilitating fat removal.

A Cross-Sectional Assessment of Literacy and Awareness, Attitudes, and Beliefs About Colorectal Cancer and Its Screening in Riyadh Region.

This study aims to explore the association between functional health literacy and awareness for, beliefs, and attitudes of patients with colorectal cancer (CRC) and CRC screening test in Riyadh, Saudi Arabia. A total of 256 participants from two different tertiary level hospitals in Riyadh, Saudi Arabia were recruited in this study. The participants were interviewed by a trained researcher between October and December 2015. All respondents answered a three-part questionnaire which included demographic data, questions related to CRC awareness, attitude, behaviour, and short Test of Functional Health Literacy in Adults (STOFHLA). More than half of the participants had an inadequate awareness of functional health literacy skills (FHLS), 16.4 % had marginal of FHLS awareness and 17.6 % had adequate awareness about FHLS as assessed by the STOFHLA. Overall, the majority of the participants in both marginal and adequate aware groups showed a limited awareness about colorectal cancer screening and testing. A significant association was found on awareness of the patients about frequencies that they should have been tested for colorectal cancer and functional health literacy. No significant association was found between functional health literacy as assessed by STOFHLA and concerns of Faecal Occult Blood Test (FOBT) (p = 0.384) and sigmoidoscopy or colonoscopy might cause embarrassment (p = 0.089), harm (p = 0.917), and pain (p = 0.849). The present study revealed a low level of health literacy among Saudi adults in Riyadh region. Although the level of literacy was low, the bigger concern is that of the poor awareness and beliefs of Saudi adults about CRC and CRC screening.