A population-based study of 15,000 people on Knowledge and awareness of lung cancer symptoms and risk factors in Saudi Arabia.

Background: Lung cancer is currently the most fatal form of cancer worldwide, ranking as the fourth most prevalent type in Saudi Arabia, particularly among males. This trend is expected to increase with growing population, lifestyle changes, and aging population. Understanding the awareness of the Saudi population regarding the risk factors and symptoms of lung cancer is necessary to attenuate the predicted increase in cases. Method: A cross-sectional, population-based survey was performed using a previously validated questionnaire (Lung CAM). Multiple linear regression analysis was used to assess variables associated with deficiency in knowledge and awareness of risk factors and symptoms of lung cancer. Results: Majority of the 15,099 respondents were male (65%), aged between 18 and 30 years (53%), 50% of which were educated up to a bachelor's degree level. Overall awareness of lung cancer signs and symptoms was 53%, with painful cough and coughing up blood being the best-known symptoms. Conversely, persistent shoulder pain (44%) and clubbing fingers (47%) were the least known lung cancer symptoms. Also, 60% of the respondents showed low confidence in identifying the signs and symptoms of lung cancer. The overall awareness of the risk factors for lung cancer development was 74%, with first-hand (74%) and second-hand (68%) smoking being the most known risk factors. However, only ≤ 62% know the other non-smoking risk factors. Awareness of the risk factors and symptoms of lung cancer depended on age, gender, education, marital and employment status (p < 0.001). Conclusion: Public awareness of the risk factors and symptoms of lung cancer in Saudi Arabia is inadequate and heavily dependent on education and socio-economic status. Awareness can be improved through campaigns to raise awareness about other lesser-known lung cancer risk factors and symptoms.

Managing High Frequency of Ambulance Calls in Hospitals: A Systematic Review.

Background: This study addresses the critical issue of high-volume emergency calls in hospitals, focusing on the strain caused by frequent caller patients on ambulance services. The aim was to synthesize various management methods for handling high-frequency hospital calls. Methods: The systematic review was conducted following the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines and guided by the Cochrane Handbook for systematic reviews. Inclusion criteria encompassed studies focusing on the management of emergency departments in hospitals, exploring various medical conditions requiring ambulance attention, and reporting on the impact of a high volume of ambulance calls on hospitals. Databases including PubMed, Web of Science, and Google Scholar were searched from January 1, 2005, to May 1, 2022. The quality of included studies was assessed using the Critical Appraisal Skills Programme (CASP) Checklist. Results: Out of 2390 identified citations, 18 studies met the inclusion criteria. These studies, from 12 countries, presented diverse methods categorized into country policy-based management, modeling approaches, and general strategies. Key findings included the effectiveness of risk stratification models and community-based interventions in managing high call frequencies and improving patient care. Our review identified effective strategies such as risk stratification models and community-based interventions, which have shown significant impacts in managing high call frequencies, aligning closely with our objective. These approaches have been pivotal in reducing the burden on emergency services and improving patient care. Conclusion: The study synthesizes effective management methods for high-frequency ambulance calls, including predictive modeling and community interventions. It highlights the need for multi-faceted management strategies in different healthcare settings and underscores the importance of continued research and implementation of these methods to improve emergency service efficiency.

Are There Interindividual Responses of Cardiovascular Disease Risk Markers to Acute Exercise? A Replicate Crossover Trial.

PURPOSE: Using a replicated crossover design, we quantified the response heterogeneity of postprandial cardiovascular disease risk marker responses to acute exercise. METHODS: Twenty men (mean (SD) age, 26 (6) yr; body mass index, 23.9 (2.4) kg·m -2 ) completed four 2-d conditions (two control, two exercise) in randomized orders. On days 1 and 2, participants rested and consumed two high-fat meals over 9 h. Participants ran for 60 min (61 (7)% of peak oxygen uptake) on day 1 (6.5 to 7.5 h) of both exercise conditions. Time-averaged total area under the curve (TAUC) for triacylglycerol, glucose, and insulin were calculated from 11 venous blood samples on day 2. Arterial stiffness and blood pressure responses were calculated from measurements at baseline on day 1 and at 2.5 h on day 2. Consistency of individual differences was explored by correlating the two replicates of control-adjusted exercise responses for each outcome. Within-participant covariate-adjusted linear mixed models quantified participant-by-condition interactions and individual response SDs. RESULTS: Acute exercise reduced mean TAUC-triacylglycerol (-0.27 mmol·L -1 ·h; Cohen's d = 0.29, P = 0.017) and TAUC-insulin (-25 pmol·L -1 ·h; Cohen's d = 0.35, P = 0.022) versus control, but led to negligible changes in TAUC-glucose and the vascular outcomes (Cohen's d ≤ 0.36, P ≥ 0.106). Small-to-moderate, but nonsignificant, correlations were observed between the two response replicates ( r = -0.42 to 0.15, P ≥ 0.066). We did not detect any individual response heterogeneity. All participant-by-condition interactions were P ≥ 0.137, and all individual response SDs were small with wide 95% confidence intervals overlapping zero. CONCLUSIONS: Large trial-to-trial within-subject variability inhibited detection of consistent interindividual variability in postprandial metabolic and vascular responses to acute exercise.

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model's effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model's efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications.

Exploring the Relationship Between Stress and Satisfaction During Clinical Training Among Respiratory Therapy Students: A Nationwide Cross-Sectional Survey.

Background: Although clinical training is an important component of healthcare education, it is nevertheless a significant source of stress for students. There is limited information on stress and satisfaction perceived by clinical-level undergraduate students studying respiratory therapy (RT) in Saudi Arabia. Methods: A descriptive, cross-sectional study design with an online questionnaire was employed to conduct this study. Data collection occurred during the academic year 2022-2023 among RT students throughout Saudi Arabia. Patricians responded to demographic questions, the Students Stress Scale and Students Professional Satisfaction questionnaires. Descriptive, inferential, and correlational statistics were used to analyze the collected responses. Results: A total of 1001 undergraduate RT students completed the online survey. RT students and interns had an overall moderate to high stress level (mean (SD); 3.55 (0.49)), while satisfaction was perceived as mild to moderate (mean (SD): 2.56 (0.65)). In addition, 38% of the study participants have considered quitting the RT program. Female students showed higher stress levels in the following domains: inadequate knowledge and training, adverse and embarrassing experiences, clinical supervision, patients' pain, and Education-reality conflict (p<0.005) compared to male students. Additionally, students who considered quitting the RT program revealed higher stress levels in all domains (p<0.005). There were negative correlations between satisfaction and stress domains: inadequate knowledge and training (r = -0.32; p = 0.001), adverse and embarrassing experience (r = -0.31; p = 0.025), close supervision (r = -0.24; p = 0.001), insufficient hospital resources (r = -0.30; p 0.002), patients' pain and suffering (r = 0.28; p = 0.04), and education - reality conflict (r = -0.30; p = 0.001). Conclusion: During clinical training, respiratory therapy students experience moderate to high-stress levels and low satisfaction. There need to be tailored interventions to reduce stress and intention to quit and increase students' clinical experience satisfaction.

Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers.

In order for a country's economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT, for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases.

Exploring the acute effects of running on cerebral blood flow and food cue reactivity in healthy young men using functional magnetic resonance imaging.

Acute exercise suppresses appetite and alters food-cue reactivity, but the extent exercise-induced changes in cerebral blood flow (CBF) influences the blood-oxygen-level-dependent (BOLD) signal during appetite-related paradigms is not known. This study examined the impact of acute running on visual food-cue reactivity and explored whether such responses are influenced by CBF variability. In a randomised crossover design, 23 men (mean ± SD: 24 ± 4 years, 22.9 ± 2.1 kg/m2 ) completed fMRI scans before and after 60 min of running (68% ± 3% peak oxygen uptake) or rest (control). Five-minute pseudo-continuous arterial spin labelling fMRI scans were conducted for CBF assessment before and at four consecutive repeat acquisitions after exercise/rest. BOLD-fMRI was acquired during a food-cue reactivity task before and 28 min after exercise/rest. Food-cue reactivity analysis was performed with and without CBF adjustment. Subjective appetite ratings were assessed before, during and after exercise/rest. Exercise CBF was higher in grey matter, the posterior insula and in the region of the amygdala/hippocampus, and lower in the medial orbitofrontal cortex and dorsal striatum than control (main effect trial p ≤ .018). No time-by-trial interactions for CBF were identified (p ≥ .087). Exercise induced moderate-to-large reductions in subjective appetite ratings (Cohen's d = 0.53-0.84; p ≤ .024) and increased food-cue reactivity in the paracingulate gyrus, hippocampus, precuneous cortex, frontal pole and posterior cingulate gyrus. Accounting for CBF variability did not markedly alter detection of exercise-induced BOLD signal changes. Acute running evoked overall changes in CBF that were not time dependent and increased food-cue reactivity in regions implicated in attention, anticipation of reward, and episodic memory independent of CBF.

New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images.

This paper introduces a new method for real-time high-density impulsive noise elimination applied to medical images. A double process aimed at the enhancement of local data composed of Nested Filtering followed by a Morphological Operation (NFMO) is proposed. The major problem with heavily noisy images is the lack of color information around corrupted pixels. We show that the classic replacement techniques all come up against this problem, resulting in average restoration quality. We only focus on the corrupt pixel replacement phase. For the detection itself, we use the Modified Laplacian Vector Median Filter (MLVMF). To perform pixel replacement, two-window nested filtering is suggested. All noise pixels in the neighborhood scanned by the first window are investigated using the second window. This investigation phase increases the amount of useful information within the first window. The remaining useful information that the second window failed to produce in the case of a very strong connex noise concentration is then estimated using a morphological operation of dilatation. To validate the proposed method, NFMO is first evaluated on the standard image Lena with a range of 10% to 90% impulsive noise. Using the Peak Signal-to-Noise Ratio metric (PSNR), the image denoising quality obtained is compared to the performance of a wide variety of existing approaches. Several noisy medical images are subjected to a second test. In this test, the computation time and image-restoring quality of NFMO are assessed using the PSNR and the Normalized Color Difference (NCD) criteria. Finally, an optimized design for a field-programmable gate array (FPGA) is suggested to implement the proposed method for real-time processing. The proposed solution performs excellent quality restoration for images with high-density impulsive noise. When the proposed NFMO is used on the standard Lena image with 90% impulsive noise, the PSNR reaches 29.99 dB. Under the same noise conditions, NFMO completely restores medical images in an average time of 23 milliseconds with an average PSNR of 31.62 dB and an average NCD of 0.10.

Burnout, Resilience, Supervisory Support, and Quitting Intention among Healthcare Professionals in Saudi Arabia: A National Cross-Sectional Survey.

Although personal resilience and supervisory support are known to reduce the impact of burnout and quitting intention, there is limited data available to explore these relationships among healthcare professionals (HCPs) in Saudi Arabia. This study aimed to assess the prevalence of burnout and explore its association with resilience, supervisory support, and intention to quit among Saudi Arabian HCPs. METHODS: A cross-sectional survey was distributed to a convenience sample of HCPs between April and November 2022. Participants responded to socio-demographic questions, the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS (MP)), the Connor-Davidson resilience scale 10 (CD-RISC 10), and the Perceived of Supervisor Support Scale (PSS). Descriptive, inferential, correlation, and logistic regression tests were performed for data analyses. RESULTS: Of the 1174 HCPs included in the analysis, 77% were presented with high burnout levels: 58% with emotional exhaustion (EE), 72% with depersonalization (DP), and 66% with low personal accomplishment (PA). Females were associated with increased odds of burnout (OR: 1.47; 95% CI: 1.04-2.06) compared to males. Burnout and its subscales were associated with higher intention to leave practice, with 33% of HCPs considering quitting their jobs. Furthermore, HCPs reported a low resilience score overall, and negative correlations were found between EE (r = -0.21; p < 0.001) and DP (r = -0.12; p < 0.01), and positive correlation with low PA (r = 0.38; p < 0.001). In addition, most HCPs perceived supervisory support as low, and it is associated with increased burnout and quitting intention. CONCLUSION: Burnout is common among HCPs across all clinical settings and is associated with higher intention to quit and low resilience and supervisory support. Workplace management should provide a supportive workplace to reduce burnout symptoms and promote resiliency.

Acute Running and Coronary Heart Disease Risk Markers in Male Cigarette Smokers and Nonsmokers: A Randomized Crossover Trial.

PURPOSE: Cigarette smoking is an independent risk factor for coronary heart disease and is associated with impaired postprandial metabolism. Acute exercise reduces postprandial lipemia and improves other coronary heart disease risk markers in nonsmokers. Less is known about responses in cigarette smokers. METHODS: Twelve male cigarette smokers (mean ± SD; age = 23 ± 4 yr, body mass index = 24.9 ± 3.0 kg·m-2) and 12 male nonsmokers (age = 24 ± 4 yr, body mass index = 24.1 ± 2.0 kg·m-2) completed two, 2-d conditions (control and exercise) in a randomized crossover design. On day 1, participants rested for 9 h (0800-1700) in both conditions except a 60-min treadmill run (65% ± 7% peak oxygen uptake, 2.87 ± 0.54 MJ) was completed between 6.5 and 7.5 h (1430-1530) in the exercise condition. On day 2 of both conditions, participants rested and consumed two high-fat meals over 8 h (0900-1700) during which 13 venous blood samples and nine resting arterial blood pressure measurements were collected. RESULTS: Smokers exhibited higher postprandial triacylglycerol and C-reactive protein than nonsmokers (main effect group effect size [Cohen's d] ≥ 0.94, P ≤ 0.034). Previous day running reduced postprandial triacylglycerol, insulin, and systolic and diastolic blood pressure (main effect condition d ≥ 0.28, P ≤ 0.044) and elevated postprandial nonesterified fatty acid and C-reactive protein (main effect condition d ≥ 0.41, P ≤ 0.044). Group-condition interactions were not apparent for any outcome across the total postprandial period (0-8 h; all P ≥ 0.089), but the exercise-induced reduction in postprandial triacylglycerol in the early postprandial period (0-4 h) was greater in nonsmokers than smokers (-21%, d = 0.43, vs -5%, d = 0.16, respectively; group-condition interaction P = 0.061). CONCLUSIONS: Acute moderate-intensity running reduced postprandial triacylglycerol, insulin, and resting arterial blood pressure the day after exercise in male cigarette smokers and nonsmokers. These findings highlight the ability of acute exercise to augment the postprandial metabolic health of cigarette smokers and nonsmokers.