Molecular Detection of Candidatus Orientia chuto in Wildlife, Saudi Arabia.

Scrub typhus is a zoonosis caused by 3 species of Orientia bacteria, including Candidatus Orientia chuto. This species is known only from a human case in Dubai and infections in wildlife in Kenya. We report molecular detection of Candidatus O. chuto in 2 wild rodent species from Saudi Arabia.

Analysis of a monocentric computed tomography dosimetric database using a radiation dose index monitoring software: dose levels and alerts before and after the implementation of the adaptive statistical iterative reconstruction on CT images.

OBJECTIVE: To analyze dosimetric data of a single center by a radiation dose index monitoring software evaluating quantitatively the dose reduction obtained with the implementation of the adaptive statistical iterative reconstruction (ASIR) on Computed Tomography in terms of both the value of the dose length product (DLP) and the alerts provided by the dose tool. METHODS: Dosimetric quantities were acquired using Qaelum DOSE tool (QAELUM NV, Leuven-Heverlee, Belgium). Dose data pertaining to CT examinations were performed using a General Electric Healthcare CT tomography with 64 detectors. CT dose data were collected over 4 years (January 1, 2017 to December 31, 2020) and included CT dose length product (DLP). Moreover, all CT examinations that triggered a high radiation dose (twice the median for that study description), termed alerts on Dose tool, were retrieved for the analysis. Two radiologists retrospectively assessed CT examinations in consensus for the images quality and for the causes of the alerts issued. A Chi-square test was used to assess whether there were any statistically significant differences among categorical variable while a Kruskal Wallis test was considered to assess differences statistically significant for continuous variables. RESULTS: Differences statistically significant were found for the DLP median values between the dosimetric data recorded on 2017-2018 versus 2019-2020. The differences were linked to the implementation of ASIR technique at the end of 2018 on the CT scanner. The highest percentage of alerts was reported in the CT study group "COMPLETE ABDOMEN + CHEST + HEAD" (range from 1.26% to 2.14%). A reduction year for year was relieved linked to the CT protocol optimization with a difference statistically significant. The highest percentage of alerts was linked to wrong study label/wrong study protocol selection with a range from 29 to 40%. CONCLUSIONS: Automated methods of radiation dose data collection allowed for detailed radiation dose analysis according to protocol and equipment over time. The use of CT ASIR technique could determine considerable reduction in radiation dose.

[The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images].

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography.

OBJECTIVE: To determine the optimal pre-adaptive and post-adaptive level statistical iterative reconstruction V (ASiR-V) for improving image quality and reducing radiation dose in coronary computed tomography angiography (CCTA). METHODS: The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into 5 groups (A, B, C, D, and E) with progressive scanning from 40% to 80% pre-ASiR-V with 10% intervals and reconstructing with 70% post-ASiR-V. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was assessed using a 5-point scale. The CT dose index volume (CTDIvol) and dose-length product (DLP) of each patient were recorded and the effective radiation dose (ED) was calculated after statistical analysis by optimizing for the best pre-ASiR-V value with the lowest radiation dose while maintaining overall image quality. In part II, the images were reconstructed with the recommended optimal pre-ASiR-V values in part I (D group) and 40%-90% of post-ASiR-V. The reconstruction group (D group) was divided into 6 subgroups (interval 10%, D0:40% post-ASiR-V, D1:50% post - ASiR-V, D2:60% post-ASiR-V, D3:70% post-ASiR-V, D4:80% post-ASiR-V, and D5:90% post-ASiR-V).The SNR and CNR of D0-D5 subgroups were calculated and analyzed using one-way analysis of variance, and the consistency of the subjective scores used the k test. RESULTS: There was no significant difference in the SNRs, CNRs, and image quality scores among A, B, C, and D groups (P > 0.05). The SNR, CNR, and image quality scores of the E group were lower than those of the A, B, C, and D groups (P < 0.05). The mean EDs in the B, C, and D groups were reduced by 7.01%, 13.37%, and 18.87%, respectively, when compared with that of the A group. The SNR and CNR of the D4-D5 subgroups were higher than the D0-D3 subgroups, and the image quality scores of the D4 subgroups were higher than the other subgroups (P < 0.05). CONCLUSION: The wide-detector combined with 70% pre-ASiR-V and 80% post-ASiR-V significantly reduces the radiation dose of CCTA while maintaining overall image quality as compared with the manufacture's recommendation of 40% pre-ASiR-V.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose. METHODS: The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared. RESULTS: For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%). CONCLUSION: Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

An algorithm for the robust estimation of the COVID-19 pandemic's population by considering undetected individuals.

Due to the current COVID-19 pandemic, much effort has been put on studying the spread of infectious diseases to propose more adequate health politics. The most effective surveillance system consists of doing massive tests. Nonetheless, many countries cannot afford this class of health campaigns due to limited resources. Thus, a transmission model is a viable alternative to study the dynamics of the pandemic. The most used are the Susceptible, Infected and Removed type models (SIR). In this study, we tackle the population estimation problem of the A-SIR model, which takes into account asymptomatic or undetected individuals. By means of an algebraic differential approach, we design a model-free (no copy system) reduced-order estimation algorithm (observer) to determine the different non-measured population groups. We study two types of estimation algorithms: Proportional and Proportional-Integral. Both shown fast convergence speed, as well as a minimal estimation error. Additionally, we introduce random fluctuations in our analysis to represent changes in the external conditions and which result in poor measurements. The numerical results reveal that both model-free estimators are robust despite the presence of these fluctuations. As a point of reference, we apply the classical Luenberger type observer to our estimation problem and compare the results. Finally, we consider real data of infected individuals in Mexico City, reported from February 2020 to March 2021, and estimate the non-measured populations. Our work's main goal is to proportionate a simple and therefore, an accessible methodology to estimate the behavior of the COVID-19 pandemic from the available data, such that the competent authorities can propose more adequate health politics.

Evaluation of Pesticide Residues in Vegetables from the Asir Region, Saudi Arabia.

This study's aim was to determine the pesticide residues in 10 different vegetable commodities from the Asir region, Saudi Arabia. We evaluated 211 vegetable samples, collected from supermarkets between March 2018 and September 2018, for a total of 80 different pesticides using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS) after extraction with a multi-residue method (the QuEChERS method). The results were assessed according to the maximum residue limit (MRL) provided by European regulations for each pesticide in each commodity. All lettuce, cauliflower, and carrot samples were found to be free from pesticide residues. A total of 145 samples (68.7%) contained detectable pesticide residues at or lower than MRLs, and 44 samples (20.9%) contained detectable pesticide residues above MRLs. MRL values were exceeded most often in chili pepper (14 samples) and cucumber (10 samples). Methomyl, imidacloprid, metalaxyl, and cyproconazole were the most frequently detected pesticides. Based on the results of this study, we recommend that a government-supported program for the monitoring of pesticide residues in vegetables be established to promote consumers' health and achieve sustainable farming systems.

Observers' response to facial disfigurement from head and neck cancer.

OBJECTIVE: Our long-term goal is to develop a normative feedback intervention to support head and neck cancer patients in forming realistic expectations about how other people in non-social group settings will respond to their appearance. This study aimed to evaluate the relationship between observer ratings of facial disfigurement and observer ratings of emotional response when viewing photographs of faces of head and neck cancer patients. METHODS: Seventy-five (75) observers rated their emotional response to each of 144 facial photographs of head and neck cancer patients using the Self-Assessment-Manikin and rated severity of facial disfigurement on a 9-point scale. Body image investment of the observers was measured using the Appearance Schemas Inventory-Revised. A standardized multiple regression model was used to assess the relationship between observer ratings of facial disfigurement and observer ratings of emotional response, taking into consideration the age and sex of the patient depicted in the stimulus photograph, as well as the age, sex, and body image investment of the observer. RESULTS: Observers who had a strong emotional response to a patient's facial photograph tended to rate the patient's facial disfigurement as more severe (standardized regression coefficient ß = 0.328, P < 0.001). Sex and age of the observer had more influence on the rating of facial disfigurement than did the patient's demographic characteristics. Observers more invested in their own body image tended to rate the facial disfigurement as more severe. CONCLUSIONS: This study lays the groundwork for a normative database of emotional response to facial disfigurement.

Adaptive statistical iterative reconstruction use for radiation dose reduction in pediatric lower-extremity CT: impact on diagnostic image quality.

BACKGROUND: For the past several years, increased levels of imaging radiation and cumulative radiation to children has been a significant concern. Although several measures have been taken to reduce radiation dose during computed tomography (CT) scan, the newer dose reduction software adaptive statistical iterative reconstruction (ASIR) has been an effective technique in reducing radiation dose. To our knowledge, no studies are published that assess the effect of ASIR on extremity CT scans in children. OBJECTIVE: To compare radiation dose, image noise, and subjective image quality in pediatric lower extremity CT scans acquired with and without ASIR. MATERIALS AND METHODS: The study group consisted of 53 patients imaged on a CT scanner equipped with ASIR software. The control group consisted of 37 patients whose CT images were acquired without ASIR. Image noise, Computed Tomography Dose Index (CTDI) and dose length product (DLP) were measured. Two pediatric radiologists rated the studies in subjective categories: image sharpness, noise, diagnostic acceptability, and artifacts. RESULTS: The CTDI (p value = 0.0184) and DLP (p value <0.0002) were significantly decreased with the use of ASIR compared with non-ASIR studies. However, the subjective ratings for sharpness (p < 0.0001) and diagnostic acceptability of the ASIR images (p < 0.0128) were decreased compared with standard, non-ASIR CT studies. CONCLUSION: Adaptive statistical iterative reconstruction reduces radiation dose for lower extremity CTs in children, but at the expense of diagnostic imaging quality. Further studies are warranted to determine the specific utility of ASIR for pediatric musculoskeletal CT imaging.

A new low-dose multi-phase trauma CT protocol and its impact on diagnostic assessment and radiation dose in multi-trauma patients.

PURPOSE: Computed tomography (CT) examinations, often using high-radiation dosages, are increasingly used in the acute management of polytrauma patients. This study compares a low-dose polytrauma multi-phase whole-body CT (WBCT) protocol on a latest generation of 16-cm detector 258-slice multi-detector CT (MDCT) scanner with advanced dose reduction techniques to a single-phase polytrauma WBCT protocol on a 64-slice MDCT scanner. METHODS: Between March and September 2015, 109 polytrauma patients (group A) underwent acute WBCT with a low-dose multi-phase WBCT protocol on a 258-slice MDCT whereas 110 polytrauma patients (group B) underwent single-phase trauma CT on a 64-slice MDCT. The diagnostic accuracy to trauma-related injuries, radiation dose, quantitative and semiquantitative image quality parameters, subjective image quality scorings, and workflow time parameters were compared. RESULTS: In group A, statistically significantly more arterial injuries (p = 0.04) and arterial dissections (p = 0.002) were detected. In group A, the mean (±SD) dose length product value was 1681 ± 183 mGy*cm and markedly lower when compared to group B (p < 0.001). The SDs of the mean Houndsfield unit values of the brain, liver, and abdominal aorta were lower in group A (p < 0.001). Mean signal-to-noise ratios (SNRs) for the brain, liver, and abdominal aorta were significantly higher in group A (p < 0.001). Group A had significantly higher image quality scores for all analyzed anatomical locations (p < 0.02). However, the mean time from patient registration until completion of examination was significantly longer for group A (p < 0.001). CONCLUSIONS: The low-dose multi-phase CT protocol improves diagnostic accuracy and image quality at markedly reduced radiation. However, due to technical complexities and surplus electronic data provided by the newer low-dose technique, examination time increases, which reduces workflow in acute emergency situations.

The incidence, mortality and disease burden of cardiovascular diseases in China: a comparative study with the United States and Japan based on the GBD 2019 time trend analysis.

Background: Cardiovascular diseases (CVDs) are not only the primary cause of mortality in China but also represent a significant financial burden. The World Health Organization highlight that as China undergoes rapid socioeconomic development, its disease spectrum is gradually shifting towards that of developed countries, with increasing prevalence of lifestyle-related diseases such as ischemic heart disease and stroke. We reviewed the rates and trends of CVDs incidence, mortality and disability-adjusted life years (DALYs) burden in China and compared them with those in the United States (US) and Japan for formulating CVDs control policies. Methods: Data on CVDs incidence, death and DALYs in China, the US and Japan were obtained from the GBD 2019 database. The Joinpoint regression model was used to analyze the trends in CVDs incidence and mortality in China, the US and Japan, calculate the annual percentage change and determine the best-fitting inflection points. Results: In 2019, there were approximately 12,341,074 new diagnosed cases of CVDs in China, with 4,584,273 CVDs related deaths, causing 91,933,122 DALYs. The CVDs age-standardized incidence rate (ASIR) in China (538.10/100,000) was lower than that in the US and globally, while age-standardized death rate (ASDR) (276.9/100,000) and age-standardized DALY rate (6,463.47/100,000) were higher than those in the two regions. Compared with the US and Japan, from 1990 to 2019, the CVDs incidence rate in China showed an increasing trend, with a lower annual decrease in ASDR and a younger age structure of disease burden. Furthermore, the disease spectrum in China changed minimally, with stroke, ischemic heart disease, and hypertensive heart disease being the top three leading CVDs diseases in terms of incidence and disease burden, also being the major causes of CVDs in the US and Japan. Conclusion: The prevention and control of CVDs is a global issue. The aging population and increasing unhealthy lifestyles will continue to increase the burden in China. Therefore, relevant departments in China should reference the established practices for CVDs control in developed countries while considering the diversity of CVDs in different regions when adjusting national CVDs control programs.

Application value of individualized tube voltage, contrast injection, and adaptive statistical iterative reconstruction V algorithm based on body mass index in renal computed tomography angiography for radiation and iodinated contrast dose reduction.

OBJECTIVES: To explore the application value of body mass index (BMI)-based kilovoltage peak (kVp) selection and contrast injection protocol combined with different adaptive statistical iterative reconstruction V (ASIR-V) strengths in renal computed tomography angiography (CTA) in reducing radiation and contrast medium (CM) doses. METHODS: One-hundred renal CTA patients were prospectively enrolled and were divided into individualized kVp group (group A, n = 50) and conventional 100 kVp group (group B, n = 50), both with automatic tube current modulation and CM of Iohexol at 350 mgI/mL concentration. Group A: 70 kVp, noise index (NI) of 18 and CM dose rate of 17 mgI/kg/s for 10 s for BMI <25 kg/m2 patients; 80 kVp, NI = 17, and CM dose rate of 19 mgI/kg/s for 10 s for 25 kg/m2≤BMI≤30 kg/m2 patients. Group B: 100 kVp, 50 mL of CM at the flow rate of 4.5 mL/s. The objective image quality, effective radiation dose, CM dose, injection rate, and image quality were compared between the 2 groups. RESULTS: There was no significant difference in patient characteristics between the 2 groups (P > .05). Compared to group B, group A significantly reduced effective radiation dose by 28.4%, CM dose by 27.2%, and injection rate by 22.7% (all P < .001). The 2 groups had similar SD values in erector spine (P > .05). Group A had significantly higher CT values, SNR, and CNR values of the renal arteries than group B (all P < .001). The 2 radiologists had excellent agreement (Kappa value > 0.8) in the subjective scores of renal CTA images and showed no statistically significant difference between the 2 groups (4.57 ± 0.42 vs 4.41 ± 0.49) (P > .05). CONCLUSIONS: BMI-based scan and reconstruction protocol in renal CTA significantly reduces radiation and contrast doses while maintaining diagnostic image quality. ADVANCES IN KNOWLEDGE: (i) BMI-based individualized tube voltage selection and contrast injection protocol in renal CTA reduces both radiation and contrast doses over conventional protocol. (ii) The combination of lower kVp and higher weight ASIR-V maybe used to improve image quality in terms of contrast enhancement and image noise under lower radiation and contrast dose conditions. (iii) Renal CTA of normal size (BMI ≤ 30 kg/m2) patients acquired at low radiation dosage and low iodine contrast dose through the combination of low tube voltage and ASIR-V algorithm achieves excellent diagnostic image quality with a good inter-rater agreement.

Prevalence, Pattern, and Predictors of Premenstrual Syndrome (PMS) and Premenstrual Dysphoric Disorder (PMDD) in Asir Region, Saudi Arabia.

BACKGROUND: The prevalence and consequences of premenstrual dysphoric disorder (PMDD) and premenstrual syndrome (PMS) among Saudi women are not well understood. Consequently, the purpose of this research is to ascertain the frequency, course, and contributing factors of PMDD and PMS in the Asir area of Saudi Arabia. METHODS: A cross-sectional study was conducted in Asir, Saudi Arabia, using a pre-designed questionnaire distributed among adult female patients to five primary healthcare facilities (PHCs) that were chosen at random in the Asir Region. Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS, version 15). RESULTS: The study included 506 participants; 81% of them were Saudi and 19% were non-Saudi. A percentage (46.2%) of the participants were aged between 21 and 30 years old. A total of 369 (72.9%) participants have PMS. Regarding PMS symptoms, 79.1% reported mood changes, 59.3% tantrums, 56.7% change in appetite, 47% acne, 41.9% back and joint pain, and 43.1% reported sleep disorders. Almost half (42.1%) of the participants receive treatment to relieve the symptoms of PMS (72% of them receive paracetamol and 6.1% receive nonsteroidal anti-inflammatory drugs (NSAIDs)). Some (6.4%) exercise during the menstrual cycle to relieve symptoms of PMS, while 31% eat healthy foods at the time of period to relieve the symptoms of PMS. CONCLUSION: The prevalence of PMS/PMDD was among the reported figures worldwide. There was a significant association between age and the use of contraceptives with symptoms of PMS/PMDD among the studied sample.

Burden of psoriasis in young adults worldwide from the global burden of disease study 2019.

Background: To determine the global burden of psoriasis in young adults, i.e., those aged 15-49, from 1990 to 2019 and predict trends in this burden for 2020 to 2030. Methods: Age-standardized disease burden indicators and their estimated annual percentage changes were assessed and used to compare the estimated burden between regions. In addition, generalized additive models were used to predict the burden in this population from 2020 to 2030. Results: From 1990 to 2019, the overall burden of psoriasis in young adults worldwide trended downward, as the age-standardized incidence rate and the age-standardized disability-adjusted life year rate decreased. From 1990 to 2019, there were gender differences in the burden of psoriasis between regions with different Socio-demographic index. Specifically, there was a smaller increase in the burden in young men than in young women in middle- and low-middle-Socio-demographic index areas. In 2019, Western Europe, Australasia, and Southern Latin America had the highest age-standardized incidence rate of psoriasis in young adults, whereas age-standardized disability-adjusted life year rates of psoriasis in young adults were highest in high-income North America. In 2019, the psoriasis burden in young adults was the highest in high-Socio-demographic index areas and the lowest in low-Socio-demographic index regions. We predict that from 2020 to 2030, the incidence rate and disability-adjusted life year rate of psoriasis in all age groups of young adults will continue to decline, but the burden in those aged 30-39 will increase. Conclusion: From 1990 to 2019, the overall burden of psoriasis in each age group trended downward in this period. We predict that from 2020 to 2030, the burden of psoriasis in those aged 30-39 will increase.

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis.

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models-Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism-were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as 'Very High' susceptibility-more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN's 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted 'Soil Texture', 'Geology', 'Distance to Road', and 'Slope' as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

The Prevalence and Burden of Psychiatric Disorders in Primary Health Care Visits in Asir Region Saudi Arabia.

BACKGROUND: Psychiatric disorders encompass a wide range of conditions that affect an individual's mental health and well-being. These disorders can manifest in various ways. The recognition, diagnosis, treatment, and referral of psychological conditions heavily rely on general practitioners, who handle consultations that involve a psychological aspect in at least one third of cases. OBJECTIVES: To assess the prevalence and burden of common psychiatric disorders at primary health care centers (PHCs) using the Kessler Psychological Distress Scale (K6) in the general population, aged 18-65, and examine their symptom patterns and comorbidity. METHODOLOGY: A descriptive online cross-sectional survey was conducted over a one-month period, spanning from September to October 2023. The survey targeted the population living in the Asir region. To fulfill the objectives, the K6 scale was used. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS) version 25. P values were considered statistically significant when they were ≤0.05. RESULTS: The survey included a total of 1,595 participants. Of these, 21.3% of respondents were male, while 78.7% were female. The majority of respondents fall within the 18-25 age group, accounting for 50.5% of the total. A significant portion of the population experiences some form of psychological distress, with 4.6% reporting low psychological distress, 36.1% reporting mild psychological distress, and a substantial 59.2% reporting severe psychological distress. Age (p-value=0.024), gender (p-value=0.001), educational level (p-value=0.001), occupation (p-value=0.008), and monthly income (p-value=0.001) had significant associations with the psychological distress score of psychiatric disorders. CONCLUSION: The prevalence and burden of psychiatric disorders in primary health care visits in Saudi Arabia is a significant public health concern. Our findings showed that the majority of participants reported having severe to mild psychological distress.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

BACKGROUND: Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. OBJECTIVE: The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. RESULTS: High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. CONCLUSION: The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Trends in gastric cancer incidence and mortality in Asia and association analysis with human development index, 1990-2019.

OBJECTIVES: To describe the epidemiological time trends and gender, age and regional differences of gastric cancer in Asia during 1990-2019, and to analyze the association between the human development index (HDI) and the statistical indicators of the burden of disease. METHODS: Describing trends in age-standardized incidence rates (ASIR) and age-standardized mortality rate (ASMR) in Asia from 1990 to 2019 based on GBD-reported population-based surveillance of gastric cancer in Asia. Obtained ASIR, ASMR, and mortality to incidence ratios (MIR) for gastric cancer in different countries in 2019, with association analysis by Kruskal-Wallis nonparametric test. RESULTS: The annual percentage change in ASIR and ASMR in Asia from 1990 to 2019 was - 1.20% and - 1.91%. Male gastric cancer patients have higher ASIR and ASMR than female gastric cancer patients. Decreasing trends in ASIR and ASMR for the total population in five Asian regions. From 1990 to 2019, the average annual change in ASMR was - 2.45%, - 1.43%, - 0.53%, - 0.62%, and - 0.27% for Central Asia, East Asia, high-income Asia-Pacific, South Asia, and Southeast Asia, respectively (p < 0.05). Both incidence and mortality were concentrated in the age groups of 85-89 and 89-94 years. Classifying Asian countries into different levels of HDI, only MIR was associated with HDI levels. CONCLUSION: ASIR and ASMR of gastric cancer in the total population, different regions, and countries in Asia from 1990 to 2019 showed an overall decreasing trend. The MIR index is suggestive of survival rates and the role of cancer care in individual countries. Asian countries should develop different strategies for gastric cancer screening and prevention according to high-risk age, high-risk gender and HDI.

The global burden of breast cancer in women from 1990 to 2030: assessment and projection based on the global burden of disease study 2019.

Background and aim: This study aims to analyze the worldwide prevalence, mortality rates, and disability-adjusted life years (DALYs) attributed to breast cancer in women between 1990 and 2019. Additionally, it seeks to forecast the future trends of these indicators related to the burden of breast cancer in women from 2020 to 2030. Methods: Data from the Global Burden of Disease Study (GBD) 2019 was analyzed to determine the age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of DALYs due to breast cancer in women across 204 countries and territories from 1990 to 2019. Socio-economic development levels of countries and regions were assessed using Socio-demographic Indexes, and trends in the burden of breast cancer in women worldwide from 2020 to 2030 were projected using generalized additive models (GAMs). Results: The estimated annual percentage change (EAPC) in the ASIR breast cancer in women globally was 0.36 from 1990 to 2019 and is expected to increase to 0.44 from 2020 to 2030. In 2019, the ASIR of breast cancer in women worldwide was 45.86 and is projected to reach 48.09 by 2030. The burden of breast cancer in women generally rises with age, with the highest burden expected in the 45-49 age group from 2020 to 2030. The fastest increase in burden is anticipated in Central sub-Saharan Africa (EAPC in the age-standardized death rate: 1.62, EAPC in the age-standardized DALY rate: 1.52), with the Solomon Islands (EAPC in the ASIR: 7.25) and China (EAPC in the ASIR: 2.83) projected to experience significant increases. Furthermore, a strong positive correlation was found between the ASIR breast cancer in women globally in 1990 and the projected rates for 2030 (r = 0.62). Conclusion: The anticipated increase in the ASIR of breast cancer in women globally by 2030 highlights the importance of focusing on women aged 45-49 in Central sub-Saharan Africa, Oceania, the Solomon Islands, and China. Initiatives such as breast cancer information registries, raising awareness of risk factors and incidence, and implementing universal screening programs and diagnostic tests are essential in reducing the burden of breast cancer and its associated morbidity and mortality.

Awareness of Keratoconus and Its Association With Eye Rubbing Among the Population in Aseer Province.

BACKGROUND: Keratoconus (KC), is a non-inflammatory, bilateral, asymmetrical, progressive disease characterized by ectasia, thinning, increasing corneal curvature, and loss of visual acuity. In Saudi Arabia, it was discovered that KC has been the main reason for corneal transplantation in the past 20 years. Eye rubbing is considered one of the most significant risk factors for KC based on available data. PARTICIPANTS AND METHODS: A cross-sectional study was conducted among adults in Aseer province, Saudi Arabia in 2023. Data were collected through a pre-designed online questionnaire consisting of 17 questions divided into three sections. The questionnaire was preceded by informed consent and insured to maintain the confidentiality of the data. RESULTS: A total of 498 respondents were included in this study. The majority of the study's participants were in the 18 to 25 age group and females made up the majority. The majority of participants had a university education. 41.6% of individuals reported experiencing an allergic disorder, 59.0% reported having knowledge about KC, and the overall participants' awareness regarding KC was poor at 85.74%, with the highest percentage of good awareness (22.2%) detected among the 18-25 age group. CONCLUSION: The study revealed a concerning lack of awareness about KC among the population in Aseer Province, Saudi Arabia. Additionally, a significant percentage of participants engaged in eye rubbing, a behavior associated with increased risk for KC. There is a need for increased awareness about KC and the importance of avoiding eye rubbing.