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Myocardial perfusion imaging (MPI) provides physiologic and functional information about the heart muscle and its blood flow. Extracardiac radioactivity can interfere with visualization of the inferior wall of the myocardium, leading to poor-quality images, difficulties in interpretation, and delays in routine practice. This study aimed to identify the efficiency of having the patient consume a carbonated lemon drink to minimize the extracardiac radioactivity of 99mTc-sestamibi in comparison to 99mTc-tetrofosmin during MPI. Methods: This was a retrospective study that recruited 158 patients with known or suspected coronary artery disease referred to undergo 99mTc-sestamibi or 99mTc-tetrofosmin rest/stress single-day MPI. The patients were divided into 2 groups of mixed sexes and different ages. The first group comprised 78 patients injected with 99mTc-sestamibi, and the second group comprised 80 patients injected with 99mTc-tetrofosmin. For both groups, the patients drank 30 mL of fresh lemon juice diluted with 150 mL of soda water, and then we gave the patients about 100 mL of straight soda water, before imaging for both the rest and the stress phases. Results: Generally, in both groups, the 99mTc-tetrofosmin produced a good-quality image in comparison with the 99mTc-sestamibi. The mean rank of the total score for 99mTc-tetrofosmin (62.75) was less than that for 99mTc-sestamibi (96.68), and this difference was highly statistically significant (P = 0.000). There were statistically significant differences in the ratios and mean ranks for both groups in favor of 99mTc-tetrofosmin in patients having coronary artery disease. Conclusion: The use of a carbonated lemon drink minimizes extracardiac activity from both 99mTc-labeled MPI radiopharmaceuticals. This finding was more statistically significant for 99mTc-tetrofosmin MPI, providing better image quality and earlier imaging in both the rest and the stress phases because of faster hepatobiliary clearance.
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This study on Buddleja polystachya highlights its phytochemical composition, antimicrobial activity, and cytotoxic impacts. The study emphasizes the plant's potential to treat ocular diseases by identifying important compounds involved in the bioactivity through GC-MS analysis. This study explores the antimicrobial and cytotoxic potential of Buddleja polystachya (stem and leaves) extracts, with a focus on their application in treating bacterial ocular infections and their efficacy against MCF7, HT29, and HepG2 cancer cells. Through comprehensive GC-MS analysis, a diverse array of phytochemicals was identified within Buddleja polystachya stem and leaves extracts, including carbohydrates, phenolic derivatives, fatty acids, and steroidal components. The extracts were then evaluated for their biological activities, revealing significant antimicrobial properties against a range of bacterial strains implicated in ocular infections. The research findings demonstrate that stem extracts derived from Buddleja polystachya demonstrated high to moderate cytotoxic effects on cancer cell lines MCF7, HT29, and HepG2. Notably, these effects were characterized by varying IC50 values, which suggest distinct levels of sensitivity. In contrast, leaf extracts exhibited reduced cytotoxicity when tested against all these cell lines, although they did so with a significantly higher cytotoxicity aganist HepG2 cells. The results of this investigation highlight the potential therapeutic utilization of Buddleja polystachya extracts in the management of ocular infections and cancer. These results support the need for additional research to elucidate the underlying mechanisms of action of these extracts and explore their potential as drugs.
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INTRODUCTION: Fine needle aspiration cytology (FNAC) for thyroid nodules has a high diagnostic accuracy, according to several studies worldwide. Patients who experienced preoperative FNAC had more optimal surgical treatment than others who did not perform FNAC. Therefore, achieving an accurate FNAC procedure appears to be an important tool for the proper management of thyroid nodules. We aimed to study the accuracy and challenges of the thyroid FNAC diagnostic tool in the Al-Baha region, Kingdom of Saudi Arabia. METHODS: The study involves 52 patients with thyroid nodules who underwent preoperative FNAC and postoperative histopathology with the same surgery and pathology team at Al-Baha region in 2022-2023. RESULTS AND CONCLUSION: The mean age of the included patients was 47.7 years, with a female predominance. The diagnostic accuracy was 90%, and the main cause of inaccurate diagnosis was processing challenges, where the majority of cases were taken on the palpation-only technique, a few cases were ultrasound-guided, and the only technique used in the laboratory was conventional smears. The applied interrater reliability Cohen kappa coefficient (κ) for the clinical-histopathological agreement was "moderate agreement". We recommend using and evaluating more cytological techniques in addition to the currently used conventional smears in pathology laboratories to enhance the efficacy of the FNAC diagnosis of thyroid lesions.
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This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.
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Background: The inflammation plays a role in the pathophysiology of type-2 diabetes progression, and the mechanism remains unclear. The systemic immune-inflammation index (SII) is a novel inflammatory marker for type 2 diabetes patients and integrates multiple indicators in complete blood counts and routine blood tests. Aim: Since there is no international diagnostic standard for dry eye disease (DED), this study uses low-cost inflammatory blood biomarkers to investigate the correlation between SII and DM2-DED and determine the diagnosis indices of other biomarkers in DM2-DED. Methodology: A case-control retrospective analysis of totel patients n = 293 randomly selected and categorized into four groups: DED, DM2, DM2-DED, and healthy subjects. Demographic and blood biomarker variables were classified as categorical and continuous variables. The platelet-to-lymphocyte ratio (PLR), lymphocytes-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio (NLR), and SII were calculated platelet count multiply by NLR and analyzed for their correlation for all groups. Results: Focusing on DM2-DED patients was more common in females, 59.6%, than in males, 40.2%. The mean ages were 60.7 ± 11.85 years, a statistically significant difference with all groups. In the study group DM2-DED, there was an increase in all blood markers compared to all remaining groups except PLR. Only neutrophil, hemoglobin A1c (HbA1c), and fasting blood sugar levels were statistically significant differences in DM2-DED patients (p > 0.001, p < 0.001, and p < 0.001, respectively) compared to all groups. There was a positive correlation between HbA1c and PLR, HbA1c and NLR, and HbA1c and SII (r = 0.037, p = 0.705; r = 0.031, p = 0.754; and r = 0.066, p < 0.501, respectively) in the DM2-DED group. Conclusion: This study demonstrated that elevated SII values were linked to elevated HbA1c in DM2-DED patients. The potential of SII and HbA1c as early diagnostic indicators for ocular problems associated with diabetes mellitus is highlighted by their favorable connection in diagnosing DM2-DED.
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Introduction The landscape of medical education is constantly evolving, with innovative assessment methods being integrated to better align with the requirements of modern healthcare education. Among these, open-book exams (OBEs) represent a significant shift from traditional closed-book exams (CBEs), promising to enhance learning outcomes and better evaluate students' understanding of medical concepts. This study aims to explore the multifaceted impact of OBEs on medical students, including their perceptions, study behaviors, stress levels, and the cultivation of critical thinking and self-directed learning skills. Methodology This is a cross-sectional study, which utilized a mixed-methods approach, conducted at Al Baha University's College of Medicine, to explore the impact of OBEs on self-directed learning among 129 medical students over a 15-day period in October 2023. The research combined quantitative data from online questionnaires, assessing students' experiences, stress, understanding, and study strategies, with qualitative insights from in-depth interviews and open-ended survey questions. Participants were final-year medical students with prior experience in OBEs, selected to minimize bias. Data analysis was performed using Statistical Product and Service Solutions (SPSS, version 25; IBM SPSS Statistics for Windows, Armonk), focusing on descriptive and inferential statistics, while qualitative data underwent thematic analysis to identify patterns in students' perceptions of self-directed learning opportunities. The study was ethically approved, ensuring participant confidentiality and informed consent. Result Regarding the medical student perspectives on OBEs, the study revealed that the majority of medical students strongly perceive OBEs as less stressful (77, 59.69%) and easier to prepare for (79, 61.24%) compared to traditional exams. A significant proportion also believe OBEs accurately assess their comprehension (106, 82.17%) and prefer them as a mode of assessment (106, 82.17%). Furthermore, most students (87, 67.44%) reported performing better on OBEs compared to CBEs. Regarding the assessment of self-directed learning using the OBE method, students predominantly utilized highlighting important points (70, 54.26%) as a preparation method for OBEs. A large majority (85, 65.89%) considered OBEs as a fair assessment of self-directed learning and believed that they encourage self-directed learning (114, 88.37%). Conclusion OBEs represent a promising direction for medical education, offering a way to better prepare students for the complexities of real-world medical practice. Future strategies should include not only the refinement of OBE methodologies but also the integration of practice opportunities that enable students to hone their skills in applying knowledge effectively.
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BACKGROUND: Metastatic secondary ocular tumors spread from systemic malignancies, including breast cancer. This study aimed to evaluate the cytotoxicity of extracts from 5 medicinal plants native to Saudi Arabia. METHODS: For preliminary activity screening, cytotoxicity using the MTT assay and selectivity index determinations were made for medicinal plant extracts against various cancer cell-lines. The most promising extract was subjected to GC-MS analysis to determine the phytochemical composition. Clonogenic assays were performed using the most promising extract to confirm the initial results. Finally, western blot analysis was used to determine the modulation in expression of survivin and P27 suppressor genes in the human breast adenocarcinoma (MCF7) cell-line to understand the potential mechanistic properties of the active plant extract. RESULTS: The 5 plant extracts showed various cytotoxic activity levels using IC50. The most active extract was found to be the leaves of Capparis spinosa L. (BEP-07 extract) against the MCF7 breast cancer cell-line (IC50 = 3.61 ± 0.99 µg/ml) and selectivity index of 1.17 compared to the normal human fetal lung fibroblast (MRC5) cells. BEP-07 extract showed a dose dependent clonogenic effect against the MCF7 colonies which was comparable with the effect of doxorubicin. BEP-07 extract caused a significant decrease of survivin and increase in P27 expression compared to control GAPDH at its highest dose (14 µg/ml). The GC-MS chromatogram of Capparis spinosa L. (BEP-07 extract) revealed the existence of 145 compounds, belonging to the diverse classes of phytoconstituents. Fatty acids and their derivatives represent 15.4%, whilst octadecanoic acid, 2,3-dihydroxypropyl ester was the principal component (7.9%) detected. CONCLUSION: Leaves of Capparis spinosa L. (BEP-07 extract) exhibited a significant cytotoxic effect particularly against breast cancer cells. It exhibited this effect through survivin inhibition and via P27 upregulation. The detected phytoconstituents in the plant extract might be involved in tested cytotoxic activity, while further investigations are required to complete the drug candidate profile.
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Extratos Vegetais , Plantas Medicinais , Humanos , Arábia Saudita , Extratos Vegetais/farmacologia , Plantas Medicinais/química , Células MCF-7 , Linhagem Celular Tumoral , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Feminino , Survivina/metabolismo , Antineoplásicos Fitogênicos/farmacologia , Cromatografia Gasosa-Espectrometria de Massas/métodos , Compostos Fitoquímicos/farmacologiaRESUMO
Background: Diabetes mellitus is a chronic disease that can lead to depression and anxiety disorders if it is not controlled and managed properly. This study aimed to estimate the prevalence of depression and anxiety disorders among diabetic patients and to determine whether patient knowledge is a risk factor. Methods: The study included 220 patients with diabetes who attended the Diabetic Care Center. Socio-demographic data on the patients was collected. The Hospital Anxiety and Depression Scale questionnaire was used to assess patients' depression and anxiety levels. The Diabetes Knowledge Test 2 was used to assess the patients' knowledge of diabetes. Results: From 220 patients with diabetes, anxiety was detected in 78.2%. From the recordings of these patients with diabetes, it was observed that 32.7%, 29.5%, and 15.9% suffered from mild, moderate, and severe anxiety, respectively. Depression was diagnosed in 170 patients (77.2%), with the majority sowing a moderate degree (32.3%) of depression. Furthermore, 29.5% and 15.5% patients had recorded mild and severe degrees of depression, respectively. Patients' understanding of diabetes was inadequate in this study, with the majority of patients (70.5%) having a low level of overall diabetes knowledge. In terms of general knowledge of diabetes, 64.1% patients had a low knowledge level, whereas 74.5% patients had a low knowledge level related to insulin therapy. Conclusion: The patients' diabetes knowledge is significantly correlated with their anxiety and depression symptoms. Patients with diabetes should be regularly screened for anxiety and depression symptoms. Our findings indicate that the educational diabetes program could be a useful intervention for reducing depression and anxiety.
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The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.
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OBJECTIVE: The aim of this study was to measure the knowledge about breast cancer and to identify the barriers in screening among Saudi women in the Al-Baha region. To achieve this, a cross-sectional study was conducted, involving 468 women, to assess their understanding of breast cancer and to explore the obstacles they face in accessing breast cancer screening services. METHODS: The cross-sectional study included 468 women from Al Baha, Saudi Arabia, starting from May 17, 2022, to May 17, 2023. Participants were interviewed by well-trained team members of the research, and their responses were subsequently entered into a Google Form. This process aimed to evaluate their awareness, knowledge, and barriers to breast cancer screening. RESULTS: The majority of participants (48.9%) were in the 18-28 age group. The findings reveal a high level of awareness (96.4%) among participants regarding the significance of early breast cancer detection. For the effectiveness of breast cancer treatment, 59% believed there is an effective treatment, while 32.9% were uncertain or did not know. Knowledge about various risk factors for breast cancer varied. Smoking (73.5%), genetic factors (65.6%), and a family history of breast cancer (70.7%) were well-recognized as risk factors. Education and occupation significantly influenced knowledge about breast cancer (p-value of 0.000, and 0.035 respectively). CONCLUSION: this research highlights strong awareness of breast cancer's importance but gaps in knowledge regarding lesser-known factors. Education is crucial, requiring tailored campaigns and healthcare professional engagement.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/epidemiologia , Arábia Saudita/epidemiologia , Estudos Transversais , Detecção Precoce de Câncer , Conhecimentos, Atitudes e Prática em SaúdeRESUMO
BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. OBJECTIVE: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. METHODS: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. RESULTS: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. CONCLUSION: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.
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Neoplasias Encefálicas , Redes Neurais de Computação , Humanos , Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Encefálicas/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodosRESUMO
Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.
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BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia, with a growing number of patients worldwide. The association between AD and treatment with drugs targeting the beta-adrenergic receptor is controversial. The aim of this study is to assess the association between the initiation of AD medication and beta-adrenoceptor antagonists (beta-blockers) in adults. MATERIALS AND METHODS: We conducted a prescription sequence symmetry analysis using the University of Groningen IADB.nl prescription database. We determined the order of the first prescription for treating AD and the first prescription for beta-blockers, with the dispensing date of the first prescription for AD defined as the index date. Participants were adults over 45 years old starting any AD medication and beta-blockers within two years. We calculated adjusted sequence ratios with corresponding 95% confidence intervals. RESULTS: We identified 510 users of both AD and beta-blockers, and 145 participants were eligible. The results were compatible with either a significant decrease in the incidence of AD after using beta-blockers (adjusted sequence ratio (aSR) = 0.52; 95% CI: 0.35-0.72) or, conversely, an increase in beta-blockers after AD medication (aSR = 1.96; 95% CI: 1.61-2.30). CONCLUSIONS: There is a relationship between the use of beta-blockers and AD medications. Further research is needed with larger populations to determine whether drug therapy for AD increases the risk of hypertension or whether beta-blockers have potential protective properties against AD development.
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A 46-year-old male, with no chronic medical illness, complained of pain, tearing, and redness for one-month duration, with no photophobia, discharge, or decrease in visual acuity. Examination revealed a small, painful, red swelling in the left sclera. Slit-lamp examination using a narrow bright slit beam revealed edema of the episcleral layer and injection of the superficial episcleral blood vessels. The rest of the anterior segment exam and fundoscopy were normal. The laboratory investigations and systemic workup were normal. The patient was initially treated with prednisolone acetate (Pred Forte) 1% every three hours per day for one week, and then four times per day for another week, and tapered gradually over eight weeks with systemic nonsteroidal anti-inflammatory drugs (NSAIDs) as diclofenac sodium for eight weeks with mild improvement of clinical symptoms, but the size of the lesion remained without any change and the patient started to have a relapse of symptoms at the end of the course. Topical tacrolimus drops of 0.1% concentration were prepared in the pharmacy under complete sterile precautions and were used four times per day for the following six weeks duration instead of the initial therapy (steroids and NSAIDs). Tacrolimus drops were then tapered gradually over another six weeks duration. The patient showed dramatic suppression of inflammation and exceptional remission of symptoms with complete resolution of the episcleritis. Topical tacrolimus is very effective in the treatment of nodular episcleritis, which is resistant to steroid therapy. Patients with nodular episcleritis suffer from prolonged bouts of inflammation that are characteristically more painful than the diffuse type and may be associated with other systemic diseases. The case is steroid-resistant nodular episcleritis, which did not respond to the usual treatment and showed a good response to treatment with tacrolimus, which was first introduced in episcleritis. Tacrolimus is being used in other ocular diseases, but its use in episcleritis is unique.
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This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.
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AIM: This study measured and evaluated the socioeconomic burden of people living with keratoconus in Saudi Arabia. METHODS: This study employed a cross-sectional design, a Keratoconus Economic Burden Questionnaire, and a convenient sample of 89 keratoconus patients (58.4% male) drawn from multiple regions in Saudi Arabia. It was conducted using online surveys, and the data were analyzed using appropriate quantitative techniques. RESULTS: The mean age and annual income of the participants were 33.24 (SD = 6.82) years and USD 9046.52 (SD = 16,866.48), respectively, with only 37% being employed for wages. Up to 94.4% needed glasses or contact lenses at least once a week, and 73.0% received care from optometrists. The condition forced 45.9% of the respondents to change careers or leisure activities, with a further 51.3% having to take time off work. The mean annual out-of-pocket expenses for buying and maintaining glasses or contact lenses, as well as traveling and accommodation for keratoconus-related treatment were USD 2341.76 (SD = 3053.09), with 48.32 incurring upwards of USD 3240 over the period. The treatment costs increased with disease duration, r(89) = 0.216, p < 0.05. Regression results showed that the existence of comorbid eye disease, changing glasses at least once a year, and wearing either glasses or contact lenses at least once a week individually had statistically significant negative effects on the total annual keratoconus treatment costs, while disease duration, utilization of optometrists, and taking time off had a statistically significant increase on the total cost (p < 0.05). CONCLUSION: With a prevalence rate of 1 in 375, progressive debilitation, and the lifetime nature of the disease, keratoconus is a critical public health concern in Saudi Arabia. The resulting visual impairment and discomfort, as well as both direct and indirect economic burdens, have considerable impacts on the patient's quality of life.
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Preconcentration for on-site detection or subsequent determination is a promising technique for selective sensing explosive markers at low concentrations. Here, we report divinylbenzene monolithic polymer in its blank form (neat-DVB) and as a composite incorporated with sodalite topology zeolite-like metal-organic frameworks (3-ZMOF@DVB), as a sensitive, selective, and cost-effective porous preconcentrator for aliphatic nitroalkanes in the vapor phase as explosive markers at infinite dilution. The developed materials were fabricated as 18 cm gas chromatography (GC) monolithic capillary columns to study their separation performance of nitroalkane mixture and the subsequent physicochemical study of adsorption using the inverse gas chromatography (IGC) technique. A strong preconcentration effect was indicated by a specific retention volume adsorption/desorption ratio equal to 3 for nitromethane on the neat-DVB monolith host-guest interaction, and a 14% higher ratio was observed using the 3-ZMOF@DVB monolithic composite despite the low percentage of 0.7 wt.% of sod-ZMOF added. Furthermore, Incorporating ZMOF resulted in a higher percentage of micropores, increasing the degree of freedom more than bringing stronger adsorption and entropic-driven interaction more than enthalpic. The specific free energy of adsorption (ΔGS) values increased for polar probes and nitroalkanes, denoting that adding ZMOFs earned the DVB monolithic matrix a more specific character. Afterward, Lewis acid-base properties were calculated, estimating the electron acceptor (KA) and electron donor (KB) constants. The neat-DVB was found to have a Lewis basic character with KB/KA = 7.71, and the 3-ZMOF@DVB had a less Lewis basic character with KB/KA = 3.82. An increased electron-accepting nature can be directly related to incorporating sod-ZMOF into the DVB monolithic matrix. This work considers the initial step in presenting a portable explosives detector or preconcentrating explosive markers trace prior to more sophisticated analysis. Additionally, the IGC technique allows for understanding the factors that led to the superior adsorption of nitroalkanes for the developed materials.
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Substâncias Explosivas , Estruturas Metalorgânicas , Zeolitas , Polímeros , Alcanos , Bases de LewisRESUMO
BACKGROUND: In urolithiasis, urinary calculi are formed in the urinary system. Stone development does not initially result in any symptoms, but later renal colic, flank pain, hematuria, obstruction of urine flow, and/or hydronephrosis may indicate renal stone disease. In addition to age, gender, ethnicity, and local climate, urolithiasis can be caused by several other factors. The prevalence and recurrence rate of kidney stone disease is rising globally, while few effective treatment options currently exist. METHODS: Between June and October 2022, a cross-sectional study was conducted. An electronic questionnaire subdivided into three categories was used to determine the prevalence and identify the factors that increase the likelihood of developing urolithiasis among the population in Bisha. The collected data were reviewed and analyzed via IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. RESULTS: A total of 1,002 participants filled out the questionnaire. The age of the participants ranged from 18 to over 60 years, with an average age of 26.1 ± 13.9 years. There were 451 female participants (45%), and 927 (92.5%) were Saudis. According to the participants' body mass index, 98 (9.8%) were underweight, 388 (38.7%) were normal weight, 300 (29.9%) were overweight, and 216 (21.6%) were obese. The total number of participants with urolithiasis was 161 (16.1%), and 420 (41.9%) had a family history of renal stones. Urolithiasis was found to be significantly associated with family history, smoking, diabetes, hypertension, hyperthyroidism, gout, and chronic kidney disease. Older age and female gender were also associated with the risk of having urolithiasis. CONCLUSION: This study found urolithiasis to be highly prevalent among the Bisha population. In terms of risk factors, body mass index, smoking, and diabetes were the most significant. Based on the findings of this study, the authors recommend more public education regarding urolithiasis and its risk factors, emphasizing the importance of preventing the disease and the ways of treating urolithiasis through medical campaigns and social media.
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Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.
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BACKGROUND AND AIM: Electronic cigarette (e-cigarette) smoking is a significant public health problem in Saudi Arabia, particularly among youth who use it as an alternative to traditional cigarettes. This study aimed to evaluate the prevalence and beliefs towards e-cigarette use among Taif University students in Saudi Arabia. METHODS: A cross-sectional study was conducted among male Taif University students using a self-administered questionnaire. A sample of 319 students was selected through stratified sampling. The questionnaire included questions about socio-demographic characteristics, smoking history, awareness of e-cigarettes, prevalence of e-cigarette use, beliefs towards e-cigarettes, and reasons for e-cigarette use. RESULTS: The study revealed a high prevalence of e-cigarette use among Taif University students, with 40.1% of participants having used e-cigarettes at least once during their lifetime and 43.7% believing that e-cigarettes are less dangerous than traditional cigarettes. Participants studying sciences had 0.76 times the odds of believing that e-cigarettes help smokers to quit compared to participants studying literature. Compared to smokers, ex-smokers had an OR of 34.1 (p<0.001) and non-smokers had an OR of 35.9 (p<0.001) for experimentation of e-cigarettes. Smokers who had friends that tried e-cigarettes had an OR of 6.6 (p<0.001) for trying e-cigarettes, compared to smokers who did not have such friends. CONCLUSION: The study found that 40.1% of participants have used e-cigarettes at least once during their lifetime with a significant proportion of participants unaware of the potential health hazards of e-cigarettes, and many believed that e-cigarettes are less dangerous than traditional cigarettes. These findings emphasize the need for targeted educational interventions to address misconceptions and promote awareness among university students.