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Our aim was to describe the incidence and mortality of genitourinary (GU) cancers in rural and urban Pennsylvania counties. We calculated age-adjusted incidence and mortality rates of GU (prostate, bladder, and kidney) cancers from 1990 to 2019 in the Pennsylvania Cancer Registry. We defined rurality using the Center for Rural Pennsylvania's population density-based definition. We modeled average annual percent changes (AAPC) in age-adjusted incidence and mortality rates using joinpoint regression. Overall GU cancer incidence decreased in rural and urban counties (AAPC −7.5%, p = 0.04 and AAPC −6.6%, p = 0.02, respectively). Prostate cancer incidence decreased in rural and urban counties by −10.5% (p = 0.02) and −9.1% (p = 0.01), respectively. Kidney cancer incidence increased in both rural and urban counties, respectively (AAPC = +11.2, p = 0.002 and +9.3%, p = 0.01). GU cancer mortality decreased in rural and urban counties (AAPC = −11.6, p = 0.047 and AAPC −12.2, p = 0.01, respectively). Prostate cancer mortality decreased at similar rates in rural and urban counties (AAPC −15.5, p = 0.03 and −15.4, p = 0.02, respectively). Kidney cancer mortality decreased in urban (AAPC −6.9% p = 0.03) but remained stable in rural counties. Bladder cancer incidence and mortality were unchanged in both types of counties. Over three decades, GU cancer incidence and mortality decreased across Pennsylvania counties. [ABSTRACT FROM AUTHOR]

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the 'Many' class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Chat Generative Pre-Trained Transformer (ChatGPT) has previously been shown to accurately predict colon cancer screening intervals when provided with clinical data and context in the form of guidelines. The National Comprehensive Cancer Network® (NCCN®) guideline on non-muscle invasive bladder cancer (NMIBC) includes criteria for risk stratification into low-, intermediate-, and high-risk groups based on patient and disease characteristics. The aim of this study is to evaluate the ability of ChatGPT to apply the NCCN Guidelines to risk stratify theoretical patient scenarios related to NMIBC. Methods: Thirty-six hypothetical patient scenarios related to NMIBC were created and submitted to GPT-3.5 and GPT-4 at two separate time points. First, both models were prompted to risk stratify patients without any additional context provided. Custom instructions were then provided as textual context using the written versions of the NMIBC NCCN® Guidelines, followed by repeat risk stratification. Finally, GPT-4 was provided with an image of the NMIBC risk groups table, and the risk stratification was again performed. Results: GPT-3.5 correctly risk stratified 68% (24.5 of 36) of scenarios without context, slightly increasing to 74% (26.5 of 36) with textual context. Using GPT-4, the model had accuracy of 83% (30 of 36) without context, reaching 100% (36 of 36) with textual context (P =.025). GPT-4 with image context maintained similar accuracy to GPT-4 without context, with accuracy 81% (29 of 36). ChatGPT generally performed poorly when stratifying intermediate risk NMIBC (33%-63%). When risk stratification was incorrect, most responses were overestimations of risk. Conclusions: GPT-4 can accurately risk stratify patients with respect to NMIBC when provided with context containing guidelines. Overestimation of risk is more common than underestimation, and intermediate risk NMIBC is most likely to be incorrectly stratified. With further validation, GPT-4 can become a tool for risk stratification of NMIBC in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Patient satisfaction levels serve as a crucial performance indicator of the quality of health care in any health institute. Aim: This study aimed to investigate the satisfaction level of patients with the dental care services provided by Taif University Dental Hospital (TUDH) in an effort to highlight areas that need further improvements. Settings and Design: This study was conducted at TUDH in Taif, Saudi Arabia, and employed a cross-sectional analytical observational research approach. Materials and Methods: A simple random sampling technique was employed over a period of 3 months from November 2023 to January 2024. A self-administrated questionnaire was used to assess patient satisfaction with the dental services provided by final-year dental students. Statistical Analysis: The Statistical Package for the Social Sciences (SPSS) software (Version 25.0, Chicago, IL, USA) was used for all statistical analyses. Results: This study involved 94 patients; satisfaction levels were assessed across various domains within a health-care setting. Notably, doctors' interactions received consistently high mean scores (ranging from 1.10 to 1.20), as did faculty supervisors' interactions (ranging from 1.36 to 1.52). X-ray procedures and personnel demonstrated similar satisfaction levels (mean scores between 1.17 and 1.23), while dental assistants received positive evaluations (mean scores around 1.24). Reception staff satisfaction was moderate (mean scores from 1.26 to 1.54), and patient relation office satisfaction varied (mean scores from 1.38 to 1.72). Younger patients (under 40 years) reported higher satisfaction across all domains compared to older participants. In addition, males and non-Saudi nationals expressed greater satisfaction than their female and Saudi counterparts, although these findings were not statistically significant. Education level also played a role, with more educated individuals (holding a College/University degree or higher) showing greater satisfaction, particularly in reception staff and patient relation office domains (with borderline significant effects; P = 0.03 and P = 0.093, respectively). Conclusions: The majority of the patients were satisfied with the provided dental care services at TUDH. [ABSTRACT FROM AUTHOR]

Purpose: Percutaneous thermal ablation is an effective treatment for primary and metastatic liver tumors and is a recommended local therapy for early-stage hepatocellular carcinoma (HCC). Reported evidence shows an increase in the ablation zone volume over the first 24-h post-liver ablation. This report compares ablation zone volumes immediately at the completion (T = 0) of 26 microwave ablations of liver tumors to 24-h post-procedure (T = 24) volumes. Materials and methods: 20 patients, 13 (65%) males, underwent a total of 26 hepatic microwave ablations (MWA) under ultrasound guidance. Contrast-enhanced CT (CECT) or MRI was performed immediately and another CECT 24 h post operatively. Evaluation of the ablation zone and comparison of the two post-operative scans were done using BioTrace software. The expansion of ablation zones on post-op CECTs was matched point by point per direction. The distance between each 2 points was measured and grouped by distance. The incidence of each specific distance was then converted into a percentage, first for each case separately, then for all cases altogether. Data were tested by a matched paired one-sided t test. Results: The median lesion diameter was 1.5 cm (range 0.5–3.3) with 16 (62%) HCC cases and 9 hepatic metastases (4 neuroendocrine carcinoma, 4 colorectal carcinomas, 1 breast carcinoma, 1 pancreatic cancer). The data show a consistent volume expansion greater than 30% (p = 7.7e−5) 24-h post-ablation, where the median expansion is 57%. Distances between T = 0 and T = 24 equal to 3–7 mm occur in over 35% of the cases. Conclusion: The ablation zone expansion at 24-h post-op was not uniform. The final ablation zone is difficult to predict at the time of the procedure. The awareness of the ablation zone expansion is important when treating near-critical structures, managing the heat sink effect, and preserving liver parenchyma. [ABSTRACT FROM AUTHOR]

Background: A pandemic classification for COVID-19 was later issued. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was the cause of it. It spreads quickly and can result in severe respiratory failure at an early stage. Clinical research has demonstrated that the risk of infection is increased by both aging and chronic conditions. As such, blood group effects on the COVID-19 infection and its progression remain unknown. Aim: This study investigates possible relationships between patient blood group types, risk of SARS-CoV-2 infection, and clinical outcomes in COVID-19 in diabetic patients. Methodology: Age (41-60) was shown to be the age group most susceptible to infection with the SARS-COVID-19 virus, according to a statistical analysis of data from 90 individuals infected with COVID-19. Based on gender, the findings indicated that women had a greater incidence of diabetes than men did, with women having a 46% incidence of diabetes and men having a 44% infection rate. The blood types A and O were the most prevalent. According to the results of the current investigation, blood type O+ individuals had a higher prevalence of diabetes than blood type O-individuals. Based on gender, the findings indicated that women had a greater incidence of diabetes than men did, with women having a 46% incidence of diabetes and men having a 44% infection rate. Results: The results of the present study suggest that while blood group A might have a role in increased susceptibility to COVID-19 infection, blood group O might be somewhat protective. However, blood group type does not seem to influence clinical outcomes once infected. [ABSTRACT FROM AUTHOR]

Background: MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions. Methods: The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017–2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS). Results: Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43–90), 7.82 ng/dL (5.6–11.2), 54 cm3 (12–173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%. Conclusions: For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy. [ABSTRACT FROM AUTHOR]

The research aims to conduct a standard economic analysis of the most important economic indicators related to sustainable development affecting the GDP of the sample countries (Iraq, Egypt, Jordan), Based on the premise that despite the vigorous pursuit of sustainable development, developing countries remain below the required level through the reduction of many indicators of sustainable development and the challenges facing sample States, The research relied on the descriptive and analytical approach using the Eviews10 programmer using time chains and two periods before forecast (2015-2022) and for forecasting (2023-2030). The data were divided into quarterly views to increase the number of views and to see the impact of some economic indicators of sustainable development on the adopted variable of sample States' GDP. The researcher concluded that sustainable development is not the level required and desired from it between (2015-2022) and that the variables got worse during the prediction that the sample states did not pay this great attention. The researcher recommended paying attention to the economic aspect, which is central to the dimensions and indicators of sustainable development, focusing on the environmental, technological, and social aspects as well, focusing on the establishment of financial funds to support sustainable development programs, and supporting research, media and cultural projects towards the establishment of environmentally friendly and beneficial projects for poor classes. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results. [ABSTRACT FROM AUTHOR]

A straightforward and efficient spectrum technique was created using Ortho-chloranil as the electron acceptor (-acceptor) in a charge transfer (CT) complex formation reaction to determine the concentration of famotidine (FMD) in solutions. Compared to the double-distilled blank solution, the reaction result detected a definite violet colour at a maximum absorption wavelength of 546 nm, For concentrations range 2—28 µg/ml, the technique demonstrated excellent compliance with Beer-Law and Lambert's, as evidenced by its molar absorptivity of 2159.648 L mol−1 cm–1. Lower detection limits of 0.3024 µg/ml and 1.471 µg/ml, respectively, were discovered. The complexes of famotidine and Ortho-chloranil were found to have a 2:1 stoichiometry. Additionally, the suggested approach effectively estimated famotidine concentrations in pharmaceutical formulations, particularly in tablet form. [ABSTRACT FROM AUTHOR]

Applying the gradient discretisation method (GDM), the paper develops a comprehensive numerical analysis for nonlinear equations called the reaction–diffusion model. Using only three properties, this analysis provides convergence results for several conforming and non-conforming numerical schemes that align with the GDM. As an application of this analysis, the hybrid mimetic mixed (HMM) method for the reaction–diffusion model is designed, and its convergence is established. Numerical experiments using the HMM method are presented to facilitate the study of the creation of spiral waves in the Barkley model and the ways in which the waves behave when interacting with the boundaries of their generating medium. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, we introduce a new technique for enhancing medical image diagnosis through transfer learning (TL). The approach addresses the issue of limited labelled images by pre-training deep learning models on similar medical images and then refining them with a small set of annotated medical images. Our method demonstrated excellent results in classifying the humerus and wrist, surpassing previous methods, and showing greater robustness in various experiments. Furthermore, we demonstrate the adaptability of the approach with a CT case, which showed improvements in the results. Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements. [ABSTRACT FROM AUTHOR]

Aims: There is increasing evidence that diabetes stigma has negative impacts on behavioural and psychological outcomes among people with type 2 diabetes (T2D). However, research has focused largely on Caucasian and certain Asian groups. The aim of this study was to examine associations of diabetes stigma with diabetes distress and self‐care, and investigate the moderating effects of self‐esteem and social support, in Arabic‐speaking communities. Methods: A cross‐sectional study was conducted at 21 outpatient clinics and diabetes‐specialist centres in the United Arab Emirates. Besides the Arabic Type‐2 Diabetes Stigma Assessment Scale, participants completed other validated questionnaires assessing distress, self‐care, social support, and self‐esteem. General linear models were used to estimate the mean difference in diabetes‐specific distress and self‐care for every 1‐point increase in diabetes stigma total score. Results: Among 327 adults with T2D, the mean total score of diabetes stigma was 43.55 ± 13.95. Every 1‐point increase in diabetes stigma was associated with significantly increased diabetes distress (β = 0.113, 95% CI: 0.078 to 0.147; p = 0.003) and decreased self‐care behaviours: diet (β = −0.029, 95% CI: −0.048 to −0.009; p = 0.008), physical activity (β = −0.022, 95% CI: −0.038 to −0.006; p = 0.013) and foot care (β = −0.043, 95% CI: −0.059 to −0.026; p < 0.001). Self‐esteem mitigated the effect of diabetes stigma on diabetes distress. Conclusions: Perceived and experienced diabetes stigma was independently associated with increased diabetes distress and decreased engagement in diabetes self‐care among Arabic‐speaking adults with T2D. These findings are crucial to help clinicians provide more effective assessment and counselling and guide public health interventions to decrease diabetes stigma in these communities. [ABSTRACT FROM AUTHOR]

In the era of the 5.0 industry, the use of the Internet of Things (IoT) has increased. The data generates from sensors through IoT industrial systems, any fault in those systems affects their performance and leads to real disaster. Protecting them from any possible attacks is an essential task. to secure any system, it needs to predict in the first place possible attacks and faults that could happen in the future. Predicting and initiating the attack type and the accuracy of these predictions can be done with machine learning models nowadays on the datasets produced with IoT networks. This paper classifies several attacks type based on several criteria and techniques to enhance the performance of machine learning (ML) models such as Voting techniques beside six ML models; Random Forest (RF), Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) using Enriching IoT dataset [1]. The results showed that 100% accuracy was achieved in estimating process with the XGBoost model. [ABSTRACT FROM AUTHOR]

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN. [ABSTRACT FROM AUTHOR]

The research aims to study and measure the impact of macro policies (monetary and financial) on the agricultural sector and economic growth in some Arab countries, namely (Saudi Arabia and Jordan), with special reference to Iraq. The research relied on the descriptive approach with quantitative analysis using the statistical program (Eviews10). The research included a time series over a period of thirty-one years (1990- 2020), where the co-integration mechanism and the error correction methodology (ECM) were used to find out the impact of some independent financial and monetary economic indicators on the variable value added of the agricultural sector of the sample countries, and then measure the value added of the agricultural sector. Estimated from the first model on the economic growth of the second stage v Using the two-stage method, in order to reach more accurate results, and one of the most important conclusions reached by the research is that all economic variables are stable at the first level and difference, so the ARDL autoregressive vector model was chosen to estimate the relationship between the long and short term variables. This model was used in order to know the nature of the relationship shown by the macro policies (financial and monetary) in the agricultural sector and the economic growth rates of all the sample countries, as it was found that the policies followed by the sample countries were ineffective. Which contributed to the low added value of the agricultural sector and thus reduced economic growth, especially in Iraq and Saudi Arabia as they are oil countries, while in Jordan the added value increased economic growth. The researcher recommended raising the efficiency of fiscal and monetary policies by relying on mechanisms that raise the agricultural added value, which in turn works to raise the economic growth rates of the sample countries. [ABSTRACT FROM AUTHOR]

The evolution of 5G and 6G networks has enhanced the ability of massive IoT devices to provide real-time monitoring and interaction with the surrounding environment. Despite recent advances, the necessary security services, such as immediate and continuous authentication, high scalability, and cybersecurity handling of IoT cannot be achieved in a single broadcast authentication protocol. This paper presents a new hybrid protocol called Hybrid Two-level µ-timed-efficient stream loss-tolerant authentication (Hybrid TLI-µTESLA) protocol, which maximizes the benefits of the previous TESLA protocol variants, including scalability support and immediate authentication of Multilevel-µTESLA protocol and continuous authentication with minimal computation overhead of enhanced Inf-TESLA protocol. The inclusion of three different keychains and checking criteria of the packets in the Hybrid TLI-µTESLA protocol enabled resistance against Masquerading, Modification, Man-in-the-Middle, Brute-force, and DoS attacks. A solution for the authentication problem in the first and last packets of the high-level and low-level keychains of the Multilevel-µTESLA protocol was also proposed. The simulation analysis was performed using Java, where we compared the Hybrid TLI-µTESLA protocol with other variants for time complexity and computation overhead at the sender and receiver sides. We also conducted a comparative analysis between two hash functions, SHA-2 and SHA-3, and assessed the feasibility of the proposed protocol in the forthcoming 6G technology. The results demonstrated the superiority of the proposed protocol over other variants in terms of immediate and continuous authentication, scalability, cybersecurity, lifetime, network performance, and compatibility with 5G and 6G IoT generations. [ABSTRACT FROM AUTHOR]

In a social network (SN), link prediction (LP) is the process of estimating whether a link will exist in the future. In prior LP papers, heuristics score techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%. [ABSTRACT FROM AUTHOR]

This study examines the typical value of wheat crop production in Nineveh Governorate for the production season 2020, and calculates costs, the United States and profits and diagnoses the challenges and problems of crop production. (134) questionnaire forms were distributed to a random sample of wheat crop farmers in Nineveh Governorate for the agricultural season 2019-2020. showing that the average revenue per ton of wheat crop amounted to (497, 578,832) dinars/ton, and that the average total value added per ton was estimated at (384,200,121) dinars/ton, and the average total costs (production and marketing) per ton amounted to (223.621.841) dinars/ton. The results of the research indicated that there are problems and obstacles with regard to the process of preparing the state for production requirements for farmers in terms of inappropriate timings for cultivation and low quantities distributed, This leads to higher variable costs than production costs. and the presence of delays in the process of marketing and receiving the yield from the silos, The study recommends early development of the agricultural plan, opening centers to collect the crop in the harvest season, close to the farms in order to reduce costs and facilitate the marketing process, and work to develop a marketing mechanism and the receipt of the crop by the silos, and expedite the process of disbursing the financial dues to farmers. [ABSTRACT FROM AUTHOR]

Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy. [ABSTRACT FROM AUTHOR]

Electrolyte disturbances are common in ill patients. Several conditions in the intensive care unit (ICU) might be responsible for developing electrolyte disorders, and medications may also contribute to these disturbances. The current study aimed to determine the frequency of electrolyte disturbances and assess the pattern of electrolyte imbalance in hospitalized patients, determining the possible effects of these electrolyte disorders. This cross-sectional study included patients admitted to the intensive care unit, respiratory care unit (RCU), and coronary care unit (CCU) at the Al-Sadar teaching hospital, Najaf, Iraq, from November 2020 to April 2021. The study collected data from two hundred patients regarding demographics, categories of ICUs at admission, comorbidities, and laboratory values at admission. Also, electrolyte levels at ICU admission and during hospitalization were collected from the medical database record. In addition, the patient's age, sex, fasting blood sugar (FBS), body mass index (BMI), B.urea, and creatinine were matched. Na+, K+, ionized Ca++, and Cl serum levels were significantly different during hospitalization. Comorbidities with predominant hypokalemia were found in 80.5%, hypochloremia in 73%, hypocalcaemia in 72%, and hyponatremia in 56.7% of hospitalized patients. Studying the effect of co-morbidities indicated a higher percentage (44%) of admitted patients with ischemic heart diseases, 38 (19%) with digestive diseases, 21 (10.5%) with orthopedic surgery in an emergency, 14 (7%) with pneumonia and lung diseases, 12 (6%) with diabetics, 18 (9%) with sepsis, and 9(4.5%) with seizure. Hospitalized patients may be at higher risk of developing combined electrolytes disorder associated with decreased serum levels of K+, Na+, Ca++, and Cl-. Thus, doctors and clinicians are recommended to observe electrolyte changes and correct them as they seem to negatively impact the outcome and prognosis. [ABSTRACT FROM AUTHOR]

Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW. [ABSTRACT FROM AUTHOR]

We therefore sought to explore the test patterns in patients with IRDs compared with the general population, investigating whether higher SARS-CoV-2 infection rates in patients with IRDs could be explained by more frequent use of testing. (A) Percentage of patients and individuals tested, (B) percentage of tests positive for SARS-CoV-2, and (C) number of tests per individual, in patients with different inflammatory rheumatic diseases and their matched comparators. In Denmark, nationwide lockdowns and a mass-testing strategy were used as tools to contain and continuously monitor the spread of coronavirus during the coronavirus disease 2019 (COVID-19) pandemic. [Extracted from the article]

Malware designers and developers persistently endeavor to target smartphone users with the aim of collecting personal information, infringing on their privacy, and jeopardizing their security. Researchers from both academia and industry have leveraged deep learning algorithms to develop theoretical and practical approaches for detecting potential threats. This survey provides an extensive discussion of recent and relevant approaches that have been published, unveiling emerging technologies and highlighting lingering challenges in malware detection. [ABSTRACT FROM AUTHOR]

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market. • A comprehensive review of Deep Learning (DL) in orthopaedics. • The use of DL in orthopaedic applications. • The main challenges and solutions in applying DL to orthopaedics. • Trustworthy Requirements for DL in orthopaedics. • Data fusion in orthopaedics. [ABSTRACT FROM AUTHOR]

The high toxicity of hexavalent chromium [Cr (VI)] could not only cause harmful effects on humans, including carcinogenicity, respiratory issues, genetic damage, and skin irritation, but also contaminate drinking water sources, aquatic ecosystems, and soil, impairing the reproductive capacity, growth, and survival of organisms. Due to these harmful effects, detecting toxic Cr (VI) is of great significance. However, the rapid, simple, and efficient detection at a low Cr (VI) concentration is extremely challenging, especially in an acidic condition (existing as HCrO 4 −) due to its low adsorption free energy. A diketopyrrolopyrrole-based small molecule (DPPT-PhSMe) is designed and characterized to act as a chemosensor, which allows a high selectivity to Cr (VI) at an acidic condition with a low limit of detection to 10−8 M that is two orders of magnitude lower than the cut of limit (1 μM) recommended by World Health Organization (WHO). Mechanism study indicates that the rich sulfur atoms enhance the affinity to HCrO 4 −. Combining with favorable features of diketopyrrolopyrrole, DPPT-PhSMe not only allows dual-mode detection (colorimetric and spectroscopic) to Cr (VI), but also enables disposable paper-based sensor for naked-eye detection to Cr (VI) from fully aqueous media. The investigation of DPPT-PhSMe chemosensor for the quantification of Cr (VI) in real life samples demonstrates a high reliability and accuracy with an average percentage recovery of 102.1 % ± 4 (n = 3). DPPT-PhSMe represents the first diketopyrrolopyrrole-derived chemosensor for efficient detection to toxic Cr (VI), not only providing a targeted solution to the bottleneck of Cr (VI) detection in acidic conditions (existing as HCrO 4 −) caused by its low adsorption free energy, but also opening a new scenario for simple, selective, and efficient Cr (VI) detection with conjugated dye molecules. [Display omitted] • A first diketopyrrolopyrrole-derived chemosensor (DPPT-PhSMe) for toxic Cr (VI) detection is designed and characterized. • The chemosensor provides a targeted solution to the bottleneck of HCrO 4 − detection caused by low adsorption free energy. • DPPT-PhSMe allows a low LOD to Cr (VI) detection of 10−8 M and an accurate quantitative analysis in real world samples. • This work opens a new scenario for simple, selective and efficient Cr (VI) detection with conjugated dye molecules. [ABSTRACT FROM AUTHOR]

The study aims to find the added value of the agricultural industrialization of mills in Nineveh Governorate for the year 2020, and to calculate costs, revenues and profits and to diagnose the challenges and problems of the wheat grain milling industry. For the year 2020, out of a total of (41) mills distributed throughout the governorate, the results of the study indicated that the average total value added for grinding one ton of wheat grain amounted to (27,632.414) dinars / ton, and that the average net added value of grinding one ton amounted to (26556,617) dinars / ton, and the average total cost per ton of the crop amounted to (14,394.891) dinars / ton. and the average profit for grinding per ton was (13237.951) dinars / ton, and as for the average revenue per ton, it amounted to (56824.958) dinars / ton. The results indicated that the greater the share supplied to the mill by the General Company for the manufacture of grains, the production costs would decrease, which leads to that the added value would increase, and then the profits of the mill would increase and an increase in the national income would be achieved. One of the problems that mills suffer from is the low grinding wages per ton of wheat crop. The study recommended increasing the milling wages per ton of wheat crop paid to the mills in order to cover the high costs. [ABSTRACT FROM AUTHOR]

Mineral and hydrocarbon exploration relies heavily on geological and geotechnical information extracted from drill cores. Traditional drill-core characterization is based purely on the subjective expertise of a geologist. New technologies can provide automatic mineral analysis and high-resolution drill core images in a non-destructive manner. However, automated rock mass characterization presents a significant challenge due to its lack of generalization and robustness. To date, the automated estimation of rock quality designation (RQD), a key parameter for rock mass classification, is based mostly on digital image processing techniques with significant user biases. Alternatively, we propose using computer vision and machine learning-based algorithms for drill core characterization using drill core images to determine the RQD. A convolutional neural network (CNN) is used to detect and classify intact and non-intact cores, and to filter out empty tray areas and non-rock objects present in the core trays. The model calculates the length of the detected intact cores and estimates the RQD. We train the CNN model with thousands of sandstone core images from different drill holes in South Australia. The proposed method is tested on 540 sandstone core rows and 90 limestone core rows (~ 1 m each), which produces average error rates of 2.58% and 3.17%, respectively. Highlights: Core tray images are automatically analyzed for estimating rock quality designation. A convolutional neural network for core image classification is developed. The proposed approach is tested on sandstone and limestone images and results in an error rate of 3% approximately. [ABSTRACT FROM AUTHOR]

Introduction: Bioequivalence and bioavailability of antimicrobial drugs are considered as a critical issue in treatment of infectious diseases. Recently, some variations have been detected in clinical response to some antimicrobial agents. Ciprofloxacin is one of broad spectrum bactericidal agent that has been used widely in community which motivate us to assess its quality and quantity within available brands. Methods: Ciprofloxacin tablets from brand company (A) and other generic companies (B, C and D) have been selected, a standard powder in specific amount was dissolved and injected directly into HPLC to interpolate with the quantity of ciprofloxacin from each company tablet. In vitro dissolution rate was evaluated in every sample by calculating the difference and similarity factors. Antimicrobial effect was evaluated by minimum inhibitory concentration (MIC) method for reference strains of low to high level quinolone resistant bacteria. Results: Ciprofloxacin content was approved within accepted range for all companies except D that exceed the rang value. The dissolution testing was very rapid for A and B (>85%) within 15 minutes, while for D was <80% during the first 30 minutes. MIC for 24 hours of 0.128-500mg/L ciprofloxacin for all strains showed better results seen in A and B than D. Conclusion: Ciprofloxacin tablet of A, B and C companies were approved under the light of the quality test and antibacterial efficacy in comparison with D company, that urge for continuous monitoring and evaluation the generic drugs to avoid drug usefulness and antibiotic resistance. [ABSTRACT FROM AUTHOR]

Cytokine response to Ancylostoma duodenale (A. duodenale) infection was measured after starting treatments with piperazine. This study aims to determine the impact of cytokine production after infection with A. duodenale before and after treatment with piperazine. Blood and stool samples of 50 patients with A. duodenale infection and 28 healthy individuals (control) were collected. In this study, IFNγ, IL-5, IL-12, and IL-13 in serum (using ELISA-based methods) were measured. Stool samples were examined using the Kato-Katz technique to detect A. duodenale parasites. Blood and stool samples were analyzed 14 days after starting piperazine treatment for A. duodenale infection. The medium concentration of IFNγ, IL-5, IL-12, and IL-13 in the serum samples with A. duodenale infection is higher than that of the control group. IFNγ, IL-5, IL-12, and IL-13 levels were significantly higher in the infected individuals (10.5±7.4 pg/ml, 14.6±5.1 pg/ml, 8.5±3.2 pg/ml and 13.6±7.5 pg/ml respectively) than the control group (4.7±2.4 pg/ml, 7.8±4.06 pg/ml, 6.3±3.4 pg/ml and 3.5±2.7 pg/ml respectively). Also, piperazine treatment can significantly reduce cytokines levels (IFN-γ: P=0.043, IL-5: P=0.02, and IL-12, p=0.001). This study shows that piperazine treatment can reduce cytokines profiles in patients with A. duodenale infection. [ABSTRACT FROM AUTHOR]

Nationwide databases have implicated an increased incidence of renal cell carcinoma (RCC). The Pennsylvania (PA) Cancer Registry was queried to better define incidence, geographic distribution, and statewide trends of new RCC cases over a 27-year period. JoinPoint Trend Analysis Software modeled average annual percent changes (APCs) in age-adjusted rates (AAR). Maps plotting county-level incidence rates and stage distribution of disease across the state in 5-year time intervals were created using R 4.0.2 software. Overall, 59,628 cases of RCC were recorded in PA from 1990 to 2017. Eighty six percent of patients were >50 years of age, 61% were males, and 89% were Caucasian. Stage distribution using the SEER staging system included 64% local, 17% regional, and 16% distant. Over the study interval, AAR of all RCC cases increased from 9.9 to 18.0 patients per 100,000 population with an APC of 2.3% (p < 0.01). AAR of local disease increased from 5.4 to 12.7 patients per 100,000 population with an APC of 3.2% (p < 0.01). AAR of regional disease also increased from 1.9 to 2.9 patients per 100,000 population with an APC of 1.0% (p = 0.01). Younger patients (<50 years) had a greater rate of increase than older counterparts (APC 3.8% vs. 2.0%, p < 0.05). Geospatial investigation of new RCC cases noted certain geographic concentrations of greater disease incidence. The incidence of RCC in PA has increased over the past 27 years in PA. One-third of the cases are regional or metastatic at presentation and rates of increase were most notable in younger patients. [ABSTRACT FROM AUTHOR]

Bacillus subtilis, an isolate of bacillus genus, was obtained from the laboratories of Ministry of Science and Technology. The best efficient Bacillus subtilis isolate in cellulose and semi-cellulose hydrolysis was treated with Dielectric-barrier Discharge (DBD). Atmospheric cold plasma technique (non-thermal) was used by exposing them at different times (2, 3, 4 and 5 mins) separately as a first stage, and then 60 seconds after any treatment separately as a second stage. After 48 hours, the difference between the plasma source and the sample was fixed at 0.5 cm. The results showed a variation in the growth of the isolate according to the exposure time by the appearance of culture turbidity and the estimation of optical density. Positive results appeared between exposure times, the amount of optical density, and the cellulose and semi-cellulose decomposition into glucose. Bacillus subtilis increased its efficacy in cellulosic hydrolysis and semi-cellulosic materials. Bacillus subtilis showed malleability and the ability to increase the efficiency in cellulose and semi)cellulose materials hydrolysis. We conducted a new and extensive study by using cold plasma technique to increase the hydrolysis efficacy of food microorganisms [ABSTRACT FROM AUTHOR]

Background: As a specialty, internal medicine's historical and philosophical roots lie in the German medical tradition of the second half of the 19th century. However, what uniquely defines the specialty of internal medicine is certainly not fully captured in its name. Objective: To assess the knowledge of Saudi population regarding the scope of internal medicine speciality, 2020. Methods: This is a cross-sectional study that utilized an online questionnaire for data collection. All those who consent and can fill the Arabic online form were eligible to participate in the study. The only exclusion criteria were health-care workers and non-Arabic speakers. Data were analyzed using the Statistical Package for the Social Sciences software (SPSS, version 26.0). Results: A total of 1692 responses were analyzed, of which 60.7% were females and (76.8%) had university education. The majority (77.5%) see that the term is clear. Of the total sample, only (35.8%) answered correctly when asked about the role of internal medicine doctor. When asked if the term (internal medicine) is clear, educational level and region were significantly associated (P = 0.004, 0.03), respectively. Conclusion: The Arabic contrast of the term (internal medicine) may not describe the true meaning or the job of internal medicine doctors as approximately only one third (35.8%) knew the role of medical internists. A variety of factors other than the Arabic synonymous of the term (internal medicine) can influence the understanding of the public to what is internal medicine. [ABSTRACT FROM AUTHOR]

Despite major developments in malaria control over the past two decades, the disease continues to scourge the human population across the globe. Rising concerns such as insecticide resistance amongst vector mosquitoes are a cause of huge fear amongst healthcare providers and policymakers. Amidst such dire circumstances, a recent development may form the blueprint for future malaria control as for the first time ever researchers were able to decimate an entire mosquito population using gene-drive technology within a span of one year in a multi-generation, ecologically challenging study. Despite some concerns, the technology displayed a high potential of becoming a powerful tool in malaria control. [ABSTRACT FROM AUTHOR]

Obesity is a global health concern associated with the dysbiosis of intestinal microbial composition. In this study, we investigated the potentials of urolithin A (Uro-A) and urolithin B (Uro-B), two gut microbiota-derived metabolites of ellagitannins, in reducing body weight gain through the modulation of the gut microbiota. We established a high-fat diet (HFD)-induced obesity model in rats that were later administered with either 2.5 mg/kg of Uro-A or Uro-B. Serum biochemical parameters were quantified, and changes in the composition of the gut microbial community were analysed using 16S rDNA gene sequencing. Our results showed that the urolithins significantly decreased the body weight in HFD-fed rats and restored serum lipid profile. The taxonomic analysis showed that both Uro-A and Uro-modulated gut microbes related to body weight, dysfunctional lipid metabolism and inflammation. Overall, our results suggest that Uro-A and Uro-B possess anti-obesity properties, which may be related to the modulation of the gut microbial composition. [ABSTRACT FROM AUTHOR]

Background: There is a global call for upscaling and optimising the role of community pharmacists (CPs) in the control of non-communicable diseases (NCDs). In the United Arab Emirates (UAE), where NCDs are classified as a public health pandemic, upscaling CPs contributions has become more critical. Several contextual, professional, and educational challenges constrain the role of CPs. Objective: To synthesise the perspectives of key stakeholders in the UAE healthcare system and propose a roadmap for advancing the role of CP s in controlling NCDs in the UAE. Methods: This research followed a qualitative design using the International Pharmaceutical Federation (FIP) framework for quality assurance of pharmacy profession development. Data were collected using semi-structured interviews with 28 experts and senior leaders, then analysed using the thematic analysis technique with the assistance of NVivo software. Results: The analysis yielded three main themes that outlined the prospective roadmap: education, work environment, and policy. Some of the generated subthemes were establishing accredited NCD-specialised programmes, building a national framework for interprofessional education and collaboration, and upscaling the engagement of CPs in public health platforms and initiatives. Conclusion: Improving the role of CPs in controlling the NCD pandemic in the UAE requires coherent and well-structured multidisciplinary endeavours from health policymakers, educational institutions, and all groups of healthcare professionals, including the CPs themselves. [ABSTRACT FROM AUTHOR]

Introduction: diastolic dysfunction refers to impaired ventricular relaxation or filling regardless of ejection fraction and symptoms. It accounts for 8% and 25% in the hospitalized and general population, respectively. The present study was conducted to determine the prevalence and correlates of diastolic dysfunction in hypertensive patients living in Saudi Arabia. Methods: a multicentric, cross-sectional study was conducted from February 2019 to February 2020 at King Khalid Hospital and Prince Sultan Center for Health Services, Prince Sattam Bin Abdulaziz University hospital in Al Kharj, and Al Kharj Military Industries Corporation hospital, KSA. All patients with hypertension who underwent an echocardiography were included in the study. Logistic regression analysis was performed to determine factors associated with left ventricular diastolic dysfunction (LVDD). Results: the study included a total of 104 participants, where 51.9% were females and the mean age of the patients was 48.01±12.81 years. Most patients had an abnormal echocardiography finding (64.4%, n = 67). The most common abnormalities were left ventricular (LV) hypertrophy (44.2%, n = 46), and diastolic dysfunction, (35.6%, n = 37). The study revealed that age (aOR: 6.1, 95% CI 1.17-31.3; p = 0.032) and dyslipidemia (aOR: 3.45, 95% CI 1.16-10.24; p = 0.026) have significant association with LVDD in the patients with hypertension. Conclusion: in conclusion, diastolic dysfunction is prevalent among older hypertensive patients and those with dyslipidaemia. Age and dyslipidaemia were non-modifiable and modifiable factors associated with LVDD in hypertensive patients, respectively. [ABSTRACT FROM AUTHOR]

Aims: Aim of this study is to evaluate the capacity of a pharmacist‐delivered screening model for type 2 diabetes and cardiovascular disease (CVD) in identifying and referring individuals at risk. Method: A screening programme was implemented in 12 community pharmacies in three cities in the United Arab Emirates. Trained pharmacists screened adults (≥40 years) without a previous diagnosis of diabetes or CVD. Most participants were recruited during their visits to the pharmacies; pharmacy‐based advertising and social media were also used. The screening included medical history, anthropometric measurements, point‐of‐care glycated haemoglobin (HbA1c) levels, and a lipid panel. High‐risk individuals (HbA1c ≥ 5.7% [39 mmol/mol], a high diabetes risk score, or a 10‐year CVD risk ≥7.5%) were given a referral letter and advised to visit their physician. Risk factors for elevated HbA1c were identified by logistic regression. Results: Of the 568 screened participants, 332/568 (58%) were identified to be at risk: HbA1c levels were consistent with diabetes 67/560 (12%) or prediabetes 148/560 (26%), high diabetes risk score 243/566 (43%), CVD risk score > 7.5% 79/541 (15%). Obese people were more likely to have prediabetes or diabetes OR (95% CI): 3.2 (1.3, 7.5), as were those who spent more than 11 h/day sitting: 5.7 (1.8, 17.6). Of the 332 at‐risk participants, 206 (62%) responded to a telephone follow‐up at six weeks; one‐third had discussed screening results with their physician. Conclusions: Community pharmacists detected and referred individuals at risk for diabetes or CVD, although participant follow‐up with their physician could be improved. Pharmacy screening is feasible and will potentially improve outcomes. [ABSTRACT FROM AUTHOR]

Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers have developed innovative methods to overcome data scarcity and enhance deep model learning capabilities. Two prevalent techniques that have gained significant attention are transfer learning and self-supervised learning. Transfer learning leverages knowledge learned from pre-training on a large-scale dataset, such as ImageNet, and applies it to a target task with limited labelled data. This approach allows models to benefit from the learned representations and effectively transfer knowledge to new tasks, resulting in improved learning performance and generalisation. On the other hand, self-supervised learning focuses on training models using pretext tasks that do not require manual annotation, allowing them to learn valuable representations from large amounts of unlabelled data. These learned representations can then be fine-tuned for downstream tasks, mitigating the need for extensive labelled data. In recent years, transfer and self-supervised learning have found applications in various fields, including medical image processing, video recognition, and natural language processing. These approaches have demonstrated remarkable achievements, enabling breakthroughs in areas such as disease diagnosis, object recognition, and language understanding. However, while these methods offer numerous advantages, they also have limitations. For example, transfer learning may face domain mismatch issues between the pre-training and target domains, while self-supervised learning requires careful design of pretext tasks to ensure meaningful representations. This review paper explores the recent applications of these pre-training methods in various fields within the past three years. It delves into the advantages and limitations of each approach, assesses the performance of models employing these techniques, and identifies potential directions for future research. By providing a comprehensive review of current pre-training methods, this article offers guidance for selecting the best technique for specific deep learning applications to address the data scarcity issue. • Differences between Transfer Learning and Self-Supervised Learning. • Applications of Transfer Learning and Self-Supervised Learning. • Advantages and Limitations of Transfer Learning and Self-Supervised Learning. • Recommendations for the Use of Each Approach in Specific Applications. [ABSTRACT FROM AUTHOR]

In this paper, the free vibration and the buckling of a continuously suspended simply-supported beam are analyzed. A semi-analytical approach is used to calculate the natural circular frequencies and the critical forces of the beam. The length of the suspension is used as a parameter, and the natural circular frequencies and the critical forces are presented in a frequency map or a buckling map. The maps are analyzed in view of the trivial solutions, and the frequency map is compared to the map of discrete cable-stayed beams. Finally, for the validation of the results a numerical, finite element analysis is performed. [ABSTRACT FROM AUTHOR]

The exponential timestepping Euler algorithm with a boundary test is adapted to simulate an expected of a function of exit time, such as the expected payoff of barrier options under the constant elasticity of variance (CEV) model. However, this method suffers from a high Monte Carlo (MC) statistical error due to its exponentially large exit times with unbounded samples. To reduce this kind of error efficiently and to speed up the MC simulation, we combine such an algorithm with an effective variance reduction technique called the control variate method. We call the resulting algorithm the improved Exp algorithm for abbreviation. In regard to the examples we consider in this paper for the restricted CEV process, we found that the variance of the improved Exp algorithm is about six times smaller than that of the Jansons and Lythe original method for the down-and-out call barrier option. It is also about eight times smaller for the up-and-out put barrier option, indicating that the gain in efficiency is significant without significant increase in simulation time. [ABSTRACT FROM AUTHOR]

Objectives: To report our immediate and delayed outcomes of nonstented tubularized incised plate (TIP) distal hypospadias repair.Methods: We retrospectively reviewed all charts of children who underwent distal hypospadias repair in a single children's hospital from 2013 to 2018. Patients' demographics, hypospadias characteristics, operative technique, and immediate and delayed outcomes were recorded.Results: Of 280 consecutive distal hypospadias repairs that were identified, 74 were excluded due to the use of a repair other than TIP. Eleven stented TIP repairs were excluded as well. Of 195 nonstented repairs, immediate postoperative voiding complications were recorded in 11 (5.6%) and included multiple/split stream in 6 (3%), dysuria and voiding difficulty in 2 (1%), urinary retention in 2 (1%), and gross hematuria that spontaneously resolved in 1 (0.5%). Late follow up was recorded in 142 of 195 (72.8%) repairs. Delayed urethroplasty/glansplasty complications were recorded in 12 (8.5%) and included urethrocutaneous fistula in 10 (7.0%), meatal stenosis in 6 (4.2%) and glans/urethroplasty dehiscence in 2 (1.4%).Conclusion: Avoiding postoperative urethral stents in distal hypospadias TIP repair reduces the morbidity associated with the stent and is a feasible option that carries acceptable immediate and delayed complication rates. Avoiding the stent eliminates stent-related bladder spasms, the need for other medications, and the short-term office visit for stent removal, therefore reducing parental anxiety, patient discomfort, and reducing cost. [ABSTRACT FROM AUTHOR]

Background: The global prevalence of Alzheimer's disease (AD) and its treatment costs are projected to increase significantly, placing increasing pressure on health systems to create new models of care. Community pharmacists are well-positioned to provide medication management for people with AD. In Arabic-speaking countries, little is known about pharmacists' knowledge and practices in caring for people with AD.Objective: To evaluate community pharmacists' knowledge of AD and its management, counseling skills, and dispensing patterns when caring for people with AD and their caregivers in the United Arab Emirates (UAE).Methods: A large-scale cross-sectional survey of community pharmacists was conducted in three cities in the UAE using stratified random sampling. The questionnaire comprised of validated tools to measure knowledge and open-ended questions. A logistic regression model was conducted to predict counseling comprehensiveness.Results: A total of 325 community pharmacists completed the questionnaire. The mean knowledge scores about AD and its pharmacotherapy were 57.0% and 67.6%, respectively. Major shortcomings in pharmacists' practices were identified; history-taking, adherence assessment, and counseling were provided by 2.2%, 9.3%, and 17.3%, respectively. A minority provided comprehensive counselling; the multivariate analysis yielded new insights into pharmacist characteristics associated with such counseling.Conclusion: Pharmacists did not provide structured patient-centered care for people with AD. Community pharmacists did not provide adequate counseling, did not assess adherence-related issues appropriately, and had deficient knowledge. To develop patient-centered pharmacy-based services for Arabic-speaking communities, a multifaceted approach is required that goes beyond improving pharmacy workforce knowledge and communication skills to address broader sociocultural, legislative, and financial factors. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the association between pretreatment blood flow velocity in the choroid and optic nerve head (ONH) and retinal oxygen metabolism in the acute uveitic phase and the development of 'sunset glow fundus' in Vogt‐Koyanagi‐Harada (VKH) disease. Methods: Retrospective analysis of 41 patients (82 eyes). Laser speckle flowgraphy and retinal oximetry measurements were performed at the presentation. The main outcome measure was the development of 'sunset glow fundus'. Results: Twenty patients (40 eyes) presented in the phase preceding anterior segment inflammation (early presentation), and 21 patients (42 eyes) presented with anterior segment inflammation (late presentation). In ONH, mean blur rate (MBR)‐vessel, representing blood flow velocity in retinal vessels, was significantly lower in the late presentation group, while choroidal MBR was not significantly different. The late presentation group had significantly lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with the early presentation group. Eyes that subsequently developed 'sunset glow fundus' had significantly lower ONH MBR‐vessels, lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with eyes without 'sunset glow fundus'. ONH MBR‐vessel had a significant negative correlation with arteriovenous oxygen saturation difference and a significant positive correlation with calibre of retinal arterioles. Conclusions: In the acute uveitic phase of VKH disease, the development of 'sunset glow fundus' is associated with pretreatment reduced retinal blood flow velocity, calibre of retinal arterioles and oxygen saturation in retinal venules, as well as an increased arteriovenous oxygen saturation difference. [ABSTRACT FROM AUTHOR]