Factors Affecting Length of Stays in the Emergency Department of a Teaching Hospital: A Retrospective Study From Najran, Saudi Arabia.

BACKGROUND:  Reducing the frequency of emergency department (ED) patient visits for treatment, particularly in urgent instances, is a global healthcare objective. Additionally, a more extended stay in the ED can harm a patient's prognosis during later hospitalization. This study aims to investigate the factors affecting the length of stay in the ED in a teaching hospital. METHODS: A retrospective chart review study was done between January 1, 2021, and February 31, 2021, involving 122 adult patients who had delayed ED visits to King Khalid Hospital in Najran, Saudi Arabia. Data on the patient's characteristics, visit time, and the causes for the delay based on the Canadian Triage and Acuity Scale (CTAS) were gathered and analyzed. Factors associated with more than six hours of delay were investigated in a univariate analysis. RESULT: The mean age was 52.3 ±13.5 years, and 42 (34.4%) were more than 65 years of age. More than half of the study population were female (n=66; 54.1%). Most delays occurred among CTAS 4 and 5 cases (47.5%), and 22 (18.0%) occurred during holidays. The mean delay time was 6.1 ±1.8 hours. The leading delay causes were multiple consultations with further investigations (37.7%) and conflict between the teams (36.1%). In univariate analysis, ED visiting at holiday time (OR: 0.14; 95% CI: 0.04-0.40, p <0.001) and CTAS 4 and 5 (OR: 2.22; 95% CI: 0.95-5.30, p = 0.003) significantly had more delay. Factors associated with delay in univariate analysis were multiple consultations with further investigations (OR: 2.82; 95% CI: 1.32-6.26, p = 0.013), various assessments in different ED areas with a late arrival of the specialist (OR: 0.43; 95% CI: 0.20-0.91, p = 0.042), and conflict between the teams (OR: 2.50; 95% CI: 1.17-5.54, p = 0.031). CONCLUSION: In this study, multiple assessments in different ED areas and conflict between the teams were the main factors that caused delays in ED. Implementing a timeframe monitoring system for consultations while emphasizing accelerated decision-making and disposition for patients and understanding teamwork collaboration may reduce patients' length of stay in the ED. Implementing these strategies and evaluating their impact on the length of stay in the ED requires further investigation.

Charles Kelman: The Father of Phacoemulsification.

Charles D. Kelman was a brilliant American ophthalmologist who revolutionized cataract surgery by introducing phacoemulsification to replace extracapsular cataract extraction. He used an ultrasonic probe to emulsify and aspirate the lens through a small incision (3-4 mm). Kelman's technique met initial resistance at first, but it gained global acceptance after proving its safety and effectiveness in the management of cataractous eyes, and it has been the preferred technique until now. Today, the entire surgery is performed in 5-7 minutes. This technique also helped to reduce hospitalization after the surgical removal of a cataract. Kelman is one of the greatest surgeons of the last century.

Cancer Care During the COVID-19 Pandemic: A Retrospective Study From a Najran Oncology Center.

Background The COVID-19 pandemic significantly impacted healthcare systems globally, with cancer patients representing a particularly vulnerable group. This study aims to evaluate the influence of COVID-19 on cancer, focusing on infection rates, types of care, therapy adjustments, and factors associated with COVID-19 infection. Materials and methods This single-center retrospective analysis included adult cancer patients who underwent anticancer therapy at King Khalid Hospital in Najran, Saudi Arabia, from December 20, 2020, to January 23, 2022. Data on patient and cancer characteristics, COVID-19 specifics, treatment delays, outcomes, and factors associated with COVID-19 were collected and analyzed. Results A total of 257 chemotherapy recipients were interviewed. The mean age was 52.6 ± 14.4 years, with 44 (17.1%) over 65 years old. Females comprised 160 (62.3%) of the patients. The most common malignancies were gastrointestinal (71, 27.6%), breast (70, 27.2%), and hematological (50, 19.5%). Metastasis was present in 116 patients (45.1%). Common comorbidities included diabetes (68, 26.5%) and hypertension (55, 21.4%). Most patients (226, 87.9%) were vaccinated against COVID-19. COVID-19 tested positive in 22 patients (8.6%), with a lower infection rate in vaccinated patients (7 vs. 15, p < 0.001). Most cases were mild (18, 81.8%), with fever (19, 7.4%) and cough and fatigue (17, 6.6%) being the most common symptoms. The median time to resume treatment post-infection was 30 days. Factors associated with higher infection rates included diabetes (OR: 4.73, 95% CI: 1.94-12.03, p = 0.001), coronary artery disease (OR: 4.13, 95% CI: 1.07-13.30, p = 0.049), chronic lung disease (OR: 15.58, 95% CI: 5.37-45.79, p < 0.001), chronic liver disease (OR: 7.64, 95% CI: 2.38-22.98, p < 0.001), and multiple comorbidities (OR: 2.04, 95% CI: 1.46-2.90, p < 0.001), cancer patients who received chemotherapy (OR: 1.02, 95% CI: 0.12-12.79, p = 0.027), and immunotherapy (OR: 3.37, 95% CI:1.27-8.43, p = 0.012). Conclusion The incidence of COVID-19 in cancer patients is proportional to the prevalence in the general population of similar geographic areas. Diabetes, coronary artery disease, chronic lung disease, chronic liver disease, receiving chemotherapy or immunotherapy, and multiple comorbidities were associated with higher COVID-19 infection rates.

Allvar Gullstrand: The Only Ophthalmologist Who Won the Nobel Prize.

Allvar Gullstrand, the Swedish ophthalmologist and Nobel laureate, was a self-taught mathematician who applied mathematics and higher-order equations to understand the optic system. His inventions, the slit lamp, and the ophthalmoscope are used in clinical practice for the diagnosis of eye diseases. With his efforts, he explained the accommodation, the process of changing the shape of the lens to focus on near or distant objects. In 1911, he was awarded the Nobel Prize in Physiology or Medicine. In 1913, he was elected as the first president of the Swedish Ophthalmological Society. In 1927, he was awarded the Graefe Medal of the Deutsche Ophthalmologische Gesellschaft.

Postoperative elevated bed header position versus supine in the management of chronic subdural hematoma: a systematic review and meta-analysis.

BACKGROUND: Chronic subdural hematoma (CSDH) is a prevalent type of intracranial hemorrhage. Surgical interventions, such as Twist Drill Craniostomy and Burr Hole Craniostomy, are employed for its treatment. However, limited information exists regarding the impact of postoperative head position (supine vs. elevated) on clinical outcomes. We aim to assess whether patients' head position after surgery influences their prognosis. METHOD: We conducted a PRISMA-compliant systematic review and meta-analysis. Our search encompassed PubMed, Cochrane CENTRAL, Scopus, Web of Science, and Embase databases to identify relevant published studies. Data were meticulously extracted, pooled using a fixed model, and reported as risk ratios (RR) with 95% confidence intervals (CI). Statistical analysis was performed using R and Stata MP v.17. RESULTS: Five studies involving 284 patients were included in our meta-analysis. We focused on three primary clinical outcomes, comparing the supine and elevated header positions. Notably, there was no statistically significant difference between the supine and elevated positions in terms of recurrence rate (RR 0.77, 95% CI [0.44, 1.37]), second intervention for recurrence (RR 1.07, 95% CI [0.42, 2.78]) and postoperative complications (RR 1.16, 95% CI [0.70, 1.92]). CONCLUSION: Current studies have proved no difference between supine and elevated bed header positions regarding recurrence rate, second intervention for recurrence, and postoperative complications. Future RCTs with long-term follow-ups are recommended.

Factors Affecting Breast Cancer Screening Behavior Among Women in Saudi Arabia: A Retrospective Cross-Sectional Study.

Background Early detection of breast cancer is crucial for effective treatment and minimizing mortality, requiring effective screening methods like self-examination, clinical examination, and mammography. However, not all women in Saudi Arabia comply with these examinations, and studies examining its practice and barriers of low uptake are scant. The aim of this study is to investigate factors influencing breast cancer screening behavior among women in Saudi Arabia. Methods This cross-sectional study involving 806 women from October to November 2022 used an online questionnaire for the data collection process, including questions about demographic characteristics, awareness assessment, breast cancer screening behavior, symptoms, risk factors, and screening programs. Factors affecting the screening behavior were analyzed using the logistic regression model with adjusted odds ratio (AOR) and 95% confidence interval (CI). Results Among the 806 women who participated in the study, 479 (59.4%) were under 40 years old, and half of them were urban residents (n = 394, 48.9%). Only 134 subjects (16.6%) had a history of breast screening. Social media (n = 519, 64.5%) was the predominant source of screening information. The primary obstacles to breast cancer screening were the absence of tumor symptoms (n = 333, 41.3%), insufficient knowledge about early detection (n = 249, 31%), lack of time (n = 245, 30%), fear of discovering a tumor (n = 187, 23%), and lack of awareness about screening centers (n = 155, 19%). In regression analysis, predictive factors for breast cancer screening behavior were as follows: age over 40 years old (AOR: 2.56; 95% CI: 1.70-3.87), residents of big cities (AOR: 3.57; 95% CI: 1.02-12.56), positive family history of breast cancer (AOR: 2.53; 95% CI: 1.50-4.28), proximity to the screening center (AOR: 2.56; 95% CI: 1.22-5.39), and using contraceptive pills for more than five years (AOR: 1.78; 95% CI: 1.04-3.04), and were statistically significant (all p-values < 0.05). Conclusions In this study, the most perceived barriers to BSE were the absence of tumor symptoms, followed by insufficient knowledge about early detection, lack of time, fear of discovering a tumor, and lack of awareness about screening centers. Additionally, the predictive factors for breast cancer screening behavior were as follows: age over 40 years old, residents of big cities, positive family history of breast cancer, proximity to the screening center, and using contraceptive pills for more than five years. Given the identified factors affecting breast self-examination behavior in this study, public education initiatives are crucial for raising awareness, facilitating self-examination, and ultimately improving health outcomes and reducing breast cancer treatment costs in society.

Clinical and Therapeutic Characteristics of Hospitalized Patients with Advanced Lung Cancer in Najran, Saudi Arabia: A Retrospective Study.

BACKGROUND: Lung cancer is one of the top causes of cancer deaths globally, including in Saudi Arabia. Although several prognostic markers have been established, the clinical features and outcomes of lung cancer in Saudi Arabia are not well understood. This study aimed to describe the clinical and therapeutic characteristics of advanced lung cancer in Najran, Saudi Arabia. METHOD: A retrospective chart review of 44 patients diagnosed with advanced lung cancer between June 2018 and September 2021 and treated at the Oncology Center of King Khalid Hospital in Najran City, Saudi Arabia. The clinicopathological features, treatment used, response, and survival outcomes were collected and analyzed. RESULT: The mean age was 69.3 ± 10.7 years, most of them (n = 35, 79.5%) were male and older than 70 years (n = 24, 54.5%). Adenocarcinoma was the most observed cancer (n = 35, 79.5%), followed by squamous cell carcinoma in six (13.6%). Most cases (n = 42, 95.5%) were in stage IV. Epidermal growth factor receptor (EGFR) mutations were positive in two (4.5%) cases and ALK mutation was positive in two (4.5%) cases. Metastasis to pleura with pleural effusion was the common presentation (n = 41, 93%). Chemotherapy was administered as the first line in 19 cases (43.2%) while 25 cases (56.8%) received chemoimmunotherapy. The commonest chemoimmunotherapy regimen used was carboplatin-pemetrexed-pembrolizumab in 16 (36.4%), followed by carboplatin-paclitaxel-pembrolizumab in 9 (20.5%) cases. The response to initial systemic therapy was as follows disease progression, stable disease, and complete remission in 10 (22.7%), 33 (75.0%), and 1 (2.3%), respectively. Median progression-free survival was 8.7 months (interquartile range (IQR): 5.7-11.4), and the median overall survival was 12.3 months (IQR: 11.1-13.4). Among the total documented 36 (81.8%) dead cases, disease progression was the main cause of death in 25 cases (56.8%). Using chemoimmunotherapy as the first-line therapy was associated with numerical survival improvement compared to using chemotherapy alone (HR: 0.75; 95% CI: 0.39-1.46) however, it was not statistically significant (p = 0.397). CONCLUSION: In this study, the majority of lung cancer patients were male and over 70 years old. Adenocarcinoma was the most common histological type. Metastasis to pleura with pleural effusion was the common presentation. The most common treatment used was chemoimmunotherapy with a regimen of carboplatin-pemetrexed-pembrolizumab. Addressing the possible causes of delayed diagnosis of lung cancer is crucial for improved survival outcomes.

Efficacy and safety of aldafermin in non-alcoholic steatohepatitis: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Non-alcoholic steatohepatitis (NASH) is an advanced subtype of non-alcoholic fatty liver disease (NAFLD). NASH prevalence is increasing exponentially and carries a high risk for disease progression, cirrhosis, and liver-related mortality. Aldafermin, a fibroblast growth factor 19 (FGF19) analog, is one of the evolving therapeutic agents with the potential to regulate multiple pathways involved in the pathogenesis of NASH. We aimed to investigate the efficacy and safety of aldafermin in patients with NASH. METHODS: PubMed, Scopus, Cochrane Library, and Web of Science were searched till November 2023 to identify eligible randomized controlled trials (RCTs). Continuous data were pooled as mean difference (MD), while dichotomous data were pooled as risk ratios (RR) with a 95 % confidence interval. A subgroup meta-analysis was conducted to evaluate the efficacy of the two doses (1 mg and 3 mg) of aldafermin. RESULTS: Four RCTs with a total of 491 patients were included. Aldafermin showed a dose-dependent improvement in the ≥30 % reduction in the liver fat content (RR: 2.16, 95 % CI [1.41 to 3.32]) and (RR: 5.00, 95 % CI [1.34 to 18.64]), alanine aminotransferase levels (MD: -19.79, 95 % CI [-30.28 to -9.3]) and (MD: -21.91, 95 % CI [-29.62 to -14.21]), aspartate aminotransferase levels (MD: -11.79, 95 % CI [-18.06 to -5.51]) and (MD: -13.9, 95 % CI [-18.59 to -9.21]), and enhanced liver fibrosis score (ELF) (MD: -0.13, 95 % CI [-0.29 to 0.02]) and (MD: -0.33, 95 % CI [-0.50 to -0.17]), in the 1 mg and 3 mg subgroups respectively. No significant differences were detected in the aldafermin group regarding histologic endpoints, lipid profile, metabolic parameters, and overall adverse effects, except for the increased occurrence of diarrhea in the aldafermin 3 mg subgroup. CONCLUSION: Aldafermin is a promising well-tolerated therapeutic agent for NASH with evidence supporting its ability to reduce liver fat content, fibrosis serum biomarkers, and liver enzymes. However, its effectiveness in improving histologic fibrosis, while showing numerical trends, still lacks statistical significance. Larger and longer NASH trials are warranted to enhance the robustness of the evidence.

Effect of acidic environment on color and translucency of different indirect restorative materials.

PURPOSE: The aim of the current study was to evaluate the effect of simulated gastric acid on the color and translucency of different indirect restorative materials. MATERIALS AND METHODS: A total of 36 disc-shaped samples were cut by using an isomet saw and divided into four equal groups (n = 9) according to the material type: Group Z: translucent zirconia (Ceramill® Zolid ht.+ preshade, Amann Girrbach, Koblach, Austria); Group E: lithium disilicate (IPS e.max CAD, Ivoclar Vivadent AG, Schaan, Liechtenstein); Group C: resin nanoceramic (Cerasmart, GC, Tokyo, Japan); Group P: polyether ether ketone (PEEK) (Bettin Zirconia Dentale Italy) veneered with indirect high impact polymer composite (HIPC) (breCAM HIPC, Bredent GmbH & Co. KG, Germany). The samples were immersed in simulated gastric acid (HCl, pH 1.2) for 96 hours at 37 °C in an incubator. The color change (ΔE00) and translucency (RTP00) were measured every 9.6 hours (one-year clinical simulation) of immersion in simulated gastric acid. RESULTS: For color change (∆E00) and translucency (RTP00) among the tested materials, there was a highly statistically significant difference (P < 0.001) after every year of follow-up. The color change in both Z and G groups was the lowest after 1 year of acid immersion, followed by that in group H, and the highest change in color was recorded in group P. CONCLUSION: High translucent zirconia is recommended in patients who are concerned about esthetic, especially with acidic oral environment.

Modeling of electromagnetic propagation inside jet engine environment using statistical electromagnetic approach.

In this paper, the distribution of the electromagnetic field inside a complex jet engine environment is analyzed using statistical electromagnetics. The jet engine environment is an extremely complex geometry and exhibits random behavior due to the presence of moving metallic parts. This renders traditional analytical and simulation modeling techniques highly inefficient. To address this issue, two different approaches are proposed to model the propagation characteristics of the jet engine environment. The first is an innovative dynamic system approach based on dynamic system simulation which is inspired by the analysis of mechanically stirred reverberation chambers. In the dynamic system simulation the dynamic system, which is characterized by the rotation of a distinct set of blades, is primarily studied through the simulation program. A dimension scaling method is also introduced along with the dynamic system simulation to solve the complex jet engine environment. In the second approach, a novel statistical excitation method is applied to develop an equivalent model for the dynamic jet engine system. The studied jet engine is considered as a static jet engine system without blade rotation (static blades), but with a random excitation.A small signal analysis method is used to integrate the static and dynamic system parameters to generate random excitation characteristics of the static system. The extracted electric field values from the dynamic jet engine simulation environment and the static system field values from the small signal analysis have been analyzed statistically to prove the statistical equality between the two systems. The numerical results of the static system model are presented and verified through comparison with finite element method simulation packages.

Repaglinide-Solid Lipid Nanoparticles in Chitosan Patches for Transdermal Application: Box-Behnken Design, Characterization, and In Vivo Evaluation.

Background: Repaglinide (REP) is an antidiabetic drug with limited oral bioavailability attributable to its low solubility and considerable first-pass hepatic breakdown. This study aimed to develop a biodegradable chitosan-based system loaded with REP-solid lipid nanoparticles (REP-SLNs) for controlled release and bioavailability enhancement via transdermal delivery. Methods: REP-SLNs were fabricated by ultrasonic hot-melt emulsification. A Box-Behnken design (BBD) was employed to explore and optimize the impacts of processing variables (lipid content, surfactant concentration, and sonication amplitude) on particle size (PS), and entrapment efficiency (EE). The optimized REP-SLN formulation was then incorporated within a chitosan solution to develop a transdermal delivery system (REP-SLN-TDDS) and evaluated for physicochemical properties, drug release, and ex vivo permeation profiles. Pharmacokinetic and pharmacodynamic characteristics were assessed using experimental rats. Results: The optimized REP-SLNs had a PS of 249±9.8 nm and EE of 78%±2.3%. The developed REP-SLN-TDDS demonstrated acceptable characteristics without significant aggregation of REP-SLNs throughout the casting and drying processes. The REP-SLN-TDDS exhibited a biphasic release pattern, where around 36% of the drug load was released during the first 2 h, then the drug release was sustained at around 80% at 24 h. The computed flux across rat skin for the REP-SLN-TDDS was 2.481±0.22 µg/cm2/h in comparison to 0.696±0.07 µg/cm2/h for the unprocessed REP, with an enhancement ratio of 3.56. The REP-SLN-TDDS was capable of sustaining greater REP plasma levels over a 24 h period (p<0.05). The REP-SLN-TDDS also reduced blood glucose levels compared to unprocessed REP and commercial tablets (p<0.05) in experimental rats. Conclusion: Our REP-SLN-TDDS can be considered an efficient therapeutic option for REP administration.

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning.

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner's search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners' improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson's Disease Using Complex and Large Vocal Features.

Parkinson's disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world's population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject's voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network's potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution's space and time complexity.

Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization.

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications.

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials' exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm's regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon's rank-sum and ANOVA tests.

Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization.

INTRODUCTION: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. METHODOLOGY: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. RESULTS: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. CONCLUSIONS: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

Genome sequencing of Enterococcus faecium NT04, an oral microbiota revealed the production of enterocin A/B active against oral pathogens.

Objective: This study aimed to isolate and investigate a bacterium from an Egyptian adult's healthy oral cavity, focusing on its probiotic properties, especially its antagonistic activity against oral pathogens. Methods: The isolated bacterium NT04 using 16S rRNA gene sequencing, was identified as Enterococcus faecium. In this study, the whole genome of Enterococcus faecium NT04 was sequenced and annotated by bioinformatics analysis tools. Results: Numerous genes encoding the production of diverse metabolic and probiotic properties, such as bacteriocin-like inhibitory substances (Enterocin A and B), cofactors, antioxidants, and vitamins, were confirmed by genomic analysis. There were no pathogenicity islands or plasmid insertions found. This strain is virulent for host colonization rather than invasion. Conclusion: Genomic characteristics of strain NT04 support its potentiality as an anti-oral pathogen probiotic candidate.