Explore the vast range of titles available.

Nanoparticles of blanched green banana peels (BGBP) were prepared using physical technique (by grinding) in order to avoid any harm effect on human health that could occurred when metals were used for preparing nanoparticles size (NPs) of banana peels. This work was designed to study the preparation of nano scale (70–135 nm for TEM) (243.4–933.9 nm for SEM) and normal size (0.12–0.25 µm for TEM) (1.150 µm for SEM) from BGBP after milling and evaluate the activities of their extracts as antioxidant, antimicrobial and anticancer agents. The size and shape of nanoparticles were analyzed using Scanning Electron Microscope (SEM) and it cleared the appearance of particle aggregation was attributed to mechanical pressure and friction resulting from the interaction between the abrasive balls and the pulverizing vessel’s inner surface. Also, Transmission Electron Microscope (TEM) shows presence of different spherical shapes ranging between 70 and 135 nm, along with the emergence of slender fibrillary shapes., on the other hand, Fourier transform IR (FT-IR) cleared that the higher extraction yields of phenolic compounds and greater antioxidant activities were achieved due to the increased surface area of nano-scale samples following milling. In addition, X-ray diffraction (XRD) determined the materials crystalline structure. Bioactive compounds (mainly phenolic compounds) were recovered by extracting banana peels weather from normal size or (NPs). The extracted bioactive compounds were subjected to evolution as antioxidant, antimicrobial and anticancer agents. Dealing with this study, it was concluded that bioactive compounds extracted from NPS of BGBP showed antioxidant, antimicrobial and anticancer activities higher than those extracted from the normal size ones. So, it is strongly recommended to use NPs of BGBP for producing these bioactive compounds since these compounds are important to protect humans against a lot of dangerous diseases. Finally, the potential applications of these compounds in the pharmaceutical or food industries would be beneficial.

The aim of the current work was to produce sodium alginate (SA) maltodextrins (MD) based functional films incorporated with phenolic extract of Azolla pinnata leaves fern (AF) by solution molding technique. AF with different concentrations (0.8, 1.2 and 1.6% w/w) were integrated inside SA.MD films. The resulted films were characterized to investigate the surface structure by scanning electron microscope (SEM), thermal disposal by (DSC), crystallization by X-ray diffraction (XRD), potential interaction by (FT-IR) and some mechanical properties. The SEM micrographs indicated that the higher concentration (1.6%) of AF extract caused development of wrinkles on the surface of films. And as a result, there were a significant decrease of elongation at break (EB) and tensile strength properties of films to 55.01 and 58.42%, respectively. By continues addition of AF extract to SA.MD films, the film thickness increased from 0.124 to 0.181 mm, the scavenging and antimicrobial properties were enhanced by the attendance of ferulic acid, rutin, thiamine, tamarixetin, astragalin, quercetin, chlorogenic acid and epicatechin inside extracts. Furthermore, the films solubility, swelling degree and water vapor permeability were decreased to 13.08%, 26.41% and 1.662 × 10− 10 g H2O/m s p.a. The resulted films could be utilized as composite packaging material for different food applications.

In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer’s disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer’s disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer’s disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset.

Successful antibiotic treatment of infections relies on accurate and rapid identification of the infectious agents. Pseudomonas aeruginosa is implicated in a wide range of human infections that mostly become complicated and life threating, especially in immunocompromised and critically ill patients. Conventional microbiological methods take more than three days to obtain accurate results. Pyocyanin is a distinctive electroactive biomarker for Pseudomonas aeruginosa. Here, we have prepared polyaniline/gold nanoparticles decorated ITO electrode and tested it to establish a rapid, diagnostic and highly sensitive pyocyanin sensor in a culture of Pseudomonas aeruginosa clinical isolates with high selectivity for traces of pyocyanin when measured in the existence of different interferences like vitamin C, uric acid, and glucose. The scanning electron microscopy and cyclic voltammetry techniques were used to characterize the morphology and electrical conductivity of the constructed electrode. The determined linear range for pyocyanin detection was from 238 μM to 1.9 μM with a detection limit of 500 nM. Compared to the screen-printed electrode used before, the constructed electrode showed a 4-fold enhanced performance. Furthermore, PANI/Au NPs/ITO modified electrodes have demonstrated the ability to detect pyocyanin directly in Pseudomonas aeruginosa culture without any potential interference with other species.

Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due to the scarcity of COVID-19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID-19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID-19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID-19 cases using chest x-ray images. The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters’ values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4-class cases (COVID-19, pneumonia bacterial, pneumonia viral, and normal). The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1-score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47%, 0.9849, 98.50%, 98.47%, 99.50%, 0.9983, 0.9847, 0.9860, and 0.9879 respectively.

Background The rapid adoption of ChatGPT in academic settings has raised concerns about its impact on learning, research, and academic integrity. This study aimed to develop and validate a comprehensive ChatGPT Usage Scale specifically tailored to postgraduate students, addressing the need for a psychometrically sound instrument to assess the multidimensional nature of ChatGPT usage in higher education. Methods A cross-sectional survey design was employed, involving 443 postgraduate students from two Egyptian universities. The initial 39-item scale underwent Exploratory Factor Analysis (EFA) using principal component analysis with Varimax rotation. Confirmatory Factor Analysis (CFA) was conducted to assess the model fit and psychometric properties of the final 15-item measure. Internal consistency reliability was evaluated using Cronbach’s alpha and McDonald’s omega. Results EFA revealed a three-factor structure explaining 49.186% of the total variance: Academic Writing Aid (20.438%), Academic Task Support (14.410%), and Reliance and Trust (14.338%). CFA confirmed the three-factor structure with acceptable fit indices (χ2(87) = 223.604, p  < .001; CMIN/DF = 2.570; CFI = 0.917; TLI = 0.900; RMSEA = 0.060). All standardized factor loadings were statistically significant ( p  < .001), ranging from 0.434 to 0.728. The scale demonstrated good internal consistency (Cronbach’s α = 0.848, McDonald’s ω = 0.849) and composite reliability (CR = 0.855). The average variance extracted (AVE) was 0.664, supporting convergent validity. Conclusions The validated ChatGPT Usage Scale provides a reliable and valid instrument for assessing postgraduate students’ engagement with ChatGPT across multiple dimensions. This tool offers valuable insights into AI-assisted academic practices, enabling more nuanced investigations into the effects of ChatGPT on postgraduate education.

Colorectal cancer (CRC) in Egypt presents a significant public health challenge, ranking as the 7th most common cancer and the 8th leading cause of cancer deaths. Understanding patients' experiences is crucial to inform tailored screening and supportive care. This study aims to explore Egyptian CRC patients' experiences of diagnoses, treatment, and perceptions of a healthy diet and physical activity. Utilizing the Social Ecological Model (SEM) as a theoretical framework, we conducted one-hour, face-to-face, semi-structured interviews with 19 CRC patients in Alexandria, Egypt, between August and September 2023. Audio recorded interviews were transcribed and analyzed thematically to identify patterns across individual, interpersonal, organizational, social context (culture/community), and policy levels of the SEM. The participants, 12 males and 7 females with a mean age of 54.8 (±10.2) years, predominantly came from low-income backgrounds, with the majority being either unemployed (47.4%) or disabled (31.6%). We identified facilitators and barriers to optimal care at several levels of the SEM. The main barriers were as follows: lack of awareness, symptom neglect, fear and embarrassment of colonoscopy, limited diet and activity from chemotherapy and colostomy, job loss, and poverty (individual); peers fear of colonoscopy and judgment (interpersonal); misdiagnosis, inadequate patient education, and lack of structured diet and activity programs (health organization); unsupportive work environments, cultural and religious beliefs, health literacy, and cancer stigma (social context); and unclear screening policies, lack of equipment, lack of insurance, high costs, and limited rural healthcare (policy). The main facilitators were faith resilience and positive perceptions of diet and activity (individual); family and friend support (interpersonal); positive doctor-patient relationships (health organization); NGO support (social context); and free healthcare at university hospitals (policy). The study highlights the complex interplay of barriers and facilitators CRC patients encounter throughout their experience with cancer. The findings emphasize the need for improved awareness, education, support systems; enhanced healthcare access; and targeted policy changes, especially in rural areas, to improve early diagnosis, treatment outcomes, and patient quality of life.

Pseudomonas aeruginosa is the most common pathogenic gram-negative bacteria causing corneal ulcers globally. In severe cases, often after trauma and eye injury, corneal destruction progresses rapidly and may be completed within 24–48 h causing blindness. In our preliminary work, we have established an ultrasensitive polyaniline (PANI)/gold nanoparticles (Au NPs)/indium tin oxide (ITO) modified sensor for rapid detection of pyocyanin (PYO) in P . aeruginosa infections with a linear range from 238 μM to 1.9 μM and a detection limit of 500 nM. In the present study, we evaluated the efficiency of the established modified electrochemical sensor in the diagnosis of P . aeruginosa in 50 samples collected from patients suffering from corneal ulcers. The obtained results were compared with the results gained by the screen-printed electrode, conventional techniques, automated identification method, and the amplification of the 16 s rRNA gene by PCR as a gold standard test for P . aeruginosa identification. We have found that the electrochemical detection of PYO by square wave voltammetry technique using PANI/Au NPs modified ITO electrode was the only technique showing 100% agreement with the molecular method in sensitivity, specificity, positive and negative predictive values when compared with the SPE, conventional and automated methods.

To evaluate the value of vaginal progesterone therapy for reduction of preterm labor in asymptomatic women with twin pregnancies and sonographic short cervix. This randomized controlled study was conducted in Mansoura University Hospital and private practice settings in Mansoura, Egypt. Of 322 women with dichorionic twin pregnancy, 250 asymptomatic women with cervical length of 20-25 mm at 20-24 weeks of gestation were included in the study. All women were randomly divided into two groups; the study group (n = 125) received vaginal progesterone suppositories in a dose of 400 mg daily starting at 20-24 weeks of gestation while the control group (n = 125) received no treatment. The primary outcome measure was preterm labor before 34 weeks of gestation and the secondary outcome measures were neonatal respiratory distress syndrome (RDS) and early neonatal death (END). 224 women (116 in the study group and 108 in the control group) were subjected to final analysis. The duration of pregnancy was significantly longer in the study group and the incidence of preterm labor before 34 and 32 weeks of gestation was significantly lower in the study group. The neonatal morbidities and mortality were significantly lower in the study group as shown by lower incidence of very low (<1500 gm) birth weight, neonatal RDS, the need for mechanical ventilation and END. Vaginal progesterone administration in asymptomatic twin pregnancies with sonographic short cervix (20-25 mm) at 20-24 weeks of gestation is effective and safe treatment for reducing the incidence of preterm labor with subsequent reduction in the neonatal morbidities and mortality associated with preterm birth.

Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assessment using facial image analysis. The Suicidal Visual Indicators Prediction (SVIP) Framework leverages EfficientNetb0 and ResNet architectures, enhanced through Bayesian optimization techniques, to detect nuanced facial expressions indicating mental state. The models’ interpretability is improved using GRADCAM, Occlusion Sensitivity, and LIME, which highlight significant facial regions for predictions. Using datasets DB1 and DB2, which consist of full and cropped facial images from social media profiles of individuals with known suicide outcomes, the method achieved 67.93% accuracy with EfficientNetb0 on DB1 and up to 76.6% accuracy with a Bayesian-optimized Support Vector Machine model using ResNet18 features on DB2. This approach provides a less intrusive, accessible alternative to video-based methods and demonstrates the substantial potential for early detection and intervention in mental health care.