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Abstract The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV risk factors. The value of swept-source retinal optical coherence tomography–angiography (SS OCT-A) imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features. We report on the interest of using machine-learning (ML) and deep-learning (DL) models for CV assessment from SS OCT-A microvasculature imaging. We assessed the accuracy of ML and DL algorithms in predicting the CHA2DS2-VASc neurocardiovascular score based on SS OCT-A retinal images of patients from the open-source RASTA dataset. The ML and DL models were trained on data from 491 patients. The ML models tested here achieved good performance with area under the curve (AUC) values ranging from 0.71 to 0.96. According to a classification into two neurocardiovascular risk groups, the EfficientNetV2-B3, a well suited DL model for retinal OCT-A images, predicted risk correctly in 68% of cases, with a mean absolute error (MAE) of approximately 0.697. Our models enable a confident prediction of the CHA2DS2-VASc score from SS OCT-A imaging, which could be a useful tool contributing to the assessment of neurocardiovascular profiles in the future.

Abstract Improving rice threshing unit performance and structure significantly enhances the thresher’s overall efficiency by raising its productivity while lowering costs and power consumption. This study evaluated and optimized longitudinal axial flow threshing device performance using two threshers’ structures, namely conical thresher and drum thresher, under different rotating speeds of 1100, 1300, and 1500 rpm and feeding rates of 0.8, 1.1, and 1.4 kg/s. The studied parameters were evaluated regarding thresher throughput, efficiency, seed damage, and specific energy, and it was figured that the experimental parameters greatly affected the whole performance of the threshing unit. The drum thresher increased the threshing throughput from 2304 to 2448 kg/h and maximized the efficiency from 98.6 to 99.07% at 1500 rpm rotation speed and 1.8 kg/s feeding rate. Moreover, it reduced the specific energy from 3.37 to 3.15 kW.h/ton for the experimental variables 1100 rpm speed and 1.4 kg/s feeding. The lowest damage rate of 0.24% was recorded using the drum thresher at a feeding rate of 0.8 kg/s and 1100 rotating speed. These results highlight the distinct effect of the new thresher structure and operating parameters on the rice threshing device’s overall effectiveness for medium-sized combine harvesters.

Abstract Essential oils of basil herbs such as nyazbo or sweet basil (Ocimum basilicum), holy basil or tulsi (O. tenuiflorum), and African or clove basil (O. gratissimum) have traditionally been used for their therapeutic potential. These medicinal herbs are being cultivated and consumed globally, and the increasing demand for antimicrobial and antifungal natural products has led to the assessment of the potential of essential oils of medicinal plants to inhibit mycelial growth rather than synthetic fungicides. Thus, the present study explored natural alternatives to inhibit mycelial growth (Aspergilus flavus) and aflatoxin production. Linalool (25.40%), methyl chavicol (37.63%), and eugenol (39.52%) were identified as chief compounds in the EOs of O. basilicum, O. tenuiflorum, and O. gratissimum respectively. Ocimum tenuiflorum EO demonstrated the highest inhibitory activity at 0.75 µL mL− 1 against Aspergillus flavus and totally inhibited the synthesis of aflatoxin B1 (AFB1). The AFB2 production was completely inhibited at 0.25 µL mL− 1 by O. tenuiflorum EO, while O. basilicum and O. gratissimum EOs showed inhibition against AFB2 at 0.50 µL mL− 1, and 1.0 µL mL− 1, respectively. The present study suggests that EOs of basil herbs could be a potential natural alternative of synthetic fungicides to inhibit the fungal growth and aflatoxin production.

Abstract Background Work stressors are associated with physical and psychological health problems among workers and thus increase their intentions to leave work. Various factors, including fear of infection, workload, and inadequate support, caused nurses especially those on the front lines during the COVID-19 pandemic greater stress. Aim This study aims to identify Jordanian nurses’ perceptions of work stressors and their intention to leave, and to investigate the relationship between these variables among nurses working in isolation units during the COVID-19 pandemic. Methods A descriptive cross-sectional study was conducted. A convenience sample of 154 nurses who worked with COVID-19 patients participated in this study. Questionnaires were distributed online. Results A total of 154 nurses participated in the study, with an average age of 29.59 years. A majority of nurses (61.1%) were employed in COVID-19 isolation units for a duration of 6 to 8 months. Of the work-related stresses, the mean score was 3.17; 85.7% of respondents reported a positive level of stress. Intent to leave had a mean score of 4.03; 57.8% of respondents showed a slightly positive intention. Work stresses and intention to leave showed a modest, positive significant correlation (r = 0.52, p = 0.001). Conclusions In COVID-19 isolation units, Jordanian nurses’ work stresses were positively correlated with their intention to leave. Lower-educated and single nurses were more stressed and likely to leave. Based on the findings, targeted stress-reduction programs are needed. Nursing retention and healthcare system sustainability amid public health emergencies depend on supportive, well-resourced workplaces.

Abstract Classically, pyramidal cells of the hippocampus are viewed as flexibly representing spatial and non-spatial information. Recent work has illustrated distinct types of hippocampal excitatory neurons, suggesting that hippocampal representations and functions may be constrained and interpreted by these underlying cell-type identities. In mice, here we reveal a non-pyramidal excitatory neuron type — the “ovoid” neuron — that is spatially adjacent to subiculum pyramidal cells but differs in gene expression, electrophysiology, morphology, and connectivity. Functionally, novel object encounters drive sustained ovoid neuron activity, whereas familiar objects fail to drive activity even months after single-trial learning. Silencing ovoid neurons prevents non-spatial object learning but leaves spatial learning intact, and activating ovoid neurons toggles novel-object seeking to familiar-object seeking. Such function is doubly dissociable from pyramidal neurons, wherein manipulation of pyramidal cells affects spatial assays but not non-spatial learning. Ovoid neurons of the subiculum thus illustrate selective cell-type-specific control of non-spatial memory and behavioral preference.

Intensity-Duration-Frequency (IDF) curves are crucial for the design and management of engineering infrastructure, including storm sewers, retention ponds, dams, and flood mitigation systems. This study adopts a comparative approach to estimate IDF curves using a combination of traditional statistical methods, machine learning techniques, and advanced deep learning models. Rainfall data from Koya City, Iraq (2005e2022), was used, with the 2005e2015 period for training and 2016e2022 for validation. The models evaluated include the Gumbel Distribution, Linear Regression, Support Vector Regression (SVR), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), assessed based on three metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 ). Among these, the RNN-LSTM model demonstrated the lowest RMSE (1.44 mm/hr), lowest MAE (0.81 mm/hr), and highest R2 (0.99), outperforming the Gumbel Distribution (RMSE: 9.13 mm/hr), Linear Regression (RMSE: 10.76 mm/hr), and SVR (RMSE: 6.19 mm/hr). This establishes RNN-LSTM as the most reliable approach for IDF curve prediction. Leveraging the RNN-LSTM model, rainfall trends for 2023e2043 were forecasted, revealing an expected increase in short-duration, high-intensity rainfall events, heightening flood risks, and emphasizing the need for adaptive stormwater management strategies. The findings underscore the significant potential of deep learning models like RNNLSTM in enhancing IDF curve predictions and guiding the development of resilient hydraulic infrastructure, particularly in regions like Koya City, where complex topography exacerbates flood challenges during intense rainfall events.

Abstract Background Nurses are the cornerstone of healthcare delivery, playing a pivotal role in ensuring safe, high-quality, and patient-centered care. However, the significant demands on their time and resources often lead to the rationing or omission of essential nursing care activities, undermining patient outcomes. As frontline caregivers, nurses’ ability to collaborate effectively within teams is critical to overcoming these challenges. The Big Five Teamwork Model, which emphasizes trust, communication, and leadership, offers a transformative approach to strengthening teamwork and addressing barriers to comprehensive nursing care. Aim This study aimed to assess the impact of the Big Five Teamwork Awareness Sessions (Big 5TWAS) on the rationing of nursing care and patient-centered care. Methods A quasi-experimental pre-post-test design was conducted in critical care units at an Egyptian university hospital. A convenience sample of 60 nurses and 31 admitted patients participated in the study. Big 5TWAS interventions were implemented for nurses, and study variables were measured using nursing teamwork surveys, the Rationing of Nursing Care Observational Checklist (RONCO), and patient-centered care questionnaires before and after the intervention. Data were analyzed using descriptive and inferential statistics. Results The Big 5 Teamwork Awareness Sessions (Big 5TWAS) demonstrated a significant impact on the study variables. Post-sessions, there was a significant decrease in rationing of nursing care scores and significant increases in nursing teamwork and patient-centered care scores (p ≤ 0.001). In addition to the significant correlation values, regression analysis revealed that overall teamwork accounted for approximately 22% of the variance in rationing of nursing care (R² = 0.224, p = 0.015), with trust as the significant predictor. Furthermore, teamwork explained approximately 80% of the variance in patient-centered care (R² = 0.801, p

Abstract Sulfonamide derivatives are a significant class of medicinal compounds. Gold nanoparticles (AuNPs) offer precise cancer treatment through targeted delivery, boasting high drug-loading capacity and low toxicity. This study aimed to develop and evaluate 99mTc-labeled benzenesulfonamide derivative-entrapped gold citrate nanoparticles as a tumor-targeting agent. A novel benzenesulfonamide derivative bearing a pyridine moiety was synthesized. Compound 3 (4-((3-cyano-4-(2,4-dichlorophenyl)-6-phenylpyridin-2-yl)amino)-N-(diaminomethylene)benzenesulfonamide) exhibited remarkable anti-cancer activity against MCF-7 cells. The chemical reduction method was employed to create compound 3-citrate-AuNPs. A comprehensive examination of the synthesized nano-platform was conducted, including zeta potential, size analysis, radiochemical yield, and in-vivo biodistribution in tumor-bearing mice. The nano-platform was successfully produced with good stability, optimal particle size (9 nm diameter for AuNPs), and high radiochemical purity for [99mTc]Tc-compound 3 (88.31 ± 2.14%). In-vivo investigations revealed that intravenously administered [99mTc]Tc-compound 3-citrate-AuNPs accumulated in tumors with a high target-to-non-target ratio. The findings validate the efficacy of the novel [99mTc]Tc-compound 3-citrate-AuNPs platform as a tumor-targeting agent.

In view of modern trends towards protecting the environment of food packaging materials, along with preserving food quality and safety, especially about the use of industrial packaging materials, especially non-degradable plastic materials, and the health and environmental damage resulting from their widespread use. Therefore, the current article included highlighting the preparation of anti-microbial Edible Films from natural biological sources, as many types of bacteria, algae and yeasts can produce biopolymers with properties suitable for food packaging, at the same time they are automatically degradable in the environment. The attention of specialists turned towards Edible Films, a natural, biological alternative that is edible and safe for health, at the same time does not cause environmental pollution. This technique has been applied to different types of fresh and processed foods. There are many natural biological sources for preparing edible films, such as proteins (such as milk proteins, wheat gluten, corn zein, etc.), polysaccharides (starch, pectin, cellulose and its derivatives, chitosan, carrageenan, etc.), lipids such as (waxes and glycerides), or a mixture of these compounds. The manufacture of these membranes provides many desirable properties in food, such as maintaining the moisture of the food, preventing the loss of volatile aromatic flavor compounds, reducing the migration of fats, and prolonging the storage life of the food by inhibiting microbial growth and incorporating substances into the membrane solution such as antimicrobials or antioxidants, and food additives, thus improving the appearance, sensory and nutritional properties of the food product.

Mukhtiar Baig,1 Zohair Jamil Gazzaz,2 Hazem M Atta,1,3 Mostafa Mohamed Mostafa,1,3 Tahir Jameel,2 Manal Abdulaziz Murad,4 Fahad Anwer,4 Hashim Mohammed Albuhayri,5 Yazeed Saed Alsulami5 1Department of Clinical Biochemistry, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia; 2Department of Internal Medicine, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia; 3Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Cairo University, Cairo, Egypt; 4Department of Family and Community Medicine, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia; 5Medical Graduates, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi ArabiaCorrespondence: Mukhtiar Baig, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, Email drmukhtiarbaig@yahoo.comPurpose: Patient safety (PS) is a basic principle of healthcare worldwide. In Saudi Arabia, medical colleges have integrated PS modules into their regular curricula. This study investigated undergraduate medical students’ and interns’ attitudes regarding PS at King Abdulaziz University (KAU), Jeddah.Methods: The survey was conducted at the Faculty of Medicine, Rabigh, KAU, Jeddah. Data were collected using the APSQ-III online questionnaire from medical students and interns. The questionnaire comprises 26 items covering various PS issues across nine domains.Results: 493 students and interns participated (233 females, 47.3%, and 260 males, 52.7%). The participants were distributed as follows: 114 (23.1%) from the fourth year, 102 (20.7%) from the fifth year, 145 (29.4%) from the sixth year, and 132 (26.8%) interns. The participants’ scores on most items showed a negative attitude. A gender comparison showed that males mean score was significantly higher than females in the few key domains such as “patient safety training received” (p = 0.001), “working hours as a cause of error” (p = 0.002), “team functioning” (p < 0.001), “patient involvement in reducing errors” (p = 0.002), and the “importance of patient safety in the curriculum” (p < 0.001). No significant variance was noticed between interns’ and medical students’ mean scores across the nine key domains.Conclusion: The findings highlight that most participants’ attitudes needed to be more positive. Gender disparities were found in PS attitudes among Saudi medical students and interns, while no significant variance was noted between interns’ and medical students’ scores.Keywords: team functioning, attitudes, medical errors, PS training, gender disparities

Exserohilum turcicum is an emerging pathogen of maize causing the northern leaf blight (NLB) disease with severe losses. The disease prevails all over the area under production but has varying severity. A temperature of 13°C to 33°C, along with 75-84% relative humidity, favours the pathogen development and growth. Thus, the present investigation was carried out to assess the prevalence of the disease in Bihar and the cultural, morphological, and pathogenic variability among the population through extensive field surveys conducted in maize-growing areas of seven major maize-producing districts of Bihar, India. A total of twenty-one isolates were identified as E. turcicum, which showed varying colony characteristics, colony margin, margin colour, pigmentation, growth pattern, sporulation, conidial shape, conidial size, and conidial septa. However, all the isolates produced characteristic symptoms of cigar-shaped necrotic lesions with variation in disease reaction on the leaves of susceptible maize inbred line CM-202. The isolates were categorized into three phylogenetic clusters with a coefficient of 0.42 based on their variations. A similar trend was observed, and three clusters with a coefficient of 0.52 were confirmed based on the disease reaction under field conditions. The isolates constituting Cluster I were deemed high virulent, followed by Cluster II as moderate virulent, and Cluster III as low virulent. All the isolates were further identified as Setosphaeria turcica, a teleomorph stage of E. turcicum, through molecular identification using ITS sequence analysis. Conclusively, a higher variability among the pathogen population was present, thus leading to the disease emergence, and their virulence assessment will aid in the identification of races and simultaneously accelerate the resistance breeding programmes in maize.