Anticancer drug delivery: Investigating the impacts of viscosity on lipid-based formulations for pulmonary targeting.

Pulmonary drug delivery via aerosolization is a non-intrusive method for achieving localized and systemic effects. The aim of this study was to establish the impact of viscosity as a novel aspect (i.e., low, medium and high) using various lipid-based formulations (including liposomes (F1-F3), transfersomes (F4-F6), micelles (F7-F9) and nanostructured lipid carriers (NLCs; F10-F12)) as well as to investigate their impact on in-vitro nebulization performance using Trans-resveratrol (TRES) as a model anticancer drug. Based on the physicochemical properties, micelles (F7-F9) elicited the smallest particle size (12-174 nm); additionally, all formulations tested exhibited high entrapment efficiency (>89 %). Through measurement using capillary viscometers, NLC formulations exhibited the highest viscosity (3.35-10.04 m2/sec). Upon using a rotational rheometer, formulations exhibited shear-thinning (non-Newtonian) behaviour. Air jet and vibrating mesh nebulizers were subsequently employed to assess nebulization performance using an in-vitro model. Higher viscosity formulations elicited a prolonged nebulization time. The vibrating mesh nebulizer exhibited significantly higher emitted dose (ED), fine particle fraction (FPF) and fine particle dose (FPD) (up to 97 %, 90 % and 64 µg). Moreover, the in-vitro release of TRES was higher at pH 5, demonstrating an alignment of the release profile with the Korsmeyer-Peppas model. Thus, formulations with higher viscosity paired with a vibrating mesh nebulizer were an ideal combination for delivering and targeting peripheral lungs.

Video dataset of sheep activity for animal behavioral analysis via deep learning.

A primary dataset capturing five distinct types of sheep activities in realistic settings was constructed at various resolutions and viewing angles, targeting the expansion of the domain knowledge for non-contact virtual fencing approaches. The present dataset can be used to develop non-invasive approaches for sheep activity detection, which can be proven useful for farming activities including, but not limited to, sheep counting, virtual fencing, behavior detection for health status, and effective sheep breeding. Sheep activity classes include grazing, running, sitting, standing, and walking. The activities of individuals, as well as herds of sheep, were recorded at different resolutions and angles to provide a dataset of diverse characteristics, as summarized in Table 1. Overall, a total of 149,327 frames from 417 videos (the equivalent of 59 minutes of footage) are presented with a balanced set for each activity class, which can be utilized for robust non-invasive detection models based on computer vision techniques. Despite a decent existence of noise within the original data (e.g., segments with no sheep present, multiple sheep in single frames, multiple activities by one or more sheep in single as well as multiple frames, segments with sheep alongside other non-sheep objects), we provide original videos and the original videos' frames (with videos and frames containing humans omitted for privacy reasons). The present dataset includes diverse sheep activity characteristics and can be useful for robust detection and recognition models, as well as advanced activity detection models as a function of time for the applications.

An explainable machine learning approach for Alzheimer's disease classification.

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.

Knowledge, attitudes, and practices toward seasonal influenza vaccination among healthcare workers and pregnant women in Pakistan: A mixed methods approach.

Vaccine hesitancy is a significant public health issue globally. We aim to document the barriers toward seasonal influenza vaccine uptake among healthcare workers (HCWs) and pregnant women (PW) in Pakistan. We performed a concurrent mixed methods study in four cities (Karachi, Islamabad, Quetta, and Peshawar) across Pakistan from September to December 2021. The quantitative component consisted of independent cross-sectional surveys for PW and HCWs, and the qualitative component comprised of in-depth interviews (IDIs) and focus group discussions (FGDs) among HCWs. Simple linear regression was used to determine the association of sociodemographic variables with knowledge, attitudes, and practices. Overall, 750 PW and 420 HCWs were enrolled. Among the PW, 44% were willing to receive the vaccine if available free of cost. Only 44% of the HCWs were vaccinated; however, 86% intended to get vaccinated and were willing to recommend the vaccine to their patients. HCWs refused vaccine due to side-effects (65%), cost (57%), and allergies (36%). An education level of secondary school and above was predictive of higher attitude and knowledge scores while having received the COVID-19 vaccine was associated with higher practice scores for both PW and HCWs. Several themes emerged from the interviews: 1) HCWs' knowledge of influenza and its prevention, 2) HCWs' perception of motivators and barriers to influenza vaccine uptake and 3) HCWs' attitudes towrd vaccine promotion. We report low influenza vaccine coverage among HCWs and PW in Pakistan. Educational campaigns addressing misconceptions, and improving affordability and accessibility through government interventions, can improve vaccine uptake.

Performance enhancement of high order Hahn polynomials using multithreading.

Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for various values of DHaPs parameters, sizes, and different values of threads. In comparison to the unthreaded situation, the results demonstrate an improvement in the processing time which increases as the polynomial size increases, reaching its maximum of 5.8 in the case of polynomial size and order of 8000 × 8000 (matrix size). Furthermore, the trend of continuously raising the number of threads to enhance performance is inconsistent and becomes invalid at some point when the performance improvement falls below the maximum. The number of threads that achieve the highest improvement differs according to the size, being in the range of 8 to 16 threads in 1000 × 1000 matrix size, whereas at 8000 × 8000 case it ranges from 32 to 160 threads.

A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors.

Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants' attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head).

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.

Transfer Learning for Classification of Alzheimer's Disease Based on Genome Wide Data.

Alzheimer's disease (AD) is a type of brain disorder that is regarded as a degenerative disease because the corresponding symptoms aggravate with the time progression. Single nucleotide polymorphisms (SNPs) have been identified as relevant biomarkers for this condition. This study aims to identify SNPs biomarkers associated with the AD in order to perform a reliable classification of AD. In contrast to existing related works, we utilize deep transfer learning with varying experimental analysis for reliable classification of AD. For this purpose, the convolutional neural networks (CNN) are firstly trained over the genome-wide association studies (GWAS) dataset requested from the AD neuroimaging initiative. We then employ the deep transfer learning for further training of our CNN (as base model) over a different AD GWAS dataset, to extract the final set of features. The extracted features are then fed into Support Vector Machine for classification of AD. Detailed experiments are performed using multiple datasets and varying experimental configurations. The statistical outcomes indicate an accuracy of 89% which is a significant improvement when benchmarked with existing related works.

State and situation of avian influenza in the Eastern Mediterranean Region.

Avian influenza viruses have had a significant burden of disease on animal and public health in countries of the Eastern Mediterranean Region. In this review, we aimed at describing the state of avian influenza in the region from 2011 to 2021. We gathered information available through the peer-reviewed scientific literature, public gene sequence depositories, OIE World Animal Health Information System platform, World Health Organization FluNet, Joint External Evaluation reports, and governmental, Food and Agriculture Organization of the United Nations, and World Organization for Animal Health websites. We used an interdisciplinary perspective consistent with the One Health approach to perform a qualitative synthesis and making recommendations. Analysis showed that although avian influenza research in the Eastern Mediterranean Region has gained more attention during the last decade, it was limited to only few countries and to basic science research. Data highlighted the weakness in surveillance systems and reporting platforms causing underestimation of the actual burden of disease among humans and animals. Inter-sectoral communication and collaboration for avian influenza prevention, detection, and response remain weak. Influenza surveillance at the human-animal interface and the application of the One Health paradigm are lacking. Countries' animal health and public health sectors rarely publish their surveillance data and findings. This review suggested that surveillance at the human-animal interface, research, and reporting capacities should be enhanced to improve understanding and control of avian influenza in the region. Implementing a rapid and comprehensive One Health approach for zoonotic influenza in the Eastern Mediterranean Region is recommended.

Rapid assessment of case recruitment tools to inform integrated surveillance of influenza and other respiratory viruses in Eastern Mediterranean countries.

Influenza-like illness (ILI) and severe acute respiratory infection (SARI) case recruitment tools from 10 countries were reviewed. The contents of the existing tools were compared against World Health Organization's current guidelines, and we also assessed the content validity (accuracy, completeness and consistency). Five of the ILI tools and two of the SARI tools were rated as having high accuracy against WHO case definitions. ILI completeness ranged from 25% to 86% and SARI from 52% to 96%. Average internal consistency scores were 86% for ILI and 94% for SARI. Limitations in the content validity of influenza case recruitment tools may compromise recruitment of eligible cases and result in varying detection rates across countries.

A review of policies and coverage of seasonal influenza vaccination programs in the WHO Eastern Mediterranean Region.

Background: Although there has been an effective seasonal influenza vaccine available for more than 60 years, influenza continues to circulate and cause illness. The Eastern Mediterranean Region (EMR) is very diverse in health systems capacities, capabilities, and efficiencies, which affect the performance of services, especially vaccination, including seasonal influenza vaccination. Aims: The aim of this study is to provide a comprehensive overview on country-specific influenza vaccination policies, vaccine delivery, and coverage in EMR. Materials and Methods: We have analyzed data from a regional seasonal influenza survey conducted in 2022, Joint Reporting Form (JRF), and verified their validity by the focal points. We also compared our results with those of the regional seasonal influenza survey conducted in 2016. Results: Fourteen countries (64%) had reported having a national seasonal influenza vaccine policy. About (44%) countries recommended influenza vaccine for all SAGE recommended target groups. Up to 69% of countries reported that COVID-19 had an impact on influenza vaccine supply in the country, with most of them (82%) reporting increases in procurement due to COVID-19. Discussion: The situation of seasonal influenza vaccination in EMR is varied, with some countries having well established programs while others having no policy or program; these variances may be due to resources inequity, political, and socioeconomic dissimilarities. Few countries have reported wide vaccination coverage over time with no clear trend of improvement. Conclusion: We suggest supporting countries to develop a roadmap for influenza vaccine uptake and utilization, assessment of barriers, and burden of influenza, including measuring the economic burden to enhance vaccine acceptance.

Knowledge, attitudes, and practices towards seasonal influenza vaccination among pregnant women and healthcare workers: A cross-sectional survey in Afghanistan.

Background: Despite recommendation by the World Health Organization (WHO), influenza vaccination coverage among high-risk groups remains suboptimal in Afghanistan. This study aims to document the knowledge, attitudes, and practices of seasonal influenza vaccine uptake among two priority groups, pregnant women (PWs) and healthcare workers (HCWs). Methods: This cross-sectional study enrolled PWs and HCWs in Kabul, Afghanistan, from September to December 2021. Data on vaccine intention and uptake, knowledge, and attitudes towards vaccination were collected. Simple linear regression was used to predict the impact of sociodemographic characteristics on the KAP score. Results: A total of 420 PWs were enrolled in Afghanistan. The majority (89%) of these women had never heard of the influenza vaccine but 76% intended to receive the vaccine. Of the 220 HCWs enrolled, 88% were unvaccinated. Accessibility and cost were factors which encouraged vaccination among HCWs. Fear of side effects and affordability were identified as key barriers. HCWs reported high level of vaccine intention (93%). PWs aged under 18 years (ß: 6.5, P = 0.004), between 18 and 24 years (ß: 2.9, P = 0.014), currently employed (ß: 5.8, P = 0.004), and vaccinated against COVID-19 (ß: 2.8, P = 0.01) were likely to have a higher attitude score. Among HCWs, being female was a predictor for poor vaccination practice (ß: -1.33, P < 0.001) whereas being vaccinated against COVID-19 was a predictor for higher practice score (ß: 2.4, P < 0.001). Conclusion: To increase influenza vaccination coverage among priority groups, efforts should be made to address issues such as lack of knowledge, limited availability, and cost barriers.

Image dataset of important grape varieties in the commercial and consumer market.

This work presents a primary dataset collected from various geographic locations in Iraq for the seedlings of eight varieties of grapes that are used for local consumption and export. Grape types included in the dataset are: deas al-annz, kamali, halawani, thompson seedless, aswud balad, riasi, frinsi, shdah. Leaves of each type of the seasoned fruit were photographed with high resolution device. A total of 8000 images (i.e., 1000 images per category) were captured using random sampling approach while maintaining the balance and diversity within grape image data. The proposed dataset is of significant potential impact and usefulness with features including (but not limited to) 8 varieties, that have different tastes and can support various industry in agriculture and food manufactures.

Towards a Virtual Fencing System: Training Domestic Sheep Using Audio Stimuli.

Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries. Accordingly, the present work has investigated the sole application of audio signals in training and managing animal behaviour. Audio cues in the range of 125-17 kHz were used to prohibit the entrance of seven Hebridean ewes from a restricted area with a feed bowl. Two trials were performed over the period of a year which were video recorded. Sound signals were activated when the animal approached a feed bowl and a restricted area with no feed bowl present. Results from both trials demonstrated that white noise and sounds in the frequency ranges of 125-440 Hz to 10-17 kHz successfully discouraged animals from entering a specific area with an overall success rate of 89.88% (white noise: 92.28%, 10-14 kHz: 89.13%, 15-17 kHz: 88.48%, 125-440 Hz: 88.44%). The study demonstrated that unaided audio stimuli were effective at managing virtual fencing for sheep.

Development of a road map to scale up the uptake and utilization of influenza vaccine in 22 countries of Eastern Mediterranean Region.

BACKGROUND: The aim of this project was to develop a road map to support countries in Eastern Mediterranean Region in developing and implementing evidence-based seasonal influenza vaccination policy, strengthen influenza vaccination delivery program and address vaccine misperceptions and hesitancy. METHODS: The road map was developed through consultative meetings with countries' focal points, review of relevant literature and policy documents and analysis of WHO/UNICEF Joint Reporting Form on immunization ((JRF 2015-2020) data. Countries were categorised into three groups, based on the existence of influenza vaccination policy and national regulatory authority, availability of influenza vaccine in the country and number of influenza vaccine doses distributed/ 1000 population. The final road map was shared with representatives of all countries in Eastern Mediterranean Region and other stakeholders during a meeting in September 2021. RESULT: The goal for next 5 years is to increase access to and use of utilization of seasonal influenza vaccine in Eastern Mediterranean Region to reduce influenza-associated morbidity and mortality among priority groups for vaccination. Countries in the Eastern Mediterranean Region are at different stages of implementation of the influenza vaccination program, so activities are planned under four strategic priority areas based on current situations in countries. The consultative body recommended that some countries should establish a new seasonal influenza vaccination programme and ensure the availability of vaccines, while other countries need to reduce vaccine hesitancy and enhance current seasonal influenza vaccination coverage, particularly in all high-risk groups. Countries are also encouraged to leverage COVID-19 adult vaccination programs to improve seasonal influenza vaccine uptake. CONCLUSION: This road map was developed through a consultative process to scale up the uptake and utilization of influenza vaccine in all countries of Eastern Mediterranean Region. The road map proposes activities that should be adopted in the local context to develop/ update national policies and programs.