107 research outputs found
Health-related quality of life in Welsh adults: psychometric properties of the SF-36v2 and normative data
No full textObjectivesThe use of normative data has become well-accepted and a common strategy to interpret individual's health outcome scores, which can help in making decisions. The objectives of this study were to obtain population normative data for the domains and component summaries of the 36-item SF-36® Health Survey (SF-36), and to evaluate its reliability and construct validity.MethodsThis study was conducted using population-based data from the Welsh Health Survey (WHS; 2011–2015). This study used version 2 of the SF-36 (SF-36v2® Health Survey). The descriptive statistics and normative data for the eight domains and two summaries, physical component summary (PCS) and mental component summary (MCS), were calculated. Reliability assessment used internal consistency methods and construct validity assessment used known group comparisons and item–scale correlations.Study design and sampleWe performed a secondary analyses of data from the Welsh Health Survey (WHS).ResultsThis study included 74,578 participants aged 16 years or older (53.6% were women). Participants aged 16–24 years scored higher on SF-36 scale than older groups on all domains. The SF-36 profiles by age group demonstrated lower scores for older age groups, with the most pronounced differences shown on the physical-related scales. Across the age groups, men had higher PCS and MCS scores than women. All SF-36 domains and PCS and MCS achieved a good to excellent internal consistency reliability exceeding 0.7. The scales demonstrated construct validity by showing associations with a range of factors known to be related to health.ConclusionsThis study provides SF-36 normative data for Wales based on a representative data and confirms the construct validity and reliability of the SF-36.Objectives: The use of normative data has become well-accepted and a common strategy to interpret individual's health outcome scores, which can help in making decisions. The objectives of this study were to obtain population normative data for the domains and component summaries of the 36-item SF-36® Health Survey (SF-36), and to evaluate its reliability and construct validity. Methods: This study was conducted using population-based data from the Welsh Health Survey (WHS; 2011–2015). This study used version 2 of the SF-36 (SF-36v2® Health Survey). The descriptive statistics and normative data for the eight domains and two summaries, physical component summary (PCS) and mental component summary (MCS), were calculated. Reliability assessment used internal consistency methods and construct validity assessment used known group comparisons and item–scale correlations. Study design and sample: We performed a secondary analyses of data from the Welsh Health Survey (WHS). Results: This study included 74,578 participants aged 16 years or older (53.6% were women). Participants aged 16–24 years scored higher on SF-36 scale than older groups on all domains. The SF-36 profiles by age group demonstrated lower scores for older age groups, with the most pronounced differences shown on the physical-related scales. Across the age groups, men had higher PCS and MCS scores than women. All SF-36 domains and PCS and MCS achieved a good to excellent internal consistency reliability exceeding 0.7. The scales demonstrated construct validity by showing associations with a range of factors known to be related to health. Conclusions: This study provides SF-36 normative data for Wales based on a representative data and confirms the construct validity and reliability of the SF-36.</p
Setting the Stage for Transformative Learning in MA TESOL Classrooms at a Saudi University
No full textThis article explores the impact of transformative theory on the learning outcomes of seven Saudi female student-teachers enrolled in a Master’s TESOL course at a Saudi university. They were actively engaged in designing learning materials for learners with special needs. In this intervention, transformative theory principles were used. They involved dialogue, authentic assessment, and structured reflection. Following the intervention, data were collected using focus group discussions and document analysis. The data were analysed using Mezirow’s transformative theory components: experience, critical reflection, reflective discourse, and action. The findings reveal the experience supported the participants’ autonomy, providing them with opportunities to reflect on their teaching practices, and improved their knowledge construction skills. Based on the results, the author makes a case for greater use of transformative theory approaches in designing and implementing teacher education
Knee Injury and Osteoarthritis Outcome Score Patellofemoral Questionnaire: Psychometric Properties among Females of Kingdom of Saudi Arabia
No full textPatellofemoral joint osteoarthritis (PFJ-OA), being a subset of knee osteoarthritis (KOA), is evident in adults, and its prevalence is greater in women in Saudi Arabia too. To assess its disease dimensions, the ‘Knee Injury and Osteoarthritis Outcome Score Patellofemoral’ questionnaire (KOOS-PF) is frequently used to measure symptoms and function among the people with PFJ-OA. Cross-cultural validation is ongoing in several languages, and it needed to be validated among females in Arabic. Therefore, aiming to translate, cross-culturally adapt and validate its psychometric properties, a cross-sectional study was designed where the Ar-KOOS-PF-F was administered among 105 females. The demographic characteristics of recruited females were 51.62 (8.49) years and 30.12 (3.70) kg/m(2). Cronbach’s alpha was used for internal consistency (IC) and the questionnaire was re-administered after 48 h to estimate the test–retest reliability (92 females, 87.61% compliance rate). Concurrent validity was also established with a visual analog scale (VAS). Factorial validity was established by principal component analysis (PCA). The psychometric properties were: excellent internal consistency of Cronbach’s alpha (α) = 0.930, intraclass correlation coefficient (ICC) for intra-ratter reliability = 0.960 (0.915–0.999), test–retest reliability, ICC = 0.893 (0.889–0.970), standard error of measurement (SEM) = 2.46, relative standard deviation/coefficient of variance (RSD/CV) = 29.9%, minimal detectable change (MDC%) = 22.96% and good concurrent validity with VAS (r = −0.783; p = 0.023). The best-fit four-factor model for confirming overall item communalities ranged from 0.529 to 0.867, which indicates moderate to high communalities, and confirms the homogeneity of Ar-KOOS-PF-F using PCA. The floor (0.9%) and ceiling effects (13.6%) were also within the limits. This scale can be used among females, as it has acceptable psychometric properties of scale validation
Healing center in allith
No full textThe major goal of this study is to gather information for the creation of a therapeutic facility. Studying the hot springs and their history comes after looking for great architects who have worked on projects similar to this one, after defining the project statement and the chosen city, defining the project's vision and its relevance to the Saudi Vision 2030, providing supporting case studies, and analyzing all priorities.
Hot springs are regarded as one of the most significant geographical features that offer significant advantages for treating a variety of diseases as well as providing relaxation through the inhalation of water vapor. Such a project needs to be thoroughly researched in all of its facets and all requirements must be carefully considered.
The creation of an unique project in Saudi Arabia will start with this research. According to the Tourism Vision 2030, it will support in enhancing national tourism
Benessere, attività fisica e qualità di vita in un’ampia coorte di pazienti con malattie croniche
No full textINTRODUZIONE
In tutte le fasce d’età, l’inattività fisica è uno dei fattori legati allo stile di vita associato di più con lo sviluppo di malattie croniche non trasmissibili (MCNT), quali malattie cardiovascolari, diabete, cancro e broncopneumopatia cronica ostruttiva (BPCO), e che è stato definito una «pandemia» (Kohl et al., 2012) per i suoi effetti sulla salute. Anche tra gli adolescenti e i giovani si assiste a una crescente incidenza delle MCNT, prima di tutto il diabete di tipo 2.
OBIETTIVI
Indagare la relazione dose-risposta tra attività fisica e la qualità della vita correlata alla salute (HRQoL) in un ampio campione di persone con malattie croniche.
METODO
Il campione è costituito da 29.271 persone di età superiore ai 16 anni (15.315 donne) con malattie croniche e che hanno partecipato al Welsh Health Survey (Galles, Regno Unito; raccolta dati 2011–2015). I partecipanti sono stati classificati, in base ai minuti settimanali di attività fisica da moderata a vigorosa (MVPA), in quattro gruppi: inattivi (nessuna attività fisica), non sufficientemente attivi (<150 min/settimana), sufficientemente attivi (≥150 –<300) e molto attivi (≥300).
Per misurare la qualità di vita correlata alla salute è stata utilizzata la Short-Form 36 Health Survey (SF-36).
RISULTATI
Dalle analisi di correlazione e di regressione è emersa un’associazione significativa tra MVPA e HRQoL: anche i partecipanti insufficientemente attivi hanno una qualità di vita correlata alla salute migliore dei partecipanti inattivi. I risultati mostrano che livelli più elevati di attività fisica sono associati a punteggi più elevati in ogni sottoscala della SF-36: coloro che erano molto attivi avevano una qualità di vita superiore, seguiti da coloro che erano sufficientemente attivi, e poi dai partecipanti non sufficientemente attivi.
CONCLUSIONI
Studi futuri potrebbero individuare modi sempre più efficaci per motivare le persone con malattie croniche a impegnarsi nell’attività fisica, considerati i suoi benefici sia per la salute fisica che mentale. Le prove a sostegno dell’utilità dell’esercizio fisico regolare in individui con malattie croniche di tutte le fasce d’età sono robuste, e i pazienti dovrebbero essere incoraggiati a dedicare regolarmente più tempo all’attività fisica per contrastare i danni arrecati dalla malattia e migliorare la loro salute e il loro benessere
Molecular Pharmacology of Glucagon-Like Peptide 1-Based Therapies in the Management of Type Two Diabetes Mellitus and Obesity
No full textAbdullah M Alzahrani,1– 3 Ghada A Alshobragi,1 Abdullah M Alshehri,1 Majed S Alzahrani,1 Hasan A Alshehri,1 Rami M Alzhrani,4 Samah Basudan,5 Ayed A Alkatheeri,6 Salman A Almutairi,7 Yahya A Alzahrani2,6 1Pharmaceutical Care Department, Ministry of National Guard—Health Affairs, Jeddah, 22384, Saudi Arabia; 2King Abdullah International Medical Research Center, Jeddah, 21423, Saudi Arabia; 3College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, 22384, Saudi Arabia; 4Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, 21944, Saudi Arabia; 5Department of Pharmacy, King Abdullah Medical Complex, Ministry of Health, Jeddah, Saudi Arabia; 6Drug Information Center, Department of Pharmacy, East Jeddah Hospital, Ministry of Health, Jeddah, 23816, Saudi Arabia; 7General Administration of Medical Services, Jeddah Medical Center, Jeddah, Saudi ArabiaCorrespondence: Yahya A Alzahrani, Drug Information Center, Department of Pharmacy, East Jeddah hospital, Ministry of health, Jeddah, 23816, Saudi Arabia, Tel +966557700178, Email [email protected]: The global increase in type 2 diabetes mellitus (DM2) and obesity presents a significant public health challenge, as these interconnected conditions contribute to severe complications, including cardiovascular disease, stroke, and certain cancers. The incretin system, particularly glucagon-like peptide-1 (GLP-1), has emerged as a promising therapeutic target due to its role in glycemic control and weight management.Objective: This review explores the molecular pharmacology of GLP-1 and its receptor agonists, evaluating their therapeutic efficacy in managing DM2 and obesity.Methods: A comprehensive literature review was conducted, analyzing recent advancements in GLP-1-based therapies, their mechanisms of action, and their clinical applications. The review also highlights the pharmacokinetic modifications developed to enhance the stability and efficacy of GLP-1 receptor agonists.Results: GLP-1 receptor agonists have demonstrated significant benefits in improving glycemic control, reducing body weight, and addressing metabolic complications. Novel therapeutic approaches, including dual and triple incretin receptor agonists, are showing enhanced efficacy in both diabetes and obesity management. However, challenges remain in optimizing treatment outcomes, addressing patient variability, and improving long-term adherence.Conclusion: GLP-1-based therapies have revolutionized the management of DM2 and obesity. Continued research is essential to refine these treatments, overcome existing limitations, and develop personalized approaches to maximize patient outcomes.Keywords: GLP-1 receptor agonists, type 2 diabetes mellitus, obesity management, incretin hormones, molecular pharmacolog
From Sigmoid to SoftProb: A novel output activation function for multi-label learning
No full textMulti-label classification is a crucial machine learning task that assigns multiple labels to a single instance, making it distinct from traditional single-label classification. The sigmoid activation function, commonly used in multi-label learning, suffers from saturation and vanishing gradient issues, which can hinder model performance. To address these limitations, we propose SoftProb, a novel output activation function designed to improve gradient flow and predictive performance while maintaining computational efficiency. We conduct a comprehensive theoretical and empirical analysis comparing SoftProb and sigmoid across shallow, medium, and deep multilayer perceptrons on six benchmark datasets. The results demonstrate that SoftProb achieves statistically significant improvements in key metrics, including a 5.15% increase in Macro F1-score and a 2.60% improvement in Average Precision Score (APS), while maintaining comparable training times to sigmoid (p>0.05). Although SoftProb showed a marginal 0.40% increase in Hamming Loss, it provides better balance between precision and recall, particularly in deeper network architectures. Notably, SoftProb’s simplified mathematical formulation avoids exponential operations, offering potential implementation advantages. Statistical validation using the Wilcoxon signed-rank test confirms the significance of the performance improvements (p<0.05 for F1 and APS). These findings establish SoftProb as a robust alternative to sigmoid for multi-label classification, combining enhanced predictive performance with stable computational characteristics
Fabrication of anisotropic polymer colloid particles
No full textThe fabrication of complex colloidal particles with anisotropic "patchy" e.g. Januslike,
morphology will be studied. Known approaches towards "Janus particles" focus
mostly on the micron-sized domain, with common fabrication routes based on
monolayer modification or microfluidic production (restricts scale-up). We operate
in the submicron regime (typically 100-500 nm) and use scalable emulsion
polymerization strategies, in combination with entropic phase separation of swollen
cross-linked latex particles and living radical polymerization, i.e. SET-LRP, to
prepare our "patchy" amphiphilic particles.
In this research, various Cross-linked densities (typically from 1 - 8wt%)
poly(styrene) latexes (typically 100-500 nm) functionalized with tert-bromine
functional groups, by batch or shot addition of (2-methacryloxyethyl -2-
bromoisobutyrate) made via soap-free emulsion polymerization used as the precursor
particles.
Two synthetic pathways were investigated to make the targeted hairy Janus Particles.
Approach one: in which we carried out the domain formation step prior to the
fabrication of the polymer brushes, We found out that the effective synthetic way to
make Janus hairy dumbbell particles in a reproducible manner is to start the
synthesis with light cross-linked density of (1.9 to 3.0 wt% DVB) precursor
poly(styrene) latex particles (150-250 nm diameter) made by shot addition method.
The direct entropic phase separation from these latex particles leads to the formation
of only one new domain with dumbbell shape morphology, when the swelling ratio
used between monomer and latex is between 2.0 and 4.0, and with low DVB concentration in the swelling monomer (between 0.15-1.0wt%) using AIBN as
initiator.
Formation of hydrophilic polymer brushes by SET-LRP resulted in targeted hairy
Janus particles with sub-micrometer diameter, in a reproducible manner. The length
of the polymer brushes can be controlled by addition of water soluble ATRP initiator
to produce shorter polymer brushes. The rate of SET-LRP was ultrafast and the rate
can be reduced by addition of deactivator CuBr2.
The second approach: water-soluble polymer brushes were grafted onto the surface
of latex particles by SET-LRP. These “hairy” cross-linked colloids were swollen
with additional monomers and initiator. Elevation of temperature causes entropic
phase separation inducing new domains, which were polymerized. This approach
leads to mainly popcorn and raspberry particles with some limited cases that are able
to make hairy Janus Particles with non reproducible manner.
The obtained complex particles show some interesting application such as a
stabilization agent for Carbon Nanotubes (CNTs) in aqueous medium, Pickering
emulsion stabilizer, and they self assembled upon addition of dilute electrolyte
solution
Theoretical investigations on modeling blood flow through vessel for understanding effectiveness of magnetic nanocarrier drug delivery systems
No full textFor cancer therapy, the focus is now on targeting the chemotherapy drugs to cancer cells without damaging other normal cells. The new materials based on bio-compatible magnetic carriers would be useful for targeted cancer therapy, however understanding their effectiveness should be done. This paper presents a comprehensive analysis of a dataset containing variables x(m), y(m), and U(m/s), where U represents velocity of blood through vessel containing ferrofluid. The effect of external magnetic field on the fluid flow is investigated using a hybrid modeling. The primary aim of this research endeavor was to construct precise and dependable predictive models for velocity, utilizing the provided input variables. Several base models, including K-nearest neighbors (KNN), decision tree (DT), and multilayer perceptron (MLP), were trained and evaluated. Additionally, an ensemble model called AdaBoost was implemented to further enhance the predictive performance. The hyper-parameter optimization technique, specifically the BAT optimization algorithm, was employed to fine-tune the models. The results obtained from the experiments demonstrated the effectiveness of the proposed approach. The combination of the AdaBoost algorithm and the decision tree model yielded a highly impressive score of 0.99783 in terms of R2, indicating a strong predictive performance. Additionally, the model exhibited a low error rate, as evidenced by the root mean square error (RMSE) of 5.2893 × 10−3. Similarly, the AdaBoost-KNN model exhibited a high score of 0.98524 using R2 metric, with an RMSE of 1.3291 × 10−2. Furthermore, the AdaBoost-MLP model obtained a satisfactory R2 score of 0.99603, accompanied by an RMSE of 7.1369 × 10−3
5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques
No full textThe development and implementation of Internet of Things (IoT) devices have
been accelerated dramatically in recent years. As a result, a super-network is
required to handle the massive volumes of data collected and transmitted to
these devices. Fifth generation (5G) technology is a new, comprehensive
wireless technology that has the potential to be the primary enabling
technology for the IoT. The rapid spread of IoT devices can encounter many
security limits and concerns. As a result, new and serious security and privacy
risks have emerged. Attackers use IoT devices to launch massive attacks; one of
the most famous is the Distributed Denial of Service (DDoS) attack. Deep
Learning techniques have proven their effectiveness in detecting and mitigating
DDoS attacks. In this paper, we applied two Deep Learning algorithms
Convolutional Neural Network (CNN) and Feed Forward Neural Network (FNN) in
dataset was specifically designed for IoT devices within 5G networks. We
constructed the 5G network infrastructure using OMNeT++ with the INET and
Simu5G frameworks. The dataset encompasses both normal network traffic and DDoS
attacks. The Deep Learning algorithms, CNN and FNN, showed impressive accuracy
levels, both reaching 99%. These results underscore the potential of Deep
Learning to enhance the security of IoT devices within 5G networks
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