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Determinants of medication adherence among older adults with type 2 diabetes using the health action process approach: A cross-sectional study
Background and aims Medication adherence is a determinant of managing chronic disease. Failure to adhere to treatment can result in disease progression, increased hospitalizations, and a higher risk of complications and mortality. This study aimed to determine the level of medication adherence in older adults with type 2 diabetes based on the Health Action Process Approach (HAPA). Methods This study is a descriptive-analytical cross-sectional study that was conducted on 179 older adults with type 2 diabetes. Data were collected using the Morisky Medication Adherence Scale (MMAS-8-Item) and the HAPA questionnaire. We used the chi-square test to compare adherence to medication by demographic characteristics and multiple binary logistic regression analysis to predict factors related to medication adherence based on the HAPA dimensions. Results A total of 179 participants (87 men and 92 women) with a mean age of 64.65 ± 4.99 years were enrolled. Low medication adherence was reported by 62% of participants. No significant associations were found between socio-demographic factors (gender, marital status, education, employment, and income) and adherence levels. Logistic regression analysis revealed that smoking (OR = 4.309, 95% CI [1.18, 15.67], p = 0.027) and perceived barriers to adherence (OR = 1.036, 95% CI [1.01, 1.06], p = 0.001) were significantly associated with increased odds of medication non-adherence. Conversely, higher recovery self-efficacy (OR = 0.924, 95% CI: 0.86–0.99, p = 0.027) and coping planning (OR = 0.963, 95% CI: 0.93–0.99, p = 0.022) were associated with reduced odds of non-adherence. The most common self-reported reasons for suboptimal adherence were lack of affordability (17.5%), lack of family support (10%), and poor understanding of the disease (9.4%). Conclusion This study highlights that older people had suboptimal adherence to medication. Smoking and perceived barriers were significant risk factors, increasing the likelihood of poor adherence. Conversely, higher levels of recovery self-efficacy and coping planning served as protective factors, reducing the risk of non-adherence. Policymakers and planners should consider the mentioned factors in designing interventions to change behavior for chronic diseases like diabetes.
A systematic review and meta-analysis of randomized controlled trials evaluating the effect of whole body vibration training on fibromyalgia
Examining the viability of five Salmonella enterica subsp. enterica in thymol at 4°C and 25°C using flow cytometry
Salmonella spp., a major cause of foodborne illness, requires effective control strategies to improve food safety. Thymol, an antibacterial agent derived from natural essential oils, has been assessed for use as an antimicrobial agent and preservative in the food industry, due to its safety and low cost. This study used flow cytometry and Tryptic Soy Agar (TSA) plate counts, to assess the viability of five serotypes of Salmonella enterica subsp. enterica over 56 days in thymol at 4°C and 25°C, during long-term storage in distilled water. The minimum inhibitory concentration (MIC) of thymol against Salmonella serotypes was found to be 256 µg/mL at both 25°C and 4°C for all serotypes at the initial time point (day 0) and after 154 days of incubation in water. Flow cytometry successfully counted viable cells in the control group, which contained 2% ethanol and 128 µg/mL thymol. However, plate count numbers completely declined after day 7 at both 25°C and 4°C for all thymol concentrations. After exposure to sub-MIC levels and subsequent spiking with 256 µg/mL at 25°C and 4°C, neither flow cytometry nor plate counts detected viable cells. These findings emphasize the importance of advanced techniques such as flow cytometry for the detection of microorganisms and demonstrate thymol’s potential as an environmentally friendly solution in food safety strategies to reduce Salmonella contamination in water sources over extended periods.
Optimizing IoV cloud trust with adaptive blockchain and reinforcement learning
Bargaining risk governance within the context of China’s belt and road initiative: Perspectives derived from the Kindleberger Trap theory
“Belt and Road” infrastructure projects frequently encounter bargaining risks. This study aims to explore the causes of bargaining behavior and investigate how to control bargaining behavior in projects. The study identifies the factors affecting bargaining risk and employs evolutionary game theory to examine the strategic decisions of Chinese contractors and host governments. The results show that lower negotiation and bargaining costs promote sustained cooperation in the negotiation process. When the value of the contract is substantial and the contractor receives less benefit from the contract than it claims from the host government, terminating the project is good for the contractor.
The effect of individual nutrition counseling on the life quality and weight in patients with gastric cancer following total gastrectomy
Diabetes and oral health: A comparative cross-sectional analysis of DMFT index among diabetic, pre-diabetic, and non-diabetic adults
Objective The objective of this study was to compare the DMFT index (decayed, missing, and filled teeth) between diabetic, pre-diabetic, and non-diabetic subjects and to determine whether sociodemographic (including age, education, or socioeconomic status) or health-related factors (such as BMI, smoking, or physical activity) were associated with DMFT levels in people with diabetes. Methods This study is cross-sectional and part of the PERSIAN Guilan cohort study. Demographic information, body mass index (BMI), cigarettes and hookah, alcohol and drug use, co-morbidity diseases, socio-economic status (SES), and DMFT of all 35–70-year-olds were investigated. Classification of diabetes status was done based on the result of the FBS test or self-report of the participant, or the use of hypoglycemic drugs. Results Out of 10520 people who participated in the study, 2531 people had diabetes, 1837 people had pre-diabetes, and 6152 people were non-diabetics. The average DMFT in diabetic, pre-diabetic, and non-diabetic participants was 16.03, 14.63, and 13.94, respectively, and the differences in DMFT between the three groups were statistically significant (P < 0.05). The risk for higher DMFT was older patient age, lower educational status, lower BMI, less physical activity, smoking, alcohol consumption, and not brushing. However, drug use is considered a risk factor only for diabetics. Conclusion In all groups, higher DMFT risk factors included older age, lower education, reduced BMI, less physical activity, smoking, alcohol consumption, and inadequate teeth brushing. Notably, drug use is regarded as a risk factor exclusively among participants with diabetes.
I deeply scan timber to reduce wasting this precious resource
Multiple oestradiol functions inhibit ferroptosis and acute kidney injury
Electrophoretic deposition and characterization of CS/nanoHAp/AgNPs composite coatings on titanium from ethanol-based suspensions
Measurement accuracy of CT systems: The importance of calibration phantoms
This study aims to evaluate the measurement accuracy of computed tomography (CT) systems, focusing on the necessity of using calibration phantoms for enhanced precision. Both clinical CT and micro-CT systems were evaluated using a specially designed two-ball phantom, which provides a reliable reference for spatial resolution and geometric accuracy. The study involved scanning the phantom with two micro-CT devices (the oversize micro-CT SkyScan 1173 and the high-resolution micro-CT SkyScan 1272) and a clinical CT device, a third-generation dual-source CT scanner (SOMATOM Force), measuring the distance between the centres of two ruby balls. The results showed significant differences in measurement accuracy between the devices. The high-resolution micro-CT provided the most consistent measurements with minimal variance, indicating its superiority in applications requiring high precision. In contrast, the oversize micro-CT exhibited larger errors, particularly at smaller voxel sizes, suggesting that internal calibration affected its accuracy. The dual source CT system had the smallest mean error but a larger standard deviation, indicating less consistency compared to micro-CT systems. Calibration with the two-ball phantom improved measurement accuracy across all devices. This improvement underscores the importance of using calibration phantoms to ensure accurate measurements, especially in fields that require high precision, such as clinical diagnostics and materials science. We concluded that routine calibration with phantoms is essential to achieve high measurement accuracy in CT imaging, thereby increasing the reliability of CT-based analyses in various disciplines.
Effects of oral glucose tolerance test on microvascular and autonomic nervous system regulation in young healthy individuals
Optimal harvesting of a continuously age-structured population with density dependence
We consider harvesting of a population with continuous age-structure and where density dependence is implemented through interaction of the population with a food source. Using a Von Bertalanffy length-age relation, the continuous age-structure is equivalent to a continuous length-structure. We allow the harvesting rate to be an arbitrary function of length. This allows for a comparison of harvesting strategies, including conventional harvesting and balanced harvesting. As a particular example, we consider plaice (Pleuronectes platessa, Pleuronectidae). The harvesting rate which gives the maximum sustainable yield is consistent with conventional harvesting: there exists a body size such that individuals smaller than that size are not harvested and individuals larger than that size are maximally harvested.
Ground settlement induced by NATM tunneling and surface loads in Shiraz metro station
Seasonal variation in egg nutrient composition under a pasture-based layer hen system: Implications for sustainable agriculture
Sustainability in poultry production emphasizes systems that promote environmental health, animal welfare, and the potential to produce a more nutrient dense product. Pasture based poultry systems align with these sustainability goals by supporting soil fertility, biodiversity, and more natural behaviors. Access to pasture allows chickens to consume a diverse range of plants and insects, potentially enhancing the nutritional value of their eggs. However, environmental variability across the grazing season may influence egg nutrient profiles, impacting both nutritional quality and system resilience. This study evaluated how seasonal changes in forage quality, soil composition, and climate affect the nutrient profile of eggs produced under a regenerative, pasture-based system in Southern Ohio. Monthly collections of forage (n = 3) and eggs (n = 24, pooled into 12 replicates) occurred from May to December. Fatty acid composition was assessed using gas chromatography-mass spectrometry, while carotenoid and phenolic levels were measured colorimetrically. Vitamin and mineral content were analyzed through liquid chromatography and Inductively Coupled Plasma Optical Emission Spectroscopy. Pasture quality, assessed by total digestible nutrients (TDN), peaked in October. Egg protein quality met USDA “Grade AA” standards every month except August (p > 0.001). The highest yolk pigmentation score was recorded in December (9.5 ± 1.3; p < 0.001). Vitamin A levels were significantly greater in late summer (p < 0.001), while vitamin E gradually increased across the season, reaching its highest value in November (118.1 ± 24.0 µg/g fresh yolk; p < 0.001). Carotenoid concentrations were elevated in mid-summer and late autumn (p < 0.001). Total omega-3 fatty acids were significantly higher in September and October than in mid-summer and late fall, while the n-6:n-3 ratio was lowest in early summer, and fall compared to July (p < 0.001). Sparse partial least squares discriminant and random forest analyses demonstrated that eggs produced from September to November contained higher levels of vitamins A and E, greater essential omega-3 fatty acids, and a more favorable n-6:n-3 balance than eggs from other months. These findings highlight the need to account for seasonal variability in pasture-based systems and suggest targeted management practices could enhance year-round nutritional quality, supporting both consumer health and sustainable food production.
Real-time molecular recorders expose the inner lives of cells
Commercial sexual behaviour among university students who engaged in casual sexual behaviour in Eastern China: a cross-sectional study
FocusGate-Net: A dual-attention guided MLP-convolution hybrid network for accurate and efficient medical image segmentation
Although recent advances in CNNs and Transformers have significantly improved medical image segmentation, these models often struggle to balance segmentation accuracy, inference speed, and architectural simplicity. Lightweight MLP-based methods have emerged as a promising alternative, but they frequently lack the ability to capture fine-grained spatial context, leading to suboptimal boundary localization. To address this issue, a hybrid architecture can be introduced that integrates the computational efficiency of MLPs with the spatial feature extraction strengths of convolutional or transformer-based modules. This design aims to deliver high segmentation accuracy while preserving low latency and minimal architectural complexity, thereby enhancing applicability in real-time clinical settings. Medical image segmentation remains a challenging task requiring both accuracy and computational efficiency in clinical settings. This paper introduces FocusGate-Net, a novel hybrid architecture combining shifted token MLP blocks, convolutional feature extractors, and dual-attention mechanisms for robust medical image segmentation. Our approach leverages the spatial dependency modeling capabilities of MLP architectures while enhancing feature selectivity through Convolutional Block Attention Module (CBAM) and Attention Gate (AG) mechanisms. We evaluate FocusGate-Net on three diverse medical image datasets: ISIC2018 for skin lesion segmentation, PH2 for dermatoscopic images, and Kvasir-SEG for polyp segmentation. Comprehensive ablation studies verify the contribution of each architectural component, demonstrating the effectiveness of our hybrid design. When benchmarked against state-of-the-art models like UNet, UNet++, and ResUNet, FocusGate-Net achieves superior performance, with a Dice coefficient of 92.47% and IoU of 86.36% on ISIC2018. Furthermore, our model demonstrates exceptional cross-dataset generalization capability, achieving Dice scores of 97.25% on PH2 and 94.83% on Kvasir-SEG. These results highlight the potential of MLP-based hybrid architectures with attention mechanisms for improving medical image segmentation accuracy while maintaining computational efficiency suitable for clinical deployment.