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Intermittent fasting and a no-sugar diet for Long COVID symptoms: a randomized crossover trial

Age-dependent ACE2/TMPRSS2 expression and SARS-CoV-2 household transmission in Gran Canaria

Background This study aimed to assess whether the expression of ACE2 and TMPRSS2 is associated with susceptibility to and severity of COVID-19 across age groups. We also evaluated the role of children in household transmission of SARS-CoV-2. Methods We conducted a cross-sectional observational study including 258 households in Gran Canaria between March 10 and June 2, 2020. A total of 650 individuals (including 89 children under 18 years of age) were evaluated using a combined serological testing strategy to confirm past SARS-CoV-2 infection. Gene expression of ACE2 and TMPRSS2 was quantified from saliva samples. Demographic, clinical, and household exposure data were collected for analysis. Results The combined serological approach increased diagnostic sensitivity by 10%. Antibody levels decreased with age in children but increased with age and disease severity in adults. ACE2 expression was slightly elevated in younger children; however, after correction for multiple comparisons, there was no statistically significant association between ACE2 expression and age, antibody titers, or symptom severity.. TMPRSS2 expression did not correlate with any studied variable. Children were less frequently infected (OR = 0.56), and when infected, they experienced milder symptoms and reduced disease severity. Risk factors for transmission included older age and sharing a bedroom with the index case. In adults, risk increased with age; in children, younger age was associated with higher transmission risk. Conclusions Our findings do not support a strong relationship between ACE2 or TMPRSS2 expression levels and susceptibility to or severity of COVID-19. Children appear to be less susceptible to SARS-CoV-2 infection and tend to experience a milder disease course.

Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach

Fault analysis and performance improvement of grid-connected doubly fed induction generator through an enhanced crowbar protection scheme

The main problem associated with a doubly fed induction generator (DFIG) during fault is large inrush currents induced in rotor winding, which has detrimental effects on the machine’s AC excitation converter. A simple conventional resistance inclusion (crowbar) is employed with a PI controller to protect a DFIG from transient current, but it is observed that this method is not enough to keep transient over-current to an admissible level. In this paper, an effective current limiting technique along with reactive power control is proposed in order to maintain stability, reduce transient current surge to an acceptable level, and enhance the Fault Ride Through capacity of DFIG. The proposed dynamic control technique not only limits the fault current during voltage dip to a permissible level but also controls the reactive power during fault. The behavior of a proposed technique is analyzed by introducing unsymmetrical faults in the MATLAB-based model of DFIG. An enhanced crowbar-based fault ride-through is employed for the rotor side controller to limit inrush current and control reactive power.

Thermodynamic, isotherm and kinetic studies lead ions adsorption onto Manihot esculenta chaff surface

Expression of Concern: Knowledge, attitude, and preventive practices towards COVID-19 and associated factors among adult hospital visitors in South Gondar Zone Hospitals, Northwest Ethiopia

Salinity stress amelioration through selenium and zinc oxide nanoparticles in rice

Abstract Salinity is one of the most dominant abiotic stresses limiting growth and productivity in rice (Oryza sativa L.), thereby posing a serious threat to global food security. To enhance plants’ tolerance to salinity stress, the application of green-synthesized nanoparticles presents a novel and eco-friendly approach. This research article investigates the ameliorative effects of selenium (Se-NPs) and zinc oxide (ZnO-NPs) nanoparticles, both individually and in combination, on rice plants under salinity stress. Our results revealed that salinity stress significantly impaired rice growth and productivity, reducing plant height, root length, and yield-related traits, including tiller count, number of grains per spike, and grain weight. Furthermore, it induced oxidative stress, as evidenced by elevated levels of malondialdehyde and proline. The elevated levels of reactive oxygen species were visibly confirmed through histochemical staining. However, treatment with Se-NPs and ZnO-NPs significantly alleviated these adverse effects by enhancing the plant’s antioxidant defense mechanism. Activities of key antioxidant enzymes such as superoxide dismutase (50.06%), catalase (59.92%), ascorbate (104.28%), and peroxidase (85%) were significantly elevated, contributing to efficient ROS scavenging and reduced lipid peroxidation. The combined nanoparticle application was particularly effective in restoring physiological and biochemical parameters to near-normal levels, with increases of 46.32% in plant height, 70.53% in root length, and 100.7% in grains per spike under salinity stress. Furthermore, the enhanced accumulation of minerals such as Zn (31.8 ppm), Se (0.57 ppm), and Fe (7.4 ppm) in rice grains was also observed, indicating a dual benefit of stress alleviation and nutritional enrichment. Green-synthesized Se-NPs and ZnO-NPs, particularly when combined, offer a promising strategy for mitigating salinity stress in rice. Beyond enhancing stress tolerance and growth, the nanoparticles also contribute to the biofortification of rice grains, thereby improving both crop resilience and nutritional value in saline environments.

RETRACTED: Physical fitness characteristics and comprehensive physical fitness evaluation model of basketball players based on association rule algorithm

Physical fitness refers to the health of all body functions, including cardiorespiratory endurance, muscle strength, flexibility, stamina, and body composition, which can help individuals effectively cope with daily activities and sports challenges. This paper explores the physical characteristics of basketball players, aiming to improve training effects through unique physical evaluation indicators and provide a theoretical framework for improving college basketball performance and training standards. The study adopted the Apriori association rule algorithm in data mining. First, the physical data of basketball players were collected and preprocessed. Then, frequent item sets were extracted through the association rule mining algorithm, association rules were generated, and the key factors affecting the physical performance of athletes were analyzed. The article’s results revealed the potential relationship between different physical characteristics and emphasized the application prospects of association rule mining in the physical evaluation of basketball players.

An integrative analysis of cell-specific transcriptomics and nuclear proteomics of sleep-deprived mouse cerebral cortex

MOSRS: An engineering multi-objective optimization through Einsteinian concept

Multi-objective optimization stands at the intersection of mathematics, engineering, and decision-making, and metaheuristics offer a promising avenue for tackling such challenges. The literature shows they are the best, and there is space for new algorithms to deliver Pareto Fronts (PFs) with more convergence and coverage at lower computational costs. This paper presents the Multi-objective Special Relativity Search (MOSRS) for the first time. It relies on principles inspired by the theory of special relativity physics, which iteratively refines solutions toward optimality and self-adapts its parameters using these laws. Unlike most algorithms in the literature today, the user sets only the number of iterations and particles (or population). To test the performance, MOSRS is applied to the most challenging test functions set (CEC 2009) and 21 real and constrained world problems, being compared with a total of eleven metaheuristics: NSGA-II, NSGA-III, MOEA/D, MOPSO, MOGWO, ARMOEA, TiGE2, CCMO, ToP, and AnD. Inverted Generational Distance, Spacing, Maximum Spread, and Hypervolume are used to identify the best algorithm. MOSRS was robust in finding the best PF in most studied problems. The source codes of the MOSRS algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.

Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors

Abstract The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score < 5%, eight regions per slide). Performance metrics based on the coordinates of detected cells were used to assess the capability of image analysis tools to detect (i) total and (ii) Ki67 positive tumor cells, and consequently calculate the (iii) Ki67 score. Manual scoring by an experienced pathologist was used as the reference standard. The ML compared to the non-ML tool showed better performance metrics (F-score 0.90 vs. 0.74) in detecting the tumor cells. Also, the ML tool had a higher agreement with the reference standard in detecting tumor cells (ICC 0.91 vs. 0.62), Ki67 positive tumor cells (ICC 0.70 vs. 0.24), and the Ki67 score (ICC 0.86 vs. 0.45). Our findings highlight the enhanced accuracy of ML-based image analysis in detecting the correct tumor cells, outperforming traditional methods.

Usability of a hearing test mobile app across generations

Introduction Traditional diagnostic methods of hearing assessment, such as pure tone audiometry, may not be equally accessible to everyone due to geographical or mobility limitations. Utilizing a mobile application (app) for self-assessment of hearing is a promising alternative. However, the effectiveness of apps, as well as their usability across different age groups, remains largely unexplored. The objective of the present study was to assess, across different age groups, the usability of the “Hearing Test” app which allows self-testing of hearing on a mobile phone. Materials and methods The study was conducted on 77 participants from three age groups (16–39 years, 40–59 years, 60 years and older) who self-tested their hearing thresholds using the mobile app and who later underwent pure tone audiometry with an audiologist. The usability of the app was evaluated using a questionnaire based on the Mobile App Rating Scale (MARS), which was complemented by participant observation and interview. Results The app generally yielded results comparable to pure tone audiometry. However, older age groups tended to report higher levels of difficulty across several usability dimensions. Specifically, the oldest group rated the app lower in terms of functionality (M = 2.30; SD = 1.27) and engagement-customization (M = 2.11; SD = 1.28). For the oldest participants, the greatest difficulties related to installation (48%), and interpretation of results (26%). None of the participants aged 60 or older were able to complete the test independently, in contrast to 67% of the youngest participants and 28% of the middle-aged who did not require assistance. All age groups expressed a preference for a conventional hearing test over an app-based assessment, although the youngest group showed the greatest openness to using mobile apps. Conclusions The “Hearing Test” app has demonstrated its potential as a tool for initial hearing assessment, particularly among younger users. However, older individuals often encounter difficulties with installation, interpretation of results, and overall usability. Adapting the interface to meet the specific needs of older users, including user-friendly tutorials and clear presentation of results, is crucial for enhancing its usability.

The albumin-globulin ratio is associated with periodontitis in American adults: results from the NHANES 2009–2014

Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh

Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected from a particular region. An image dataset has been developed for wood species classification of Bangladeshi forest. Our aim is to address the gaps by comparing and contrasting our developed sequential Convolutional Neural Network based BdWood model with several deep learning, ensemble technique, Machine learning classification models on specific wood species identification for Bangladeshi forests. Using our own dataset, comprising more than 7119 high-quality captured images representing seven types of wood species of Bangladesh. It is found that DenseNet121 is the clear winner in our thorough evaluation among seven pre-trained models. The highest accuracy of DenseNet121 is achecived 97.09%. In addition, our customized BdWood model, which is adapted to the desired outcome, produced results that are excellent. BdWood model achieves a training accuracy of 99.80%, validation accuracy of 97.93%, an F1-score of 97.94%, and an outstanding ROC-AUC of 99.85%, demonstrating its effectiveness in wood species classification. Gradient-weighted Class Activation Mapping (Grad CAM) is used to interpret the model’s predictions, providing insights into the features contributing to the classification decisions. Finally, to make our research practically applicable, we have also developed an Android application as a tangible outcome of this work.

Correction: Influence of fermented whey protein fractions on the growth performance, haematological traits, serum biochemistry, faecal and caeca microbiota of broiler chickens

Expression of Concern: Menstrual hygiene practices among high school girls in urban areas in Northeastern Ethiopia: A neglected issue in water, sanitation, and hygiene research

Improvement of anti-swelling property and anticorrosion performance of epoxy coatings by the introduction of nanocrystalline cellulose composite

Guiding principles for accelerating change through health inequities research and practice: A modified Delphi consensus process

Despite a preponderance of evidence and considerable resources, health and social inequities persist, and in many cases, are widening. These inequities are not simply the result of passive structural and economic conditions but are actively maintained through institutional processes, norms, and ideologies that uphold the status quo. Reform within health inequities research, policy, and health and social care practice is therefore critical to disrupting these entrenched systems and catalysing both bottom-up and top-down change. We aimed to develop agreement for an iterative set of guiding principles underpinning ways of working for a newly formed Health and Social Equity Collective comprising researchers, community leaders, policymakers, and health and care professionals, seeking to address inequity by identifying and engaging the levers of change within and across institutions. The principles aim to inform a more inclusive and translational knowledge base through research practices, tackling entrenched inequalities in education, training, and capacity-building; and centring communities affected by health inequities through engagement and advocacy. We carried out a modified Delphi consensus process between March and September 2022 with Collective members and networks through online workshops and surveys. Out of 24 consensus statements developed and refined over a workshop and three successive survey rounds, we identified eleven key principles agreed upon by a majority of respondents. Two of these were rated high priority by over 75% of respondents, four by over 60% and five by over 50%. These could be grouped into three main topics detailing ways of working and change needed within: ‘Knowledge and framing of health and social inequities, and incorporation into practice’, ‘Community engagement, involvement and peer research’, and ‘Organisational culture change’, respectively. Given the pressing need to address inequities, these principles offer a grounding for future consensus building initiatives which also incorporate a wider diversity of perspectives, and which should be iteratively updated with ongoing learning from health equity initiatives nationally and internationally.

Enhancing grid connected wind energy conversion systems through fuzzy logic control optimization with PSO and GA techniques

Plasma-activated water modulates taxanes production and phenylalanineammonia-lyase activity in Taxus baccata cell culture

Paclitaxel, an anti-cancer compound from the Taxus baccata L. (yew tree), is limited in availability from natural sources. This study explores the use of plasma-activated water (PAW) as an elicitor in T. baccata suspension cell cultures to enhance the production of paclitaxel and its precursor, 10-Deacetylbaccatin III (10-DAB III). The effects of PAW on various factors, including fresh and dry weight, cell viability, and phenylalanine ammonia-lyase (PAL) activity were investigated. The PAW treatment was conducted at different concentrations (200, 300, and 400 μL), with a pH of 5.6 to 5.8, and was applied at different time points (0, 7, 14, and 21 days). The results revealed that 10-DAB III was increased (14.04 µg/g) significantly at a concentration of 400 μL of PAW on day 21. In contrast, the highest paclitaxel content (3.342 µg/g) was achieved in the control group on day 21. The PAW treatment reduced cell viability by 32.25% compared to day 0 (86.25%), and PAL activity increased initially before declining, but remained higher than in the control group. This study is the first to demonstrate the potential of PAW to enhance taxanes production in T. baccata cell cultures, warranting further investigation into the underlying mechanisms.