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Defining informal caregivers by their characteristics safety roles and training needs in Europe
Deep crypt secretory cells shape region-specific mucin glycosylation patterns in the mouse colon
The colonic mucin layer, comprising highly glycosylated mucin proteins, is crucial for maintaining colonic health. Its region-specific glycosylation patterns are indispensable for adapting to distinct physiological and microbial environments along the colon, thus ensuring appropriate mucin layer function. However, the mechanisms underlying this region-specific glycosylation remain unknown. Here, using fluorescence-based immunohistological analyses of the colon from experimental mice, we demonstrated that along with contribution of goblet cells, as conventionally believed, mucin glycosylation involves deep crypt secretory (DCS) cells, a specialized mucin-producing cell population in the colon. Based on cKit/CD117 as a DCS cell marker, DCS and goblet cells are inversely distributed along the mouse colon: DCS cells predominate proximally, constituting nearly 70% of mucin-producing cells, whereas goblet cells are more abundant distally, indicating a dynamic shift in the predominant mucin-producing cell population along the colon. Immunofluorescence staining revealed that DCS cells produce distinctive mucin-glycans, including those with the Core3-glycan motif that exhibit region-specific distributions in the mucin layer. We found that the gradient distribution of DCS cells predominantly shapes their region-specific distribution, whereas the inverse distribution of goblet cells corresponds to the distal distribution of sulfated and sialylated glycans. Furthermore, the in situ Proximity Ligation Assay for specifically detecting Muc2 with distinct glycosylation, revealed that DCS and goblet cells produce different types of α1,2-fucosylated glycans on Muc2, indicating that the shift in the predominant mucin-producing cells drives region-specific α1,2-fucosylation on Muc2 across colonic regions. Although DCS cells are implicated in supporting the stem cell niche, their involvement in mucin production was unclear. We highlight the critical role of DCS cells in establishing regional glycosylation patterns. Our findings provide new insights into the cellular basis of mucin glycosylation, as well as their potential impact on colonic health and disease susceptibility in specific colonic regions.
Serotonin activates dermal papilla cells and promotes hair growth
LINE-1 transposition into murine Thyroglobulin results in congenital thyroid dysplasia
A spontaneous mutation in the wild type C57BL/6NTac mouse was discovered that is associated with early-onset histopathologic sequalae typical of thyroid dysplasia. The spontaneous mutation resulted from insertion of a L1 long interspersed nuclear element (LINE-1) into an intron within the Thyroglobulin ( Tg ) gene. The mouse genome contains a significant amount of retrotransposon DNA, and these mobile genetic elements routinely change genomic location through retrotransposition, including in germ cells. Analysis of the thyroid transcriptome suggested that the presence of the LINE-1 interferes with the Tg gene splicing, resulting in exclusion of exon 26 from most Tg transcripts in animals homozygous (HOM) for the insertion. The LINE-1 insertion allele of the Tg gene has been designated Tg tdys-Tac . The resulting phenotype is inherited in an autosomal dominant manner with affected mice exhibiting thyroid follicular cell dysplasia that progresses to thyroid adenoma by 9 months of age with complete penetrance in homozygotes. Serum thyroid hormone measurements revealed a decrease in triiodothyronine (T3) levels in homozygotes at 12 months of age, as well as a decrease in tetraiodothyronine (T4) levels at 6–9 months and at 12 months of age in both heterozygotes and homozygotes. In addition, serum Thyroid Stimulating Hormone (TSH) level was strongly increased in homozygotes at 6–8 months of age, consistent with hypothyroidism. Computational molecular modeling showed that omission of the 64 amino acids from the TG protein arm domain, which is the consequence of exon 26-skipping in the Tg transcript, results in decreased local stability. This result in combination with the observed up-regulation in unfolded protein response (UPR) pathways in the thyroids of affected animals, identifies the arm domain of TG as important for its proper cellular distribution. This report describes a spontaneous retrotransposon insertion causatively linked to dysregulated physiological phenotypes in a widely used inbred mouse strain.
Gross-Pitaevskii systems of fractional order with respect to multicomponent solitary wave dynamics
HPV vaccine uptake among adolescent girls in Nigeria: The complex role of caregivers’ education
Introduction Cervical cancer remains a leading cause of cancer-related deaths among women in low- and middle-income countries (LMICs), with sub-Saharan Africa (SSA) bearing a disproportionate burden of the disease. Human papillomavirus (HPV) vaccination offers a critical intervention, yet uptake remains suboptimal due to vaccine hesitancy, misinformation, and socio-economic disparities. This study examines factors associated with HPV vaccine uptake among adolescent girls whose caregivers use social media. Methods We conducted a cross-sectional survey in October and November 2024 among 4,830 caregivers of adolescent girls 9–17 in Abuja, Nasarawa, and Adamawa states. Participants were recruited via advertisements on Facebook and Instagram. Data on adolescents’ HPV vaccination was collected from caregivers. Caregiver also provided data on their own education, motivation, ability, and exposure to HPV vaccine messaging. Multivariate logistic regression was used to identify predictors of vaccine uptake, adjusting for socio-demographic factors, motivation, and ability. Results The HPV vaccination rate among adolescent girls 9–17 was 53.9%. Caregivers with no formal education had higher exposure to HPV campaign messaging than caregivers with Higher National Diploma (HND) or Bachelor’s (BSc) education (95.3% vs 53.8%, p < 0.001). The least educated caregivers were also more likely to report a three times higher odds ratio of HPV vaccination compared to caregivers with Higher National Diploma (adjusted odds ratio [aOR] = 3.01, 95% CI: 1.52–5.93). Exposure to HPV vaccine messaging was associated with a seven times higher odds ratio of HPV vaccine uptake (aOR = 6.87, 95% CI: 6.20–7.61). Motivation and ability were positively associated with HPV vaccination. Regional differences were observed, with Nasarawa demonstrating higher a vaccination rate than Abuja and Adamawa. Conclusion Exposure to HPV vaccine messages is higher among less educated compared to more educated caregivers. Moreover, the impact of advertising exposure on vaccine uptake is stronger among less educated caregivers. Educational disparities in campaign exposure and campaign effects highlight the need for strategies to increase campaign reach to more educated caregivers and to ensure that HPV messages resonate with them. Our findings suggest that existing campaigns may need to be restructured to more effectively reach educated and skeptical audiences.
Evaluating third generation cervical Cancer intracavitary interstitial brachytherapy applicator efficacy and safety
Neurophysiological outcomes of combined transcranial and peripheral electromagnetic stimulation on DOMS among young athletes: A randomized controlled trial
This study investigates the potential benefits of a combined electromagnetic stimulation therapy, involving both transcranial and peripheral stimulation (paired-associative electromagnetic stimulation), to address Delayed Onset Muscle Soreness (DOMS). Forty-eight young athletes participated in this randomized controlled trial and were allocated to the control group (n = 12), the peripheral group (n = 13), the transcranial group (n = 11), and the combined group (n = 12). Surface electromyography (EMG) during leg extension and peak force were used to assess the response of the peripheral nerves. Additionally, force dynamometry and the Counter Movement Jump (CMJ) test were employed to evaluate the progression of lower limb sports performance over the study period. All assessments were performed before and after the eccentric exercise session that induced DOMS, as well as at 24-, 48-, and 72-hours post-exercise. The combined group exhibited significantly greater muscle activation in both electromyographic recordings compared to the other groups (p < 0.001), with large effect sizes for EMG peak in vastus medialis (η²p = 0.786), vastus lateralis (η²p = 0.821), and rectus femoris (η²p = 0.816). Moreover, the combined group demonstrated a marked improvement in both force dynamometry (η²p = 0.593) and CMJ performance (η²p = 0.520), with significant differences observed compared to the other groups (p < 0.001). In conclusion, paired-associative electromagnetic stimulation shows promise in enhancing muscle activity and improving lower limb performance by facilitating recovery from DOMS in young athletes. The study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623000677606) on June 23 rd , 2024 ( https://anzctr.org.au/ ).
Pulmonary function and motoric cognitive risk syndrome in older adults
Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks
Purpose Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As shown for recommender systems and molecular property predictions, Graph Neural Networks (GNNs) may represent a powerful alternative to exploit the inherently graph-based properties of clinical data. The main goal of this study is to evaluate when GNNs represent a valuable alternative for analyzing large clinical data from the clinical routine on the example of Complete Blood Count Data. Methods In this study, we evaluated the performance and time consumption of several GNNs (e.g., Graph Attention Networks) on similarity graphs compared to simpler, state-of-the-art machine learning algorithms (e.g., XGBoost) on the classification of sepsis from blood count data as well as the importance and slope of each feature for the final classification. Additionally, we connected complete blood count samples of the same patient based on their measured time (patient-centric graphs) to incorporate time series information in the GNNs. As our main evaluation metric, we used the Area Under Receiver Operating Curve (AUROC) to have a threshold independent metric that can handle class imbalance. Results and Conclusion Standard GNNs on evaluated similarity-graphs achieved an Area Under Receiver Operating Curve (AUROC) of up to 0.8747 comparable to the performance of ensemble-based machine learning algorithms and a neural network. However, our integration of time series information using patient-centric graphs with GNNs achieved a superior AUROC of up to 0.9565. Finally, we discovered that feature slope and importance highly differ between trained algorithms (e.g., XGBoost and GNN) on the same data basis.
A transformer-based architecture for collaborative filtering modeling in personalized recommender systems
Cross-cultural adaptation and validation of The Resilience Scale for Kidney Transplantation (RS-KTPL) in a Chinese population
Objective This study aimed to translate and validate The Resilience Scale for Kidney Transplantation (RS-KTPL) into Chinese and assess its reliability and validity among kidney transplant patients in China. Methods With authorization from the original authors, the RS-KTPL was translated following Brislin’s translation model, including forward translation, back translation, author review, cross-cultural adaptation, and a pilot study, resulting in a Chinese version of the RS-KTPL. A total of 358 kidney transplant recipients were recruited through convenience sampling and completed the questionnaire. Statistical analyses included item analysis, content validity, structural validity, convergent validity, discriminant validity, and reliability. Results Item analysis led to the removal of certain items that did not meet the criteria, resulting in a final version of the scale with 22 items across four dimensions. For content validity, the item-level content validity index (I-CVI) ranged from 0.83 to 1.000, and the scale-level content validity index (S-CVI/Ave) was 0.91, indicating good content fit. Structural validity was confirmed through exploratory and confirmatory factor analyses, supporting a four-factor structure with a cumulative variance contribution rate of 64.913% and all factor loadings exceeding 0.5. Convergent and discriminant validity analyses showed that the composite reliability (CR) values ranged from 0.741 to 0.938, and the average variance extracted (AVE) values ranged from 0.5 to 0.704, with the square root of AVE being higher than the inter-factor correlation coefficients, indicating good internal consistency and discriminating ability. Reliability testing showed a Cronbach’s α coefficient of 0.944 for the overall scale, with subscale Cronbach’s α coefficients all above 0.696, and a split-half reliability of 0.891, demonstrating high internal consistency and stability of the scale. Conclusion The Chinese version of the RS-KTPL exhibits good reliability and validity among kidney transplant patients in China and can be effectively used to assess psychological resilience in this population.
Comparative analysis of reinforcement learning and artificial neural networks for inverter control in improving the performance of grid-connected photovoltaic systems
Abstract This research aims to explore the potential applications of artificial intelligence (AI) methods, such as reinforcement learning (RL) and artificial neural networks (ANN), in controlling inverter systems and enhancing the performance of photovoltaic (PV) systems. PV systems are essential for producing sustainable energy, as they improve the reliability and efficiency of renewable power resources by utilizing AI to control inverters. This study examines the application of AI techniques to manage PV systems, given the increasing importance of energy generation through PV systems on a global scale. The goal of the project is to investigate the potential applications of RL algorithms for achieving maximum power point tracking (MPPT) and managing PV system maintenance and operation. According to the results, control of the inverter by RL yields better results than the ANN controller in all cases. Globally, increasing the use of PV systems for energy generation is a top goal to satisfy rising energy demands sustainably. By improving efficiency and dependability, AI control of PV systems helps to meet this challenge and further efforts in environmental sustainability and energy security. In terms of efficiency, reliability, and overall system performance, the research findings demonstrate that RL-based control of inverters outperforms ANN controllers. This comparison highlights how well RL works to control PV systems adaptively and efficiently in various environmental conditions. Total Harmonic Distortion (THD) for both current and voltage is compared and evaluated under ramp and random conditions. The results show that by consistently achieving reduced THD values, the RL controller outperforms the ANN controller in both dynamic and uncertain scenarios. This study reveals that RL exhibits superior adaptability and achieves lower THD compared to ANN, particularly under varying operational conditions. This comparative analysis fills a significant research gap, as comprehensive evaluations of this nature have not been adequately addressed in previous works. These results highlight how RL approaches may increase the dependability and efficiency of PV systems, advancing sustainable energy technology.
Diversity of Aβ aggregates produced in a gut-based Drosophila model of Alzheimer’s disease
Selection of stable reference genes for accurate reverse-transcription quantitative PCR in cotton-herbivore studies using virus-induced gene silencing
Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network
Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model—Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)—to enhance bank risk identification within this context. The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. Experiments were conducted on three public datasets: Bank Marketing, Lending Club, and German Credit. Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). These findings demonstrate the model’s effectiveness and practical value in dynamic international trade scenarios, offering a reliable approach for enhanced bank credit risk evaluation.
KIN17 modulates the WNT/β-catenin pathway and epithelial mesenchymal transition in non-small cell lung cancer
Onset of extensive human fire use 50,000 y ago
Fire is a pivotal aspect of human involvement in the carbon cycle. However, the precise timing of the large-scale human fire use remains uncertain. Here, we report a pyrogenic carbon record of East Asian fire history over the past 300,000 y from the East China Sea. This record suggests a rapid increase in fire activity since approximately 50,000 y ago, indicating a decoupling from the monsoon climate, and this pattern is consistent with fire histories in Europe, Southeast Asia, and Papua New Guinea-Australia regions. By integrating extensive archaeological data, we propose that the intensified global expansion of modern human and population growth, coupled with the rising demand for fire use during cold glacial periods, resulted in a significant increase in fire utilization from 50,000 y onward. This suggests that a measurable human imprint on the carbon cycle via fire likely predates the Last Glacial Maximum.
Diagnostic accuracy of self-reported food consumption and shaking chills in predicting bacteremia in outpatients: A prospective, multicenter observational study
Bacteremia, a critical condition that can lead to sepsis, is often diagnosed using blood cultures, which may yield false positives, leading to unnecessary treatments. Although clinical indicators, such as shaking chills and food consumption, have been identified as predictors of bacteremia, their diagnostic accuracy in outpatients, particularly when considering the timing of blood collection, remains unclear. This study aimed to assess the diagnostic accuracy of self-reported food consumption and shaking chills in detecting bacteremia, focusing on the time interval between the last meal and blood culture collection. This prospective, multicenter, observational study included outpatients aged > 16 years who could eat orally and underwent blood cultures in the emergency or general medicine department from April 2019 to March 2021. Food consumption before blood culture was self-reported using a medical questionnaire and categorized as “normal” (≥80%) or “poor” (<80%). The presence of chills was also assessed. Among 534 patients (mean age 68.3 ± 21.9 years, 51.3% men), 68 had bacteremia. The absence of poor food consumption (i.e., normal food consumption) had a negative predictive value of 91.2% (95% confidence interval, 88.8–93.6) and a negative likelihood ratio of 0.66 (0.23–1.94). Excluding the blood cultures collected between 10 pm and 8 am, these values increased to 96.2% (94.5–97.8) and 0.32 (0.12–0.89), respectively. Shaking chills had a positive likelihood ratio of 3.74 (2.75–4.73), increasing to 4.21 (3.22–5.19) after the same exclusion. Self-reported shaking chills were good positive predictors of bacteremia in outpatients, whereas self-reported normal food consumption, when accounting for the time between meals, ruled out bacteremia. These findings could help improve the early diagnosis and management of bacteremia, particularly in outpatient settings, and may contribute to the development of self-report tools for clinical decision-making.