Browse Articles

Fermented pickles improve gut microbiota and immune profile in women in a community trial in rural Pakistan

RETRACTED: SPAG6 hypermethylation silences a novel tumor suppressor and inhibits renal cell carcinoma progression via PI3K/AKT/mTOR pathway

Background Renal cell carcinoma (RCC) ranks among the most prevalent malignancies of the genitourinary system, with a steadily rising incidence. Despite growing attention, the etiology and underlying mechanisms of RCC remain incompletely understood. Epigenetic modifications, particularly DNA methylation, have emerged as critical regulators in various malignancies, including RCC. Sperm-associated antigen 6 (SPAG6), initially identified in human testicular tissue and considered a marker for testicular tumors, has been associated with the pathophysiology of several malignancies. This study aimed to elucidate the role of aberrant SPAG6 methylation in RCC progression. Methods We first analyzed SPAG6 expression and methylation patterns in RCC and adjacent normal tissues using data from The Cancer Genome Atlas (TCGA) and the Epigenome-Wide Association Study (EWAS) databases. Clinical tissue specimens from Peking University First Hospital were then examined to explore the association between SPAG6 expression/methylation and the clinicopathological features of RCC patients. The correlation between SPAG6 expression and promoter methylation was further validated in RCC cell lines. Functional roles of SPAG6 in cell proliferation, invasion, cell cycle regulation, and apoptosis were investigated through in vitro cellular assays and in vivo xenograft models. Finally, transcriptome sequencing was performed to explore the molecular mechanisms by which SPAG6 affects RCC development. Results SPAG6 expression was markedly downregulated in RCC tissues compared to adjacent non-tumorous counterparts, because of promoter CpG hypermethylation. SPAG6 expression was associated with tumor stage in RCC patients. Functional assays demonstrated that SPAG6 suppresses RCC cell proliferation, invasion, and cell cycle progression, while promoting apoptosis. Mechanistically, SPAG6 inhibited RCC progression by negatively regulating the PI3K/AKT/mTOR signaling pathway. Conclusions SPAG6 functions as a tumor suppressor in RCC, with its silencing driven by promoter hypermethylation. Through modulation of the PI3K/AKT/mTOR pathway, SPAG6 plays a vital role in restraining RCC initiation and progression.

Digital PCR-Based characterization of a Zhonghuang 6106 soybean genomic DNA reference material for its food and feed detection

How social and economic policies have affected the genome of mezcal agaves: The contrasting stories of Bacanora and Espadín

Plant domestication in Mesoamerica gave the world crops of global significance, such as maize, beans, squashes, cocoa, and cotton. Additionally, it has introduced species of regional economic importance, which still display intermediate levels of domestication, including Agave, Opuntia, columnar cacti, Amaranthus, and various ornamental species. Agaves, in particular, hold immense cultural and economic significance in Mexico and play a crucial ecological role in wild plant communities. However, current agricultural practices have negatively impacted both wild populations of agave through overexploitation and habitat destruction, as well as cultivated plants by reducing available landraces and promoting the use of homogeneous plant material. Using genomic data (RADseq) and over 50,000 SNPs, we aimed to assess how local social and political decisions may have influenced genomic diversity and differentiation in intensively managed, clonally propagated Agave angustifolia (Espadín) in the state of Oaxaca, the cultivated A. angustifolia used to produce mezcal known as Bacanora in the state of Sonora, and their wild counterparts from both regions. We found evidence suggesting that Espadín recently arose through farmer selection of a clonal lineage with desirable mezcal production attributes (i.e., hybrid vigor), apparently from an unsampled cultivated variety, a cross between genetically distinct wild populations, or by hybridization between wild and cultivated plants. Espadín samples were represented by closely related heterozygous genotypes, with considerable genetic differentiation from wild plants. On the other hand, the genomic composition of cultivated Bacanora agave appears to have been influenced by a recently lifted ban (in 1992) on its cultivation and distillation, which, along with the relatively lower popularity of this beverage, allowed cultivated Bacanora agave to maintain the genetic diversity found in wild populations of the region. We found that social and political decisions may have important impacts on crop genomic diversity and differentiation.

Block segmentation in feature space for realtime object detection in high granularity images

Development and validation of a risk predication model for nutritional risk based on water swallowing test in post-stroke with dysphagia

This study aims to develop and validate a prediction model for nutritional risk in Post-Stroke Dysphagia (PSD). This study retrospectively analyzed data on stroke patients with dysphagia from January 2022 to December 2023. A stepwise logistic regression model was used to construct the prediction model, and internal validation was performed using the bootstrap resampling (1000 iterations), the nomogram was developed for clinical applications. The final prediction model incorporated the following factors: age, marital status, mechanical ventilation, dysphagia treatment, fasting duration, atrial fibrillation, oral care frequency, serum potassium levels, and National Institute of Health stroke scale (NIHSS) score. The model demonstrated strong discriminatory power, with area under the ROC curve (AUC) values of 0.916 in the development set and 0.878 in the validation set. Calibration curves and the Hosmer-Lemeshow (H-L) test further confirmed the strong correlation between predicted and observed nutritional risks.The prediction model developed in this study exhibits high accuracy, consistency, and practical applicability, making it a valuable tool for predicting nutritional risk in PSD patients. The code library: https://osf.io/p3hjm.

Investigation of physical education classroom teaching using AHP with IV-CIFS-based aggregation operators

Expression of Concern: Economic policy uncertainty and stock market in G7 Countries: A panel threshold effect perspective

Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces

A lightning cluster identification method considering multi-scale spatiotemporal neighborhood relationships

Rapid and accurate identification and tracking of lightning clusters from massive lightning detection data are crucial for real-time thunderstorm nowcasting and climatological analyses of thunderstorm activity. Although density-based clustering algorithms can identify clusters of arbitrary shapes at fine scales, their performance is often hindered by large data volumes and significant variations in lightning density. To address these challenges, we propose a multi-scale spatiotemporal lightning clustering framework, termed CC3D-CSCAP. It consists of two main components. First, the 3-D connected component algorithm (CC3D) performs coarse-scale segmentation by dividing the lightning dataset into spatiotemporally disconnected subsets using 26-connectivity. Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of lightning clusters. Since the lightning subset may still contain multiple thunderstorms with varying lightning densities, CSCAP adaptively determines clustering parameters based on the statistical characteristics (time difference and spatial distance) of subset. Compared with fixed-parameter methods, CC3D-CSCAP identifies more clusters (771,033) while retaining a high percentage of usable lightning strokes (98.988%). The clustering results align well with the theoretical criteria for optimal clustering and are promising for global applications in lightning data analysis, nowcasting, and climatological studies of convective systems.

Comparative study of indoor positioning datasets focusing on localization accuracy success rate and floor classification

Community wellbeing and lived experiences during urban redevelopment in New Zealand: Te Hotonga Hapori – Connecting communities qualitative study protocol

Cities around the world are growing rapidly, and the demand for housing is subsequently increasing. Many governments have initiated large-scale redevelopment projects to address the problem but planning and implementation can significantly impact the wellbeing of residents. Evidence has shown that people-centred urban planning and redevelopment, including walkable areas, natural environments, and appealing public spaces, can enhance physical, mental, and social wellbeing. The impact of these designs on wellbeing is complex, and without community involvement, there is a risk that redevelopment may not meet the wellbeing needs of the residents. Community Wellbeing and Lived Experiences study is part of Te Hotonga Hapori – Connecting Communities research programme which aims to provide the evidence to improve liveability and wellbeing in neighbourhoods that are undergoing redevelopment. It seeks to empower communities by involving them directly in the redevelopment process. Guided by the culturally informed Te Hotonga Hapori Engagement Framework it comprises 5 components: i) Active relationship building; ii) Historical/cultural realities; iii) Community aspirations; iv) Building bridges; and v) Activation of neighbourhood urban and natural environments. Data collection will be undertaken using the Community Science Aotearoa process that is contextually specific to communities in New Zealand and adapted from the Our Voice Citizen Science Research method. Residents using Te Hotonga Hapori app will collect photos and narratives and share information about their lived experiences during the redevelopment using a six-step process of Engage, Discover, Discuss, Advocate, Change and Re-engage. Descriptive statistics will be produced in relation to the number and typology of photos and narratives collected. Qualitative thematic analysis will be used for narrative data obtained from downloaded app data, and community and group sessions. Braun and Clarke’s Thematic Analysis Framework will guide researchers through an iterative process and themes will be developed using the Attride-Stirling Thematic Networks Analytic Tool.

Goal-setting improves movement accuracy during unsupervised training in stroke patients

Abstract This pilot study investigated whether goal-setting leads to good behavioral performance and high motivation during an unsupervised reaching task in patients with subacute stroke. Using a single-blind, stratified randomized controlled design, 50 patients with unilateral upper-extremity motor paralysis were allocated to either a goal-setting group (n = 25) or a control group (n = 25) without goal setting. Both groups performed an unsupervised reaching task during a 15-minute session. The primary outcome measures included the total time spent in training and the success rate of reaching tasks. The secondary outcome was self-reported motivation assessed using a visual analog scale. Although there were no significant differences in the median [interquartile range] training time between goal-setting and control groups (636 [500–709] sec vs. 633 [494–715] sec, p = 0.771), the goal-setting group demonstrated significantly higher success rates than the control group (92.6 [82.9–96.0]% vs. 80.7 [65.4–92.6]%, p = 0.019). Self-reported motivation scores showed no significant differences between the groups (76 [60–95] vs. 84 [49–97], p = 0.994). These findings suggest that goal setting can help patients accurately perform reaching movements during unsupervised training, potentially enhancing the quality of motor performance in self-rehabilitation.

Is live birth rate decline from frozen cycles performed during Chinese new year holiday season: A single center retrospective cohort

Purpose This study investigates the impact of the Chinese New Year (CNY) holiday on frozen embryo transfer (FET) outcomes in assisted reproductive technology (ART), particularly in vitro fertilization (IVF). Previous research has highlighted the negative effect of the CNY holiday on fresh embryo transfer outcomes, prompting an exploration of whether FET outcomes are similarly affected. Methods A retrospective cohort study was conducted at the Second Hospital of Hebei Medical University, analyzing FET cycles performed from January 2012 to December 2022. A total of 4456 women were included, with 305 undergoing FET during the CNY holiday season and 4151 during non-holiday periods. The primary outcome measure was the live birth rate. Multivariate logistic regression and propensity score matching (PSM) were applied to assess the differences between the CNY and non-CNY (N-CNY) groups. Results Multivariate logistic analysis revealed no significant difference in live birth outcomes between the CNY and N-CNY groups (OR = 1.11, 95% CI 0.86 to 1.42). PSM analysis further confirmed that live birth rates were similar between the groups, with 39.0% in the N-CNY group and 44.2% in the CNY group (p = 0.208). These findings suggest that FET outcomes are not adversely affected by the CNY holiday. Conclusion FET outcomes during the CNY holiday season remain resilient, contrasting with findings from fresh embryo transfer studies. This may be attributed to the shorter duration and less intensive preparation of FET cycles, reducing the influence of sociocultural events and psychological stress. Further multicenter studies are needed to validate these findings and explore the impact of other significant sociocultural events on ART outcomes.

Estimation of reference curves for brain atrophy and analysis of robustness to machine effects

Abstract Neurodegenerative diseases like Alzheimer’s are difficult to diagnose due to brain complexity and imaging variability. However, volumetric analysis tools, using reference curves, help detect abnormal brain atrophy and support diagnosis and monitoring. This study evaluates the robustness of three segmentation algorithms, AssemblyNet, FastSurfer and FreeSurfer, in constructing brain volume reference curves and detecting hippocampal atrophy. Using data from 3,730 cognitively normal subjects, we built reference curves and assessed robustness to magnetic field strength (1.5T vs. 3T) using four error metrics (sMAPE, sMSPE, wMAPE, sMdAPE) with bootstrap validation. We evaluated classification performance using hippocampal atrophy rates and HAVAs scores (Hippocampal-Amygdalo-Ventricular Atrophy scores). AssemblyNet shows the lowest errors across all robustness metrics. In contrast, FastSurfer and FreeSurfer exhibit greater deviations, indicating higher sensitivity to field strength variability. AssemblyNet provides consistent hippocampal atrophy rates across all reference models, despite slightly lower sensitivity, while FastSurfer and FreeSurfer display greater variability. Specificity ranges from 0.87 to 0.91 for AssemblyNet, compared to 0.76-0.93 for FastSurfer and 0.86-0.93 for FreeSurfer. Using the HAVAs score, all methods detect high atrophy rates in Alzheimer’s patients. FastSurfer achieves the highest sensitivity (0.98), while AssemblyNet reaches the best specificity (0.95) and the highest balanced accuracy (0.91). This study underscores the importance of algorithm choice for reliable brain volumetric analysis in heterogeneous imaging environments. Among the methods tested, AssemblyNet stands out as both sensitive to Alzheimer’s-related atrophy and robust to acquisition variability, making it a strong candidate when analyzing hippocampal volumes in large, multi-site datasets.

Within-individual variation of HbA1c measurements in primary care: A retrospective cohort study

Background HbA1c is a marker for diabetes mellitus that reflects average glucose concentrations over the previous eight to twelve weeks. It is used to aid in the diagnosis and management of diabetes. Variation in within-individual measured HbA1c may affect its clinical utility but estimates of this are based on limited data that is often not generalisable to real-world settings. Methods A retrospective cohort study was performed using data on HbA1c results and sociodemographic, lifestyle and comorbidity covariates extracted from the IQVIA Medical Research Database (IMRD) database using the DEXTER tool. A minimum of four measurements in the same individual was the only inclusion criterion. Within-individual measured variation was calculated as a coefficient of variation (CV) using a linear regression random effects model for the whole population and various subgroups. Results 587,023 participants were included in this study, making it the largest study of variation of HbA1c to date. The overall measured within-individual coefficient of variation (CVT) was 0.20 (95%CI 0.20 to 0.20). This is around three times higher than reported in a previous systematic review. CVT increased with patient mean HbA1c level. Strengths and limitations The large number of participants and the real-world nature of the results are important strengths of this study. Weaknesses included the problem of accounting for confounding by indication. Conclusions Estimated within-individual variation in this analysis of real-world data is very high and is higher than previously reported. Variation increases with patient mean HbA1c, that is with more severe disease status. This has important implications for the diagnosis, monitoring and clinical decision-making for diabetes.

Landing control of a magnetically actuated robotic catheter on beating heart surface

Impact of functional and technical quality on patient satisfaction in prosthetic and orthotic care: A cross-sectional study

Patient satisfaction with prosthetic and orthotic services is shaped by both functional quality, such as communication and service accessibility, and technical quality, including device durability and usability. This study examined how these dimensions are associated with patient satisfaction in diverse healthcare settings in Jordan, a low- to middle-income country. A cross-sectional survey of 307 users from government, military, private, and non-governmental (NGO) providers was conducted using a 28-item questionnaire developed from existing literature. Confirmatory factor analysis and structural equation modeling confirmed the scale’s validity and tested associations between variables. Both functional quality (β = 0.521, p < 0.001) and technical quality (β = 0.382, p < 0.001) were significantly associated with satisfaction, with functional quality showing a stronger influence. NGO users reported the highest satisfaction scores, and the model explained 41.7% of the variance in satisfaction. ANOVA results revealed statistically significant differences among provider types. These findings highlight the value of patient-centered care and suggest that enhancing communication, accessibility, and service responsiveness may improve satisfaction, particularly in public and private sectors.

Population frequency of Predicted pathogenic MisMatch Repair (MMR) gene variants in Lynch syndrome from bioinformatic analyses of the general population

DNTB: Dual-branch network model based on transformer and Bi-LSTM for energy consumption prediction in building chiller systems

Accurate prediction of chiller energy consumption is crucial for reducing building energy consumption. In this study, an innovative dual-branch network architecture DNTB (A Dual-Branch Network Model Based on Transformer and Bi-LSTM for Energy Consumption Prediction in Building Chiller Systems) was proposed to address the problems of insufficient long-term dependency modeling and noise sensitivity in current prediction models. The research goal is to develop a prediction model that can simultaneously process temporal features and global dependencies. The basic principle is to utilize the complementary characteristics of Transformer and Bi-LSTM. Transformer is sensitive to data noise and Bi-LSTM is weak in capturing long-term sequence information. It can better capture the temporal information of chiller energy consumption data and well model the relationship between variables such as chilled water, building load, chiller temperature, humidity, dew point and chiller energy consumption. In order to prove the effectiveness and generalization ability of the model, experiments were carried out on long-term and short-term tasks of chiller energy consumption prediction. The long-term prediction results had MSE (mean absolute error) of 0.0051, RMSE (mean square error) of 0.0605, and R2 (coefficient of determination) of 0.8031. The short-term prediction results had MSE of 0.0080, RMSE of 0.0738, and R2 of 0.6717. The experimental results indicate that DNTB performs excellently in both long-term and short-term chiller energy consumption prediction, making it a robust framework for chiller energy consumption prediction. The introduction of DNTB enriches the diversity of empirical model algorithms.