Recently, observational studies have reported an association between renal function and sudden sensorineural hearing loss (SSNHL), but the causal relationship remains unclear. To begin with, we filtered single nucleotide polymorphisms from genome-wide association studies at the summary level by implementing quality control measures in genome-wide association studies databases. Afterwards, we employed a two-sample Mendelian randomization (MR) analysis to indirectly evaluate the causal effect of renal function indicators on the risk of SSNHL. Finally, a multivariate MR approach was conducted to account for potential interactions among the positive findings. MR analysis was performed using several methods, including inverse variance weighting (IVW), maximum likelihood, weighted median, MR-Egger regression, and penalized weighted median. In addition, this study included individuals who underwent routine medical examinations at Dongzhimen Hospital, Beijing University of Chinese Medicine, from September 2004 to September 2024, and employed propensity score methodology to assess the potential causal relationship between renal function and SSNHL. In the IVW method, a significant causal relationship was found between urinary albumin excretion (UAER) and cystatin-C (Cys-C) and the risk of SSNHL (via IVW random effects: UAER [odds ratios [ORs] 0.239, 95% confidence intervals [CI] 0.071–0.813, P-value .022]; via IVW-fixed effects: UAER [OR 0.239, 95% CI 0.082–0.698, P-value .009]; Cys-C [OR 0.824, 95% CI 0.712–0.955, P-value .010]). These findings remained consistent in multivariate MR analysis, which accounted for mutual adjustments. (via IVW: UAER [OR 0.343, 95% CI 0.119–0.991, P value .048]; Cys-C [OR 0.808, 95% CI 0.697–0.936, P value .0005]). Additionally, our case–control study revealed a significant trend towards elevated Cys-C and reduced serum uric acid in patients with SSNHL. There appears to be a causal relationship between renal function and SSNHL, particularly the levels of UAER, Cys-C and serum uric acid. These findings imply that renal function should be specifically focused on in the prevention and treatment of SSNHL.

ABSTRACTIn this study, some safety and quality parameters such as biopreservatives, heavy metals, lipid quality indices, and levels of aflatoxin M1 of imported cheeses in the Babylon Governorate of Iraq were examined by focusing on how processing methods and geographical origins. A total of 3 different cheese types and 27 samples from Iran, Saudi Arabia, and Turkey were analyzed by novel techniques such as High‐Performance Liquid Chromatography and Liquid Chromatography‐Mass Spectrometry. The findings showed the presence of nisin A and nisin Z presence in a few samples, while natamycin was detected in only one soft cheese in an amount below the regulatory limit. Analysis of fatty acid composition revealed high variability as well as high levels of SFA among samples from different countries. Detectable levels of cadmium, lead, copper, and arsenic were also found in some cheeses, in some cases exceeding the maximum permitted levels. Aflatoxin M1 contamination was also found in a handful of cheese samples, with several exceeding the maximum permitted levels. These findings revealed significant differences in the quality and safety of cheeses based on origin and processing methods, raising potential public health concerns related to the consumption of imported cheeses. The current study also strongly emphasized the need for improved quality control measures and regular monitoring of dairy products to ensure consumer safety and compliance with international health standards. In addition, it strongly recommends further research to better understand the long‐term effects of these pollutants on public health.

F1 populations are widely used in genetic mapping studies in agriculture, where known pedigrees enable rigorous quality control measures such as segregation distortion testing. However, conventional tests for segregation distortion are inadequate for polyploids, as they fail to account for double reduction, preferential pairing, and genotype uncertainty, leading to inflated type I error rates. Prior work developed a statistical framework to address these issues in tetraploids. Here, we extend these methods to higher even ploidy levels and introduce additional strategies to mitigate the influence of outliers. Through extensive simulations, we demonstrate that our tests maintain appropriate type I error control while retaining power to detect true segregation distortion. We further validate our approach using empirical data from a hexaploid mapping population. Our methods are implemented in the segtest R package, available on the Comprehensive R Archive Network (https://doi.org/10.32614/CRAN.package.segtest).

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants found in various matrices, including water and sea foam. This study utilized both quantitative and nontargeted analysis (NTA) to investigate PFAS contamination in sea foam, bulk water, and blank samples collected from the Atlantic coast near Wilmington, North Carolina. Previous work has suggested PFAS can accumulate at higher concentrations in sea foam than the bulk water. High-resolution mass spectrometry (HRMS) was employed to identify and quantify PFAS, with strict quality control measures applied to confirm results. Quantitative analysis detected up to 42 PFAS compounds, with particularly high concentrations in condensed sea foam samples, highlighting their role in accumulating PFAS. In one foam sample, PFOS, was observed at ca. 8,000,000 ng/L (ppt) whereas, PFO5DoA, a compound predominantly found in North Carolina, was detected at levels exceeding 20,000 ng/L in some samples. Notably, emerging and regionally relevant PFAS─including unsaturated perfluorooctanesulfonic acid (UPFOS), hexafluoropropylene oxide tetramer acid (HFPO-TeA), and 3-oxaperfluorononanesulfonic acid (F53)─were identified through nontargeted analysis. Comparisons between bulk water and sea foam samples revealed distinct PFAS concentration patterns, and significantly higher concentrations in the sea foam. The study underscores the need for continued monitoring of PFAS in coastal environments to assess potential human and ecological exposure risks.

Background Ensuring data quality in self-reported online surveys remains a critical challenge in digital health research, particularly when targeting healthcare professionals [1,2]. Self-reported data are susceptible to multiple biases, including careless responding, social desirability bias, and dropout-related attrition, all of which may compromise the validity of findings [3,4]. In web-based surveys where researcher oversight is limited, structured quality control measures are essential to detect low-quality responses, minimise sampling bias, and enhance data reliability [5]. Previous studies have demonstrated that inadequate quality checks can lead to inflated error rates, reduced statistical power, and misleading conclusions [6]. Objective This study presents a comprehensive methodological framework for optimising data quality in web-based medical surveys, applied to a national study on AI awareness among Italian physicians. Integrating pre-survey validation, real-time dashboards, response-time filtering, and post-hoc careless responding detection would address key challenges in digital research, while providing a replicable model for future studies. Methods We conducted a national web-based survey using a validated instrument (doi:10.1101/2025.04.11.25325592) via the LimeSurvey platform. The survey incorporated two main sections: (1) a core module assessing knowledge, attitudes and practices regarding AI in medicine; (2) clinical scenarios evaluating diagnostic agreement with AI-generated proposals. Multiple quality control strategies were implemented throughout the survey lifecycle. In terms of survey design and logic, the questionnaire employed an adaptive flow structure, whereby respondents were routed through clinical scenarios relevant to their medical speciality. To reduce the incidence of partial completions and missing data, key questions were marked as mandatory, and completion status was actively tracked. In the monitoring and recruitment phase, a real-time dashboard monitored participant distribution (gender/geographical areas/speciality); referral links were rotated to minimise snowball bias [7]. Time-based data quality checks excluded outliers (completion time <1st or >99th percentile) [8]. Completion time for the first section was analysed for all completers to assess correlations between response speed and quality indicators. Dropout patterns were analysed using Kaplan-Meier survival analysis and logistic regression, to identify systematic attrition predictors. Data quality assessments were performed on the outlier-cleaned dataset (n=587). Response quality was assessed using complementary careless responding indicators applied specifically to opinion scale items (Likert 1-5). Two detection methods were used: low response variance analysis, identifying respondents with insufficient variability (SD < 0.5), and excessive same-response detection, flagging participants using identical responses for >75% of items. Internal consistency analysis (Cronbach's α) evaluated scale reliability across different quality levels. Results A total of 736 accesses were recorded on the survey platform. As an initial inclusion criterion, only participants who indicated current registration with the Italian Medical Council were considered eligible: 79 (10.7%) were excluded, yielding a sample of 657 eligible participants (89.3%). Among eligible respondents, 597 completed the first section, yielding a dropout rate of 9.1% (n=60). A Kaplan-Meier survival analysis using total survey time revealed that most dropouts occurred early, with critical points at 45% after demographic, 51% after personal AI knowledge items, 71% after opinion items, and 100% before clinical scenarios. Logistic regression showed no significant predictors of completion (LR χ²(6)=3.46, p= 0.7497; pseudo-R²=0.014; AUC=0.60, 95%CI: 0.50–0.70). Completion time showed no correlation with response quality (Spearman's ρ = -0.019, p = 0.645). Following outlier removal, data quality assessment among 587 who completed the first section revealed two complementary patterns of careless responding: 8.52% (n=50) exhibited low response variance, while 32 (5.45%) demonstrated excessive same response patterns. Cross-classification analysis showed 23 participants (3.92%) flagged by both indicators, with 71.88% of excessive same responders also showing low variance. Overall, 50 participants (10.05%, 95% CI: 7.9%- 12.8%) exhibited careless responding detectable by at least one indicator. Internal consistency analysis showed robust scale reliability (Cronbach's α = 0.754) that remained stable across quality levels. Conclusion The integration of real-time monitoring, adaptive design, time-based validation, and systematic careless responding detection provides a robust methodological framework for web-based medical surveys, particularly for complex topics like AI adoption. Comprehensive data quality assessment revealed a 10.05% careless responding rate among completers, which aligns with the literature. The absence of correlation between completion time and response quality shows that careless responding could reflect attentional rather than temporal factors. Our findings suggest that both phenomena likely reflect situational or contextual factors rather than systematic participant characteristics or survey design flaws. This supports the validity and generalizability of the final dataset while providing a replicable quality control framework for future web-based medical research.

Prenatal screening for trisomy 21 (Down syndrome) using first- and second-trimester biochemistry and nuchal translucency: A technical standard of the American College of Medical Genetics and Genomics (ACMG).

Implementation of circulating tumor DNA (ctDNA) testing in precision oncology: A four-year experience from a tertiary cancer center in India.

Cardiac Magnetic Resonance Imaging in the German National Cohort (NAKO): Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality Control.

3D printing personalized medications in a hospital: Rapid and non-destructive dose verification of printed medicines enabled by miniaturised spectroscopy.

The primary aim of this study is to detect and quantify the presence of Sildenafil (SDF) and Tadalafil (TDF) in dietary supplements marketed as natural sexual enhancers in Bosnia and Herzegovina. Additionally, the study seeks to utilize these findings to inform relevant authorities, enabling further testing in reference laboratories and prompting the necessary actions to remove these adulterated products from the market. Using high-performance liquid chromatography with diode array detection (HPLC-DAD), 20 samples were analysed for the PDE-5 inhibitors. The analysis revealed that seven of the samples contained either SDF or TDF, with mean concentrations ± standard deviation (SD) ranging from 2,075.57 ± 0.47 µg/g to 33,808.857 ± 99.43 µg/g, and TDF concentrations ranging from 24.16 ± 0.11 µg/g to 3,994.66 ± 6.95 µg/g. These findings indicate a significant health risk posed by the adulteration of these products. The widespread presence of these active pharmaceutical ingredients (APIs) in products falsely labelled as natural underscores the urgent need for stringent regulatory oversight and enhanced quality control measures to protect consumer safety. This study adds to the growing body of evidence concerning the adulteration of dietary supplements and emphasizes the critical importance of regulatory compliance and monitoring in safeguarding public health.

The research focused on the design and fabrication of a set of sieve shakers, an essential equipment in mineral extractive industries for particle size analysis and material separation. The fabrication process involved the selection of appropriate materials, designing a robust frame, and integrating an efficient motor and eccentric mechanism to achieve the desired vibratory motion. The components of the machine are assembled in accordance with ASTM standard by ensuring the quality control measures are implemented. Key considerations were made that include durability, ease of operation, and minimal maintenance requirements. The resulting device was evaluated based on performance metrics such as sieving efficiency, noise levels, and energy consumption, with necessary adjustments made to achieve optimal performance. The efficiency of the machine was 49.75%, which was considered averagely suitable in laboratories andnot in industrial settings, to provide a reliable tool for quality control and research applications. The noise level for the sieve shaker was found to be 52 decibels, which falls under acceptable levels according to OSHA. The energy consumption of the sieve shaker was calculated based on the 60 W motor power used as 216 KJ per hour. It is equally observed that the sieve shaker is cost effective because it was tailored to specific need by totally eliminating the costs associated with unnecessary features when compared to commercial alternative. The findings reveal that necessary modifications are required to enhance the performance of the machine, and also optimize the energy use by incorporating a speed regulator or advanced control for future design.

ABSTRACTValidating high‐resolution weather and climate models is challenged by insufficient spatial and temporal resolution of meteorological observations, particularly for the precipitation in complex terrain. Traditional datasets, which rely on sparse official weather stations and gridded datasets, often lack the spatio‐temporal resolution needed for accurate localized studies. This study serves as a first step in investigating the potential of including Personal Weather Stations (PWSs) in the validation of high‐resolution regional climate models. We performed a quality control on PWS data, flagging approximately 13% and retaining around 450 stations in Western Norway. Compared to 124 official meteorological stations (MET stations), PWSs provided significantly improved spatial coverage, especially in densely populated areas, revealing spatial variability often missed by MET stations and traditional gridded datasets. We validated simulations from the Weather Research and Forecasting (WRF) regional climate model using the combined PWS and MET observational dataset for two cases: multiple frontal passages in November 2022 and a record‐breaking convective burst in August 2023, which were sparsely captured by official MET stations. Although biases existed in the WRF dataset, the incorporation of PWSs in the observational dataset revealed a more nuanced precipitation pattern and provided enhanced spatial validation opportunities. In conclusion, PWS networks significantly enhance observational coverage, aiding high‐resolution model validation and opportunities for improved local precipitation understanding. As the number of PWSs grows, refined quality control measures will further solidify their role in meteorological research and emergency preparedness, particularly for localized extreme weather events. This integration is vital for advancing climate science and improving community resilience to weather‐related challenges.

Comprehensive evaluation of Xuezhikang by four-dimensional fingerprint combined with antioxidant.

This review explores the pivotal role of preanalytical variables in bringing liquid biopsy approaches into the clinic for brain tumors. Preanalytical variables encompass a range of critical issues, from blood sample collection and handling to the impact of tumor heterogeneity and patient-specific factors. These variables introduce challenges such as false positives, false negatives, and variability in the analysis of tumor signals, which can hinder the diagnostic and prognostic utility of liquid biopsies. Understanding the nuances of preanalytical variables is essential for the successful implementation of liquid biopsy in clinical settings. This paper delves into strategies aimed at mitigating the influence of preanalytical variables by emphasizing the importance of standardized sample collection protocols, optimized sample processing and storage, quality control measures, and the integration of multiple liquid biopsy modalities.

Tomato (Lycopersicum esculentum) is an important vegetable cultivated and consumed worldwide due to its nutritional value and culinary versatility. However, despite processing and preservation, canned tomato products remain prone to microbial contamination, particularly by fungi, which can adversely affect food safety, shelf life, and consumer health.This study aimed to isolate and identify fungal species present in two commercial brands of canned tomatoes (Gino and Sonia) and to compare the types of fungi detected in each brand to evaluate their potential health and safety implications. Standard microbiological techniques were used for fungal isolation and identification. Samples from both brands were cultured on Potato Dextrose Agar (PDA) and incubated at 37°C for 48 hours. Fungal colonies were examined for their macroscopic and microscopic features, and identification was performed using established morphological criteria. Two fungal species were isolated and identified. Aspergillus flavus was observed in the Gino sample, characterized by greenish colonies with unbranched conidiophores, while Rhizopus stolonifer was identified in the Sonia sample, showing white colonies that turned grey with broad, non-septate hyphae. The detection of A. flavus, known for its production of aflatoxins, indicates potential health risks to consumers, while R. stolonifer contributes to product spoilage and quality degradation. The presence of A. flavus and R. stolonifer in canned tomatoes demonstrates that fungal contamination can persist despite industrial canning processes, possibly due to post-processing exposure or inadequate storage conditions. Such contamination compromises food quality and safety, underscoring the need for stringent quality control measures, effective sterilization, and proper storage practices. Ensuring the microbiological safety of canned tomato products aligns with sustainable food production goals and global efforts to reduce foodborne risks in line with the United Nations Sustainable Development Goals (SDG 2 and SDG 12).

The study provides an analysis of the organization and subsequent launch of a turnkey embryology laboratory. The necessity of the study is determined by the fact that assisted reproductive technologies (ART) are a principal element of modern reproductive healthcare, where effectiveness directly depends on the organization and quality of work of the embryology laboratory. The aim of the work is to systematize key stages in the creation of turnkey laboratory infrastructure and to develop a scientifically substantiated concept of its highly effective operation. The methodological basis comprised analysis and synthesis of current scientific publications, clinical recommendations of leading international societies (ESHRE, ASRM) and regulatory documents. The result is an integrative model that combines architectural and planning solutions with projected throughput capacity in terms of number of cycles and identifies critical parameters in the choice of technical equipment and consumables. Special attention is given to strict zoning of the laboratory space (wet, clean and sterile zones), organization of high-purity medical gas supply systems and a set of measures for air quality control, including monitoring of volatile organic compound (VOC) concentrations. The conclusion of the study is that achieving stable high indicators of clinical efficacy and safety requires exclusively a comprehensive, holistic approach in which all laboratory components — from engineering and technical design to validation protocols — are regarded as a single interconnected entity. The practical value of the work lies in providing substantiated recommendations for heads of medical centers, clinical embryologists, medical equipment engineers and specialists in healthcare facility design.

Accurate, up-to-date agricultural monitoring is essential for assessing food production, particularly in countries like Kenya, where recurring climate extremes, including floods and droughts, exacerbate food insecurity challenges. In regions dominated by smallholder farmers, a significant obstacle to effective agricultural monitoring is the limited availability of current, detailed crop-type maps. Creating crop-type maps requires extensive field data. However, the high costs associated with field data collection campaigns often make them impractical, resulting in significant data gaps in regions where crop production information is most needed. This paper presents our inaugural dataset comprising 4,925 validated crop-type data points from Kenya’s 2021 and 2022 long-rain seasons. Collaborating with institutional partners and an extensive citizen science network, we collected georeferenced images across Kenya using GoPro cameras. We developed and implemented a deep learning pipeline to process images into crop-type datasets. Our methodology incorporates rigorous quality control measures to ensure the integrity and reliability of the data. The resulting dataset represents a significant contribution to open science and a valuable resource for evidence-based agricultural decision-making.

The advancement of cell therapy and cellular agricultureunderscoresthe need for noninvasive, cost-effective methods for continuous monitoringof large-scale cell production. Bioreactors, designed to mimic physiologicalconditions to facilitate cell growth, require reliable quality controlmeasures. This study investigates the potential of ultrasound technologyto characterize cellular growth and decellularization in spinach scaffolds.Ultrasound images were acquired of fresh spinach leaves, decellularizedleaves, and leaves with cells seeded in defined cloning well regionson decellularized leaves. Machine learning tools were employed toassist in the identification and segmentation of images as part ofultrasound data analysis. Statistical analysis revealed significantdifferences in mean pixel intensity values between fresh and decellularizedleaves (p < 0.001) as well as between regionswith and without cells seeded on decellularized leaves (p = 0.001). These findings were confirmed by histological analysis.The results indicate that ultrasound imaging could serve as a viable,nondestructive tool for quality assessment of plant scaffolds in cellularagriculture applications.

Renal Cell Carcinoma (RCC) is a leading cause of cancer-related mortality worldwide, with Clear Cell Renal Cell Carcinoma (ccRCC) comprising 75% of cases. Surgical resection remains the cornerstone of treatment for localized RCC, but its asymptomatic progression and lack of reliable early biomarkers often result in advanced disease at diagnosis. Collagen VI alpha-2 chain (COL6A2), an extracellular matrix protein, has been implicated in tumor progression and metastasis. Despite its established roles in other malignancies, the specific contribution of COL6A2 to ccRCC pathogenesis is poorly understood. This study aims to systematically investigate COL6A2 expression in ccRCC, its prognostic value, and its potential impact on the tumor immune microenvironment, cancer stem cell characteristics, and drug response. The mRNA and protein expression data for ccRCC were sourced from TCGA, GEO, CPTAC, and ICPC. Single-cell and spatial transcriptomic data were processed using Seurat with quality control measures. Clinical correlations and survival analyses, including immune infiltration and COL6A2 expression, were assessed using Cox regression and Kaplan-Meier curves. Cancer stemness was evaluated using six stemness indices. Differential expression and pathway analyses (GO, KEGG, GSEA) were performed with DESeq2 and clusterProfiler. Drug sensitivity and immunotherapy response were predicted using GDSC, CTRP, and TIDE databases. Functional studies, including colony formation and invasion assays, as well as in vivo xenograft models, assessed the impact of COL6A2 on tumor progression and therapy response. COL6A2 expression was significantly upregulated in ccRCC compared to normal tissues. High COL6A2 expression correlated with poorer overall survival (OS), progression-free interval (PFI), and progression-free survival (PFS), establishing it as an independent prognostic factor for ccRCC. Additionally, COL6A2 expression was positively associated with immune-suppressive cell infiltration, suggesting an immunosuppressive tumor microenvironment. COL6A2 was also linked to enhanced stem cell-like properties, invasiveness, and metastatic potential. Pathway enrichment analyses revealed that COL6A2 may influence tumor progression by regulating the epithelial-mesenchymal transition (EMT) process and activating the PI3K-Akt signaling pathway. Notably, high COL6A2 expression correlated with enhanced responsiveness to sunitinib but resistance to immunotherapy, highlighting its dual role in therapy selection. This study identifies COL6A2 as a powerful prognostic biomarker and a driver of ccRCC progression through EMT and immune suppression. Targeting COL6A2 holds promise for improving immunotherapy efficacy and personalizing treatment strategies, offering new hope for ccRCC patients facing limited options.

This study investigates the implementation methods of the PERI formwork system for reinforced concrete column structures in the KAI CO-Living Gondangdia project, an 8-story residential building in Central Jakarta. Employing a qualitative case study approach, the research documents the assembly, installation, concrete placement, and removal processes of the PERI system during a three-month internship. Key aspects analyzed include material preparation, panel assembly, alignment techniques, quality control measures, and safety protocols. The PERI system, characterized by modular steel-framed panels and phenolic-coated plywood, facilitated rapid assembly (45-60 minutes per column), high dimensional accuracy (±3mm tolerance), and excellent concrete surface quality for 85% of columns. The system’s reusability (24 cycles) and reduced labor requirements (25% less than traditional methods) enhanced project efficiency. Standardized procedures, skilled labor, and rigorous quality control were critical to success, though specialized training was necessary. The findings highlight the PERI system’s advantages in speed, quality, and cost-effectiveness for repetitive structural elements, offering practical insights for optimizing formwork in modern construction. Recommendations include investing in crew training, detailed planning, and comprehensive quality protocols to maximize system benefits.