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Testing accuracy of a chemical contaminant requires use of a testing platform that conforms to validation criteria outlined in quality literature and standards. This study explores the application of commercial field data measured by qualified analysts using a United States Department of Agriculture - Federal Grain Inspection Service approved kit for measuring fumonisin in maize to augment method validation procedures. Analysts from seven grain testing facilities were qualified in official USDA sampling, sample preparation, and testing methodology using the Charm LF-FUMQ-WETS5. A duplicate sample was tested in the Office of the Texas State Chemist (OTSC) laboratory using UPLC-MS-MS. Data were subject to four statistical techniques using continuous and categorical methodology. This approach enabled researchers to explore if a single test or multiple comparisons were best suited to assess a field kit\'s fitness for purpose across facility, toxin level, and year. The study concluded that a paired t-test and correlation analysis provided a quick and meaningful evaluation of kit performance. The correct placement of samples within the correct bin (violative versus non-violative) aligns well with market forces and regulatory compliance. The results of this study also provide a useful tool to assess all field kits\' performance at the beginning of the harvest season and subsequent years. The combination of statistical techniques presented in this research is an important tool in assessing mycotoxin field test kits fitness for purpose and represents a key step in a continuous improvement-quality systems process meant to protect the feed and food supply.

BACKGROUND: Regulations designed to protect children participating in clinical research often restrict the availability of research data necessary for the development of age-specific therapies and drug dosing. Few data exist on how children experience participation in clinical research, and studies investigating young children undergoing an intensive medical treatment are lacking.
METHODS: Mixed methods with semi-structured interviews and DISCO-RC questionnaires were used to explore young children\'s and their parents\' experiences in clinical research participation during a kidney transplantation trajectory.
RESULTS: Nine children and their parents were interviewed. Children\'s median age at kidney transplantation was 4 years (IQR 4,7); age at interview was 7 years (IQR 6,9). Thematic content analysis of interviews revealed that most children were unaware of having participated in a study. Both children and their parents frequently were unaware whether procedures were standard care or research related. The additional burden attributed to study participation varied from not at all to heavy in combination with intensive medical treatment. Positive experiences included kind healthcare professionals, effective distraction techniques, educational aspects, contributing to science and extra check-ups. Most reported negative experiences were conflicting communication, spending much time in the hospital, missing school and suboptimal planning. Venous puncture was stressful for all children, whereas the discomfort of other procedures varied.
CONCLUSIONS: Pediatric clinical research design should focus on education and fun during research procedures, smart planning, consistent communication, close collaboration between clinical and research team and age appropriate distraction techniques.

The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment\'s effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.

OBJECTIVE: To measure dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) biomarker repeatability in patients with non-small cell lung cancer (NSCLC). To use these statistics to identify which individual target lesions show early biological response.
METHODS: A single-centre, prospective DCE-MRI study was performed between September 2015 and April 2017. Patients with NSCLC were scanned before standard-of-care radiotherapy to evaluate biomarker repeatability and two weeks into therapy to evaluate biological response. Volume transfer constant (Ktrans), extravascular extracellular space volume fraction (ve) and plasma volume fraction (vp) were measured at each timepoint along with tumour volume. Repeatability was assessed using a within-subject coefficient of variation (wCV) and repeatability coefficient (RC). Cohort treatment effects on biomarkers were estimated using mixed-effects models. RC limits of agreement revealed which individual target lesions changed beyond that expected with biomarker daily variation.
RESULTS: Fourteen patients (mean age, 67 years +/- 12, 8 men) had 22 evaluable lesions (12 primary tumours, 8 nodal metastases, 2 distant metastases). The wCV (in 8/14 patients) was between 9.16% to 17.02% for all biomarkers except for vp, which was 42.44%. Cohort-level changes were significant for Ktrans and ve (p < 0.001) and tumour volume (p = 0.002). Ktrans and tumour volume consistently showed the greatest number of individual lesions showing biological response. In distinction, no individual lesions had a real change in ve despite the cohort-level change.
CONCLUSIONS: Identifying individual early biological responders provided additional information to that derived from conventional cohort cohort-level statistics, helping to prioritise which parameters would be best taken forward into future studies.
CONCLUSIONS: Dynamic contrast-enhanced magnetic resonance imaging biomarkers Ktrans and tumour volume are repeatable and detect early treatment-induced changes at both cohort and individual lesion levels, supporting their use in further evaluation of radiotherapy and targeted therapeutics.
CONCLUSIONS: Few literature studies report quantitative imaging biomarker precision, by measuring repeatability or reproducibility. Several DCE-MRI biomarkers of lung cancer tumour microenvironment were highly repeatable. Repeatability coefficient measurements enabled lesion-specific evaluation of early biological response to therapy, improving conventional assessment.

BackgroundThe Vibrio genus comprises several bacterial species present in the Baltic Sea region (BSR), which are known to cause human infections.AimTo provide a comprehensive retrospective analysis of Vibrio-induced infections in the BSR from 1994 to 2021, focusing on the \'big four\' Vibrio species - V. alginolyticus, V. cholerae non-O1/O139, V. parahaemolyticus and V. vulnificus - in eight European countries (Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland and Sweden) bordering the Baltic Sea.MethodsOur analysis includes data on infections, Vibrio species distribution in coastal waters and environmental data received from national health agencies or extracted from scientific literature and online databases. A redundancy analysis was performed to determine the potential impact of several independent variables, such as sea surface temperature, salinity, the number of designated coastal beaches and year, on the Vibrio infection rate.ResultsFor BSR countries conducting surveillance, we observed an exponential increase in total Vibrio infections (n = 1,553) across the region over time. In Sweden and Germany, total numbers of Vibrio spp. and infections caused by V. alginolyticus and V. parahaemolyticus positively correlate with increasing sea surface temperature. Salinity emerged as a critical driver of Vibrio spp. distribution and abundance. Furthermore, our proposed statistical model reveals 12 to 20 unreported cases in Lithuania and Poland, respectively, countries with no surveillance.ConclusionsThere are discrepancies in Vibrio surveillance and monitoring among countries, emphasising the need for comprehensive monitoring programmes of these pathogens to protect human health, particularly in the context of climate change.

UNASSIGNED: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect multiple organs and body systems.
UNASSIGNED: To describe the sociodemographic, clinical, and biochemical characteristics of the Lupus-IMMS-Mexico (LUPUS-IMMex) patient cohort from a tertiary-level center.
UNASSIGNED: Observational descriptive study of 160 patients with diagnosis of SLE belonging to the aforementioned cohort. Various variables were analyzed at the time of diagnosis. For quantitative variables, normality tests were applied, followed by measures of central tendency and dispersion according to their distribution. For categorical variables, frequencies and percentages were calculated.
UNASSIGNED: 81.87% of the patients were female, with a median age at diagnosis of 28 years. 18.12% had a family history of SLE, and concurrently with SLE, 32.50% had hypertension, and 11.25% had antiphospholipid syndrome. The most common clinical manifestation was joint involvement (68.12%), renal (49.37%) and hematological (43.75%) manifestations.
UNASSIGNED: SLE affects millions globally. Lack of awareness leads to delayed diagnoses, suboptimal management, and diminished quality of life. After analyzing 160 patients with SLE, their clinical, socioeconomic, and therapeutic characteristics are largely like other cohorts, with differences attributable to ethnic and geographical influences. Informing patients about SLE and providing reliable resources are essential for self-care. Awareness promotes research, therapies, and enhances medical care and the lives of patients globally.
UNASSIGNED: el lupus eritematoso sistémico (LES) es una enfermedad autoinmunitaria crónica que puede afectar a múltiples órganos y sistemas del cuerpo.
UNASSIGNED: describir las características sociodemográficas, clínicas y bioquímicas de la cohorte de pacientes Lupus-IMMS-México (LUPUS-IMMex) de un hospital de tercer nivel.
UNASSIGNED: estudio descriptivo observacional de 160 pacientes con diagnóstico de LES de la cohorte mencionada. Se analizaron diversas variables al momento del diagnóstico. Para variables cuantitativas se aplicaron pruebas de normalidad y posteriormente medidas de tendencia central y dispersión de acuerdo con su distribución. Para variables categóricas se calcularon frecuencias y porcentajes.
UNASSIGNED: 81.87% de los pacientes fueron del sexo femenino, con mediana de edad al diagnóstico de 28 años. El 18.12% tenían antecedentes familiares de LES y concomitante al LES, hipertensión (32.50%) y síndromes antifosfolípidos (11.25%). Las afecciones clínicas más frecuentes fueron la articular (68.12%), la renal (49.37%) y la hematológica (43.75%).
UNASSIGNED: el LES afecta a millones de personas globalmente. La falta de conciencia lleva a diagnósticos tardíos, manejo deficiente y baja calidad de vida. Tras analizar 160 pacientes con LES, sus características clínicas, socioeconómicas y terapéuticas son mayormente similares a otras cohortes, con diferencias atribuibles a influencias étnicas y geográficas. Informar a los pacientes sobre el LES y brindar recursos confiables es esencial para el autocuidado. La sensibilización fomenta la investigación, las terapias y mejora la atención médica y la vida de pacientes a nivel global.

BACKGROUND: Lineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent.
RESULTS: We demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias.
METHODS: Source code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.

Regular health monitoring is crucial in laboratory animal facilities to determine the presence or absence of specific pathogens. One common approach to monitoring involves the use of sentinel animals, which are periodically exposed to biological material from the cages being monitored. At a certain point, some of these sentinel animals are tested for pathogens. This article discusses designing an effective sampling scheme to meet desired quality standards. It addresses questions such as the number of sentinel animals required, the frequency of sampling biological material, the selection of cages based on facility set-up, and the optimal frequency and quantity of sentinel animal tests. While existing design formulas are available for simple random sampling, no quantitative recommendation exists for using sentinel animals to the best of our knowledge. We propose a Monte Carlo simulation-based approach in this article to address this. Our algorithm has been implemented in a publicly accessible web page at http://nolan.cnb.csic.es/sentinelcagesmanager.

OBJECTIVE: The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.
METHODS: 113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method\'s accuracy.
RESULTS: The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.
CONCLUSIONS: The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.
CONCLUSIONS: The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.