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Natural selection generally favours phenotypic variability in camouflaged organisms, whereas aposematic organisms are expected to evolve a more uniform warning coloration. However, no comprehensive analysis of the phenotypic consequences of predator selection in aposematic and cryptic species exists. Using state-of-the-art image analysis, we examine 2800 wing images of 82 moth species accessed via three online museum databases. We test whether anti-predator strategy (i.e., camouflage or aposematism) explains intraspecific variation in wing colour and pattern across northern hemisphere moths. In addition, we test two mutually non-exclusive, ecological hypotheses to explain variation in colour pattern: diel-activity or dietary-niche. In this work, taking into account phylogenetic relationships, moth phenotypic variability is best explained by anti-predator strategy with camouflaged moths being more variable in wing patterning than aposematic species.

ABSTRACT The isolated river drainages of eastern North America serve as a natural laboratory to investigate the roles of allopatry and secondary contact in the evolutionary trajectories of recently diverged lineages. Drainage divides facilitate allopatric speciation, but due to their sensitivity to climatic and geomorphological changes, neighboring rivers frequently coalesce, creating recurrent opportunities of isolation and contact throughout the history of aquatic lineages. The freshwater mussel Quadrula quadrula is widely distributed across isolated rivers of eastern North America and possesses high phenotypic and molecular variation across its range. We integrate sequence data from three genomes, including female‐ and male‐inherited mitochondrial markers and thousands of nuclear encoded SNPs with morphology and geography to illuminate the group's divergence history. Across contemporary isolated rivers, we found continuums of molecular and morphological variation, following a pattern of isolation by distance. In contact zones, hybridization was frequent with no apparent fitness consequences, as advanced hybrids were common. Accordingly, we recognize Q. quadrula as a single cohesive species with subspecific variation (Q. quadrula rumphiana). Demographic modeling and divergence dating supported a divergence history characterized by allopatric vicariance followed by secondary contact, likely driven by river rearrangements and Pleistocene glacial cycles. Despite clinal range‐wide variation and hybridization in contact zones, the process‐based species delimitation tool delimitR, which considers demographic scenarios like secondary contact, supported the delimitation of the maximum number of species tested. As such, when interpreting species delimitation results, we suggest careful consideration of spatial sampling and subsequent geographic patterns of biological variation, particularly for wide‐ranging taxa. [ABSTRACT FROM AUTHOR]

Biological sex affects the morphology, physiology, behaviour, and distribution of organisms and populations. Integrating intraspecific variation of biological traits into community dynamics has improved our understanding of food web and ecosystem dynamics and stability. However, biological sex is rarely considered. The inclusion of sex-specific variation in theoretical models has highlighted that sex-based differences can significantly influence community dynamics, but empirical tests are limited. Methodological and logistical challenges often prevent the consideration of organisms' sex in the field, limiting our understanding of the role of sex in community functioning. Where these limitations have been overcome, field studies highlighted sex-based species interactions and their cascading ecological outcomes in marine, freshwater, and terrestrial ecosystems. Understanding how natural communities and ecosystems are structured and respond to anthropogenic pressures in a rapidly changing world is key to successful management and conservation. A fundamental but often overlooked biological characteristic of organisms is sex. Sex-based responses are often considered when conducting studies at organismal and population levels, but are rarely investigated in community ecology. Focusing on kelp forests as a model system, and through a review of other marine and terrestrial ecosystems, we found evidence of widespread sex-based variation in species interactions. Sex-based variation in species interactions is expected to affect ecosystem structure and functioning via multiple trophic and nontrophic pathways. Understanding the drivers and consequences of sex-based variation in species interactions can inform more effective management and restoration. [ABSTRACT FROM AUTHOR]

Telomerase reverse transcriptase promoter mutation (pTERT MT) promotes human carcinogenesis via aberrant expression of telomerase reverse transcriptase (TERT). However, the tumorigenic impact of TERT expression independent of pTERT MT remains unclear despite numerous mechanisms of TERT being suggested. To tackle this issue, we employed comprehensive bioinformatics to assess biological variations noticed among different TERT expression mechanisms. Papillary thyroid cancer (PTC) with pTERT MT (pTERT MT PTC) presented aggressive clinical behavior and exhibited biological profiles associated with cellular immortality and genomic instability. PTC with TERT expression but without pTERT MT (TERT (+) PTC), also exhibited poor clinicopathological characteristics and was enriched with immune responses. In accordance, c-MYC/E2F and nuclear factor kappa B (NFκB) were dominant transcription factors in pTERT MT PTC and TERT (+) PTC, respectively. Notably, we revealed TERT hypermethylated oncological region (THOR) as a potential TERT expressing mechanism in TERT (+) PTC patients. Furthermore, three unique subtypes of papillary thyroid cancer were deciphered using a combination of machine learning-based scoring systems. Our proposed scoring system was clinically significant, especially in microcarcinoma, predicting survival outcomes and inferring therapeutic responses to radioactive iodine therapy. Finally, our analysis was expanded to endocrine-related cancers, unveiling various regulatory mechanisms of TERT with poor clinical outcomes and biological behaviors. [ABSTRACT FROM AUTHOR]

Background: Gastric fluid plays a key role in food digestion and drug dissolution, therefore, the amount of gastric fluid present in a fasted state may influence subsequent digestion and drug delivery. We aimed to describe intra‐ and interindividual variation in fasted gastric content volume (FGCV) and to determine the association with age, sex, and body size characteristics. Methods: Data from 24 MRI studies measuring FGCV in healthy, mostly young individuals after an overnight fast were pooled. The analysis included 366 participants who had up to 6 repeated measurements, with a total of 870 measurements. Linear mixed model analysis was performed to calculate intra‐ and interindividual variability and to assess the effects of age, sex, weight, height, weight*height as a proxy for body size, and body mass index (BMI). Results: FGCV ranged from 0 to 156 mL, with a mean (± SD) value of 33 ± 25 mL. The overall coefficient of variation within the study population was 75.6%, interindividual SD was 15 mL, and the intraindividual SD was 19 mL. Age, weight, height, weight*height, and BMI had no effect on FGCV. Women had lower volumes compared to men (MD: −6 mL), when corrected for the aforementioned factors. Conclusion: FGCV is highly variable, with higher intraindividual compared to interindividual variability, indicating that FGCV is subject to day‐to‐day and within‐day variation and is not a stable personal characteristic. This highlights the importance of considering FGCV when studying digestion and drug dissolution. Exact implications remain to be studied. [ABSTRACT FROM AUTHOR]

Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known. We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results. TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00–09:00, 08:00–12:00, 06:00–18:00, and 00:00–24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00. We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept. [ABSTRACT FROM AUTHOR]

Early life history traits of small pelagic fish are usually affected by environmental conditions. Patagonian sprat, Sprattus fuegensis, is a small pelagic fish that inhabits the continental shelf, fjords and channels of Patagonia. Their larvae are usually exposed to large variations in physical and biological conditions. We evaluated the effects of environmental conditions on early life history traits of this key species encompassing over 20 years of information. The characterization of oceanographic features, larval abundance and distribution was carried out in western Patagonia between 1996 and 2019, while the growth and mortality rates were estimated for the austral spring 2019 between 49°54′S and 53°55′S. Larval abundance decreased three to ten times from 1996 to 1997 to the rest of the study period (2008–2010 and 2014–2019), with larger abundances being found over the shelf. In spring 2019, the growth rate was 0.20 ± 0.05 mm d−1 for larvae between 9 and 19 days after hatching. The growth rate was similar in partially stratified (7–50 J m−3) and stratified waters (50–100 J m−3), but slower in mixed waters (< 7 J m−3). GAM modelling showed that the faster larval growth occurred in partially stratified waters with temperatures above 7.5°C. Natural mortality rates were higher in partially stratified waters (24% of daily loss), lower in stratified areas (14%) and the lowest in the mixed water column (12%). Accordingly, larvae living in channels had faster growth rates but higher daily mortality, while those that hatched or were advected to water parcels on the continental shelf had slower growth but lower daily mortality. Finally, these latter traits may be an example of an inverse growth‐selective survival strategy. [ABSTRACT FROM AUTHOR]

Background: In recent decades, the analytical quality of clinical laboratory results has substantially increased because of collaborative efforts. To effectively utilize laboratory results in applications, such as machine learning through big data, understanding the level of harmonization for each test would be beneficial. We aimed to develop a quantitative harmonization index that reflects the harmonization status of real-world laboratory tests. Methods: We collected 2021–2022 external quality assessment (EQA) results for eight tests (HbA1c, creatinine, total cholesterol, HDL-cholesterol, triglyceride, alpha-fetoprotein [AFP], carcinoembryonic antigen [CEA], and prostate-specific antigen [PSA]). This EQA was conducted by the Korean Association of External Quality Assessment Service, using commutable materials. The total analytical error of each test was determined according to the bias% and CV% within peer groups. The values were divided by the total allowable error from biological variation (minimum, desirable, and optimal) to establish a real-world harmonization index (RWHI) at each level (minimum, desirable, and optimal). Good harmonization was arbitrarily defined as an RWHI value ≤1 for the three levels. Results: Total cholesterol, triglyceride, and CEA had an optimal RWHI of ≤1, indicating an optimal harmonization level. Tests with a desirable harmonization level included HDL-cholesterol, AFP, and PSA. Creatinine had a minimum harmonization level, and HbA1c did not reach the minimum harmonization level. Conclusions: We developed a quantitative RWHI using regional EQA data. This index may help reflect the actual harmonization level of laboratory tests in the field. [ABSTRACT FROM AUTHOR]

Background: INCbase is an international, multicenter prospective observational study using a customizable web-based modular registry to study the clinical, biological and electrophysiological variation and boundaries of chronic inflammatory demyelinating polyneuropathy (CIDP). The primary objective of INCbase is to develop and validate a clinical prediction model for treatment response. Methods: All patients meeting clinical criteria for CIDP can be included in INCbase. Collected data include demographics, clinical history, diagnostics and various domains of clinical outcomes. Data is collected at a minimum of every 6 months for two years, and more frequently at the discretion of the investigational site to allow for assessment of unexpected changes in treatment response or clinical status. Participants can be enrolled in various sub-studies designed to capture data relevant to specific groups of interest. Data is entered directly into the web-based data entry system by local investigators and/or participants. Collection and local storage of biomaterial is optional. To develop a clinical prediction model for treatment response, newly diagnosed patients with active disease warranting start of first-line treatment will be included. The study population will be split into a development and validation cohort. Univariate and multivariate logistic regression analysis will be used to identify and combine predictors at start of treatment for treatment response at six months. Model performance will be assessed through discrimination and calibration in an external validation cohort. The externally validated prediction model will be made available to researchers and clinicians on the INCbase website. Discussion: With this study, we aim to create a clinically relevant and implementable prediction model for treatment response to first line treatments in CIDP. INCbase enrollment started in April 2021, with 29 centers across 8 countries and 303 patients participating to date. This collaborative effort between academia, patient advocacy organizations and pharmaceutical industry will deepen our understanding of how to diagnose and treat CIDP. [ABSTRACT FROM AUTHOR]

Gladiolus, a widely cultivated cut flower known for its aesthetically pleasing multicoloured spikes, has earned significant commercial popularity. A comprehensive understanding of the rhizosphere bacterial community associated with gladiolus is imperative for revealing its potential benefits. Molecular characterization is considered an effective method to gain insights into the structural and functional aspects of microbial populations. The soil characteristics and bacterial communities in the rhizosphere are typically influenced by quorum sensing (QS) and quorum quenching (QQ) mechanisms. This study aims to explore the niceties and diversity of rhizospheric bacterial populations linked with gladiolus corms, with a specific focus on understanding the dynamics of QS and QQ mechanisms in their complex interactions. The isolation of bacterial strains was achieved through the serial dilution method on nutrient agar (NA) media. The identification of the isolates was accomplished by amplifying 16 S rRNA gene sequences via polymerase chain reaction (PCR) via the use of universal primers. Sequence analysis was conducted via BLAST on the National Center for Biotechnology Information (NCBI) database. The characteristics of the isolated bacteria were elucidated via biosensors. This study identified three QS strains and five QQ strains. A consortium of quenchers was formulated utilizing five strains that demonstrated efficacy in mitigating the impact of disease on gladiolus and fostering growth. Among the three treatments—Scale, Descale, and Descale and Cut Half (DSC)—the DSC treatment emerged as the most effective. This treatment exhibited a broader range of variation in biological parameters over time, aligning with prevailing trends in the local market. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a cancer caused by abnormal cell growth due to faulty signal transduction. Cells secrete tumor suppressor factors in response to potential carcinogenic signals, inducing cellular senescence (CS) as a countermeasure. However, accurately measuring CS levels in different types of tumors is challenging due to tumor heterogeneity and the lack of universal and specific CS markers. Machine learning has revealed unique molecular traits in HCC patients, leading to clinical advantages. More research is needed to understand senescence-related molecular features in these patients. In this study, the gene expression profile features of patients with HCC were analyzed by integrating single-cell RNA sequencing and bulk RNA-seq datasets from HCC samples. The analysis identified the senescence-related pathways exhibiting HCC specificity. Subsequently, genes from these pathways were used to identify senescence-related molecular subtypes in HCC, showing significant variations in biological and clinical attributes. An HCC-specific CS risk model developed in this study revealed substantial associations between the patients' CS scores and prognosis grouping, clinical staging, immune infiltration levels, immunotherapy response, and drug sensitivity levels. Within the constructed model, G6PD was identified as a key gene, potentially serving as a senescence-related target in liver cancer. Molecular biology experiments demonstrated that overexpression of G6PD effectively promotes the proliferative, invasive, and migration capacities of HepG2 and SK-HEP-1 cells. In conclusion, this analysis offers a valuable framework for understanding senescence in HCC and introduces a new biomarker. These findings improve our understanding of senescence in HCC and have potential for future research. [ABSTRACT FROM AUTHOR]

Motivation Spatial transcriptomics (ST) technologies provide richer insights into the molecular characteristics of cells by simultaneously measuring gene expression profiles and their relative locations. However, each slice can only contain limited biological variation, and since there are almost always non-negligible batch effects across different slices, integrating numerous slices to account for batch effects and locations is not straightforward. Performing multi-slice integration, dimensionality reduction, and other downstream analyses separately often results in suboptimal embeddings for technical artifacts and biological variations. Joint modeling integrating these steps can enhance our understanding of the complex interplay between technical artifacts and biological signals, leading to more accurate and insightful results. Results In this context, we propose a hierarchical hidden Markov random field model STADIA to reduce batch effects, extract common biological patterns across multiple ST slices, and simultaneously identify spatial domains. We demonstrate the effectiveness of STADIA using five datasets from different species (human and mouse), various organs (brain, skin, and liver), and diverse platforms (10x Visium, ST, and Slice-seqV2). STADIA can capture common tissue structures across multiple slices and preserve slice-specific biological signals. In addition, STADIA outperforms the other three competing methods (PRECAST, fastMNN, and Harmony) in terms of the balance between batch mixing and spatial domain identification, and it demonstrates the advantage of joint modeling when compared to STAGATE and GraphST. Availability and implementation The source code implemented by R is available at https://github.com/zhanglabtools/STADIA and archived with version 1.01 on Zenodo https://zenodo.org/records/13637744. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the metric stability of the one-month handgrip test (HGT) in order to define the contractile characteristics of the biological variation of maximal isometric strength (Fmax) and maximal isometric rate of force development (RFDmax) of the handgrip in two different testing regimes (classic and impulse). The study was conducted with a total of 16 participants (11 men and 5 women). Testing was performed using an isometric handgrip probe with a standardized test protocol and equipment sports medical solutions (SMS). The results of Fmax showed a low relative standard error of the mean (RS = 1.33 %), a high value of inter-class correlation (ICC = 0.996), and no statistically significant change in trend (p > 0.05) during the testing period. Therefore, can conclude that the HGT procedure in classic mode can be used as a stable parameter in a human subject sample. However, the RFDmax results showed a low RS (2.13 %) and a high ICC value (0.996), but a statistically significant change of trend (p < 0.05) during the measurement period. The regression constant (RCO) trend was 42.629 N/s, which can be attributed to learning or to the adaptive effects of the test procedure, which triggered similar adaptation processes as the training. In general, it can be concluded that the handgrip can be used to sensitively measure the effects of different long-term health improvements, or the effects of different medical/health exercises, rehabilitation programs, effects of medication applications, or dietary supplements for Fmax. However, further research should be conducted for the RFDmax considering the metric stability parameters. [ABSTRACT FROM AUTHOR]

Simple Summary: Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability is measured by the frequency distribution of observed data, allowing for an understanding of the fundamental principles influencing diversity across different levels of biological organization. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper examines the different aspects of uncertainty in biology. It specifically looks at using CDP-based platforms to improve fuzzy algorithms and tackle some of the challenges related to biological and medical uncertainties. The goal is to produce algorithm outputs more relevant to biology and clinical applications. Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant. [ABSTRACT FROM AUTHOR]

Simple Summary: Moringa oleifera L. is a multipurpose tree species widely used for its high nutritional value. It has also been used for centuries due to its high medicinal value and antifungal, antiviral, antidepressant, and anti-inflammatory properties, and has wider applications in the nutraceutical, pharmaceutical, and cosmetics industries. Due to greater demand in the local and international market, the exploitation of high-yielding moringa varieties and their cultivation techniques is needed. However, the breeding approaches adopted in Malaysia and achievements made for the exploitation of this crop were rather slow due to the unavailability of elite planting material and germplasm collection. Germplasm is a gene pool conserved in a wide array of crop species, landraces, and wild populations by identifying elite accessions with specific desirable genetic traits to increase yield and productivity in agricultural and horticultural crop species. Moringa germplasm collected across the country showed wider genetic variations for morphological traits and chemical content. Thus, moringa germplasm has been collected and conserved for the development of improved varieties with high-yielding potential for commercial cultivation in Malaysia. Hence, it is imperative to understand the genetic potential of each moringa genotype for the development of superior varieties through continuous breeding programs. Improvements in selection criteria, collection, and clonal propagation methods of moringa genotypes can result in the development of elite planting material with improved pharmacological and industrial properties and ensure the production of a high-quality end product. Moringa oleifera L. is a valuable multipurpose tree species widely planted for centuries due to its high medicinal value and antifungal, antiviral, antidepressant, and anti-inflammatory properties in the food industry. However, its cultivation is hindered by production constraints such as the unavailability of planting material and the inadequate number of high-yielding clones. Thus, a study was initiated to select high-yielding clones in terms of growth and chemical content for the mass propagation of superior moringa trees. Screening on high-yielding clones with high astragalin content was conducted through the high-performance liquid chromatography (HPLC) analysis of moringa leaf extract. Selected genotypes were evaluated for their anti-inflammatory potential through in vitro bioactivity assays of leaf methanol extract. The effects of the rooting hormone, rooting substrates, and size of the cutting on the rooting response of branch cuttings of moringa were investigated. Results found that samples collected from different ecological zones of Peninsular Malaysia show significant variation in terms of astragalin content. The extracts were observed to show considerable variation in biological activity against the pro-inflammatory enzymes. The size of the cuttings had significant effects on the rooting of the cuttings as longer cuttings with bigger diameters rooted better than shorter cuttings with smaller diameters. Several genotypes of M. oleifera with superior phenotypic characteristics and bioactive compounds have been identified. Factors affecting the rooting efficiency and optimal conditions of rooting are suggested, which provides valuable information for the propagation of the superior planting material of moringa. This effort will ensure the sustainable production and supply of good quality raw materials for the production of quality end-products for the food and pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets. [ABSTRACT FROM AUTHOR]

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design. Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals. Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %. This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design. [ABSTRACT FROM AUTHOR]

There is a growing interest in the relevance of salivary cortisol and cortisone concentrations in stress-related research. To correctly attribute the magnitude of salivary cortisol and cortisone variation as an effect of a stressful event, a coherent understanding of the day-to-day intra-individual and inter-individual variability across the diurnal cycle of the two steroids is required. However, such information is currently lacking. This study aimed to overcome these existing limitations by performing an investigation of the biological variation (BV) of salivary cortisol and cortisone within one day and between five days using an LC-MS/MS method. Saliva samples were collected from 20 healthy volunteers immediately after waking up, at 8:00, 12:00, 15:00, 19:00 and 23:00 on each day over five days. All samples were analyzed in duplicate in one run. Nested ANOVA was used to calculate the sums of squares for analytical and biological components of variation. The within-subject BV of salivary cortisol and cortisone (CVI) ranged from a minimum of 29.3 and 19.0 % to a maximum of 56.5 and 49.1 %, respectively, while the between-subject biological variation (CVG) ranged from 29.7 and 29.0 % to 51.6 and 43.6 %. The reference change values (RCVs) ranged from 96 to 245 % for cortisol and from 55 to 194 % for cortisone. A medium index of individuality was observed for both compounds at all time points. This study provides updated BV estimates and RCVs for different times of day that can be used to assess the magnitude of change in biomarkers in future stress-related research. [ABSTRACT FROM AUTHOR]

The interpretation of laboratory data is a comparative procedure. Physicians typically need reference values to compare patients' laboratory data for clinical decisions. Therefore, establishing reliable reference data is essential for accurate diagnosis and patient monitoring. Human metabolism is a dynamic process. Various types of systematic and random fluctuations in the concentration/activity of biomolecules are observed in response to internal and external factors. In the human body, several biomolecules are under the influence of physiological rhythms and are therefore subject to ultradian, circadian and infradian fluctuations. In addition, most biomolecules are also characterized by random biological variations, which are referred to as biological fluctuations between subjects and within subjects/individuals. In routine practice, reference intervals based on population data are used, which by nature are not designed to capture physiological rhythms and random biological variations. To ensure safe and appropriate interpretation of patient laboratory data, reference intervals should be personalized and estimated using individual data in accordance with systematic and random variations. In this opinion paper, we outline (i) the main variations that contribute to the generation of personalized reference intervals (prRIs), (ii) the theoretical background of prRIs and (iii) propose new methods on how to harmonize prRIs with the systematic and random variations observed in metabolic activity, based on individuals' demography. [ABSTRACT FROM AUTHOR]

The secretion of thyroid stimulating hormone (thyrotropin, TSH), is characterized by a marked circadian rhythm. Plasma or serum TSH values are significantly lower in the afternoon and in the evening as compared to the early morning. As in clinical practice, blood sampling time shows an important variation, a reliable assessment of thyroid status is often not an easy task for the clinician. The biological variation of TSH plays a major role in the intra-individual variability of TSH results in serum or plasma. The observed intra-day variation largely exceeds the reported inter-vendor variation and the coefficient of variation of clinical TSH assays. Therefore, a mathematical solution was sought for correcting interpretation of TSH results for sampling time.We have developed a cosinor model which allows to compensate TSH decision values for the fluctuating serum or plasma TSH concentrations throughout the day.The following mathematical function could be derived: corrected TSH cutoff_value (mIU/L)=0.40 + 0.24*cos(((π/12) *T) + 6) in which T represents the time (hours). This mathematical function can be easily implemented into a laboratory’s informatics system and furthermore allows a better tailored diagnosis of (subclinical) hyperthyroidism, regardless the blood sampling time.Implementing the corrected cut-off values result in a marked reduction of apparent (false positive) hyperthyroidism diagnosis, in particular in the afternoon. [ABSTRACT FROM AUTHOR]

• The Zulus engage in complementary and alternative medicine through a traditional healing system known as izangoma. • The first review of the antidiabetic medicinal plants commonly used by the Zulus of Kwazulu-Natal province of South Africa. • The list includes local and botanical names, the parts commonly used, isolated bioactive principles and their reported mechanisms of action. • 38 plants covering 20 families were identified with Asteraceae accounting for 34.2% and the leaves was the most used plant part (50 %). • 29 have been scientifically validated while nine lack scientific verification. Diabetes mellitus is a chronic metabolic disorder that affects millions of people worldwide. Its conventional management involves diet modification, exercise, and majorly the use of synthetic drugs, which may be ineffective, have adverse effects, and of relatively expensive costs. To mitigate the downsides of the available treatments, alternative therapies from medicinal plants are commonly sought by different ethnic groups including the Zulus. This paper appraised the antidiabetic plants used by the Zulus, their local and botanical names, the parts commonly used, isolated bioactive principles and their reported mechanisms of action. 38 plants covering 20 families were identified with Asteraceae accounting for 31.57 % and the leaves was the most used plant part (50 %). Out of the 38 plants identified, 28 have been scientifically validated using in vitro/in vivo models but identifying the plant with the most potent hypoglycemic activity remains challenging due to variations in biological activities. Important antidiabetic agents such as quercetin, squalene, kaempferol, isomukaadial acetate, phytol, sitosterol, apigenin, catechin and their derivates have been isolated and characterized from the verified plants though, more scientific exploration is required to develop these principles into valuable antidiabetic medications with establish mechanism of action. Scientists therefore needs to beam their searchlight and dedicate resources to the rich botanicals of the Zulu land with the view to providing indigenous solutions that can be incorporated into modern medicine for diabetes management. [ABSTRACT FROM AUTHOR]

The pearl oyster (Pinctada radiata), a marine species native to the Arabian Gulf in the Indian Ocean is common in Qatari waters where it comprises 95% of pearl oyster stocks found naturally there. P. radiata therefore, has been used extensively as a natural bio-indicator for monitoring pollution in the Arabian Gulf. Sustainability of P. radiata wild stocks are now significantly threatened in the Arabian Gulf region as a result of both natural (e.g. extreme salinity, high temperatures, high evaporation rates and low flushing rates) and anthropogenic factors (e.g. rapid development of coastline areas, overfishing and heavy exploitation). Long term sustainability and conservation of this local resource in the Arabian Gulf has therefore become a serious issue, but their survival can significantly contribute to maintenance of healthy marine ecosystems across the region. The current study assessed natural levels and patterns of genetic variation in Arabian Gulf populations of the native pearl oyster, P. radiata to define wild population structure. Potential intrinsic (e.g. pelagic larval phase and life history traits) and extrinsic factors (e.g. water current, wave action, water temperature and prevailing wind direction) that could influence wild population structure were investigated. MtDNA (COI) sequences were used here to define levels and patterns of genetic diversity within and among three sample sites in Qatar territorial waters. Results of the statistical analysis that partitioned variation (AMOVA) were significant (P=0.0234) and showed that all variation was present within P. radiata sampled sites (94.61%) and differentiation among sites was relatively low (5.39%). [ABSTRACT FROM AUTHOR]

Background: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). Results: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. Conclusions: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations. [ABSTRACT FROM AUTHOR]

Automation is critically important for sustainability in meat production, where heavy reliance on human labour is a growing challenge. In this work, a novel robotic Meat Factory Cell (MFC) platform presents the opportunity for unconventional automation in pork meat processing, particularly abattoirs. Instead of following line-based approaches, which are the main option today, it uses robotics and Artificial Intelligence (AI) to perform complex cutting and manipulation operations on entire unchilled pork carcasses, with awareness of biological variation and deformation. The long-term goal of the MFC is to take a pork carcass as an input and produce seven primal outputs: hams, shoulders, saddle, belly and entire organ set. However, the MFC platform is under continuous development – therefore, this paper aims to demonstrate it through a specific use-case: shoulder removal. The system is evaluated based on data from testing and development sessions (June–November 2022), with a total of 34 attempted shoulder removals. Data regarding the MFCs' ability to handle variation, in addition to success rate and process timing models are presented. Qualitative feedback from skilled butchers is also discussed. The authors propose that, as well as technical development of the platform, it is important to consider new ways of comparing unconventional systems with their conventional counterparts. Innovative manufacturing systems have more to offer than raw speed and volume; traits such as flexibility, robustness and scalability – particularly economic scalability – should play a prominent role. Future legislation and standards must also encourage innovation rather than hinder innovative robotics solutions. [ABSTRACT FROM AUTHOR]

Recently, certain biological field experiments and research findings have revealed that predators not only reduce prey populations through direct predation (lethal effect) but also indirectly affect the growth rate of prey by inducing fear; these nonlethal effects can be carried over seasons or generations. In this paper, we proposed an autonomous and a nonautonomous Leslie–Gower model incorporating some biological factors such as fear and its carry‐over effects, prey refuge, and nonlinear predator harvesting. In the autonomous model, first, we examined the well‐posedness, positivity solutions, and their boundedness. Also, it is shown that new equilibrium points emerge and disappear with the change in intrinsic growth rate of predator. Further, we compute and analyzed the local and global stability at the interior equilibrium points. Furthermore, Hopf bifurcation and direction and stability of limit cycle at interior equilibrium points are established. Moreover, the sensitivity analysis of the biological parameters is carried out numerically with two statistical methods via Latin hypercube sampling and partial rank correlation coefficients. It was found that at the small values of fear factor, carry‐over effect, and refuge behavior of prey parameters, the autonomous system exhibits oscillatory behavior, whereas for small values of prey birth rate and harvesting effort, the system remains stable. However, when intrinsic growth rate of predator increases from a low value to a higher value, the system shows transition from stability to instability and back to stability. We also explore the effects of seasonal variations in biological parameters in the nonautonomous model. Additionally, we investigate the theoretical existence of positive periodic solutions using the continuation theorem. The influences of seasonal variations in some parameters such as prey's birth rate, fear, and its carry‐over effects; refuge behavior of prey; intrinsic growth rate of predator; and harvesting effort are then numerically investigated. The results reveal the emergence of positive periodic solutions and bursting patterns in the seasonally forced model. [ABSTRACT FROM AUTHOR]

Effective analysis of single-cell RNA sequencing (scRNA-seq) data requires a rigorous distinction between technical noise and biological variation. In this work, we propose a simple feature selection model, termed "Differentially Distributed Genes" or DDGs, where a binomial sampling process for each mRNA species produces a null model of technical variation. Using scRNA-seq data where cell identities have been established a priori, we find that the DDG model of biological variation outperforms existing methods. We demonstrate that DDGs distinguish a validated set of real biologically varying genes, minimize neighborhood distortion, and enable accurate partitioning of cells into their established cell-type groups. Author summary: Single-cell omics technologies measure tens of thousands of genes in up to millions of individual cells. Yet, the sheer dimensionality of the data poses a challenge to its intelligibility. A typical first step in reducing the dimensionality is to apply a feature selection model that distinguishes real biological signals from technical noise. Yet without an appropriate model of technical noise, feature selection can introduce bias into the downstream analysis of the data. In this work, we demonstrate that, in the analysis of single-cell RNA sequencing data, the standard approach of finding Highly Variable Genes (HVGs) induces severe distortion and bias into the analysis of data, when compared to true biological variation that is known a priori. To address this issue, we present a new feature selection model and demonstrate that our model outperforms existing methods in its ability to accurately identify real biological variation. [ABSTRACT FROM AUTHOR]

Summary Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a gr aph a ttention network designed for sp atial transcriptomics data integration with unbalanced o ptimal t ransport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. Availability and implementation Graspot software is available at https://github.com/zhan009/Graspot. [ABSTRACT FROM AUTHOR]

Laboratory medicine in sport and exercise has significantly developed during the last decades with the awareness that physical activity contributes to improved health status, and is present in monitoring both professional and recreational athletes. Training and competitions can modify concentrations of a variety of laboratory parameters, so the accurate laboratory data interpretation includes controlled and known preanalytical and analytical variables to prevent misleading interpretations. The paper represents a comprehensive summary of the lectures presented during the 35th Annual Symposium of the Croatian Society of Medical Biochemistry and Laboratory Medicine. It describes management of frequent sport injuries and sums up current knowledge of selected areas in laboratory medicine and sports including biological variation, changes in biochemical parameters and glycemic status. Additionally, the paper polemicizes sex hormone disorders in sports, encourages and comments research in recreational sports and laboratory medicine. In order to give the wider view, the connection of legal training protocols as well as monitoring prohibited substances in training is also considered through the eyes of laboratory medicine. [ABSTRACT FROM AUTHOR]

Objectives: The Six Sigma methodology is also frequently used by clinical laboratories as an objective and quantitative way to measure quality. In our study, we aimed to evaluate the analytical performance of 80 tests using the Six Sigma methodology according to the CLIA (Clinical Laboratory Improvement Amendments) 2019, RICOS BV (Dr. Carmen RI-COS Biological Variation) Desirable, and EFLM BV (European Federation of Clinical Chemistry and Laboratory Medicine Biological Variation) Desirable criteria. Methods: The sigma values of 80 tests were calculated according to the TEa (Total Allowable Error) limits allowed by all three references using internal quality control and external quality control data. They were calculated monthly for 12 months, and the annual average was taken. Sigma values were calculated with the Six Sigma formula. Results: Considering the total number of goals reached, the highest success rate of 60% was achieved according to the CLIA goals, while the lowest success rate of 36% was obtained according to the EFLM BV Desirable criteria. Although exactly the same laboratory data are used, this gap between the sigma values obtained according to the selected reference is especially noticeable in tests such as Na (Sodium), K (Potassium), Cl (Chloride), Calcium, HbA1c (Hemoglobin A1c), and Troponin T. Conclusion: The Six Sigma protocol is one of the effective and universal tools for evaluating the performance of clinical laboratories. However, one of its biggest limitations is the lack of standardization in tolerance limits. The obtained performance varies according to the preferred reference. Therefore, we think that in the Six Sigma methodology, it is more feasible to select Total Allowable Error criteria from different references according to their suitability for the test. [ABSTRACT FROM AUTHOR]

The modifications were in response to changing constraints, including time, money, space, student background, and my knowledge and comfort. The lab went from emphasizing experiments with the attendant troubleshooting and data analysis skills to a lab focused more on giving prehealth professional students the motivation to learn physiology problem-solving skills by providing real cases. In the lab, students watched and listened to a random student try to use these problem-solving skills to solve the problem. This made them appreciate how much others also struggle in solving the problem. Some students with imposter syndrome think their classmates immediately know how to solve a problem, and therefore, seeing others also struggle has the potential to reduce imposter syndrome. Rather than having the students do experiments, they did kinesthetic activities with mechanical models to generate data without biological variation. They then graphed their data, thus improving their ability to actually read graphs rather than memorize patterns. NEW & NOTEWORTHY: I learned to explicitly recognize the current and projected constraints of instructor comfort, money, space, student background (poor graph reading and problem-solving skills), student safety, and time and energy on the possible goals and methods to attain them for an undergraduate physiology lab. I cannot decide if changing constraints allowed me to reexamine my goals or whether it forced me to reexamine my goals. In either case, the reexamination of my goals (and their priorities), within the context of the constraints, allowed me to redesign the labs to better meet my new goals within this new context. [ABSTRACT FROM AUTHOR]

The impact of seven hemoglobin variants (Hb Q-Thailand, Hb G-Honolulu, Hb Ube-2, Hb New York, Hb J-Bangkok, Hb G-Coushatta, and Hb E) on the outcome of HbA1c was investigated for six methods by comparing with liquid chromatography-tandem mass spectrometry (LC/MS/MS) reference method. Twenty-nine normal and 112 variant samples were measured by LC/MS/MS, Sebia Capillarys 3 TERA, Intelligene Biosystems QuanTOF, Premier Hb9210, Arkray HA-8190V, Bio-Rad D-100, and Tosoh G11, then evaluated for correlation, consistency, and mean relative bias among six methods. The lowest biological variation bias of ±2.8 % was an acceptable standard. All methods showed poor correlation and consistency with LC/MS/MS for Hb E. The unacceptable biases were observed for Capillarys 3 TERA (−14.4 to −3.7 % for Hb Q-Thailand, Hb Ube-2, Hb New York, Hb J-Bangkok and Hb E), QuanTOF (−8.3 to −2.9 % for Hb Ube-2, Hb New York and Hb G-Coushatta), Premier Hb9210 (−18.3 to −3.6 % for Hb Q-Thailand, Hb Ube-2, Hb New York, Hb J-Bangkok and Hb E), HA-8190V variant mode (−17.3 to 6.6 % for Hb G-Honolulu, Hb Ube-2, Hb New York, Hb G-Coushatta and Hb E). All variant samples showed larger biases than ±2.8 % comparing HA-8190V fast mode, D-100, and G11 with LC/MS/MS. The accuracy of different HbA1c methods was influenced by some Hb variants, especially Hb Ube-2 and Hb New York. Thus, laboratories need to choose appropriate methods to measure HbA1c with different Hb variants. [ABSTRACT FROM AUTHOR]

Laboratory results are increasingly interpreted against common reference intervals (CRIs), published clinical decision limits, or previous results for the same patient performed at different laboratories. However, there are no established systems to determine whether current analytical performance justifies these interpretations. We analysed data from a likely commutable external quality assurance program (EQA) to assess these interpretations. The use of CRIs was assessed by evaluating instrument group medians against minimum specifications for bias. The use of clinical decision limits was assessed using specifications from professional bodies, and the monitoring of patients by testing at different laboratories was assessed by comparing all-laboratory imprecision to within-subject biological variation. Five of the 18 analytes with Australasian CRIs did not meet specification for all instrument groups. Among these, calcium and magnesium failed for one instrument group out of seven, while bicarbonate, chloride, and lipase failed for two instrument groups. Of the 18 analytes reviewed currently without CRIs in Australasia, 10 candidates were identified. Among analytes with clinical decision limits, i.e. lipids, glucose, and vitamin D, only triglycerides met both bias and imprecision specifications, while vitamin D met the imprecision specification. Monitoring patients by testing at different laboratories was supported for 15 of the 46 (33 %) analyte-method principles groups that met minimum imprecision specifications. Analysis of data from commutable EQA programs can provide a mechanism for monitoring whether analytical performance justifies the interpretations made in contemporary laboratory practice. EQA providers should establish systems for routinely providing this information to the laboratory community. [ABSTRACT FROM AUTHOR]

Background: Mild traumatic brain injury (mTBI) is defined as a Glascow Coma Score of between 13 and 15. The diagnosis and rule out of individuals suffering from mTBI on an acute basis is imperfect and involves subjective measures. Serum biomarkers that exhibit narrow within-individual biological variation can be used for the early rule-out of mTBI, when baseline levels are compared during health. Methods: This is a descriptive study that applies published biological variation data of serum mTBI biomarkers for early rule out of sports-related injury. Results: Laboratory tests such as glial fibrillary acidic protein, fatty acid binding protein 7, and phosphorylated protein enriched in astrocytes have low within-individual variances and are potential candidates. Aldolase C also rises early in blood but the biological variation is of this marker is currently unknown. Conclusions: The use of blood-based biomarkers, measured in real time using point-of-care testing devices when compared to a pre-competition baseline instead of a population-based reference interval, can provide early rule out of mTBI, and possibly enable on-field evaluations and a medical decision for a return to competition. [ABSTRACT FROM AUTHOR]

Background: Regression describes the relationship of results from two analyzers, and the generated equation can be used to harmonize results. Point‐of‐care (POC) analyzers cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. Harmonization (uniformity) of laboratory results facilitates the use of common reference intervals and medical decision thresholds. Objective: Our aims were to characterize the relationship of results for multiple biochemistry analytes on a POC and a commercial laboratory analyzer (CL) with three regression techniques and to use regression equations to harmonize the POC results with those of the CL. Harmonized results were assessed by recognized quality goals. We used harmonized results to assess the regression techniques. Methods: After analyzer imprecision assessments, paired clinical samples were assessed with one dataset to calculate regression parameters that were applied to a second dataset. Three regression techniques were performed, and each was used to harmonize the POC results with those from the CL. POC results were assessed for bias and the number of results reaching quality goals before and after harmonization. Results: All regression techniques could be used to harmonize most analytes so that 95% of results were within ASVCP TEa guidelines. Harmonization could be further improved with alternate regression techniques or exclusions. Conclusions: Regression offers a means to harmonize POC and CL analyzers. Further work is needed to assess how few samples can reliably be used and to assess likely species differences. No regression technique reliably describes the relationship between methods when correlation is poor. [ABSTRACT FROM AUTHOR]

Background: Point‐of‐care (POC) biochemistry analyzers are widely used in small animal clinical practice but infrequently independently assessed for performance. Objective: To assess the performance of two current model point‐of‐care biochemistry analyzers (Heska Element DC and IDEXX Catalyst) compared with a commercial laboratory analyzer (Cobas 8000). Methods: One hundred twenty‐one cats from a feline hospital population were sampled with plasma results from a single lithium heparin tube assessed on all three analyzers. Plasma biochemistry results from each POC analyzer were compared with the commercial laboratory analyzer using Bland–Altman difference plots and by determining whether the limits of agreement (LOAs) (95% of differences) fell within various quality goals after correcting for inherent bias. Results: Only 7 of 14 analytes on the Heska analyzer and 2 analytes on the IDEXX analyzer attained the most stringent LOA quality goal, which was being within desirable total error based on biologic variation (TEdes). The number of analytes achieving quality goals increased with less stringent standards such as American Society of Veterinary Clinical Pathologists allowable total error (ASVCP TEA) guidelines or if <95% of clinical comparisons reaching these quality goals is considered acceptable. Widespread bias was found between both POC analyzers and the commercial laboratory analyzer. Conclusions: The performance of both POC biochemistry analyzers was variable compared with a commercial laboratory analyzer. Performance goals were only able to be attained after the bias for each analyzer was accounted for by offsetting the LOAs and quality goals set by the mean bias for each analyte on each analyzer. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine factors influencing observed increased metal biomarkers of exposure levels in a group of 116 Quebec apprentice welders during a longitudinal follow-up of exposure. Analysis of 14 metals was carried out in hair, fingernail, and toenail samples taken from participants over the course of their welding curriculum at 6 different times. Personal and socio-demographic characteristics, lifestyle habits, and other potential confounding factors were documented by questionnaire. Multivariate linear mixed-effect models were used to assess main predictors of metal concentrations in each biological matrix including increasing time of exposure throughout the curriculum (defined as the repeated measure "time" variable"). Significant associations between repeated measure "time" variable and metal levels in hair, fingernails, and toenails were found for chromium, iron, manganese and nickel. Significant associations with "time" were also noted for arsenic levels in hair and fingernails, and for barium, cobalt and vanadium levels in fingernails and toenails. The repeated measure "time" variable, hence increasing time of exposure throughout the curriculum, was the predominant predictor of elevated biological metal levels. Reduced spaces and simultaneous activities such as oxyfuel-cutting and welding in the same welding room were suspected to contribute to higher metal levels. Age, ethnicity, and annual household income exerted an effect on metal levels and considered as confounders in the models. Variations observed in metal levels between hair and nails of apprentice welders also emphasized the relevance and importance of performing multi-matrix and multi-element biomonitoring to assess temporal variations in biological metal concentrations during welding curriculum. [ABSTRACT FROM AUTHOR]

The article discusses the development and release of 11 new wild-derived inbred mouse strains known as the "Nachman panel," which offer a unique genetic and phenotypic diversity platform for biological research. These strains were developed from wild-caught mice from different geographic regions in North and South America, providing a wide range of genetic and phenotypic variation. The Nachman strains have been extensively characterized genetically and phenotypically, offering a rich resource for biological discovery and disease modeling. Despite some limitations, such as smaller litter sizes and wild behavior, these strains present promising prospects for future research in basic, biomedical, and preclinical investigations. [Extracted from the article]

In the field of control engineering, there has been a growing emphasis on systems with feedback loops that involve stochastic factors. Simultaneously, with the advancements in probability theory, feedback loops with stochastic processes have gained practicality due to their customizability and the potential for practical applications. Essentially, the effect of adding additive random signals in linear feedback systems can be viewed as the introduction of filtered stochastic noise. However, when random signals enter the feedback loop multiplicatively, a richer set of state behaviors emerges. This study utilizes multiplicative feedback loops to construct Markov chains and provides a probabilistic modeling approach to investigate the stability and asymptotic behavior of Markov feedback loops, offering insights for microsystems. This paper derives properties of such stochastic processes and provides corresponding theoretical proofs. The theoretical foundation of this probabilistic modeling can be applied in economics, biological variation processes, epidemic control predictions, and linear perturbation models, offering a theoretical perspective for feedback systems with uncertain triggers. [ABSTRACT FROM AUTHOR]

This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches (p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the prevalence, dimensions, and positions of accessory mental foramen (AMF) and lateral lingual foramen (LLF) in an Iranian population using cone beam computed tomography (CBCT). Methods and materials: In this cross-sectional study, CBCT images of 2082 patients were retrospectively reviewed based on inclusion criteria, including images from the mandibular region taken in the past five years with accurate demographic data. Exclusion criteria included images with significant pathologies, fractures, or poor quality. CBCT images were captured, and AMF and LLF presence (primary outcome variable), dimensions, and positions (secondary outcome variables) were recorded relative to the mental foramen and adjacent teeth. Data analysis was performed using the Chi-square test with a 5% significance level. Results: A total of 2082 CBCT samples (46.54% females and 53.46% males) with a mean age of 48.30 ± 12.70 years were examined. Among these, 246 AMFs were observed in 222 individuals with a prevalence at the foramen level of 11.815%, with 24 having two AMFs. AMF was mostly observed unilaterally, with bilateral occurrence noted in 10.81% of patients with AMF. Additionally, 86 patients were found to have LLF with a prevalence at the foramen level of 5.907%. There were no significant differences between genders in AMF and LLF prevalence. Conclusion: AMF and LLF were detectable using CBCT in the selected Iranian population. AMF was commonly situated posterior and superior to the mental foramen, while LLF was often found near the first premolar. This knowledge is crucial for preventing nerve injury during surgery and ensuring effective nerve block procedures. [ABSTRACT FROM AUTHOR]

With about 60%-70% of medical decisions based on laboratory investigations, modern medicine is increasingly more reliant on laboratory medicine. Pediatric healthcare particularly depends on laboratory testing, with reference intervals (RIs) serving as critical tools for accurate result interpretation. However, establishing pediatric RIs poses distinctive challenges due to developmental, growth, nutritional, and disease-related variations across age groups, along with technical, ethical, and logistical challenges. Despite these limitations, there are several ongoing initiatives for developing pediatric RIs globally, underscoring the importance of developing accurate pediatric RIs. This review outlines the particularities of pediatric laboratory medicine and the specific challenges in establishing pediatric RIs, highlighting the recent initiatives in this area. It also addresses biological and analytical variability issues and explores future perspectives aimed at enhancing diagnostic accuracy and elevating patient care standards.

Abstract Background Although the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes. Methods Participants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan–Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes. Results Sixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54–3.90) for severe liver disease (SLD), and 2.81 (2.60–3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes. Conclusions High PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management.

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github. [ABSTRACT FROM AUTHOR]

Personalized reference intervals (prRI) have been proposed as a diagnostic tool for assessing measurands with high individuality. Here, we evaluate clinical performance of prRI using carcinoembryonic antigen (CEA) for cancer detection and compare it with that of reference change values (RCV) and other criteria recommended by clinical guidelines (e.g. 25 % of change between consecutive CEA results (RV25) and the cut-off point of 5 μg/L (CP5)).Clinical and analytical data from 2,638 patients collected over 19 years were retrospectively evaluated. A total 15,485 CEA results were studied. For each patient, we calculated prRI and RCV using computer algorithms based on the combination of different strategies to assess the number of CEA results needed, consideration of one or two limits of reference interval and the intraindividual biological variation estimate (CVI) used: (a) publicly available (CVI-EU), (b) CVI calculated using an indirect method (CVI-NOO) and (c) within-person BV (CVP). For each new result identified falling outside the prRI, exceeding the RCV interval, RV25 or CP5, we searched for records identifying the presence of tumour at 3 and 12 months after the test. The sensitivity, specificity and predictive power of each strategy were calculated.PrRI approaches derived using CVI-EU, and both limits of reference interval achieve the best sensitivity (87.5 %) and NPV (99.3 %) at 3 and 12 months of all evaluated criteria. Only 3 results per patients are enough to calculate prRIs that reach this diagnostic performance.PrRI approaches could be an effective tool to rule out new oncological findings during the active surveillance of patients. [ABSTRACT FROM AUTHOR]

Sepsis pathobiology is complex. Heterogeneity refers to the clinical and biological variation within sepsis cohorts. Sepsis subtypes refer to subpopulations within sepsis cohorts derived based on these observable variations and latent features. The overarching goal of such endeavors is to enable precision immunomodulation. However, we are yet to identify immune endotypes of sepsis to achieve this goal. The sepsis subtyping field is just starting to take shape. The current subtypes in the literature do not have a core set of shared features between studies. Thus, in this narrative review, we reason that there is a need to a priori state the purpose of sepsis subtyping and minimum set of features that would be required to achieve the goal of precision immunomodulation for future sepsis. [ABSTRACT FROM AUTHOR]

Summary Recent methodology advances in computational signal deconvolution have enabled bulk transcriptome data analysis at a finer cell-type level. Through deconvolution, identifying cell-type-specific differentially expressed (csDE) genes is drawing increasing attention in clinical applications. However, researchers still face a number of difficulties in adopting csDE genes detection methods in practice, especially in their experimental design. Here we present cypress , the first experimental design and statistical power analysis tool in csDE genes identification. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of multiple influencing factors, by various statistical metrics, to help researchers optimize experimental design and conduct power analysis. Availability and implementation cypress is an open-source R/Bioconductor package at https://bioconductor.org/packages/cypress/. [ABSTRACT FROM AUTHOR]

Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non‐synonymous SNP (ns‐SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns‐SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns‐SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune‐related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses. [ABSTRACT FROM AUTHOR]

Biological hallmarks of splenic marginal zone lymphoma (SMZL) remain poorly described. Herein, we performed in‐depth SMZL characterization through multimodal single‐cell analyses of paired blood/spleen samples. The 3'‐single‐cell RNA‐sequencing, Cellular Indexing of Transcriptomes and Epitopes by sequencing, and 5'‐V(D)J single‐cell RNA‐sequencing datasets were integrated to characterize SMZL transcriptome profiles, including B‐cell receptor and T‐cell receptor repertoires. Hyperexpanded B‐cell clones in the spleen were at a memory‐like stage, whereas recirculating tumor B‐cells in blood encompassed multiple differentiation stages, indicating an unexpected desynchronization of the B‐cell maturation program in SMZL cells. Spatial transcriptomics showed the enrichment of T‐effector and T‐follicular helper (TFH) signatures in the nodular subtype of SMZL. This latter also exhibited gene‐based cell–cell interactions suggestive of dynamic crosstalk between TFH and cancer cells in transcriptomics, further substantiated by using imaging mass cytometry. Our findings provide a comprehensive high‐resolution description of SMZL biological hallmarks and characterize, for the first time in situ, inter‐ and intra‐patient heterogeneity at both transcriptomic and protein levels. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

This paper proposes a full-automatic high-efficiency Mueller matrix microscopic imaging (MMMI) system based on the tissue microarray (TMA) for cancer inspection for the first time. By performing a polar decomposition on the sample's Mueller matrix (MM) obtained by a transmissive MMMI system we established, the linear phase retardance equivalent waveplate fast-axis azimuth and the linear phase retardance are obtained for distinguishing the cancerous tissues from the normal ones based on the differences in their polarization characteristics, where three analyses methods including statistical analysis, the gray-level co-occurrence matrix analysis (GLCM) and the Tamura image processing method (TIPM) are used. Previous MMMI medical diagnostics typically utilized discrete slices for inspection under a high-magnification objective (20×–50×) with a small field of view, while we use the TMA under a low-magnification objective (5×) with a large field of view. Experimental results indicate that MMMI based on TMA can effectively analyze the pathological variations in biological tissues, inspect cancerous cervical tissues, and thus contribute to the diagnosis of postoperative cancer biopsies. Such an inspection method, using a large number of samples within a TMA, is beneficial for obtaining consistent findings and good reproducibility. [ABSTRACT FROM AUTHOR]

Background: Although the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes. Methods: Participants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan–Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes. Results: Sixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54–3.90) for severe liver disease (SLD), and 2.81 (2.60–3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes. Conclusions: High PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management. [ABSTRACT FROM AUTHOR]