Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

Total error in lymphocyte subpopulations by flow cytometry-based in state of the art using Spanish EQAS data.

OBJECTIVES: Flow cytometry analyses of lymphocyte subpopulations (T, B, NK) are crucial for enhancing clinical algorithms and research workflows. Estimating the total error (TE) values for the percentage and absolute number of lymphocyte subpopulations using the state-of-the-art (SOTA) approach with real data from an external proficiency testing (EPT) scheme was performed. A comparison with previously published Biological Variability (BV)-based specifications was carried out. METHODS: A total of 44,998 results from 86 laboratories over 10 years were analysed and divided into two five-year periods (2012-2016) and (2017-2021). Data come from the IC-1 Lymphocytes scheme of the Spanish External Quality Assurance System (EQAS) GECLID Program. This quantitative scheme includes percentages and absolute numbers of CD3+, CD3+CD4+, CD3+CD8+, CD19+, and CD3-CD56+CD16+ NK cells. The percentage of TE was calculated as: |reported value - robust mean|*100/robust mean for each laboratory and parameter. The cut-off for TE is set at 80 % best results of the laboratories. RESULTS: A significant reduction in the SOTA-based TE for all lymphocyte subpopulations in 2017-2021 was observed compared to 2012-2016. The SOTA-based TE fulfils the minimum BV-based TE for percentages of lymphocyte subpopulations. The parameter with the best analytical performance calculated with SOTA (2017-2021 period)-based TE was the percentage of CD3+ (TE=3.65 %). CONCLUSIONS: The values of SOTA-based specifications from external quality assurance program data are consistent and can be used to develop technical specifications. The technological improvement, quality commitment, standardization, and training, reduce TE. An update of TE every five years is therefore recommended. TE assessment in lymphocyte subsets is a helpful and reliable tool to improve laboratory performance and data-based decision-making trust.

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.

BACKGROUND: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE: The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). METHODS: A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. RESULTS: We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. CONCLUSIONS: The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.

Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating.

Soluble solids content (SSC) is particularly important for kiwifruit, as it not only determines its flavor, but also helps assess its maturity. Visible/near-infrared (Vis/NIR) spectroscopy has been widely used to evaluate the SSC of kiwifruit. Still, the local calibration models may be ineffective for new batches of samples with biological variability, which limits the commercial application of this technology. Thus, a calibration model was developed using one batch of fruit and the prediction performance was tested with a different batch, which differs in origin and harvest time. Four calibration models were established with Batch 1 kiwifruit to predict SSC, which were based on full spectra (i.e., partial least squares regression (PLSR) model based on full spectra), continuous effective wavelengths (i.e., changeable size moving window-PLSR (CSMW-PLSR) model), and discrete effective wavelengths (i.e., competitive adaptive reweighted sampling-PLSR (CARS-PLSR) model and PLSR-variable importance in projection (PLSR-VIP) model) respectively. The Rv2 values of these four models in the internal validation set were 0.83, 0.92, 0.96, and 0.89, with corresponding RMSEV values of 1.08 %, 0.75 %, 0.56 %, and 0.89 %, and RPDv values of 2.49, 3.61, 4.80, and 3.02, respectively. Clearly, all four PLSR models performed acceptably in the validation set. However, these models performed very poorly in predicting the Batch 2 samples, with their RMSEP values all exceeding 1.5 %. Although the models could not be used to predict exact SSC, they could still interpret the SSC values of Batch 2 kiwifruit to some extent because the predicted SSC values could fit a specific line. To enable the CSMW-PLSR calibration model to predict the SSC of Batch 2 kiwifruit, the robustness of this model was improved by calibration updating and slope/bias correction (SBC). Different numbers of new samples were randomly selected for updating and SBC, and the minimum number of samples for updating and SBC was finally determined to be 30 and 20, respectively. After calibration updating and SBC, the new models had average Rp2, average RMSEP, and average RPDp values of 0.83 and 0.89, 0.69 % and 0.57 %, and 2.45 and 2.97, respectively, in the prediction set. Overall, the methods proposed in this study can effectively address the issue of poor performance of calibration models in predicting new samples with biological variability and make the models more robust, thus providing important guidance for the maintenance of SSC online detection models in practical applications.

The magnitude and time-course of physiological responses to 9 weeks of incremental ramp testing.

PURPOSE: The aims of this study were to assess (1) the day-to-day variability in, and (2) the magnitude and time-course of adaptation of physiological parameters (i.e., maximal oxygen uptake [VO2 max], heart rate [HR], blood lactate concentration, respiratory exchange ratio [RER], ratings of perceived exertion [RPE], and time-to-exhaustion [TTE]) in response to an intervention involving three incremental ramp tests per week for 9 weeks. METHODS: Twelve participants (25 ± 4 yrs, VO2 max, 47.8 ± 5.2 mL∙min-1 ∙kg-1 (means ± SD)) completed the entire experimental procedure. The tests comprised a 5-min constant workload to obtain submaximal parameters followed by an incremental protocol until exhaustion. RESULTS: The mean day-to-day variability for the maximal value of VO2 was 2.8%, 1.1% for HR, 18.1% for blood lactate concentration, 2.1% for RER, 1.1% for RPE, and 5.0% for TTE. The values for the corresponding submaximal variables were 3.8% for VO2 , 2.1% for HR, 15.6% for blood lactate concentration, 2.6% for RER and 6.0% for RPE. VO2 max (+4.7% ± 3.5%), TTE (+17.9% ± 8.6%), and submaximal HR (-3.2 ± 3.5%) improved significantly. Except for RPE (p < 0.01), there were no alterations in the coefficient of variation for any parameter. On the group level, the first changes greater than the day-to-day variability in VO2 max, TTE, and submaximal HR were observed after 21, 12, and 9 training sessions, respectively. CONCLUSION: Based on our findings, we recommend that training studies include assessment of the reliability of the measurements, for example, the CVs in the specific laboratory to be able to judge if the changes detected are actually physiological.

Reference change value of global longitudinal strain in clinical practice: A test-rest quality implementation project.

BACKGROUND: Reference change value (RCV) is used to assess the significance of the difference between two measurements after accounting for pre-analytic, analytic, and within-subject variability. The objective of the current study was to define the RCV for global longitudinal strain (GLS) using different semi-automated software in standard clinical practice. METHODS: Using a test-retest study design, we quantified the median coefficient of variation (CV) for GLS using AutoStrain and Automated Cardiac Motion Quantification (aCMQ) by Philips. Triplane left-ventricular ejection fraction (LVEF) was measured for comparison. Multivariable regression analysis was performed to determine factors influencing test-retest CV including image quality and the presence of segmental wall motion abnormalities (WMA). RCV was reported using a standard formula assuming two standard deviations for repeated measurements; results were also translated into Bayesian probability. Total measurement variation was described in terms of its three different components: pre-analytic (acquisition), analytic (measuring variation), and within-subject (biological) variation. RESULT: Of the 44 individuals who were screened, 41 had adequate quality for strain quantification. The mean age of the cohort was 56.4 ± 16.8 years, 41% female, LVEF was 55.8 ± 9.8% and the median and interquartile range for LV GLS was -17.2 [-19.3 to -14.8]%. Autostrain was more time efficient (80% less analysis time) and had a lower total median CV than aCMQ (CV = 7.4% vs. 17.6%, p < .001). The total CV was higher in patients with WMA (6.4% vs. 13.2%, p = .035). In non-segmental disease, the CV translates to a RCV of 15% (corresponding to a probability of real change of 80%). Assuming a within-subject variability of 4.0%, the component analysis identified that inter-reader variability accounts for 3.7% of the CV, while acquisition variability accounts for 4.0%. CONCLUSION: Using test-retest analysis and CVs, we find that an RCV of 15% for GLS represents an optimistic estimate in routine clinical practice. Based on our results, a higher RCV of 17%-21% is needed in order to provide a high probability of clinically meaningful change in GLS in all comers. The methodology presented here for determining measurement reproducibility and RCVs is easily translatable into clinical practice for any imaging parameter.

Between-day reliability of cytokines and adipokines for application in research and practice.

Purpose: This study assessed the biological reliability of peripheral human cytokines and adipokines, and the influence of participant characteristics on total error. This has essential application to interventional cytokine measurement to ensure that reported results are interpreted with confidence. Methods: Participants (49% female, 18-85 years, n = 84) completed two consecutive-day testing sessions. Participants provided a venous blood sample at the same time of day across two consecutive days, under standardized participant presentation, including 24-h rested and 12-h fasted conditions. Multiplex immunoassay was used to assess inflammatory analytes from samples (predominantly plasma). Repeat measurements were conducted between-day for total precision quantification, and technical (technique) error was negated from the total to provide an estimate of biological (attributed to participant presentation) error. Results: Whilst there was no evidence of statistically significant biological error, a small amount of biological error was consistently present across most analytes (∼3.3%/0.07 pg/ml), which was largest for measurement of leptin (7.3%/210 pg/ml). There was also an influence of sex on reliability of leptin and adiponectin (total model explained 6-7% of error variation), where females demonstrated the greatest error. Conclusion: Biological error reported in this study should be applied to any future study or individual with a repeated measurement of cytokine concentrations over time that maintain best practice procedures (12-h fasted, 24-h rested). In most cases, raw error should be used, with exceptions for women for measurement of leptin and adiponectin. This approach will ensure that results are reported with certainty for improved reporting of intervention efficacy.

Current use of bone turnover markers in the management of osteoporosis.

The adult bone is continuously being remodelled to repair microdamage, preserve bone strength and mechanical competence as well as maintain calcium homeostasis. Bone turnover markers are products of osteoblasts (bone formation markers) and osteoclasts (bone resorption markers) providing a dynamic assessment of remodelling (turnover). Resorption-specific bone turnover markers are typically degradation products of bone collagen molecules (N- [NTX] and C-telopeptide cross-linked type 1 collagen [CTX]), which are released into the circulation and excreted in urine; or enzymatic activities reflecting osteoclastic resorption, tartrate-resistant acid phosphatase [TRACP]. Formation-specific bone turnover markers embrace different osteoblastic activities: type 1 collagen synthesis (Procollagen type I N- propeptide [PINP]), osteoblast enzymes (bone-specific alkaline phosphatase [BALP]), or bone matrix proteins [osteocalcin]. Among individuals not receiving osteoporosis treatment, resorption and formation markers are tightly linked and highly correlated (r = 0.6-0.8). Significant biological variability was reported in the past, but these issues have been greatly improved with automated assays and attention to pre-analytical and analytical factors that are known to influence bone turnover marker levels. Bone turnover markers are not useful in the diagnosis of osteoporosis, the individual prediction of bone loss, fracture, or rare complications, or in the selection of pharmacological treatment. Despite remaining issues with reference intervals and assays harmonization, bone turnover markers have proven to be useful in elucidating the pharmacodynamics and effectiveness of osteoporosis medications in clinical trials. As an alternative to BMD testing, BTMs may be useful to monitor osteoporosis therapies.

Single-Cell Proteomics: The Critical Role of Nanotechnology.

In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.

The Head of Fannia pusio (Fanniidae: Diptera) as A Novel Source of Morphometric Data for Assessing of Variation Along Geographic and Biological Lines.

Fannia Robineau-Desvoidy, 1830 is the most diverse genus in the family Fanniidae (Diptera), with 288 species, many of which are include many of sanitary, economic and legal interest. The morphological homogeneity within the genus often makes species determination difficult. The best option for correct identification is to combine molecular and morphological analyses. The variation in the shape of a selection of body characters can be assessed by Geometric Morphometrics using the head as an innovative structure. Sex must be accounted for as a key covariate in this kind of study, since Fannia, as many other Diptera, has a sexually dimorphic head structure, with holoptic males and dicoptic females. Firstly, we analysed a set of Fannia sp. specimens sampled across the Iberian Peninsula (2012-2015), of which Fannia pusio (Wiedemann, 1830) was found to be the most abundant species. Our analyses provide significant morphological information. Fannia pusio exhibits clear intraspecific morphometric variation along an Iberian-wide East-West axis. A similar pattern emerged when comparing a laboratory-bred colony and wild samples.

Highly Variable Dietary RNAi Sensitivity Among Coleoptera.

Many herbivorous beetles (Order Coleoptera) contribute to serious losses in crop yields and forest trees, and plant biotechnology solutions are being developed with the hope of limiting these losses. Due to the unprecedented target-specificity of double-stranded RNA (dsRNA), and its utility in inducing RNA interference (RNAi) when consumed by target pest species, dsRNA-based plant biotechnology approaches represent the cutting edge of current pesticide research and development. We review dietary RNAi studies in coleopterans and discuss prospects and future directions regarding RNAi-based management of coleopteran plant pests. Herein, we also provide a balanced overview of existing studies in order to provide an accurate re-assessment of dietary RNAi sensitivity in coleopterans, despite the limitations to the existing body of scientific literature. We further discuss impediments to our understanding of RNAi sensitivity in this important insect order and identify critical future directions for research in this area, with an emphasis on using plant biotechnology approaches.

Interindividual Biological Variability of Reticulocytes and Their Maturation Fractions in the Pediatric Population.

OBJECTIVES: Because published data about the variability of reticulocyte counts in children are scarce, the interindividual biological variability of the automated reticulocyte count and its maturation fractions according to age and sex were analyzed. METHODS: A retrospective, observational, analytical study was designed to establish and compare normal values of the automated reticulocyte count and its maturation fractions in different age and sex groups. The sample was drawn from results of CBC counts performed in children aged between 2 months and 18 years using an indirect sampling methodology. RESULTS: A total of 9,362 CBC counts were analyzed. Automated reticulocyte count decreased between 2 months and 3 years of age and slowly increased thereafter, showing higher values in girls up to the age of 9 years, and equalized by sex thereafter. Immature reticulocyte fraction increased until 7 months of age; decreased progressively until 4 years of age; and then showed a discreet but constant rise, with significantly higher values in boys older than 1 year. The low-fluorescence fraction was relatively steady, with significantly higher values in girls aged 8 months and older. CONCLUSIONS: The automated reticulocyte count and its maturation fractions show significant variations related to age and sex in pediatric patients.

Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data.

BACKGROUND: RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. RESULTS: A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. CONCLUSIONS: This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.

Biological variations of hematologic and biochemical parameters in cows during early lactation.

The aim of the study was to determine biologic variations of the main parameters in cows in early lactation and to compare intra-individual variations of parameters in cows and other species. 50 cows were included in the experiment. Blood samples were taken at the moment of calving, then on 1st, 7th, 14th and 28th day after calving. CVi had the following values: 1.9-5% for MCV, MCH, MCHC, GLU, TProt, ALB, UREA, Ca, P and Mg; 5.1-10% for RBC, Hgb, Hct, WBC, NEU, LYM, BHB, ALT, CHOL, TGC and >10% for PLT, NEFA, TBil, AST and GGT. For RBC, Hgb, Hct, MCV, MCH subject-based reference values or the reference change value should be used. For all other parameters except AST, population-based reference intervals should be used with caution. For LYM, NEU, PLT, GLU, TProt, ALB, CHOL and TGC index of individuality was close to 0.6 which means that subject-based reference values can be much more adequate to use then population-based reference intervals. For AST population-based intervals should be used. There is little effect of parity (increase CVi with parity) and no effect of milk production. CVi values obtained in cows in early lactation are specific because they deviate from the CVi values of other animal species and human. Calculated RCVs varied from 5.5% for MCHC to 41% for AST. High individuality index for many metabolic parameters opens possibility for development of model for longitudinal monitoring of metabolic status of individual animals during lactation. That requires further research.

Modeling Between-Subject Variability in Subcutaneous Absorption of a Fast-Acting Insulin Analogue by a Nonlinear Mixed Effects Approach.

Despite the great progress made in insulin preparation and titration, many patients with diabetes are still experiencing dangerous fluctuations in their blood glucose levels. This is mainly due to the large between- and within-subject variability, which considerably hampers insulin therapy, leading to defective dosing and timing of the administration process. In this work, we present a nonlinear mixed effects model describing the between-subject variability observed in the subcutaneous absorption of fast-acting insulin. A set of 14 different models was identified on a large and frequently-sampled database of lispro pharmacokinetic data, collected from 116 subjects with type 1 diabetes. The tested models were compared, and the best one was selected on the basis of the ability to fit the data, the precision of the estimated parameters, and parsimony criteria. The selected model was able to accurately describe the typical trend of plasma insulin kinetics, as well as the between-subject variability present in the absorption process, which was found to be related to the subject's body mass index. The model provided a deeper understanding of the insulin absorption process and can be incorporated into simulation platforms to test and develop new open- and closed-loop treatment strategies, allowing a step forward toward personalized insulin therapy.

Should clinical laboratories adapt to the reality of chronic kidney disease in the determination of parathyroid hormone?

Objectives: The contribution of the clinical laboratory to diagnostics is increasingly important since a great deal of clinical decisions rely on laboratory test results. Content: Parathyroid hormone (PTH) measurement presents a considerable analytical variability due to the heterogeneity of its circulating forms and the antigenic configuration of the different assays commercially available. Such variability may have an impact on pathological conditions associated with significant increases in circulating PTH, as it is the case of chronic kidney disease (CKD). Summary: Despite the recent identification of new molecules involved in bone and mineral disorders associated with CKD, such as klotho or the fibroblastic factor 23 (FGF23), nephrologists still base their clinical decisions on PTH concentrations. The problem is that unawareness of these analytical considerations may cause errors in the clinical interpretation of test results. Outlook: This systematic review addresses these issues from the clinical laboratory perspective and proposes new approaches related to PTH method selection and result expression. These new strategies will help laboratory medicine specialists and nephrologist better determine the status of CKD patients.

Spoilage of fresh turkey and pork sausages: Influence of potassium lactate and modified atmosphere packaging.

Fresh poultry and pork meat products represent highly perishable products which are susceptible to spoil within a few days after production. Lactate addition and modified atmosphere packaging are common preservation strategies used to overcome spoilage. This study aimed to identify the effects of these strategies and their possible interactions on spoilage indicators simultaneously on fresh pork and turkey sausages. Ten batches of raw meat (turkey or pork) sausages were industrially produced with different lactate concentrations (0, 1 or 2% w/w in turkey and 0, 0.57 and 1.13% w/w in pork), packed under different gas mixtures (air, MAP1: 70% O2 - 30% CO2 and MAP2: 50% CO2 - 50% N2) and chill stored during 22 days. Spoilage responses including enumeration of total aerobic mesophilic and lactic acid bacteria, measurement of pH and colour, evaluation of visual defects and off-odour, were monitored. Effects of lactate and modified atmosphere packaging (MAP) as well as random effect of the batch variability were studied using a mixed effect model. Despite initial batch variability, significant effects of lactate and gas packaging were observed but in a different way in turkey and pork. Our results suggest that for fresh turkey sausages, the gas mixture enriched in oxygen enhanced off-odour perception and sausage discolouration from red to dark grey / brown colour. Unlike turkey sausages, in pork sausages, lactate did not significantly influence the monitored spoilage responses, whereas MAP (70% O2-30% CO2) reduced the off-odour perception. The developed model could be useful to estimate the effect of preservation strategies on spoilage occurrence while considering industrial batch variability.

Microbiological and process variability using biological indicators of inactivation (BIIs) based on Bacillus cereus spores of food and fish-based animal by-products to evaluate microwave heating in a pilot plant.

Microwave processing can be a valid alternative to conventional heating for different types of products. It enables a more efficient heat transfer in the food matrix, resulting in higher quality products. However, for many food products a uniform temperature distribution is not possible because of heterogeneities in their physical properties and non-uniformtiy in the electric field pattern. Hence, the effectiveness of microwave inactivation treatments is influenced by both intrinsic (differences between cells) and extrinsic variability (non-uniform temperature). Interpreting the results of the process and considering its impact on microbial inactivation is essential to ensure effective and efficient processing. In this work, we quantified the variability in microbial inactivation attained in a microwave pasteurization treatment with a tunnel configuration at pilot-plant scale. The configuration of the equipment makes it impossible to measure the product temperature during treatment. For that reason, variability in microbial counts was measured using Biological Inactivation Indicators (BIIs) based on spherical particles of alginate inoculated with spores of Bacillus spp. The stability of the BIIs and the uncertainty associated to them was assessed using preliminary experiments in a thermoresistometer. Then, they were introduced in the food product to analyse the microbial inactivation in different points of the products during the microwave treatment. Experiments were made in a vegetable soup and a fish-based animal by-product (F-BP). The results show that the variation in the microbial counts was higher than expected based on the biological variability estimated in the thermoresistometer and the uncertainty of the BIIs. This is due to heterogeneities in the temperature field (measured using a thermographic camera), which were higher in the F-BP than in the vegetable soup. Therefore, for the process studied, extrinsic variability was more relevant than intrinsic variability. The methodology presented in this work can be a valid method to evaluate pasteurization treatments of foods processed by heating, providing valuable information of the microbial inactivation achieved. It can contribute to design microwave processes for different types of products and for product optimization.

A new method for identification of outliers in immunogenicity assay cut point data.

The cut point is an important parameter for immunogenicity assay validation and critical to immunogenicity assessment in clinical trials. FDA (2019) recommends using a statistical approach to derive cut point, with an appropriate outlier removal procedure. In general, the industry follows the methods described in Shankar et al. (2008) and Zhang et al. (2013) among others to determine cut point. Outlier removal is a necessary step during the cut point determination exercise to reduce potential false negative classifications. However, the widely used statistical outlier removal method, namely, Tukey's box-plot method (1.5 times inter-quartile range, IQR), is often found to be overly conservative in the sense that it removes too many "outliers". Tukey's box-plot method can be used to flag potential outliers for further investigation, however, it is not a hypothesis testing based statistical method. Removing these suspected "outliers" will lead to lower cut point which might confound immunogenicity assessment due to the presence of many low false positives. Besides, the very nature of assay analytical variability has a non-negligible adverse impact on the reliability of ADA classification in terms of false positive and false negative, demanding as large as possible contribution from biological variability relative to analytical variability. A new outlier removal procedure, which takes into account the relative magnitude between biological variability and analytical variability within the sample population, is proposed and statistically justified. After sequential removal of analytical and biological outliers, a 5% false positive rate and 1% false positive rate in screening and confirmatory assays, respectively, are still targeted without increasing potential false negatives. Internal data shows that this practice has minimal impact on assay sensitivity and has the advantage of selecting true positive samples. It is shown that the new procedure is more appropriate for cut point determination.

Variability in Cerebrospinal Fluid MicroRNAs Through Life.

The development of the human brain starts in the first weeks of embryo differentiation. However, there are many relevant neurodevelopmental processes that take place after birth and during lifespan. Such a fine and changing scenario requires the coordinated expression of thousands of genes to achieve the proper specialization and inter-connectivity. In this context, microRNAs (miRNAs), which can modulate mRNA stability and translation, are gaining recognition for their involvement in both brain development and neurodevelopmental disorders. Therefore, cerebrospinal fluid (CSF) miRNAs should be perfectly differentiated in relevant age periods. In this study, we aimed to highlight the biological variability of miRNA expression in the CSF throughout life, which is also crucial for biomarker discovery in CNS pathologies, especially in children, where they are desperately needed. We analyzed the CSF microRNAome of 14 healthy children (aged 0-7.4 years) by smallRNA-Seq and compared it with previously published data in adults (N = 7) and elders (N = 11). miR-423-5p and miR-22-3p were overexpressed in the < 1 and > 3 years groups, respectively. Additionally, we detected 18 miRNAs that reached their highest peak of expression at different time-points during the lifespan and sets of miRNAs that were exclusively expressed in a specific age group. On the contrary, miR-191-5p showed stable expression in CSF from the first year of life. Our results remark the complex differential miRNA expression profile that can be observed through life, which underlines the need for including appropriate age-matched controls when the expression of CSF miRNAs is analyzed in different pathological contexts. Graphical abstract.