Eugenio Alladio

Background: The gut microbiota is an emerging factor in athletic performance. Methodological limitations persist in longitudinal microbiome studies, particularly regarding the compositional nature of microbiota data and the lack of standardized performance metrics in team sports. The present study applies a Compositional Data Analysis (CoDA) framework with ANOVA-Simultaneous Component Analysis (ASCA) to investigate longitudinal gut microbiota dynamics in elite volleyball athletes and introduces a novel Technical Performance Level (TPL) metric for objective performance quantification. Methods: Seven elite male volleyball athletes from the Italian SuperLega Championship were monitored across four sampling timepoints (T0, T1, T2, T3) during the Regular Season, Rest Period, and Tournament Period. Fecal samples underwent 16S rRNA metabarcoding. CLR-transformed data were analyzed at the Phylum, Family, and Genus level using ASCA with Season, Player, and their interaction. Statistical significance was assessed using the Freedman-Lane permutation test (10,000 iterations). Within-subject associations between TPL and CLR-transformed microbial features were assessed by repeated-measures correlation. Results: Season and Player effects were statistically significant at all taxonomic levels (p ≤ 0.0004), whereas the Season × Player interaction was significant at the Family and Genus levels (p ≤ 0.002) but not at the Phylum level (p = 0.108). The Player effect represented the largest source of variance at the Family (51.31%) and Genus (50.75%) levels. At the Phylum level, the Season × Player interaction accounted for the largest share of variance (36.23%), although it did not reach significance. Within-subject correlation analyses revealed no statistically significant association between microbial features and TPL at any taxonomic level, and none remained significant after correction for multiple comparisons. Conclusions: The application of a compositional data analysis framework modifies the interpretation of gut microbiota dynamics in elite volleyball athletes compared to standard approaches. Individual athlete identity accounted for most of the compositional variance in the microbiota, whereas exploratory within-subject analyses did not reveal statistically significant associations between taxa and TPL. Given the small cohort, any observed trends should be interpreted as exploratory and hypothesis-generating until confirmed in adequately powered cohorts. Longitudinal monitoring of gut microbiota could support individualized surveillance of gut health in elite sport.

Understanding how deeply near-infrared (NIR) radiation penetrates into matter is essential for interpreting (hyper)spectral imaging (SI) data, yet comprehensive assessments of penetration depth remain limited. Although NIR-SI is traditionally considered a surface analytical technique, subsurface contributions may affect spectral profiles, challenging this assumption. This study presents a systematic and quantitative investigation of NIR penetration in a controlled SI setup using multilayer polymeric samples manufactured by 3D printing. Layered cubes and cylinders composed of polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) were analyzed using two independent short-wave NIR-SI systems. Penetration behavior was evaluated through an integrated chemometric workflow combining exploratory analysis (principal component analysis, PCA), unmixing (classical least squares, CLS), supervised linear (partial least squares, PLS) and nonlinear (convolutional neural network, CNN) regression methods. The results demonstrated that NIR-SI can retrieve chemical information from subsurface layers to depths of ∼1 cm. Penetration depth was strongly influenced by material composition, internal structure, and illumination conditions. PCA and CLS revealed nonlinear attenuation and scattering phenomena, while regression models successfully retrieved subsurface information across the full sample height. CNN consistently outperformed PLS, highlighting the importance of nonlinear modeling approaches. The robustness of the proposed strategy arises from the use of designed ad hoc samples, two independent instruments, and multiple complementary chemometric methods converging toward consistent results. Overall, this work challenges the conventional surface-limited view of NIR-SI and provides a robust framework for investigating NIR penetration, supporting the development of depth-resolved and potentially tomographic NIR-SI approaches.

Methods Validation App (MVA) is a desktop application designed to streamline methods validation in regulated laboratories. MVA is developed using Python’s powerful libraries for data handling and statistical analysis. It provides a user-friendly interface that enables analysts to efficiently evaluate key validation parameters. By automating critical processes such as calibration, limit of detection, precision, and accuracy, MVA minimizes human error, standardizes validation protocols, and accelerates evaluation. This tool is designed to guarantee adherence to regulatory requirements while concomitantly enhancing accessibility for individuals with and without expertise in analytical methods validation. As analytical techniques evolve, MVA assist laboratories in sustaining rigorous and dependable validation workflows. MVA is freely available at https://mva.databloom.eu.

Methods Validation App (MVA) is a desktop application designed to streamline methods validation in regulated laboratories. MVA is developed using Python’s powerful libraries for data handling and statistical analysis. It provides a user-friendly interface that enables analysts to efficiently evaluate key validation parameters. By automating critical processes such as calibration, limit of detection, precision, and accuracy, MVA minimizes human error, standardizes validation protocols, and accelerates evaluation. This tool is designed to guarantee adherence to regulatory requirements while concomitantly enhancing accessibility for individuals with and without expertise in analytical methods validation. As analytical techniques evolve, MVA assist laboratories in sustaining rigorous and dependable validation workflows. MVA is freely available at https://mva.databloom.eu.

Background: Bipolar Disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression. Diagnosis relies exclusively on clinical evaluation, frequently resulting in misdiagnosis with other psychiatric disorders due to overlapping symptomatology. This diagnostic uncertainty necessitates the development of objective laboratory-based tools to complement clinical assessment. The absence of reliable biomarkers represents a critical gap that hinders accurate diagnosis and timely therapeutic intervention. This study addresses the need for objective metabolic signatures capable of distinguishing BD patients from healthy individuals through urinary metabolomics profiling.Results: Ultra High-Performance Liquid Chromatography coupled to High Resolution Mass Spectrometry (UHPLC-HRMS) was employed to analyze urine samples from 20 BD patients and 20 healthy controls in both positive and negative ionization modes. A rigorous untargeted metabolomics workflow incorporating blanks, internal standards, and pooled quality control samples ensured high data quality and reproducibility throughout the analytical sequence. Features were extracted and putatively annotated using Compound Discoverer® v3.3 software, followed by implementation of a structured filtering pipeline designed to address high-dimensional data and potential confounding effects from pharmacological treatments. Exploratory principal component analysis revealed clear group separation, while partial least squares discriminant analysis achieved excellent classification performance with accuracy exceeding 90%. The retained metabolic features demonstrated robust discriminative power, successfully distinguishing BD patients from healthy controls based on characteristic urinary metabolic profiles.Significance: This research demonstrates that a characteristic urinary metabolic fingerprint distinguishes BD patients from healthy individuals with high accuracy. The validated analytical and chemometric workflow provides a foundation for developing objective laboratory-based diagnostic tools that could complement clinical evaluation, potentially reducing misdiagnosis rates. These findings represent a significant step toward translating metabolomics research into clinically actionable biomarker strategies for psychiatric disorders.

The estimation of the post-mortem interval (PMI) in forensic skeletal remains is extremely challenging, as traditional morphological methods lose their effectiveness and accuracy as decomposition progresses. To address this issue, this study utilizes metabolomics to investigate the biochemical changes affecting bone tissue during the decomposition process. Fragments of pig mandibles were buried in an open grassland field at varying depths (0, 10, 30, and 50 cm) and collected every month up to 6 months. Bone metabolites were extracted using a single-phase methanol-water protocol, and both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied for their analysis. The primary goal of this study is to identify specific metabolic shifts associated with increasing post-mortem intervals to identify potential bone metabolomic biomarkers for PMI and to assess the impact of burial depth on these changes. The generated regression models using LC-MS/MS data were able to estimate the PMI of the skeletal fragments with an accuracy of 14 days over 6 months, an outstanding result, particularly considering the current lack of methodologies to estimate PMI from bones. Burial depth, instead, did not play a significant role on the metabolomic bone signature. This research deepens our understanding of post-mortem biochemical processes in bones, making a significant contribution to the advancing field of forensic metabolomics, and highlights the potential of metabolomics for investigating buried skeletal remains and enhancing post-mortem interval assessments.

Accurate quantification of both crystalline and amorphous phases of active pharmaceutical ingredients is crucial for ensuring drug efficacy and stability. However, amorphous content quantification poses significant challenges using traditional analytical techniques. To overcome these limitations, this work introduces a novel method combining solid‐state nuclear magnetic resonance (SSNMR) with multivariate curve resolution‐alternating least squares(MCR‐ALS). Tested on mebendazole and indomethacin as model compounds, this approach enables accurate and robust quantification of crystalline and amorphous phases by leveraging the full spectral deconvolution through advanced multivariate analysis, overcoming the intrinsic challenges of cross polarization magic angle spinning (CPMAS) quantification. The approach allows for accurate quantification by either using reference spectra of the pure forms as constraints or extracting the spectral contributions directly from mixture data, making it applicable even when pure components are not available. While demonstrated in pharmaceuticals, this fast, robust, and user‐independent approach is broadly applicable for solid‐state quantification across various fields.