Nevin Manimala

Adv Exp Med Biol. 2026;1504:303-326. doi: 10.1007/978-3-032-18966-0_15.

ABSTRACT

Human diseases are multi-factorial, affecting multiple aspects of a homeostatic system. Recent advances in high-throughput technology have allowed the generation of various omics datasets from large cohorts at affordable costs and hence made it possible to study the complex dynamical systems perturbed in human diseases. Studying the complex perturbed systems offers a mechanistic understanding to identify druggable targets and offers new avenues for individualised medical intervention. Mechanisms driving complex human diseases cannot be explored merely by single omics-focused studies. In addition, the heterogeneity among the human populations adds additional complexity and limits the possibility for inferring the regulatory mechanisms underlying these diseases. Examining the disease or phenotype of interest through the lens of multiple omics layers may allow the dissection of the perturbed biological processes associated with the disease. Studying a complex disease through multiple omics layers providing vast information is quite a challenging task and, therefore, requires statistical frameworks to achieve integrative multi-omics analysis. In this chapter, we first summarise key characteristics of each of the omics layers and the various considerations important for the implementation of statistical methods. We then shed light on the most common statistical methods used for multi-omics integration studies and highlight various published examples showing the use of these methods for addressing key biological questions. For this, we show integration examples focused on at least two prime omics layers. We next focus on methods and examples showing multi-omics integration to study dynamical systems in large cohort studies. Finally, we discuss some of the multi-omics approaches and examples from single-cell multi-omics datasets.

PMID:42071151 | DOI:10.1007/978-3-032-18966-0_15

Adv Exp Med Biol. 2026;1504:271-286. doi: 10.1007/978-3-032-18966-0_13.

ABSTRACT

Omics data allow us to study complex biological systems. Mathematical models can help us understand these systems and extract more knowledge from the data. In this chapter, we introduce three types of mathematical models that can be used to potentiate the interpretation of omics datasets: statistical, machine learning and mechanistic models. We delve into the characteristics of these modelling approaches, the essential stages involved in their development and their potential applications to omics datasets.

PMID:42071149 | DOI:10.1007/978-3-032-18966-0_13

Adv Exp Med Biol. 2026;1504:247-269. doi: 10.1007/978-3-032-18966-0_12.

ABSTRACT

Mass spectrometry (MS)-based metabolomics is a powerful tool for understanding the complexity of biochemical processes and to identify biomarkers across diverse biological systems. The vast amount of data generated by extreme resolution mass spectrometers poses significant data processing challenges, requiring robust computational approaches and workflows for meaningful data interpretation. This chapter provides a comprehensive overview of current methodologies in MS-based metabolomics data analysis, with a focus on data preprocessing and pretreatment, m/z extraction and annotation, univariate and multivariate statistical approaches, as well as data visualization. We discuss key considerations for ensuring data quality and the growing role of bioinformatics in pathway analysis and metabolite identification. We highlight the transforming role of extreme resolution and mass accuracy enabled by FT-ICR mass spectrometers, and finally, we explore emerging trends, including artificial intelligence-driven insights and real-time data processing, to guide future developments in this rapidly evolving field.

PMID:42071148 | DOI:10.1007/978-3-032-18966-0_12

Adv Exp Med Biol. 2026;1504:205-224. doi: 10.1007/978-3-032-18966-0_10.

ABSTRACT

Omics data, comprising a diverse array of high-throughput molecular datasets, present substantial statistical challenges due to their intrinsic heterogeneity and variability. Effectively distinguishing biologically meaningful variations from random noise requires the application and development of robust statistical approaches. Interdisciplinary collaboration plays a pivotal role in refining these methodologies and enhancing the understanding of intricate biological systems. This chapter reviews the importance of statistical methods in omics data analysis, highlighting the need for ongoing advancements to address key challenges, including experimental design, preprocessing, dimensionality reduction, statistical modeling of complex datasets, and the interpretation of results. The pursuit of improved reliability in biological insights creates opportunities for the development and refinement of advanced statistical methodologies.

PMID:42071146 | DOI:10.1007/978-3-032-18966-0_10

Adv Exp Med Biol. 2026;1504:119-144. doi: 10.1007/978-3-032-18966-0_6.

ABSTRACT

Metabolomics has emerged as a powerful discipline for characterizing the small molecules that define cellular physiology, environmental responses, and disease states. As technologies advance, researchers face an expanding landscape of analytical platforms, data-processing strategies, and integrative approaches that require clear guidance for effective application. This chapter was written to provide a comprehensive and accessible resource for students, clinicians, and researchers entering or advancing in the field. We outline the fundamentals of metabolomics, describe major analytical methodologies-including MS, NMR, chromatography, and imaging-and summarize key considerations for experimental design, data preprocessing, statistical analysis, and functional interpretation. We also address current challenges related to metabolite identification, reproducibility, and multi-omic integration, and highlight emerging innovations such as stable-isotope tracing, spatial metabolomics, and AI-driven analytics. Together, these elements offer a detailed roadmap for conducting robust, reproducible, and insightful metabolomic studies.

PMID:42071142 | DOI:10.1007/978-3-032-18966-0_6

J Racial Ethn Health Disparities. 2026 May 3. doi: 10.1007/s40615-026-02992-2. Online ahead of print.

ABSTRACT

Patient-physician demographic concordance has been associated with improved communication, trust, and care experiences for minoritized patient populations, yet the demographic composition of team physicians in U.S. women’s professional sports has not been evaluated. We conducted a cross-sectional study to evaluate racial, sex, and intersectional concordance between players and team physicians in the Women’s National Basketball Association (WNBA) and National Women’s Soccer League (NWSL). Official league rosters and publicly available online sources were used to identify players and team physicians, and race and sex were independently classified by multiple reviewers. Descriptive statistics summarized player and physician demographics, and chi-square tests compared racial distributions by league. Intersectional representation was assessed by quantifying Black women among team physicians. We identified 162 WNBA players, 380 NWSL players, 39 WNBA physicians, and 51 NWSL physicians. In the WNBA, 69.1% of players versus 28.2% of physicians were Black, with 64.1% of physicians White. In the NWSL, 67.1% of players versus 82.4% of physicians were White, and 18.4% of players versus 5.9% of physicians were Black. Racial distributions differed significantly between players and physicians in both leagues (p < .01). Men comprised most team physicians (53.8% in the WNBA; 68.6% in the NWSL), and only five Black women were identified across all team physician roles. These findings demonstrate substantial racial, sex, and intersectional discordance between female professional athletes and their physicians, underscoring persistent gaps in workforce diversity that may have implications for athlete care.

PMID:42071128 | DOI:10.1007/s40615-026-02992-2

Sci Rep. 2026 May 3. doi: 10.1038/s41598-026-51601-z. Online ahead of print.

ABSTRACT

This study aims to optimize the mechanical performance, durability, and environmental sustainability of alkali-activated mortars (AAM) incorporating nano-silica (NS), nano-alumina (NA), and polypropylene fiber (PPF). A three-factor, three-level Central Composite Design (CCD) within the Response Surface Methodology (RSM) framework was employed, generating 17 experimental mixtures prepared using fly ash (FA) and ground granulated blast-furnace slag (GGBS) as binder materials. The maximum compressive strength of 82 MPa was achieved in the mixture containing 2% NA, while the maximum flexural strength (12 MPa) was recorded in the mixture containing 1% NS and 0.5% PPF. ANOVA results confirmed the statistical significance of the developed models, with R² = 0.984 and R² = 0.977 for compressive and flexural strength, respectively. Nano-alumina produced a greater increase in strength than NS, and the combination of both nanomaterials enhanced the density of the microstructure through the formation of C-(A)-S-H and N-A-S-H gels. The incorporation of PPF improved durability by preventing microcrack formation and enhancing resistance to acidic and saline environments. For example, specimens containing 2% NS and 2% NA demonstrated more than 20% higher residual strength under sulfuric acid exposure compared with reference specimens. Scanning Electron Microscopy (SEM) analyses showed that the nanomaterials accelerated early strength development by filling micro-voids and creating a more homogeneous matrix structure. A CO₂ emission analysis indicated that the optimized AAM mixture emits approximately 607.4 kg CO₂/m³, representing a reduction of about 26%. The results demonstrate that alkali-activated mortars provide a strong and environmentally sustainable alternative to conventional cement-based systems, highlighting the efficiency and practical potential of this approach.

PMID:42071109 | DOI:10.1038/s41598-026-51601-z

Eur J Nucl Med Mol Imaging. 2026 May 4. doi: 10.1007/s00259-026-07888-1. Online ahead of print.

NO ABSTRACT

PMID:42071107 | DOI:10.1007/s00259-026-07888-1

Sci Rep. 2026 May 4. doi: 10.1038/s41598-026-50789-4. Online ahead of print.

ABSTRACT

Real-world environments are usually characterized by uncertainty, hesitation, and time-dependent information in decision-making problems. The current fuzzy and intuitionistic fuzzy models have the limitation that they cannot adequately model multidimensional and dynamic uncertainty without loss of information. To overcome this shortcoming, this paper develops a new dynamic multi-attribute decision-making model using complex intuitionistic fuzzy sets. A more powerful score function is initially suggested to address the comparison ambiguity that is inherent in traditional complex intuitionistic fuzzy ranking approaches. Based on this enhancement, two dynamic Dombi aggregation operators, i.e., the complex intuitionistic fuzzy dynamic Dombi weighted averaging and complex intuitionistic fuzzy dynamic Dombi weighted geometric operators, are proposed to effectively aggregate time-dependent decision information. The structural properties of the proposed operators, including closure, idempotency, monotonicity, and boundedness, are rigorously established. A systematic decision-making algorithm is then constructed under the proposed framework. The practicality and effectiveness of the approach are demonstrated through a case study involving the selection of an optimal Internet of Things platform. Comparative and sensitivity analyses confirm that the proposed methods provide stable, reliable, and more discriminative results than existing approaches. The developed framework offers a flexible and robust tool for dynamic decision-making problems in complex and uncertain environments.

PMID:42071103 | DOI:10.1038/s41598-026-50789-4

Psychiatry Res Neuroimaging. 2026 Apr 24;360:112228. doi: 10.1016/j.pscychresns.2026.112228. Online ahead of print.

ABSTRACT

Neuroimaging studies in familial high-risk (FHR) individuals are vital for identifying vulnerability markers independent of overt illness. However, research on purely non-prodromal FHR cohorts using comparative multimodal approaches remains limited. This study addresses this gap through multimodal MRI analysis-including cortical morphometry, white matter microstructure, tractography, and functional connectivity-in non-prodromal FHR for psychosis (FHR-P, n = 18), bipolar disorder (FHR-BD, n = 19), and healthy controls (HC, n = 25). FHR-BD showed increased right inferior parietal surface area and right middle temporal volume compared to HC. Conversely, FHR-P exhibited reduced right superior frontal cortical thickness compared to FHR-BD and decreased left pallidum volume compared to HC. White matter analysis revealed significantly lower fractional anisotropy in FHR-P compared to both FHR-BD and HC. FHR-BD showed higher axial diffusivity than HC in the forceps minor, uncinate fasciculus, and right-fronto-occipital fasciculus. No significant differences were found in network-based statistics or graph theoretical measures. These findings reveal shared and distinct neurobiological alterations in non-prodromal FHR-P and FHR-BD, suggesting that grey and white matter disruptions constitute endophenotypes even without clinical symptoms. The lack of network-level findings may reflect the modest sample size, requiring further investigation in larger cohorts.

PMID:42070334 | DOI:10.1016/j.pscychresns.2026.112228