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Nevin Manimala Statistics

Greek Child and Adolescent Well-Being in the Post-pandemic Normalcy

Adv Exp Med Biol. 2026;1490:155-164. doi: 10.1007/978-3-032-03402-1_17.

ABSTRACT

BACKGROUND: The measures taken to protect public health during the pandemic had a significant impact on the mental, social, and physical well-being of children and adolescents. Restrictive measures had affected their development, behavior, school life, family, and friendships.

AIM: To explore the psychological impact on children and adolescents after the withdrawal of COVID-19 restrictions.

METHODS AND MATERIALS: This study was a cross-sectional study with a convenience sample. The sample consisted of 100 children and adolescents, and it was conducted in the outpatient department of the pediatric population of the Regional Unit of Trikala. The scale used in the present study explores post-pandemic coping strategies upon life returning to normal for children and teenagers, “PPCSRN-CT.” The data were analyzed with the SPSS-12 statistical package, and multiple linear regression was performed. The level of statistical significance was set at p < 0.05.

RESULTS: In the study, nearly 60% of the participants were girls, and over 80% of the children and adolescents lived in urban areas in Trikala Prefecture. Half of them (50%) were 12 years old or older. The statistical analysis found that children and adolescents worry significantly more about the health of their loved ones when they are alone and don’t have anyone to share their concerns with (p = 0.008). Furthermore, children and adolescents who lived with both parents believe that their parents have become less strict with them after the withdrawal of restrictions due to the pandemic (p = 0.001). Additionally, children and adolescents living with both parents believe that their parents care for them more after the withdrawal of restrictions due to the COVID-19 pandemic (p = 0.013). The study also revealed that the desire of children and teenagers to learn more about COVID-19 is positively affected by whether someone close to them has been sick with SARS-CoV-2 (p = 0.009). Furthermore, children and adolescents who want to learn more about the COVID-19 pandemic and whose parents are less strict with them expect their lives to return to normal immediately after the removal of restrictions due to the COVID-19 pandemic (p = 0.016 and p = 0.006, respectively). Finally, the study found that children and adolescents’ belief that they will move on in life together with friends is positively influenced by whether they are less emotionally burdened (p = 0.024) and by whether they are more positive about their daily life (p < 0.001).

CONCLUSIONS: It is important to evaluate the perceptions of children and adolescents post-pandemic in order to implement interventions to empower this group.

PMID:41479079 | DOI:10.1007/978-3-032-03402-1_17

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Nevin Manimala Statistics

Self-Concept in Pediatric Cancer Patients

Adv Exp Med Biol. 2026;1490:145-153. doi: 10.1007/978-3-032-03402-1_16.

ABSTRACT

INTRODUCTION: Visible changes in self-concept among pediatric patients with cancer are a bothersome experience that may accompany them until adulthood.

PURPOSE: To explore the self-concept and in particular the physical appearance and popularity in children with cancer compared to healthy ones.

METHODS AND MATERIAL: A descriptive correlational study of 100 children (50 healthy and 50 diagnosed with different forms of cancer) ages 8 to 10 years. Participants were recruited from a public hospital in Athens Greece. The research instrument was “Piers-Harris Children’s Self-Concept scale” which included patients’ characteristics. The data were analyzed with the SPSS-12 statistical packet by using the following statistical tests: χ2-test, nonparametric Mann-Whitney U-test, Kruskal-Wallis test.

RESULTS: It was found that children with cancer reported more negatively or both physical appearance and popularity (p ≤ 0.001) compared to the healthy ones. Moreover, the 8-year-old children with cancer reported more negatively for physical appearance (p ≤ 0.001) but not for the factor popularity (p = 0.021), while the 10-year-old children with cancer present a more negative body image relative to the healthy ones, only for the factor popularity (p ≤ 0.001) but not for the factor physical appearance (p = 0.134). In terms of gender, female subjects with cancer presented a more negative body image relative to the healthy female subjects for both factors examined (p ≤ 0.001), while male subjects with cancer presented a more negative body image relative to the healthy male subjects only for the factor physical appearance. A positive correlation between popularity and physical appearance was found (p ≤ 0.001) but only among the children with cancer.

CONCLUSIONS: The present results highlight the differences in physical appearance and popularity among children with cancer and their healthy counterparts. The recognition of the role of physical appearance as a significant factor for children with cancer may inform the development of effective interventions for this group of children.

PMID:41479078 | DOI:10.1007/978-3-032-03402-1_16

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Nevin Manimala Statistics

Empathy and Burnout Among Nurses: a Cross-Sectional Study in a University Hospital in Central Greece

Adv Exp Med Biol. 2026;1490:25-34. doi: 10.1007/978-3-032-03402-1_4.

ABSTRACT

BACKGROUND: Empathy cultivates deeper interpersonal relationships; however, frequent exposure can trigger the risk of burnout. This study aims to predict empathy, burnout, and syndrome among nursing staff in a university hospital in Central Greece.

MATERIAL AND METHODS: This is a synchronic study on the nursing staff of the university general hospital in central Greece. The sample consisted of 210 nurses who took part in the study by completing a questionnaire that included demographic and social characteristics, the “Copenhagen Burnout Inventory” and the “Composite Empathy Scale.” The Pearson correlation coefficient (r) and linear regression analysis with a statistical significance of 0.05 were used for the statistical analysis. Univariate and multiple logistic regression analyses were used to determine the potential predictive factors associated with burnout and empathy.

RESULTS: The prevalence of burnout and empathy among nursing staff was 62.5%. A significant positive correlation between empathy with burnout was found in almost all dimensions. For burnout subscales, “Personal Burnout” was found to be at 44.1%, Operational Burnout “at 62.5%, and in” Burn related to patients “the average was 58.3%. A higher level of burnout is associated with” Workplace Burnout “for nurses on shift work. There was a significant negative correlation between “Cognitive Personal Empathy” and the “Personal Burnout.” Also, 92.9% of the nursing staff reported suffering from a disease.

CONCLUSION: The nurses in the university hospital are “aged staff” with health problems, high levels of empathy, and burnout.

PMID:41479066 | DOI:10.1007/978-3-032-03402-1_4

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Nevin Manimala Statistics

EEGs, Neuropsychological Performance and Financial Capacity in aMCI Patients: a Preliminary Longitudinal Study

Adv Exp Med Biol. 2026;1490:7-13. doi: 10.1007/978-3-032-03402-1_2.

ABSTRACT

Given the lack of relevant research, the goal of this paper is to present longitudinal data regarding electroencephalograms (EEGs) and financial capacity for amnestic mild cognitive impairment (aMCI) patients in order to examine if there are specific EEG indicators that may reveal financial capacity deficits. A detailed neuropsychological and financial capacity assessment along with EEGs was performed at three time points (baseline, 6-month retest, and 12-month retest). Strong statistically significant correlations were found exclusively for the group of aMCI patients with the lowest financial capacity performance (F1 group) between neuropsychological test performance and EEG recordings. EEGs differentiate aMCI patients into two groups: those with high financial capacity performance and those who fail in financial capacity. For the second group, EEGs measurements can be a promising source of information for predicting those aMCI individuals who need assistance in this complex cognitive domain and in order to prevent financial exploitation.

PMID:41479064 | DOI:10.1007/978-3-032-03402-1_2

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Nevin Manimala Statistics

Quality Control Measures and Statistical Strategies to Address the Challenges of High-Content Phenotypic Data

Methods Mol Biol. 2026;2989:125-150. doi: 10.1007/978-1-0716-4985-5_7.

ABSTRACT

High-content image-based cytological profiling is a powerful strategy for studying the effects of chemical and genetic perturbations on the cell. Cytological profiling assays illuminate multiple cellular compartments within each cell by multiplex fluorescent staining, followed by automated microscopy and image analysis. In this chapter, we show how to utilize data derived from images of fluorescently labeled cells and organelles while simultaneously addressing common challenges of this data type. We discuss different modes of interpreting raw cellular features and describe statistical methods for using said features to quantitatively evaluate overall assay quality and reproducibility. Data standardization is described as a two-tiered task, and the more recent EMD metric is implemented as a quantitative measure of phenotypic change. We illustrate each technique using data from an osteosarcoma (U-2 OS) high-content screening assay and describe all tools required to reproduce this work.

PMID:41479051 | DOI:10.1007/978-1-0716-4985-5_7

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Nevin Manimala Statistics

The Epigenetic Angle in the Precision Medicine Era for Blood Disorder Advancements

Subcell Biochem. 2026;114:299-353. doi: 10.1007/978-3-032-08530-6_6.

ABSTRACT

This work examined the integration of epigenetics and precision medicine in the management of various blood disorders, including anemias, antiphospholipid syndrome, hemochromatosis, hemophilia, leukemia, lymphoma, multiple myeloma, porphyria, thalassemia, thrombocytopenia, thrombocytosis, polycythemia, von Willebrand disease, and coagulopathy. It begins with an overview of key concepts and the significance of precision medicine in treating blood diseases, supported by current statistics. The role of noncoding RNAs (ncRNAs) is highlighted, detailing their mechanisms of action and clinical implications as potential biomarkers and therapeutic targets. Additionally, the chapter explores natural products used in personalized medicine, examining their sources, mechanisms, and successful case studies in blood disorders. A comprehensive review of recent clinical trials provides insights into the impact of innovative therapies and FDA approvals on treatment protocols, emphasizing the importance of combination therapies. Future directions address emerging research technologies such as clustered regularly interspaced short palindromic repeats (CRISPR) and ethical considerations surrounding genetic testing and patient consent. The synthesis of findings underscores the contributions of epigenetics and precision medicine to blood disease treatment, advocating for interdisciplinary research and ongoing education to enhance patient care and outcomes.

PMID:41479041 | DOI:10.1007/978-3-032-08530-6_6

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Nevin Manimala Statistics

Evolutionary Insights from Sequence Analysis of Interleukin 17A (IL17A)

Methods Mol Biol. 2026;2983:397-447. doi: 10.1007/978-1-0716-4901-5_32.

ABSTRACT

Nucleotide sequence analyses provide insights into changes that might have an impact on proteins and their function. With the rapid accumulation of sequence data, it is now possible to recover the evolutionary history of most genes at the population level to species and beyond. Those sequences can be compared for substitutions that might change or not change the encoded protein and its function, but they can also help to estimate evolutionary relationships. These hypotheses, as phylogenetic trees, provide visual and statistical guidance for characterizing the degree of relatedness among biological entities. In a phylogenetic tree, ancestor-descendant relationships are represented by connections, and closely related entities share most of these links, which represent their evolutionary closeness. In this chapter, I outlined a method to retrieve and label nucleotide sequences of the cytokine IL17A gene, align them to identify substitutions in homologous sites, estimate phylogenetic trees with support values, and visualize these trees as images. The methodology outlined here uses free software packages in the R environment and the Python language.

PMID:41478993 | DOI:10.1007/978-1-0716-4901-5_32

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Nevin Manimala Statistics

A review of evidence supporting amyloid beta reduction as a surrogate endpoint in Alzheimer’s disease

J Prev Alzheimers Dis. 2026 Jan 1:100458. doi: 10.1016/j.tjpad.2025.100458. Online ahead of print.

ABSTRACT

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disease driven by pathological depositions of proteins that accumulate over decades. Compelling genetic and neurobiological evidence suggests that amyloid accumulation in the brain initiates and drives early-stage AD. Measurement of fibrillar amyloid has been pivotal to the development and approval of disease-slowing treatments. Various biomarkers of AD pathophysiology provide evidence of target engagement and downstream effects on disease progression, and their use as surrogate endpoints may help identify and expeditiously bring new treatments to patients. In clinical trials, a surrogate endpoint serves as a substitute for a direct measurement of a patient’s clinical status, and its use can provide ethical, logistical, and economic advantages. Establishing biomarkers as surrogate endpoints involves evaluating scientific evidence through diverse statistical approaches to demonstrate their predictivity of clinical benefit. This article evaluated evidence supporting amyloid β plaque reduction as a surrogate endpoint in symptomatic AD by exploring regulatory considerations and guidelines for surrogate endpoints, examining the amyloid hypothesis and the current therapeutic landscape in AD, and presenting supporting evidence of surrogate endpoints from a recent clinical development program of AD.

PMID:41478826 | DOI:10.1016/j.tjpad.2025.100458

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Nevin Manimala Statistics

Identifying risk factors of young-onset dementia and evaluating evidence hierarchy: a meta-analysis and umbrella review

J Prev Alzheimers Dis. 2026 Jan 1:100467. doi: 10.1016/j.tjpad.2025.100467. Online ahead of print.

ABSTRACT

BACKGROUND: Young-onset dementia (YOD) directly affects the working-age population. The premature onset of dementia intensifies peer caregiving responsibilities and diverts medical and nursing resources. While modifiable risk factors for late-onset dementia have been well established, uncertainty remains regarding the applicability of these findings to YOD. We aim to identify modifiable risk factors for YOD and evaluate the strength of evidence.

METHODS: We searched PubMed, Embase, Web of Science, and Ovid Medline from inception to 22 May 2025 for epidemiological studies on non-genetic risk factors for YOD. We used random-effects meta-analyses with the inverse variance method to pool relative risks (RRs) and 95% confidence intervals (CIs). A series of statistical tests were designed to classify the strength of evidence of significant associations as convincing, highly suggestive, suggestive, or weak evidence.

RESULTS: From 25,731 initial and 2289 updated search records, 36 studies examining 31 non-genetic risk factors for YOD were identified. Of the 31 associations examined, 21 were nominally statistically significant at P < 0.05 based on random-effects models. Prior stroke was convincingly associated with an increased risk of YOD. Evidence of association was highly suggestive for alcohol use disorders, diabetes, depression, mood disorders, Parkinson’s disease, multiple sclerosis, use of antidepressants/antipsychotics, and asthma.

CONCLUSION: We found that the risk of dementia in young individuals may be closely related to neuropsychiatric symptoms and clinical alcohol disorders. Future research should further validate these findings and explore intervention strategies to reduce dementia risk in younger individuals.

PMID:41478824 | DOI:10.1016/j.tjpad.2025.100467

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Nevin Manimala Statistics

Multi-omics integration reveals shared genetic architecture between metabolic markers and gray matter atrophy in Alzheimer’s Disease

J Prev Alzheimers Dis. 2026 Jan 1:100452. doi: 10.1016/j.tjpad.2025.100452. Online ahead of print.

ABSTRACT

BACKGROUND: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by widespread gray matter volume (GMV) reductions. Emerging evidence links glucose and lipid metabolic dysregulation to AD pathophysiology. However, the extent to which AD-related GMV alterations and metabolic traits share a common genetic basis remains poorly understood.

OBJECTIVES: To explore the shared genetic architecture between GMV alterations in AD and metabolites related to glucose and lipid metabolism, aiming to provide biological insights into the prevention and treatment of AD.

DESIGN: This is a multimodal, cross-disciplinary study combining neuroimaging meta-analysis, transcriptome-neuroimaging association analysis, conjunctional false discovery rate (conjFDR) analysis, and functional enrichment analysis to identify the shared genetic architecture between AD-related brain structural alterations and metabolic traits.

SETTING: Public databases and European populations.

PARTICIPANTS: The meta-analysis included 49 studies (1945 CE patients and 2598 controls). The largest genome-wide association study (GWAS) summary statistics were used for AD (Ncase = 39,918; Ncontrol =358,140), two glycemic traits-glucose (GLU, N = 459,772) and glycated hemoglobin (HbA1c, N = 146,864), and three lipid traits (N = 1320,016)-high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG).

MEASUREMENTS: We conducted a voxel-based morphometric meta-analysis of GMV in AD by systematically reviewing 49 neuroimaging studies, identified through a literature search in PubMed and Web of Science using a predefined search strategy. Building upon these neuroanatomical findings, we performed a transcriptome-neuroimaging association analysis using data from the Allen Human Brain Atlas to identify genes spatially correlated with GMV alterations. To further explore the shared genetic architecture, we integrated GWAS summary statistics for AD and five metabolic markers using conjFDR analysis. Finally, functional enrichment analyses were performed to elucidate the biological relevance of the identified genes through this integrative framework.

RESULTS: Consistent GMV reductions in AD were observed in the bilateral middle temporal gyrus, right superior temporal gyrus, and other key subcortical regions. The conjFDR analysis identified 20, 17, 78, 87, and 82 genes shared between AD-related GMV reductions and GLU, HbA1c, HDL-C, LDL-C, and TG, respectively. Notably, 6 genes were shared across all five metabolic markers. Enrichment analysis implicated these genes in biological processes related to Aβ aggregation and phosphatidylinositol metabolism.

CONCLUSIONS: This study reveals a convergent genetic architecture underlying AD-related GMV atrophy and metabolic dysfunction. These findings may offer novel insights into the molecular interplay between systemic metabolism and neurodegeneration in AD and highlight potential targets for therapeutic strategies.

PMID:41478820 | DOI:10.1016/j.tjpad.2025.100452