Advances in experimental medicine and biology最新文献

The preference for the gender of physicians has been the subject of numerous studies, primarily conducted abroad. Several of these studies reveal that a considerable number of female patients prefer to consult with a female doctor, especially for issues related to gynecological care. This study aimed to: (a) investigate patient preferences regarding the gender of physicians in pulmonology, (b) explore factors related to these preferences, and (c) examine the relationship between gender preferences and health locus control among the treated individuals.

Methods: A cross-sectional study design was applied in this study. Data were collected using a self-administered questionnaire. The questionnaire consists of three parts: The first part includes questions related to the demographic characteristics of the sample (age, profession, education, etc.). The second part of the questionnaire consists of a specially designed section that examines (a) gender preferences for the pulmonologist, (b) age preferences of the physician, and (c) reasons for choosing a specific gender, such as knowledge and skills, communication, etc. Finally, the third part of the questionnaire includes the Health Locus of Control scale. The statistical software package for social sciences (SPSS), version 25.0, was utilized for data analysis. The level of statistical significance will be set at 0.05.

Results: According to the results, the majority of the participants didn't have a specific preference, but if they did, they favored female pulmonologists over men. Moreover, only monthly income emerged as a significant associated factor for pulmonologist gender preference.

Conclusion: The majority exhibited no specific preference, but among those who did, a preference for female pulmonologists over males was observed. Monthly income emerged as a significant factor associated with pulmonologist gender preference.

Introduction: Organ transplantation is a complex process and one of the most advanced procedures for patients suffering from end-stage liver disease. Transplant Life (TL) has been described as uncertainty, adjustment to later life, and body reclamation. This qualitative study explores the experiences of TL post-liver transplantation among adult individuals in Greece, as such research is pivotal in stepping into their world, but still scarce.

Aim: To investigate the experience and challenges of TL post-liver transplantation.

Methods: A phenomenological study where 12 adult participants shared their TL experiences after liver transplantation through semi-structured interviews, which were transcribed, coded, and subjected to the thematic analysis approach of Braun and Clarke. This paper presents the findings of a secondary data analysis.

Findings: The participants described TL post-liver transplantations as a transformative experience. The core theme "The lived experience of transplant life: reconstructing identity and managing a transformed body" comprised of five sub-themes: (a) Life and Reality: "finding myself in a new reality," (b) Selfhood and Others: "I am reborn, though afraid," (c) Managing a Transformed Body: "treasuring my liver transplant/graft," (d) The before and during transplantation experience: "will I wake up?," and (e) The Return to Life: "I am grateful."

Conclusions: The participants described their experience as one of growth and body reclamation, resilience, navigation through risks, acceptance and uncertainty, identity reconstruction, and body transformation through a transplanted organ.

Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. Considering that most AAAs remain asymptomatic until they are close to rupture, an efficient workflow for the accurate detection and delineation is crucial for the clinical outcome. In this study, we conduct a comparative analysis of two different AAA segmentation algorithms using X-ray Computed Tomography (CT) images from 18 patients diagnosed with AAA, which have not been used in similar studies before. The methodologies employed include an in-house segmentation algorithm based on conventional image analysis techniques, and a deep learning approach based on the nnU-Net -framework called TotalSegmentor. The CT dataset, which contained baseline studies, was processed, and the manual annotations by clinicians were used as ground truth. Results demonstrated a high degree of accuracy and robustness, with TotalSegmentor achieving an average Sorensen-Dice coefficient of 0.89 and Jaccard index of 0.81 across the dataset, compared to the proposed unsupervised method's scores of 0.85 and 0.77, respectively. These findings highlight the potential of deep learning-based models to enhance clinical workflows, ultimately improving early detection and monitoring of AAA.

Alcohol use disorder (AUD) often coexists with psychiatric disorders, complicating treatment and outcome. This pilot study highlights the psychiatric disorders that co-exist with AUD in 403 consecutive admissions to a short-term residential program of the Psychiatric Hospital of Attica, Greece. Clinical and demographic data for these patients were systematically recorded and analyzed. Subsequently, patients diagnosed with at least one other psychiatric disorder were identified and categorized. Notably, approximately 87% of the patients were diagnosed with at least one comorbid psychiatric disorder, with comorbidities reported for Substance Use Disorders, Personality Disorders, Major Depressive Disorder, Anxiety Disorders, Bipolar Disorder, Eating Disorders, and Schizophrenia. The findings indicate that the majority of patients with AUD in this program exhibit at least one additional psychiatric disorder, consistent with trends observed in the international literature.

Aim: This literature review seeks to provide a comprehensive overview of the concept of gender incongruence, addressing various aspects of the phenomenon through multiple perspectives.

Methodology: Eligible studies were sought in Google Scholar, Scopus, and PubMed databases (end-of-search May 15, 2024).

Results: Global prevalence rates, the experience of the young population, workplace equity, the controversial issues of gender transition and detransition are thoroughly presented.

Discussion: Gender dysphoria remains a highly contentious subject, even in 2024. Despite ongoing societal and scientific advancements, there remains a notable lack of awareness and comprehensive understanding of gender incongruence. Future research must strive to employ updated and inclusive methodologies while developing robust scientific frameworks to support individuals undergoing gender transitions.

Lifelong learning has become essential in modern society, driven by technological advancements and the increasing demand for continuous skill development. E-learning platforms have transformed education by enhancing accessibility, flexibility, and personalization. Artificial intelligence (AI) and data-driven technologies further refine these platforms, enabling adaptive learning, real-time feedback, and predictive analytics to improve engagement and outcomes. The COVID-19 pandemic accelerated the adoption of e-learning, shifting it from a supplementary tool to a primary mode of education.This study examines the impact of e-learning on adult education, focusing on AI-driven personalization and data analytics. In Greece, national and European policies have facilitated digital education, yet challenges such as infrastructure limitations and digital inequalities persist. Additionally, data mining in e-learning environments has the potential to optimize instructional methods and predict learning outcomes. A proposed framework for precision education integrates multimodal data sources, including biometric indicators, to enhance individualized learning experiences.While AI-powered e-learning systems offer transformative opportunities, ethical concerns surrounding data privacy and equitable access must be addressed. Ensuring responsible data management and inclusive policies will be crucial in maintaining the effectiveness of digital education and fostering lifelong learning in an increasingly technology-driven world.

The aim of the present study was to evaluate the reliability and validity of the Greek version of Tilburg Pregnancy Distress Scale (TPDS). The study was conducted from August 2021 to November 2022 and included 196 healthy pregnant adult women. The study sample was drawn from two maternity clinics, and included pregnant adult women from the Second Department of Obstetrics and Gynecology at the "Aretaieion" University Hospital and the private Obstetrics and Gynecology Center "Woman Clinic," in Athens, Greece. Study participants were asked to fill out the TPDS, to assess pregnancy distress, as well as a demographic questionnaire, the Perceived Stress Scale and the Depression Anxiety and Stress Scale 21. Cronbach's alpha coefficient was 0.85 for the Greek TPDS version, 0.870 for the "Negative Affect" factor, and 0.818 for the "Partner Involvement" factor. The Kaiser-Meyer-Olkin value was 0.841 and the significance of Bartlett's test of sphericity was p < 0.001. Exploratory factor analysis displayed two extracted factors with eigenvalue >1. The Greek version of the TPDS can be considered a reliable instrument for measuring pregnancy-related distress in women.

Introduction: Breastfeeding offers numerous benefits for both the mother and child, affecting them physically and psychosocially. Despite these well-documented benefits, breastfeeding rates up to 12 or 24 months remain quite low in developed countries. Parental positive attitudes toward breastfeeding can significantly influence the initiation and continuation of breastfeeding.

Aim: To investigate parents' perspectives and attitudes toward breastfeeding, as well as the relationship between these attitudes and their sociodemographic characteristics/parenting choices.

Methodology: A cross-sectional study that used self-administered questionnaires developed for the purpose of the study and was completed by 862 parents (mothers and fathers) who received support from a private maternity and breastfeeding support center.

Results: Attitudes toward breastfeeding after the first year of the child's life was positively correlated with female gender (p = 0.001), parents' higher educational level (p < 0.001), exclusive breastfeeding (p = 0.000), residence in other than Athens urban area (p = 0.003). Overall breastfeeding experience was positively associated with living in Athens or another urban area (p < 0.001), parents' higher educational level (p = 0.005), and the birth order of this child (p = 0.028). More positive attitudes observed among parents whose child weaned themselves or due to a new pregnancy and among parents whose child was born naturally (p = 0.000). Information and support during breastfeeding and weaning were additional factors that contributed to more positive views (p = 0.000). More positive attitudes were associated with the introduction of whole pieces of food as the first food (p = 0.000), and finally, a positive attitude after the first year and in general was linked to not following a strict sleep schedule during the first year of the child's life and a longer duration of co-sleeping in the same room (p = 0.000).

Conclusions: The study could serve as a basis for more extensive research on breastfeeding and parenting. The findings of this study can be utilized by health professionals to demonstrate greater awareness, empathy, and effectiveness regarding issues of breastfeeding and parenting.

The knowledge and attitudes of nurses caring for patients with diabetes mellitus should be regularly evaluated using reliable and valid instruments to identify any knowledge gaps and address any negative attitudes, allowing for the implementation of targeted skill development. The aim of this study was to test the validity and reliability of the Greek translation of the Diabetes Attitudes Scale (DAS-3) questionnaire. A total of 278 conveniently selected undergraduate nursing students participated in the survey and were administered a questionnaire of sociodemographic and clinical data, the Diabetes Knowledge Test tool and the DAS-3. Factor analysis revealed 4 factors explaining 50% of the variability of the DAS-3; the value of strict glucose control (Cronbach's α = 0.86), the need for special training (Cronbach's α = 0.63), the psychosocial impact of diabetes (Cronbach's α = 0.68), and finally the respect for patient autonomy (Cronbach's α = 0.65). The overall Cronbach's alpha internal consistency coefficient found for the DAS-3 questionnaire was 0.75 indicating good internal reliability. The values of the Pearson correlation coefficients in test-retest assessment varied for the four factors from 0.71 to 0.90 and reflected a moderate to very strong correlation. The good reliability and validity of the DAS-3 contribute to the evaluation of the attitudes of health professionals towards diabetes, becoming a future motivation for improving educational programs.

Precision medicine leverages genomics, metabolomics, and biomedical data sciences to develop targeted treatments based on individual genetic, environmental, and lifestyle factors. While standardized treatments work for some conditions, many require a personalized approach to improve therapeutic outcomes. The integration of artificial intelligence (AI) and big data analytics enhances precision medicine by enabling predictive models and individualized treatment plans. A key application of precision medicine is in rare disease diagnosis and treatment, where AI-driven methodologies improve diagnostic accuracy and therapeutic development. Multidisciplinary collaboration among researchers, healthcare professionals, and patient advocacy groups is essential to advancing care and ensuring equitable access to innovative treatments. Despite these advancements, ethical concerns regarding AI in precision medicine remain a challenge. Addressing these issues requires transdisciplinary research involving medical practitioners, ethicists, and data scientists to develop robust validation frameworks and certification methodologies. This paper explores the ethical implications of AI in precision medicine and rare disease treatment, emphasizing the need for responsible algorithm governance. The findings aim to provide a foundation for future research on integrating ethical considerations into AI-driven healthcare, ensuring safe, transparent, and equitable medical innovations.