Advances in experimental medicine and biology最新文献

Cognitive impairment has been closely associated with systemic metabolic disorders, such as oxygen and energy metabolism disorders. We used deep learning to estimate the risk of mild cognitive impairment (MCI) in outpatients with pain (pain group) and those without pain (non-pain control group) from general blood test data reflecting systemic metabolic disorders. Univariate analysis was performed on blood test data from both groups to calculate the estimated Mini-Mental State Examination (MMSE) scores. Additionally, principal component analysis was conducted on data obtained from eight assessment batteries, comprising 12 items commonly used by Japanese pain treatment institutions. Cluster analysis was also performed using Ward's method on seven components with cumulative proportions exceeding 90%. Our results showed that patients suffering from pain had significantly lower estimated MMSE scores than controls, despite no significant age differences. Patients suffering from pain had significantly higher white blood cell, triglyceride, glucose, and potassium values and significantly lower red blood cell, hemoglobin (Hgb), and uric acid values. Notably, Hgb values were significantly lower only in men, with no significant differences in women. Cluster analysis of the assessment battery data revealed five distinct clusters (Euclidean distance: 10.14%). The average MMSE score of the cluster with an extremely low value for the first component was 27.1, whereas the scores for the other clusters fell below the MCI cutoff. The first component primarily reflects mobility, suggesting that patients suffering from pain with reduced mobility are at increased risk of mild dementia. This finding indicates that impaired mobility due to pain can promote systemic metabolic disorders, subsequently increasing the risk of MCI.

Computational methods are presented for modeling the catalytic center of an enzyme and calculating the energy associated with given molecular geometry. First, an introduction to general theory is given, including a brief discussion of quantum mechanics (QM), molecular mechanics (MM), and hybrid QM/MM, a standard approach that divides the system into a region requiring a description of the electronic structure (QM) and a remainder that can be considered a perturbation (MM) to the system. Next, the nitrogenase enzyme will be used as a sample system to explain in detail how modeling is carried out. This discussion will review the broken symmetry (BS) approach within DFT, a computational approach that describes the spin states of multiple Fe centers. The review concludes with a discussion of machine learning (ML) and the training of a neural network (NN) potential based on QM calculations.

Temperature directly shapes insect physiology and has a preponderant effect on life history traits. Winter conditions in temperate and polar regions are especially challenging for insects. Extremely low temperatures can indeed compromise insect survival by promoting freezing of body fluids, but mild cold temperatures above 0 °C (i.e., chilling) can also lead to complex and severe physiological dysregulations. Among physiological damages due to freezing and chilling, insect lipids are one of the primary targets. As low temperatures tend to rigidify phospholipid bilayers, membrane functions are compromised in the cold. Lipid rigidification due to cold also decreases the accessibility of fat stores for metabolic enzymes, and therefore their availability for basal metabolism. These deleterious effects, combined with low food availability in winter, result in substantial nutritional challenges for overwintering insects. Consequently, lipid modifications such as homeoviscous adaptation of cell membranes, fluidity maintenance of fat reserves, cuticular lipid accumulation, and production of antifreeze glycolipids are essential components of the physiological response to cold stress. The aim of the present chapter is to present the physiological challenges caused by low temperatures, the lipid modifications linked with cold tolerance in insects, and the molecular regulation of lipid metabolism during cold exposure.

According to the World Health Organization vector-borne diseases account for more than 17% of all infectious diseases, causing more than 700,000 deaths annually. Vectors are organisms that are able to transmit infectious pathogens between humans, or from animals to humans. Many of these vectors are hematophagous insects, which ingest the pathogen from an infected host during a blood meal, and later transmit it into a new host. Malaria, dengue, African trypanosomiasis, yellow fever, leishmaniasis, Chagas disease, and many others are examples of diseases transmitted by insects.Both the diet and the infection with pathogens trigger changes in many metabolic pathways, including lipid metabolism, compared to other insects. Blood contains mostly proteins and is very poor in lipids and carbohydrates. Thus, hematophagous insects attempt to efficiently digest and absorb diet lipids and also rely on a large de novo lipid biosynthesis based on utilization of proteins and carbohydrates as carbon source. Blood meal triggers essential physiological processes as molting, excretion, and oogenesis; therefore, lipid metabolism and utilization of lipid storage should be finely synchronized and regulated regarding that, in order to provide the necessary energy source for these events. Also, pathogens have evolved mechanisms to hijack essential lipids from the insect host by interfering in the biosynthesis, catabolism, and transport of lipids, which pose challenges to reproduction, survival, fitness, and other insect traits.In this chapter, we have tried to collect and highlight the current knowledge and recent discoveries on the metabolism of lipids in insect vectors of diseases related to the hematophagous diet and pathogen infection.

Lipidomics, a specialized branch of metabolomics, investigates the diversity and functionality of lipids in biological systems. Lipids serve crucial roles in energy storage, membrane composition, and environmental acclimation in insects, underpinning processes such as development and stress responses. Advances in analytical technologies, such as liquid chromatography-mass spectrometry (LC-MS), have enabled precise identification and quantification of lipid species, providing unprecedented insights into lipid metabolism and dynamics. Key lipid classes, including triacylglycerols and phospholipids, exhibit structural and functional versatility, adapting to environmental pressures through mechanisms like homeoviscous adaptation. These dynamic lipid responses are essential for maintaining cellular and cuticular integrity and functionality under stress. By exploring lipid diversity and adaptations, lipidomics offers valuable perspectives on insect physiology, survival strategies, and evolutionary ecology. This chapter summarizes methods used to study insect lipidomes and highlights comparative lipidomic studies that have advanced our understanding of insect biology.

Hashimoto's disease or Hashimoto's thyroiditis is an autoimmune disorder of the thyroid gland. The pathophysiology of the disorder is characterized by an increased volume of the thyroid gland, infiltration of the parenchyma by lymphocytes, and the presence of antibodies specific for thyroid antigens. The diagnosis of Hashimoto thyroiditis is based on the presence of clinical symptoms, anti-thyroid antibodies, and certain histologic features. The present study aimed to collect biomarkers and create a web-based application for detecting the disease through machine learning. The web application was created using Python and Random Forest algorithm was selected to train the existing dataset. Taking advantage of machine learning algorithms, the application provides users with a practical means for predicting immortality based on their blood tests. Although there are limitations that need to be addressed, representing a valuable tool for early diagnosis and management of the disease is required.

Neurodegenerative diseases such as Alzheimer's, Parkinson's, ALS, and Huntington's disease pose a growing global health challenge due to their prevalence in aging populations and their devastating impact on cognitive and motor functions. Current treatments focus on symptom management, with no options available to reverse neuronal damage. Emerging evidence highlights the potential role of extra virgin olive oil (EVOO) polyphenols in neuroprotection, particularly in the context of the Mediterranean diet, which is associated with lower rates of neurodegenerative disorders. EVOO's rich polyphenolic compounds, including hydroxytyrosol, oleuropein, tyrosol, and oleocanthal, exhibit potent antioxidant, anti-inflammatory, and neuroprotective properties. These bioactive molecules have shown potential in modulating disease-specific pathways, such as reducing oxidative stress, inhibiting abnormal protein aggregation, and regulating neuroinflammation. This paper explores the therapeutic potential of olive oil polyphenols for neurodegenerative diseases, detailing their mechanisms of action across different conditions. Our findings suggest that incorporating EVOO into dietary and medical interventions could serve as a promising strategy for mitigating neurodegenerative disease progression and enhancing cognitive health.

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.

Introduction: Healthcare professionals working with individuals who have experienced trauma are at risk of experiencing secondary trauma.

Purpose: To explore the perspectives and experiences of addiction nurses working in substitution treatment units and harm reduction services regarding the physical or psychological trauma of individuals coping with the dual burden of addiction and trauma.

Methodology: A mixed-method study (quantitative and qualitative) involving 111 addiction nurses. For the collection of quantitative data, the Greek version of the Professional Quality of Life Scale was used, which assesses compassion satisfaction, professional burnout, and secondary traumatic stress, as well as a questionnaire capturing demographic, individual, and professional characteristics. Eight nurses participated in semistructured interviews.

Results: The majority of participants reported (96.4%) moderate to high levels of compassion satisfaction while more than half (54.4%) of the participants reported low levels of professional burnout, and about half (49.5%) reported low levels of secondary traumatic stress. Considering Stamm's interpretation of the triad of high compassion satisfaction, moderate-to-low professional burnout, and secondary traumatic stress, the results of this study fall into the positive results category. Good physical health, participants' readiness to care for individuals with trauma, high levels of cooperation, respect for teamwork, positive work climate, and seamless colleague support were positively related to higher levels of compassion satisfaction.

Conclusions: Recognizing, reflecting on, and understanding trauma processes protected participants from mirroring the helplessness, frustration, and feelings of incompetence they encountered on the trauma responses of the individuals and families they cared for.

Childhood and adolescent obesity is a growing health concern with complex multifactorial origins, encompassing genetic, environmental, physiological, and psychosocial factors. In Greece, the prevalence of childhood obesity is among the highest in Europe, indicating an urgent need to understand its underlying mechanisms. Herein, we explore the genetic basis of obesity, focusing on both monogenic and polygenic factors, and how early life stressors contribute to obesity's onset and progression. Genetic predispositions, such as mutations in leptin-melanocortin pathways, and the role of epigenetic modifications influenced by environmental factors, are examined to understand obesity's complexity. Moreover, stress-related hormonal dysregulation impacts metabolic pathways, exacerbating weight gain and obesity-related complications. Through advanced algorithms like neural networks, decision trees, and clustering techniques, ML/AI approaches have demonstrated high accuracy in predicting obesity, identifying genetic markers, and analyzing interactions between genetic and lifestyle factors. These technologies hold promise for early detection, personalized interventions, and the development of targeted prevention strategies. The integration of ML/AI with genomic, epigenomic, and clinical data offers a comprehensive understanding of childhood obesity, paving the way for more effective management and treatment. This study contributes to a deeper understanding of the genetic and environmental factors in childhood obesity and highlights the potential of AI-driven approaches in addressing this critical public health challenge.