Fingerprinting Breast Milk; insights into Milk Exosomics.

Breast milk, often referred to as "liquid gold," is a complex biofluid that provides essential nutrients, immune factors, and developmental cues for newborns. Recent advancements in the field of exosome research have shed light on the critical role of exosomes in breast milk. Exosomes are nanosized vesicles that carry bioactive molecules, including proteins, lipids, nucleic acids, and miRNAs. These tiny messengers play a vital role in intercellular communication and are now being recognized as key players in infant health and development. This paper explores the emerging field of milk exosomics, emphasizing the potential of exosome fingerprinting to uncover valuable insights into the composition and function of breast milk. By deciphering the exosomal cargo, we can gain a deeper understanding of how breast milk influences neonatal health and may even pave the way for personalized nutrition strategies.

Elucidating the role of PRMTs in prostate cancer using open access databases and a patient cohort dataset.

Protein arginine methylation is an understudied epigenetic mechanism catalyzed by enzymes known as Protein Methyltransferases of Arginine (PRMTs), while the opposite reaction is performed by Jumonji domain- containing protein 6 (JMJD6). There is increasing evidence that PRMTs are deregulated in prostate cancer (PCa). In this study, the expression of two PRMT members, PRMT2 and PRMT7 as well as JMJD6, a demethylase, was analyzed in PCa. Initially, we retrieved data from The Cancer Genome Atlas (TCGA) project and the Gene Expression Omnibus (GEO) database to explore the differential expression of various PRMT family members in patients with PCa and then applied immunohistochemistry in a patient cohort across the spectrum of PCa, including non-neoplastic prostate tissue and lymph node metastatic foci. The results from the TCGA analysis revealed that PRMT7, PRMT6 and PRMT3 expression increased while PRMT2, PRMT9 and JMJD6 levels decreased in the tumor compared to non-neoplastic prostate. Results from the GEO datasets were similar, albeit not identical with the TCGA results, with PRMT7 and PRMT3 being upregulated and PRMT2 and JMJD6 being downregulated in the tumor compared to non-neoplastic tissue in some of them. In addition, PRMT7 levels decreased with stage and grade progression in the TCGA analysis. In the patient cohort, both PRMTs and JMJD6 were overexpressed in PCa compared to non-neoplastic tissue, and nuclear PRMT2 and JMJD6 were upregulated in lymph node metastasis, too. PRMT7 and JMJD6 expression were upregulated with the progression of stage and JMJD6 was also increased with the elevation of grade. After androgen ablation therapy, nuclear expression of PRMT7 and JMJD6 were elevated compared to untreated tumors. PRMT2, PRMT7 and JMD6 were also correlated with markers of EMT and cell cycle regulators. Finally, our findings indicate that PRMTs and JMJD6 are involved in prostate cancer progression and revealed a potential interplay of PRMTs with EMT mediators, underscoring the need for therapeutic targeting of arginine methylation in prostate cancer.

Validation of Patras Immunotherapy Score model for prediction and prognosis of patients with advanced NSCLC treated with nivolumab or pembrolizumab: results from a European multicentre study.

Background: Recently, the Patras Immunotherapy Score (PIOS) has been developed to estimate the survival benefit of patients with advanced non-small-cell lung cancer (aNSCLC) treated with nivolumab or pembrolizumab. The aim of this study was to validate the clinical value of PIOS in an external cohort of aNSCLC patients. Methods: PIOS is a baseline formula produced by the combination of performance status, body mass index, age and line of treatment. In this multicentre study, 626 patients with confirmed NSCLC pathology, who had been treated with nivolumab or pembrolizumab, as well as 444 patients with aNSCLC, who had been managed with chemotherapy alone, were retrospectively enrolled. Predictive and prognostic values of PIOS were finally evaluated. Results: Patients treated with immunotherapy and higher PIOS score had an improved progression-free survival not only in univariate [hazard ratio (HR) = 0.621, p = 0.001], but also in multivariable analysis (HR = 0.651, p = 0.003). In addition, improved overall survival with increasing PIOS score was also observed (HR = 0.608, p < 0.001) with this association remaining statistically significant after adjusting for programmed-cell death ligand 1 (PD-L1) expression (HR = 0.620, p < 0.001). In addition, patients with disease progression (PD) had lower scores compared to those with stable disease (SD), partial response (PR) or complete response (CR) in a two-tier model (p < 0.001) as well as in a four-tier model (PD, SD, PR and CR; p < 0.001). Prognostic significance of PIOS score also persisted using a binary logistic regression analysis, adjusted for disease stage and PD-L1 status (p = 0.002, odds ratio: 0.578). Contrarily, PIOS had no prognostic significance in the chemotherapy group; however, upon combined analysis of the two cohorts, PIOS was found to have a significant interaction with the type of treatment (HR = 0.066 with p < 0.001), confirming its predictive value for immunotherapy. Conclusions: This study provides further validation of PIOS in aNSCLC patients treated with anti-PD-1 monotherapy.

Study of the Langat virus RNA-dependent RNA polymerase through homology modeling.

Langat virus is a member of the Flaviviridae family and a close relative of a group of important tick-borne viruses that cause human encephalitis. RNA-dependent RNA polymerase is a significant component of the replication mechanism of the Flaviviridae viral family. In the present work, a three-dimensional model of the Langat virus RNA-dependent RNA polymerase was designed through homology modeling. The experimentally determined structure of the RNA-dependent RNA polymerase of Dengue virus type II, another member of the same viral family, was employed as template for the homology modeling process. The resulting model underwent a series of optimisations and its quality was verified using the Verify3D algorithm. Important functional characteristics of the family of viral RNA-dependent RNA polymerases were identified in the generated model, thus affirming the potential for its use in the possible design of anti-viral agents for Langat virus.

Using a Virtual Reality Serious Game to Assess the Performance of Older Adults with Frailty.

OBJECTIVES: The comparison of cognitive performance of older adults with frailty and non-frail ones (according to Fried's criteria) was investigated. METHODS/DESIGN: The differences in performance between people with frailty and individuals without frailty according to Fried were tested using a Virtual Reality (VR) application. The Fried criteria for frailty were used to categorize users into study groups, while standardized batteries were used for a Comprehensive Geriatric Assessment, including Activities of Daily Living (ADL), lifestyle, cognition, and depression screening. A group of 80 elders (78.08 years old in average) played the VR game entitled Virtual Supermarket (VSM). From those, 39 were healthy controls and 30 were categorized as pre-frail and 11 as frail. The VSM application presented users with a virtual shopping experience where users had to locate and purchase items displayed in a shopping list. This application was designed to test player's ability to reproduce a typical customer behavior in a simulated environment which requires spatial orientation, short-term memory, selective attention, and cognition speed. The performance, duration, and error rate were used as measurements. RESULTS: The analysis showed that there was a statistically significant difference in game performance between the different user groups with X2 (2) = 9.929, p = 0.007. Moreover, the multinomial logistic regression model generated, which based on game performance metrics, was found to be statistically significant with X2 (4) = 15.662, p = 0.004. CONCLUSIONS: Results shed more light toward the possible use of VR for distant self-administered evaluation of the frail status.

FrailSafe: An ICT Platform for Unobtrusive Sensing of Multi-Domain Frailty for Personalized Interventions.

The implications of frailty in older adults' health status and autonomy necessitates the understanding and effective management of this widespread condition as a priority for modern societies. Despite its importance, we still stand far from early detection, effective management and prevention of frailty. One of the most important reasons for this is the lack of sensitive instruments able to early identify frailty and pre-frailty conditions. The FrailSafe system provides a novel approach to this complex, medical, social and public health problem. It aspires to identify the most important components of frailty, construct cumulative metrics serving as biomarkers, and apply this knowledge and expertise for self-management and prevention. This paper presents a high-level overview of the FrailSafe system architecture providing details on the monitoring sensors and devices, the software front-ends for the interaction of the users with the system, as well as the back-end part including the data analysis and decision support modules. Data storage, remote processing and security issues are also discussed. The evaluation of the system by older individuals from 3 different countries highlighted the potential of frailty prediction strategies based on information and communication technology (ICT).

Dark Suite: a comprehensive toolbox for computer-aided drug design.

Dark Suite is a complete interactive software pipeline aiming to exploit the advantages of free software and modern programming. Besides two commands (installation and opening) on the command line, the handling and the program operation is done through a user's friendly interface. This platform has a central graphical interface which allows the user to choose in what computational tool to work. Each computational tool has an interface. Dark Suite combines the functions of other programmes to create a pipeline for high-quality secondary effects through a friendly user interface. It is made to run on GNU / Linux distributions and its interface was built using JAVA to seamlessly integrate scientific tools written in Perl, Java, R and Python.

An in silico and in vitro pipeline for the rapid screening of helicase modulators.

To evaluate the potency of potential helicase modulators, we developed an assay of helicase enzyme activity. Using a DNA or RNA biotin labelled oligonucleotide and after the addition of a recombinant helicase, the nucleic acid unwinds, causing the emission of luminescence, which is quantified with a particular antibody. In our assay, one of the DNA oligos was biotinylated, while the other was labelled with digoxygenin (DIG), both in their 5' termini. The biotin molecule immobilises the DNA duplex on a neutravidin-coated plate and the helicase activity is measured through the unwinding of DNA, due to ATP activation. The subsequent release of DIG-labelled oligos results in a luminescence signal measured with a chemiluminescence antibody. Our goal was to provide a high throughput screening method for potential helicase inhibitors. The method described in this paper has been demonstrated to be fast, easy and reproducible and doesn't use radiochemicals.

Reliability and Validity of the Newly Diagnosed Breast Cancer Stress Scale in the Greek Population.

OBJECTIVE: To examine the validity and the reliability of a novel measurement tool, the Newly Diagnosed Breast Cancer Stress Scale (NDBCSS) in the Greek population. The tool aimed to assess distress in patients recently diagnosed with breast cancer. METHODS: We performed a principal component analysis (PCA) of the 17 items of the scale. RESULTS: The PCA resulted in 4 factors: 1. Personal life, 2. Procedural issues, 3. Facing challenges and 4. Psychological load. All subscales showed satisfactory internal consistency and variance, relative to theoretical score ranges. Subscale scores and total score were significantly correlated with perceived stress and hospital anxiety and depression scale, implying good criterion validity. Associations with social, demographic and disease related information were also found. CONCLUSIONS: The NDBCSS resulted in acceptable reliability and good validity, and was considered as a valuable tool for health-care workers and oncologists to measure psychological distress in early stage of breast cancer.

Stress Management in Women with Hashimoto's thyroiditis: A Randomized Controlled Trial.

AIM: Stress has been implicated in the pathogenesis of Hashimoto's thyroiditis (HT), nevertheless evidence is scarce regarding the effect of stress management on individuals suffering from HT. The purpose of this study was to evaluate the impact of an 8-week stress management intervention on the anti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-TG) antibodies as well as thyroid-stimulating hormone (TSH) levels of women with HT. Secondary endpoints included the effect on the patients' lifestyle, body mass index (BMI), depression, anxiety and stress. METHODS: This was a two-arm parallel group (stress management intervention vs. standard care groups) randomized controlled study. Adult women with Hashimoto's thyroiditis, completed questionnaires on stress, anxiety, depression and lifestyle, at the beginning of the programme and 8 weeks later. Laboratory thyroid function tests (anti-TPO, anti-TG antibodies and TSH) were also measured at baseline and at the end of the study. RESULTS: A total of 60 women with HT, aged 25-76 years, participated in the study (30 patients in each group). After eight weeks, patients in the intervention group demonstrated statistically significant beneficial decrements in the rate change of anti-TG titers and the levels of stress, depression and anxiety as well as better lifestyle scores, compared to the control group.

A genomic data mining pipeline for 15 species of the genus Olea.

In the big data era, conventional bioinformatics seems to fail in managing the full extent of the available genomic information. The current study is focused on olive tree species and the collection and analysis of genetic and genomic data, which are fragmented in various depositories. Extra virgin olive oil is classified as a medical food, due to nutraceutical benefits and its protective properties against cancer, cardiovascular diseases, age-related diseases, neurodegenerative disorders, and many other diseases. Extensive studies have reported the benefits of olive oil on human health. However, available data at the nucleotide sequence level are highly unstructured. Towards this aim, we describe an in-silico approach that combines methods from data mining and machine learning pipelines to ontology classification and semantic annotation. Fusing and analysing all available olive tree data is a step of uttermost importance in classifying and characterising the various cultivars, towards a comprehensive approach under the context of food safety and public health.

NOTCH3 and CADASIL syndrome: a genetic and structural overview.

CADASIL syndrome is a rare disease that belongs to a group of disorders called leukodystrophies. It is well established that NOTCH3 gene on chromosome 19 is primarily responsible for the development of the CADASIL syndrome. Herein, an attempt is made to shed light on the actual molecular mechanism underlying CADASIL syndrome, through insights extracted from comprehensive evolutionary studies and in silico modelling on Notch 3 protein. In particular, we suggest the use of optical coherence tomography angiography for the detection of early signs of small vessel diseases, which are the major precursors to a repertoire of neurodegenerative conditions, including CADASIL.

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.

The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.

Integrating an openEHR-based personalized virtual model for the ageing population within HBase.

BACKGROUND: Frailty is a common clinical syndrome in ageing population that carries an increased risk for adverse health outcomes including falls, hospitalization, disability, and mortality. As these outcomes affect the health and social care planning, during the last years there is a tendency of investing in monitoring and preventing strategies. Although a number of electronic health record (EHR) systems have been developed, including personalized virtual patient models, there are limited ageing population oriented systems. METHODS: We exploit the openEHR framework for the representation of frailty in ageing population in order to attain semantic interoperability, and we present the methodology for adoption or development of archetypes. We also propose a framework for a one-to-one mapping between openEHR archetypes and a column-family NoSQL database (HBase) aiming at the integration of existing and newly developed archetypes into it. RESULTS: The requirement analysis of our study resulted in the definition of 22 coherent and clinically meaningful parameters for the description of frailty in older adults. The implemented openEHR methodology led to the direct use of 22 archetypes, the modification and reuse of two archetypes, and the development of 28 new archetypes. Additionally, the mapping procedure led to two different HBase tables for the storage of the data. CONCLUSIONS: In this work, an openEHR-based virtual patient model has been designed and integrated into an HBase storage system, exploiting the advantages of the underlying technologies. This framework can serve as a base for the development of a decision support system using the openEHR's Guideline Definition Language in the future.

Using Octuplet Siamese Network For Osteoporosis Analysis On Dental Panoramic Radiographs.

Dental Panoramic radiography (DPR) image provides a potentially inexpensive source to evaluate bone density change through visual clue analysis on trabecular bone structure. However, dense overlapping of bone structures in DPR image and scarcity of labeled samples make learning of accurate mapping from DPR patches to osteoporosis condition challenging. In this paper, we propose a deep Octuplet Siamese Network (OSN) to learn and fuse discriminative features for osteoporosis condition prediction using multiple DRP patches. By exploring common features, OSN uses patches of eight locations together to train the shared feature extractor. Feature fusion for different location adopts both accumulation and concatenation with fully considering of patches' spatial symmetry. In our dedicated two-stage fine-tuning scheme, an augmented texture analysis dataset is employed to prevent overfitting in transferring weights learned on ImageNet to DPR dataset when using merely 108 samples. Leave-one-out test shows that our proposed OSN outperforms all other state of the art methods in osteoporosis category classification task.

Introducing Thetis: a comprehensive suite for event detection in molecular dynamics.

A suite of computer programs has been developed under the general name Thetis, for monitoring structural changes during molecular dynamics (MD) simulations on proteins. Conformational analysis includes estimation of structural similarities during the simulation and analysis of the secondary structure with emphasis on helices. In contrast to available freeware dealing with MD snapshots, Thetis can be used on a series of consecutive MD structures, thus allowing a detailed conformational analysis over the time course of the simulation.

Human Emotions on the Onset of Cardiovascular and Small Vessel Related Diseases.

BACKGROUND/AIM: The aim of the present study was to examine the relation between understanding of emotions and cardiovascular related diseases, namely coronary heart disease, diabetes mellitus and obesity. The uniqueness of this study lies in the fact that it examined the relationship between the cardiovascular related diseases named above and the understanding of emotions in the context of Emotional Intelligence (EI). PATIENTS AND METHODS: The study was conducted in 300 participants during a 3 year period. All participants completed a self-report questionnaire, assessing various aspects of EI, such as self-emotion appraisal, other emotion appraisal, emotion regulation and use of emotions. As hypothesized, coronary heart disease is a prognostic factor of regulation of emotions. RESULTS: The present study is an attempt to examine the relation between emotional understanding and cardiovascular related diseases, namely coronary heart disease, diabetes mellitus and obesity. Establishing which diseases are independent risk factors for the understanding of emotions, could have a significant impact on emotional health, through the treatment of these cardiovascular related diseases. Emotions were studied within the theoretical context of Emotional Intelligence (EI), which affects people's physical and mental health. CONCLUSION: The results of this study emphasize on the relationship of cardiovascular related diseases and psychological characteristics, such as anxiety and anger, being aspects of EI. Additionally, this work fills a gap in the relevant Greek literature, as a first attempt to examine the correlation of EI with cardiovascular related diseases.

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

Genomic big data hitting the storage bottleneck.

During the last decades, there is a vast data explosion in bioinformatics. Big data centres are trying to face this data crisis, reaching high storage capacity levels. Although several scientific giants examine how to handle the enormous pile of information in their cupboards, the problem remains unsolved. On a daily basis, there is a massive quantity of permanent loss of extensive information due to infrastructure and storage space problems. The motivation for sequencing has fallen behind. Sometimes, the time that is spent to solve storage space problems is longer than the one dedicated to collect and analyse data. To bring sequencing to the foreground, scientists have to slide over such obstacles and find alternative ways to approach the issue of data volume. Scientific community experiences the data crisis era, where, out of the box solutions may ease the typical research workflow, until technological development meets the needs of Bioinformatics.

Cyclin-dependent kinase 5 mediates pleiotrophin-induced endothelial cell migration.

Pleiotrophin (PTN) stimulates endothelial cell migration through binding to receptor protein tyrosine phosphatase beta/zeta (RPTPß/ζ) and ανß3 integrin. Screening for proteins that interact with RPTPß/ζ and potentially regulate PTN signaling, through mass spectrometry analysis, identified cyclin-dependent kinase 5 (CDK5) activator p35 among the proteins displaying high sequence coverage. Interaction of p35 with the serine/threonine kinase CDK5 leads to CDK5 activation, known to be implicated in cell migration. Protein immunoprecipitation and proximity ligation assays verified p35-RPTPß/ζ interaction and revealed the molecular association of CDK5 and RPTPß/ζ. In endothelial cells, PTN activates CDK5 in an RPTPß/ζ- and phosphoinositide 3-kinase (PI3K)-dependent manner. On the other hand, c-Src, ανß3 and ERK1/2 do not mediate the PTN-induced CDK5 activation. Pharmacological and genetic inhibition of CDK5 abolished PTN-induced endothelial cell migration, suggesting that CDK5 mediates PTN stimulatory effect. A new pyrrolo[2,3-α]carbazole derivative previously identified as a CDK1 inhibitor, was found to suppress CDK5 activity and eliminate PTN stimulatory effect on cell migration, warranting its further evaluation as a new CDK5 inhibitor. Collectively, our data reveal that CDK5 is activated by PTN, in an RPTPß/ζ-dependent manner, regulates PTN-induced cell migration and is an attractive target for the inhibition of PTN pro-angiogenic properties.