The decoding of the human genome, completed two decades ago, marked a revolutionary moment in biology by introducing a vast amount of data. This avalanche of information presented several computational challenges. Machine Learning has become the dominant method to address these challenges, with Natural Language Processing playing a significant role and offering promising results. In this systematic review, we will explore the application of Machine Learning and Natural Language Processing to the study of biological data. On the one hand, Machine Learning is widely used in Artificial Intelligence to improve automation, carry out tasks that require no human interaction, and perform analytical and physical activities. It helps advance our understanding of biology and improve healthcare and drug development processes in bioinformatics. On the other hand, improved machine-human language interaction is the aim of Natural Language Processing. Its three main goals are character sequence processing, pattern recognition, and algorithm development. The use of Natural Language Processing is becoming increasingly important for the analysis of omics data using both modern and conventional Machine Learning models, underscoring the necessity for a systematic review. In this work, 82 studies were included following the PRISMA guidelines, sourced from PubMed, Scopus and IEEE Xplore on April 4th, 2023. The evaluation of the publications was based on the type of the studied biological data and the employed NLP techniques. Through our in-depth exploration of NLP approaches, we highlight their significance and potential in advancing the field of bioinformatics.

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) controls has attracted significant attention. In this paper, we present a complementary dataset consisting of eyes-open photic stimulation recordings from the same cohort. The dataset includes recordings from 88 participants (36 AD, 23 FTD, and 29 CN) and is provided in Brain Imaging Data Structure (BIDS) format, promoting consistency and ease of use across research groups. Additionally, a fully preprocessed version is included, using EEGLAB-based pipelines that involve filtering, artifact removal, and Independent Component Analysis, preparing the data for machine learning applications. This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. It offers a valuable benchmark for both methodological comparisons and biological investigations, and it is expected to significantly contribute to the fields of neurodegenerative disease research, biomarker discovery, and EEG-based diagnostics.

Background/Objectives: Endometriosis is a chronic, estrogen-driven gynecological disorder affecting approximately 10% of reproductive-aged women worldwide, with significant physical, psychosocial, and socioeconomic impacts. Recent research suggests a possible involvement of the gut microbiome in endometriosis disease mechanisms through immune manipulation, estrogen metabolism, and inflammatory networks. This narrative review aims to summarize current evidence on gut microbiota changes in endometriosis patients, explore the mechanisms by which gut dysbiosis contributes to disease progression, and examine epidemiological links between gastrointestinal health and endometriosis risk. Methods: A narrative review was conducted to synthesize available literature on the compositional changes in gut microbiota associated with endometriosis. The review also evaluated studies investigating potential mechanisms and epidemiological patterns connecting gut health with endometriosis development and severity. Results: Alterations in gut microbiota composition were observed in endometriosis patients, suggesting roles in immune dysregulation, estrogen metabolism, and inflammation. Potential gut-oriented interventions, including dietary changes, probiotics, and lifestyle modifications, emerged as promising management options. However, methodological variability and research gaps remain barriers to clinical translation. Conclusions: Integrating gut microbiome research into endometriosis management holds potential for improving early diagnosis, patient outcomes, and healthcare system sustainability. The study emphasizes the need for further research to address existing challenges and to develop public health strategies that incorporate microbiome-based interventions in population-level endometriosis care.

Developmental dyslexia (DD) is a learning disorder. Although risk genes have been identified, environmental factors, and particularly stress arising from constant difficulties, have been associated with the occurrence of DD by affecting brain plasticity and function, especially during critical neurodevelopmental stages. In this work, electroencephalogram (EEG) findings were coupled with the genetic and epigenetic molecular signatures of individuals with DD and matched controls. Specifically, we investigated the genetic and epigenetic correlates of key stress-associated genes (NR3C1, NR3C2, FKBP5, GILZ, SLC6A4) with psychological characteristics (depression, anxiety, and stress) often included in DD diagnostic criteria, as well as with brain EEG findings. We paired the observed brain rhythms with the expression levels of stress-related genes, investigated the epigenetic profile of the stress regulator glucocorticoid receptor (GR) and correlated such indices with demographic findings. This study presents a new interdisciplinary approach and findings that support the idea that stress, attributed to the demands of the school environment, may act as a contributing factor in the occurrence of the DD phenotype.

Digital Twins are an innovative approach to medical education, allowing realistic simulation of clinical procedures. Their combination with feedback technologies, such as Artificial Intelligence (AI), significantly enhances training in complex and specialized scenarios, such as those related to neurodegenerative diseases. In this paper, we present an integrated virtual avatar system with embedded AI, designed based on data from real patients in everyday environments. This educational environment offers an immersive and interactive learning experience, targeting both healthcare professionals, caregivers and ordinary users, through questions and scenarios that simulate reality. The methodology is based on machine learning models and real-time behavioral simulations, aiming to reproduce symptoms of conditions such as Parkinson's and Alzheimer's disease. The system allows researchers to practice diagnostic and treatment strategies in a controlled environment, while providing caregivers with practical guidelines for daily patient care. In addition, integration into virtual reality (VR) environments is supported. A user study was conducted to evaluate the effectiveness of the system, focusing on user engagement, educational value and clinical applicability. Results demonstrate significant improvement in knowledge acquisition and retention, as well as in the development of diagnostic skills.

Background: Multiple biologics targeting type 2 inflammation have been evaluated for the treatment of severe asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) separately. Objective: To evaluate the efficacy and safety of biologics in patients with comorbid asthma and CRSwNP. Methods: A systematic review of randomised controlled trials (RCTs) from Medline, Embase, Web of Science and Scopus (up to 31 October 2025) was conducted. Random-effects meta-analysis assessed efficacy and safety outcomes. Results: 16 studies involving 3598 patients were included in the meta-analysis. Overall, biologics reduced asthma exacerbations by 73% (rate ratio 0.27, 95% CI 0.21–0.34), increased forced expiratory volume in 1 s by 0.21 L (95% CI 0.11–0.30), improved asthma control questionnaire score by −0.70 points (95% CI −0.83–−0.56) and asthma quality of life questionnaire score by 0.71 points (95% CI 0.49–0.93). Regarding sino-nasal outcomes, the sino-nasal outcome test 22 (SNOT-22) score was reduced by 15.15 points (95% CI −19.64–−10.66), the nasal polyp score by 1.39 points (95% CI −1.88–−0.89), the Lund–Mackay computed tomography score by 6.64 points (95% CI −8.88–−4.40) and the nasal congestion/obstruction score by 0.84 points (95% CI −1.13–−0.54). Heterogeneity across biologic classes varied by outcome, ranging from low to substantial. Overall, biologics exhibited a favourable safety profile. Conclusions: Biologics significantly reduced asthma exacerbations, improved lung function, asthma control and quality of life, and alleviated sino-nasal outcomes in patients with comorbid asthma and CRSwNP, with an acceptable safety profile.