BioMedInformatics

COVID-19 has caused millions of deaths around the world. The respiratory system is the main target of this disease, but it has also been reported to attack the central nervous system, creating a neuroinflammatory environment with the release of proinflammatory cytokines. There are several studies suggesting a possible relationship between Alzheimer’s disease and COVID-19. Therefore, in this study, machine learning microarray analysis was performed to identify key genes in COVID-19 that may be associated with Alzheimer’s disease. The dataset is identified as GSE177477, containing 47 samples. A bioconductor oligo package in the RStudio (version 4.3.3) environment was used to process and normalize the data. Subsequently, one-way ANOVA was used to obtain differentially expressed genes. We used decision tree generation to classify 47 samples. The study identified 1856 differentially expressed genes. Three decision trees were generated where three genes (DNAJC16, TREM1, and UCP2) were identified that differentiated patients. The best decision tree obtained an accuracy of 72.34%, with a sensitivity of 72.34% and a specificity of 86.17%. The genes identified with the decision trees may be involved in processes like those of Alzheimer’s disease, such as in the inflammation process, amyloid pathologies, and related to type 2 diabetes mellitus. Full article

Background: Systematic reviewers face a growing body of biomedical literature, making early-stage article screening increasingly time-consuming. In this study, we assessed six large language models (LLMs)—OpenHermes, Flan T5, GPT-2, Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o—for their ability to identify randomized controlled trials (RCTs) in datasets of increasing difficulty. Methods: We first retrieved articles from PubMed and used all-mpnet-base-v2 to measure semantic similarity to known target RCTs, stratifying the collection into quartiles of descending relevance. Each LLM then received either verbose or concise prompts to classify articles as “Accepted” or “Rejected”. Results: Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o consistently achieved high recall, though their precision varied in the quartile with the highest similarity, where false positives increased. By contrast, smaller or older models struggled to balance sensitivity and specificity, with some over-including irrelevant studies or missing key articles. Importantly, multi-stage prompts did not guarantee performance gains for weaker models, whereas single-prompt approaches proved effective for advanced LLMs. Conclusions: These findings underscore that both model capability and prompt design strongly affect classification outcomes, suggesting that newer LLMs, if properly guided, can substantially expedite systematic reviews. Full article

(1) Background: Blood pressure (BP) variability is an important risk factor for cardiovascular diseases. Still, existing BP monitoring methods often require periodic cuff-based measurements, raising concerns about their accuracy and convenience. This study aims to develop a subject-independent, cuff-less BP estimation method using finger and toe photoplethysmography (PPG) signals combined with an electrocardiogram (ECG) without the need for an initial cuff-based measurement. (2) Methods: A customized measurement system was used to record 80 readings from human subjects. Fifteen features with the highest dependency on the reference BP, including time and morphological characteristics of PPG and subject information, were analyzed. A multivariate regression model was employed to estimate BP. (3) Results: The results showed that incorporating toe PPG signals improved the accuracy of BP estimation, reducing the mean absolute error (MAE). Using both finger and toe PPG signals resulted in an MAE of 9.63 ± 12.54 mmHg for systolic BP and 6.76 ± 8.38 mmHg for diastolic BP, providing the lowest MAE compared to previous methods. (4) Conclusions: This study is the first to integrate toe PPG for more accurate BP estimation and proposes a method that does not require an initial cuff-based BP measurement, offering a promising approach for non-invasive, continuous BP monitoring. Full article

Background: This work presents an artificial intelligence-based algorithm for detecting Parkinson’s disease (PD) from voice signals. The detection of PD at pre-symptomatic stages is imperative to slow disease progression. Speech signal processing-based PD detection can play a crucial role here, as it has been reported in the literature that PD affects the voice quality of patients at an early stage. Hence, speech samples can be used as biomarkers of PD, provided that suitable voice features and artificial intelligence algorithms are employed. Methods: Advanced signal-processing techniques are used to extract audio features from the sustained vowel ‘/a/’ sound. The extracted audio features include baseline features, intensities, formant frequencies, bandwidths, vocal fold parameters, and Mel-frequency cepstral coefficients (MFCCs) to form a feature vector. Then, this feature vector is further enriched by including wavelet-based features to form the second feature vector. For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. Results: The results demonstrate that the inclusion of wavelet-based voice features enhances the performance of both the SVM and kNN models for PD detection. However, kNN provides better accuracy, detection speed, training time, and misclassification cost than SVM. Conclusions: This work concludes that wavelet-based voice features are important for detecting neurodegenerative diseases like PD. These wavelet features can enhance the classification performance of machine learning models. This work also concludes that kNN is recommendable over SVM for the investigated voice features, despite the inclusion and exclusion of the wavelet features. Full article

Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT. Full article

Background: Radiomic features have been extensively used with machine learning and other Artificial Intelligence methods in medical imaging problems. Coronavirus Disease 2019 (COVID-19), which has been spreading worldwide since 2020, has motivated scientists to develop automatic COVID-19 recognition systems, to enhance the clinical routine in overcrowded hospitals. Purpose: To develop an automated system of recognizing COVID-19 and Community-Acquired Pneumonia (CAP) using radiomic features extracted from whole lung chest Computed Tomography (CT) images. Radiomic feature extraction from whole lung CTs simplifies the image segmentation for the malignancy region of interest (ROI). Methods: In this work, we used radiomic features extracted from CT images representing whole lungs to train various machine learning models that are capable of identifying COVID-19 images, CAP images and healthy cases. The CT images were derived from an open access data set, called COVID-CT-MD, containing 76 Normal cases, 169 COVID-19 cases and 60 CAP cases. Results: Four two-class models and one three-class model were developed: Normal–COVID, COVID–CAP, Normal–CAP, Normal–Disease and Normal–COVID–CAP. Different algorithms and data augmentation were used to train each model 20 times on a different data set split, and, finally, the model with the best average performance was selected for each case. The performance metrics of Accuracy, Sensitivity and Specificity were used to assess the performance of the different systems. Since COVID-19 and CAP share similar characteristics, it is challenging to develop a model that can distinguish these diseases. Result: The results were promising for the models finally selected for each case. The accuracy for the independent test set was 83.11% in the Normal–COVID case, 88.77% in the COVID–CAP case, 93.97% in the Normal–CAP case and 94.13% in the Normal–Disease case, when referring to two-class cases, while, in the three-class case, the accuracy was 78.55%. Conclusion: The results obtained suggest that radiomic features extracted from whole lung CT images can be successfully used to distinguish COVID-19 from other pneumonias and normal lung cases. Full article

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its promise, challenges such as image heterogeneity across different centers, variability in acquisition protocols and scanners, and sensitivity to artifacts hinder the reliability and clinical integration of deep learning models. Addressing these issues is critical for ensuring accurate and practical AI-powered neuroimaging applications. We reviewed and summarized the strategies for improving the robustness and generalizability of deep learning models for the segmentation and classification of neuroimages. This review follows a structured protocol, comprehensively searching Google Scholar, PubMed, and Scopus for studies on neuroimaging, task-specific applications, and model attributes. Peer-reviewed, English-language studies on brain imaging were included. The extracted data were analyzed to evaluate the implementation and effectiveness of these techniques. The study identifies key strategies to enhance deep learning in neuroimaging, including regularization, data augmentation, transfer learning, and uncertainty estimation. These approaches address major challenges such as data variability and domain shifts, improving model robustness and ensuring consistent performance across diverse clinical settings. The technical strategies summarized in this review can enhance the robustness and generalizability of deep learning models for segmentation and classification to improve their reliability for real-world clinical practice. Full article

Background: Exosomes are a group of extracellular vesicles that are released by almost all mammalian cell types and engage in intracellular communication. Studies conducted in recent years have shown that exosomes are involved in a variety of diseases, where they may act as “vehicles” for the transmission of biomolecules and biomolecular information. Amyloidoses constitute a critical subgroup of these diseases, caused by extracellular deposition or intracellular inclusions of insoluble protein fibrils in cells and tissues. However, how exosomes are involved in these diseases remains largely unexplored. Methods: To detect possible links between amyloid proteins and exosomes, protein data from amyloidosis-isolated exosomes were collected and visualized using biological networks. Results: This biomedical informatics approach for the analysis of interaction networks, in combination with the existing literature, highlighted the involvement of exosomes in amyloidosis while strengthening existing hypotheses regarding their mechanism of action. Conclusion: This work is focused on exosomes from patients with Alzheimer’s disease and identifies important amyloidogenic proteins found in exosomes. These proteins can be used for future research in the field of exosome-based biomarkers of amyloidosis and potential prognostic or preventive approaches. Full article

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential. Full article

Survival analysis is a statistical approach widely employed to model the time of an event, such as a patient’s death. Classical approaches include the Kaplan–Meier estimator and Cox proportional hazards regression, which assume a linear relationship between the model’s covariates. However, the linearity assumption might pose challenges with high-dimensional data, thus stimulating interest in performing survival analysis using neural network models. In the present work, we implemented a deep Cox neural network (Cox-net) to predict the time of a cardiac event using patient data collected from the Myocardial Iron Overload in Thalassemia (MIOT) project. Cox-net achieved a concordance index (c-index) of 0.812 ± 0.036, outperforming the classical Cox regression (0.790 ± 0.040), and it demonstrated resilience to varying levels of censored patients. A permutation feature importance analysis identified fibrosis and sex as the most significant predictors, aligning with clinical knowledge. Cox-net was able to represent the nonlinear relationships between covariates and maintain reliable survival curve predictions in datasets with a large number of censored patients, making it a promising tool for determining the appropriate clinical pathway for thalassemic patients. Full article

Background: Critical studies have unwaveringly established the importance of peculiar single-nucleotide polymorphisms (SNPs) in apolipoproteins (Apos) genes as genetic risk factors for dyslipidemias and their related comorbidities. In this study, we employed in silico approaches to analyze mutations in Apos. Methods: A comprehensive set of computational tools was utilized. The tools for predictions derived from sequence analysis were: SIFT, PolyPhen-2, FATHMM and SNPs&GO; The tools for structure analysis were: mCSM, DynaMut2, MAESTROweb, and PremPS; for prediction of pathogenic potential were: MutPred2, and PhD-SNP; for profiling of aggregation propensity were: Camsol, and Aggrescan3D 2.0, and lastly, for residual frustration analysis, the Frustratometer was used. These approaches assess variant effects on protein structure, stability, and function. Results: We identified seventeen SNPs in total, twelve for ApoB, one for ApoC2, one for ApoC3, and three for ApoE, representing 70%, 6%, 6% and 18%, respectively. The pathogenity of ApoE, was highlighted in two SNPs the rs769452 with amino acid replacement L46P, and rs769455 with amino acid replacement R163C. The aggregation/solubility analysis revealed that the L46P leads to a decrease in ApoE aggregation. The R163C, showed a decrease in solubility in one of two tools used, resulting in destabilizing effects altering its solubility. Conclusions: The two mutations in ApoE studied with the in silico methodologies identified clinically significant genetic variants, highlighting the robustness of the integrated approach. The future direction of the research is to create a multiplex panel with the SNPs identified here in APOE and expanding to other proteins to have a panel genetic risk assessment and disease prediction in which ApoE correlates. Full article

Large language models (LLMs) have emerged as powerful tools for (semi-)automating the initial screening of abstracts in systematic reviews, offering the potential to significantly reduce the manual burden on research teams. This paper provides a broad overview of prompt engineering principles and highlights how traditional PICO (Population, Intervention, Comparison, Outcome) criteria can be converted into actionable instructions for LLMs. We analyze the trade-offs between “soft” prompts, which maximize recall by accepting articles unless they explicitly fail an inclusion requirement, and “strict” prompts, which demand explicit evidence for every criterion. Using a periodontics case study, we illustrate how prompt design affects recall, precision, and overall screening efficiency and discuss metrics (accuracy, precision, recall, F1 score) to evaluate performance. We also examine common pitfalls, such as overly lengthy prompts or ambiguous instructions, and underscore the continuing need for expert oversight to mitigate hallucinations and biases inherent in LLM outputs. Finally, we explore emerging trends, including multi-stage screening pipelines and fine-tuning, while noting ethical considerations related to data privacy and transparency. By applying systematic prompt engineering and rigorous evaluation, researchers can optimize LLM-based screening processes, allowing for faster and more comprehensive evidence synthesis across biomedical disciplines. Full article

Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population. The criticality of seizure occurrence and associated risks, combined with the overwhelming need for more precise and innovative treatment methods, has led to the development of invasive neurostimulation devices programmed to detect and apply electrical stimulation therapy to suppress seizures and reduce the seizure burden. Tiny Machine Learning (TinyML) is a rapidly growing branch of machine learning. One of its key characteristics is the ability to run machine learning algorithms without the need for high computational complexity and powerful hardware resources. The featured work utilizes TinyML technology to implement an algorithm that can be integrated into the microprocessor of an implantable closed-loop brain neurostimulation system to accurately detect seizures in real-time by analyzing intracranial EEG (iEEG) signals. Methods: A dataset containing iEEG signal values from both non-epileptic and epileptic individuals was utilized for the implementation of the proposed algorithm. Appropriate data preprocessing was performed, and two training datasets with 1000 records of non-epileptic and epileptic iEEG signals were created. A test dataset with an independent dataset of 500 records was also created. The web-based platform Edge Impulse was used for model generation and visualization, and different model architectures were explored and tested. Finally, metrics of accuracy, confusion matrices, and ROC curves were used to evaluate the performance of the model. Results: Our model demonstrated high performance, achieving 98% and 99% accuracy on the validation and test EEG datasets, respectively. Our results support the use of TinyML technology in closed-loop neurostimulation devices for epilepsy, as it contributes significantly to the speed and accuracy of seizure detection. Conclusions: The proposed TinyML model demonstrated reliable seizure detection in real-time by analyzing EEG signals and distinguishing epileptic activity from normal brain electrical activity. These findings highlight the potential of TinyML in closed-loop neurostimulation systems for epilepsy, enhancing both speed and accuracy in seizure detection. Full article

Background: Variations in the states of Gene Regulatory Networks significantly influence disease outcomes and drug development. Boolean Networks serve as a tool to conceptualize and understand the complex relationships between genes. Threshold computation methods are used for the binarization of gene expression and the Boolean representation of its Gene Regulatory Network. This study aims to provide a platform that facilitates the exploration of the impact of different threshold computation methods on the binarization of gene expression and the subsequent Boolean representation of Gene Regulatory Networks. Methods: Threshold computation methods are implemented for binarizing gene expression, enabling the Boolean representation of the Gene Regulatory Networks. Variations in gene expression discretization and threshold computation methods often lead to differing Boolean representations, which may affect the subsequent analysis. Lluberes proposed a framework for analyzing gene expression when binarization varies based on these factors. This theoretical framework was implemented using the Python Dash framework. Results: A visualization tool has been developed to implement this framework. The tool allows users to upload gene expression datasets and interact with a dashboard to explore gene expression binarization and the inferred Boolean Networks. Conclusions: The developed visualization tool provides a platform that facilitates the exploration of how different binarization methods impact the interpretation of Gene Regulatory Networks, offering insights for disease research and drug development. Full article

Background: Large Language Models (LLMs) have demonstrated strong performances in clinical question-answering (QA) benchmarks, yet their effectiveness in addressing real-world consumer medical queries remains underexplored. This study evaluates the capabilities and limitations of LLMs in answering consumer health questions using the MedRedQA dataset, which consists of medical questions and answers by verified experts from the AskDocs subreddit. Methods: Five LLMs-GPT-4o mini, Llama 3.1-70B, Mistral-123B, Mistral-7B, and Gemini-Flash were assessed using a cross-evaluation framework. Each model generated responses to consumer queries and their outputs were evaluated by every model by comparing them with expert responses. Human evaluation was used to assess the reliability of models as evaluators. Results: GPT-4o mini achieved the highest alignment with expert responses according to four out of the five models’ judges, while Mistral-7B scored the lowest according to three out of five models’ judges. Overall, model responses show low alignment with expert responses. Conclusions: Current small or medium sized LLMs struggle to provide accurate answers to consumer health questions and must be significantly improved. Full article

Background: Understanding the dynamics of HIV transmission in heterogeneous populations is crucial for effective prevention strategies. This study introduces the Risk Modulation Point (RMP), a novel threshold identifying where HIV transmission transitions from unsustainable spread to self-sustaining epidemic dynamics. Methods: Using a deterministic, risk-stratified compartmental model, we examined HIV transmission across populations stratified into 100–200 risk groups, each characterized by behavioral heterogeneity modeled through a power-law distribution. The model captures key features of HIV progression, with simulations conducted across high- (~20%), moderate- (~5%), and low (~0.2%)-prevalence regimes. Results: Our findings reveal universal patterns in HIV dynamics. The RMP marks a consistent threshold across scenarios, separating low-risk groups where transmission is minimal from higher-risk groups sustaining the epidemic. Logistic growth in HIV prevalence across risk groups, with sharp transitions near the RMP, was observed universally. The force of infection follows power-law scaling, directly reflecting the level and nature of risk behavior within each group. Importantly, the location of the RMP remains largely invariant to the underlying sexual risk distribution, population resolution, and mixing patterns, making it applicable across both generalized and concentrated epidemics. Conclusion: The RMP framework offers actionable public health insights. It identifies key populations and transition regions for targeted interventions such as antiretroviral therapy and pre-exposure prophylaxis. By tracking shifts in the RMP, it also serves as an early warning indicator for epidemic transitions, guiding resource allocation and monitoring. The focus of the model on intrinsic epidemic dynamics, excluding external interventions, highlights its utility in uncovering fundamental transmission patterns. This study bridges theoretical modeling and practical application, providing a flexible framework for understanding HIV and other stratified epidemics. The findings advance HIV modeling by revealing generic patterns that transcend specific contexts, supporting data-driven public health strategies. Full article

Background: In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet limited data and high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing computational costs, although applying these models can still be resource intensive. This study aims to present flexible and computationally efficient architecture that leverages transfer learning and delivers highly accurate results across various medical imaging problems. Methods: We evaluated three datasets with varying similarities to ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), and the Shenzhen and Montgomery CXR Set (lung segmentation). An ablation study on ISIC 2018 tested various pre-trained backbones, architectures, and loss functions. Results: The optimal configuration—DeepLabV3+ with a pre-trained ResNet50 backbone and Log-Cosh Dice loss—was validated on the remaining datasets, achieving state-of-the-art results. Conclusion: Computationally simpler architectures can deliver robust performance without extensive resources, establishing DeepLabV3+ with the ResNet50 as a baseline for future studies. In the medical domain, enhancing data quality is more critical for improving segmentation accuracy than increasing model complexity. Full article

Background: Tumor mutation burden (TMB), a genomic biomarker, has proven to be a strong predictor of immunotherapy response but is not widely adopted. This study investigates the association between TMB and immune checkpoint inhibitors (ICIs) response in TNBC patients. Methods: From the TCGA database, patients were stratified into two levels based on TMB and validated using survival analysis. Then, four machine learning models were trained to classify TNBC patients based on histological features into high and low TMB. To further validate our approach, we compared the genomic landscapes of both groups, identified differentially expressed genes (DEGs), and performed pathway enrichment analysis. Results: Our findings revealed a significant association between TMB and ICI response in TNBC. Random forest model effectively classified TNBC patients based on the representative histological features and clinical data with an accuracy of 0.82 on the validation set. The genomic analysis revealed that FAT3, TTN, and DYNC2H1 had a significantly high mutation rate in the TMB groups. Genes impacting cancer progression and immunogenicity were identified in the DEG analysis as IGF2, CLEC3A, and CASC9. Conclusions: This study constructs a model to identify suitable TNBC patients for immunotherapy and highlights the potential role of TMB associated with genomic alterations in predicting immune response in TNBC. Full article

EEG microstates are brief, stable topographical configurations of brain activity that provide insights into alterations in brain function and connectivity. Anomalies in microstates are associated with different neuropsychiatric conditions, especially schizophrenia. Recent advances in both EEG techniques and machine learning point to the potential role of microstates as diagnostic markers for psychotic disorders. This systematic review aims to gather current knowledge on machine learning applied to EEG microstate analysis in psychotic disorders. Following PRISMA guidelines, we searched Scopus, PubMed, and Scholar databases, including 10 studies. Overall results show that EEG microstates can be used to accurately classify diagnoses within the psychosis spectrum, across all stages, outperforming models based on conventional EEG measures, with a prominent role of microstate D. One study also suggests that microstate anomalies may be directly linked to symptom severity. Integrating EEG microstates with machine learning shows promise in improving our understanding of psychotic disorders and developing more precise diagnostic tools. Full article