BioMedInformatics, Volume 3, Issue 1 (March 2023) – 17 articles

Digital information storage capacity and biomedical technology advancements in recent decades have stimulated the maturity and popularization of “big data” in medicine. The value of utilizing big data as a diagnostic and prognostic tool has continued to rise given its potential to provide accurate and insightful predictions of future health events and probable outcomes for individuals and populations, which may aid early identification of disease and timely treatment interventions. Whilst the implementation of big data methods for this purpose is more well-established in specialties such as oncology, cardiology, ophthalmology, and dermatology, big data use in nephrology and specifically chronic kidney disease (CKD) remains relatively novel at present. Nevertheless, increased efforts in the application of big data in CKD have been observed over recent years, with aims to achieve a more personalized approach to treatment for individuals and improved CKD screening strategies for the general population. Considering recent developments, we provide a focused perspective on the current state of big data and its application in CKD and nephrology, with hope that its ongoing evolution and revolution will gradually identify more solutions to improve strategies for CKD prevention and optimize the care of patients with CKD. Full article

Introduction: Digital health interventions are gradually being incorporated into the management of tuberculosis to ensure treatment adherence, but only a small number of trials focusing on latent tuberculosis infection (LTBI) care have tested and evaluated them. It is anticipated that 170 million persons with LTBI may eventually develop active TB; thus, treatment of LTBI patients is an important aspect, along with ensuring treatment adherence. Digital platforms can be beneficial to ensure treatment adherence in LTBI patients, as various studies have shown the positive impact of digital interventions in improving patients’ treatment adherence and treatment outcome. This study aims to explore the various available digital interventions worldwide for treatment adherence in LTBI patients and develop an informatics platform for enhancing treatment adherence in LTBI patients. Methods: This will be a quasi-experimental study divided into three phases. In the first phase, a scoping review method will be used to conduct a systematic literature review using the PRISMA tool to report on various digital interventions focused on treatment adherence in LTBI patients. In the second phase, a text message-based digital platform will be developed, and in the third phase of the study, an evaluation of the digital platform will be done using qualitative and quantitative questionnaires. The study will be conducted using a mixed-methods approach between January 2023 and December 2023. The sample size will be 162 participants, of whom 81 will be assigned to an intervention group and 81 will receive the usual care from the respective chest clinic as a control group. Results: A descriptive analysis of demographic variables and other variables will be done. Continuous variables will be described as mean ± standard deviation (M ± SD), medians (inter-quartile ranges) (M (IQR)), and medians (5th percentile to 95th percentile) (P5-P95). A two-sample independent T-test, the chi-square test, and the Mann-Whitney test will be used for comparisons between groups. Treatment success between control and intervention will be compared through a chi-square test. Conclusions: The key finding of the study will be an understanding of the efficiency of digital platforms for improving treatment adherence in latent TB patients in India. Full article

Background: The Holter electrocardiogram (ECG) provides a long signal that represents the heart’s responses to both autonomic regulation and various phenomena, including heart tissue remodeling. Loss of information is a common result when using global statistical metrics. Method: Breaking the signal into short data segments (e.g., windows) provides access to transient heart rate characteristics. Symbolization of the ECG by patterns of accelerations and/or decelerations allows using entropic metrics in the assessment of heart rate complexity. Two types of analysis are proposed: (i) visualization of the pattern dynamics of the whole signal, and (ii) scanning the signal for pattern dynamics in a sliding window. The method was applied to a cohort of 42 heart transplant (HTX) recipients divided into the following groups: a left ventricle of normal geometry (NG), concentrically remodeled (CR), hypertrophic remodeled (H), and to the control group (CG) consisting of signals of 41 healthy coevals. The Kruskal–Wallis test was used to assess group differences. Statistical conclusions were verified via bootstrap methods. Results: The visualization of the group pattern dynamics showed severely limited autonomic regulations in HTX patients when compared to CG. The analysis (in segments) prove that the pattern dynamics of the NG group are different from the pattern dynamics observed in the CR and H groups. Conclusion: Dynamic pattern entropy estimators tested in moving windows recognized left ventricular remodeling in stable HTX patients. Full article

With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field. Full article

An interesting issue observed in some drugs is the “double peak phenomenon” (DPP). In DPP, the concentration-time (C-t) profile does not follow the usual shape but climbs to a peak and then begins to degrade before rising again to a second peak. Such a phenomenon is observed in the case of amisulpride, which is a second-generation antipsychotic. The aim of this study was to develop a model for the description of double peaks in amisulpride after oral administration. Amisulpride plasma C-t data were obtained from a 2 × 2 crossover bioequivalence study in 24 healthy adult subjects. A nonlinear mixed-effects modeling approach was applied in order to perform the analysis. Participants’ characteristics, such as demographics (e.g., body weight, gender, etc.), have also been investigated. A model for describing the double peak phenomenon was successfully developed. Simulations were run using this model to investigate the impact of significant covariates and recommend appropriate dosage regimens. For comparison purposes and to investigate the suitability of our developed model for describing the double peak phenomenon, modeling of previously published population pharmacokinetic models was also applied to the C-t data of this study. Full article

Bedridden patients risk presenting several problems caused by prolonged immobility, leading to a long recovery process. There is thus a need to develop solutions that ensure the implementation of physical rehabilitation programs in a controlled and interactive way. In this context, the ABLEFIT project aims to develop a medical device to physically rehabilitate bedridden patients with prolonged immobility. A partnership was established between the school of nursing, business enterprises and an engineering institute to develop a prototype. After creating the prototype, a pre-clinical experimental usability study was created using the user-centred multi-method approach (User and Human-Centered Design) to assess the device’s functionality, ergonomics and safety. The pre-clinical stage was initiated with a sample of 12 health professionals (that manipulated the device’s functionalities) and 10 end-users (who used the device). During the pre-clinical stage, the need to incorporate in the final version joint stabilizers was observed. Another important finding was the importance of the continuous monitorization of vital signs on Ablefit, namely, heart rate and SPO₂. Therefore, the development of the Ablefit system allows the monitoring of a set of variables and conditions inherent to immobility. At the same time, this device will be a dynamic solution (using gamification and simulation technologies) by generating personalized rehabilitation plans. Full article

The radiation adaptive response effect is a biophysical phenomenon responsible for the enhancement of repair processes in irradiated cells. This can be observed in dedicated radiobiological experiments, e.g., where the small priming dose of ionising radiation is given before the high challenging one (the so-called Raper–Yonezawa effect). The situation is more complicated when the whole complex system (the organism) is taken into consideration; many other mechanisms make the adaptive response weaker and—in some cases—practically insignificant. The recently published simplified Monte Carlo model of human lymphocytes irradiation by X-rays allows for the calculation of the level of repair enhancement by the adaptive response when every other cellular biological mechanism is implemented. The qualitative results show that the adaptive response phenomenon, observed with some probability on a basic level, usually blurs among other effects and becomes weaker than expected. Regardless, the radiation adaptive response is still an important biophysical effect which needs to be taken into consideration in low-dose radiobiological studies. Full article

Dementia is characterized as a decline in cognitive function, including memory, language and problem-solving abilities. In this paper, we conducted a Genome-Wide Association Study (GWAS) using data from the electronic Medical Records and Genomics (eMERGE) network. This study has two aims, (1) to investigate the genetic mechanism of dementia and (2) to discuss multiple p-value thresholds used to address multiple testing issues. Using the genome-wide significant threshold ($p\le 5\times {10}^{-8}$), we identified four SNPs. Controlling the False Positive Rate (FDR) level below $0.05$ leads to one extra SNP. Five SNPs that we found are also supported by QQ-plot comparing observed p-values with expected p-values. All these five SNPs belong to the TOMM40 gene on chromosome 19. Other published studies independently validate the relationship between TOMM40 and dementia. Some published studies use a relaxed threshold ($p\le 1\times {10}^{-5}$) to discover SNPs when the statistical power is insufficient. This relaxed threshold is more powerful but cannot properly control false positives in multiple testing. We identified 13 SNPs using this threshold, which led to the discovery of extra genes (such as ATP10A-DT and PTPRM). Other published studies reported these genes as related to brain development or neuro-development, indicating these genes are potential novel genes for dementia. Those novel potential loci and genes may help identify targets for developing new therapies. However, we suggest using them with caution since they are discovered without proper false positive control. Full article

Background: Nowadays, much research deals with the application of the automated meta-analysis of clinical trials through appropriate machine learning tools to extract the results that can then be applied in daily clinical practice. Methods: The author performed a systematic search of the literature from 27 September 2022–22 November 2022 in PUBMED, in the first 6 pages of Google Scholar and in the online catalog, the Systematic Review Toolbox. Moreover, a second search of the literature was performed from 7 January 2023–20 January 2023 in the first 10 pages of Google Scholar and in the Semantic Google Scholar. Results: 38 approaches in 39 articles met the criteria and were included in this overview. These articles describe in detail machine learning approaches, methods, and tools that have been or can potentially be applied to the meta-analysis of clinical trials. Nevertheless, while the other tasks of a systematic review have significantly developed, the automation of meta-analyses is still far from being able to significantly support and facilitate the work of researchers, freeing them from manual, difficult and time-consuming work. Conclusions: The evaluation of automated meta-analysis results is presented in some studies. Their approaches show positive and promising results. Full article

Bladder cancer (BCa) is one of the most diagnosed urological malignancies. A timely and accurate diagnosis is crucial at the first assessment as well as at the follow up after curative treatments. Moreover, in the era of precision medicine, proper molecular characterization and pathological evaluation are key drivers of a patient-tailored management. However, currently available diagnostic tools still suffer from significant operator-dependent variability. To fill this gap, physicians have shown a constantly increasing interest towards new resources able to enhance diagnostic performances. In this regard, several reports have highlighted how artificial intelligence (AI) can produce promising results in the BCa field. In this narrative review, we aimed to analyze the most recent literature exploring current experiences and future perspectives on the role of AI in the BCa scenario. We summarized the most recently investigated applications of AI in BCa management, focusing on how this technology could impact physicians’ accuracy in three widespread diagnostic areas: cystoscopy, clinical tumor (cT) staging, and pathological diagnosis. Our results showed the wide potential of AI in BCa, although larger prospective and well-designed trials are pending to draw definitive conclusions allowing AI to be routinely applied to everyday clinical practice. Full article

Chest X-ray (CXR) is one of the most common radiological examinations for both nonemergent and emergent clinical indications, but human error or lack of prioritization of patients can hinder timely interpretation. Deep learning (DL) algorithms have proven to be useful in the assessment of various abnormalities including tuberculosis, lung parenchymal lesions, or pneumothorax. The deep learning–based automatic detection algorithm (DLAD) was developed to detect visual patterns on CXR for 12 preselected findings. To evaluate the proposed system, we designed a single-site retrospective study comparing the DL algorithm with the performance of five differently experienced radiologists. On the assessed dataset (n = 127) collected from the municipal hospital in the Czech Republic, DLAD achieved a sensitivity (Se) of 0.925 and specificity (Sp) of 0.644, compared to bootstrapped radiologists’ Se of 0.661 and Sp of 0.803, respectively, with statistically significant difference. The negative likelihood ratio (NLR) of the proposed software (0.12 (0.04–0.32)) was significantly lower than radiologists’ assessment (0.42 (0.4–0.43), p < 0.0001). No critical findings were missed by the software. Full article

Background: Scientific and technological development has constituted a reality in the lives of populations, making it unimaginable to live without specific incentives that development has allowed. That said, given the increase in the longevity of people, it will be helpful to implement these resources in promoting health and disease prevention in the elderly. The objective of this study is to identify, in the scientific evidence, the effects of Wii games on the physical training of the elderly. Methods: A systematic review will be carried out according to the methodology recommended by the Joanna Briggs Institute. Relevant databases will be used for the research, where the words will be used: rehabilitation, exercise, physical activity, rehabilitation exercise; movement; therapeutic exercise; engine activity; rehabilitation, geriatric, gerontologic care, and aged. Results: This systematic review will include experimental and quasi-experimental studies, including randomised studies with and without a control group, pre- and post-assessment. Conclusions: To promote the autonomy of the elderly and consequently healthy and prosperous ageing, it is crucial to implement all available measures and resources. For this purpose, exergames have been shown to be effective, and it is necessary to know which ones are suitable for the physical training of the elderly. This one protocol is registered with the Open Science Framework. Full article

Protein three-dimensional structural analysis using artificial intelligence is attracting attention in various fields, such as the estimation of vaccine structure and stability. In particular, when using the spike protein in vaccines, the major issues in the construction of SARS-CoV-2 vaccines are their weak abilities to attack the virus and elicit immunity for a short period. Structural information about new viruses is essential for understanding their properties and creating effective vaccines. However, determining the structure of a protein through experiments is a lengthy and laborious process. Therefore, a new computational approach accelerated the elucidation process and made predictions more accurate. Using advanced machine learning technology called deep neural networks, it has become possible to predict protein structures directly from protein and gene sequences. We summarize the advances in antiviral therapy with the SARS-CoV-2 vaccine and extracellular vesicles via computational analysis. Full article

Recently, Artificial Intelligence (AI) has spread in orthodontics, in particular within cephalometric analysis, where computerized digital software is able to provide linear-angular measurements upon manual landmark identification. A step forward is constituted by fully automated AI-assisted cephalometric analysis, where the landmarks are automatically detected by software. The aim of the study was to compare the reliability of a fully automated AI-assisted cephalometric analysis with the one obtained by a computerized digital software upon manual landmark identification. Fully automated AI-assisted cephalometric analysis of 13 lateral cephalograms were retrospectively compared to the cephalometric analysis performed twice by a blinded operator with a computerized software. Intra- and inter-operator (fully automated AI-assisted vs. computerized software with manual landmark identification) reliability in cephalometric parameters (maxillary convexity, facial conicity, facial axis angle, posterior and lower facial height) was tested with the Dahlberg equation and Bland–Altman plot. The results revealed no significant difference in intra- and inter-operator measurements. Although not significant, higher errors were observed within intra-operator measurements of posterior facial height and inter-operator measurements of facial axis angle. In conclusion, despite the small sample, the cephalometric measurements of a fully automated AI-assisted cephalometric software were reliable and accurate. Nevertheless, digital technological advances cannot substitute the critical role of the orthodontist toward a correct diagnosis. Full article

Ultrasound elastography (USE) or elastosonography is an ultrasound-based, non-invasive imaging method for assessing tissue elasticity. The different types of elastosonography are distinguished according to the mechanisms used for estimating tissue elasticity and the type of information they provide. In strain imaging, mechanical stress is applied to the tissue, and the resulting differential strain between different tissues is used to provide a qualitative assessment of elasticity. In shear wave imaging, tissue elasticity is inferred through quantitative parameters, such as shear wave velocity or longitudinal elastic modulus. Shear waves can be produced using a vibrating mechanical device, as in transient elastography (TE), or an acoustic impulse, which can be highly focused, as in point-shear wave elastography (p-SWE), or directed to multiple zones in a two-dimensional area, as in 2D-SWE. A general understanding of the basic principles behind each technique is important for clinicians to improve data acquisition and interpretation. Major clinical applications include chronic liver disease, breast lesions, thyroid nodules, lymph node malignancies, and inflammatory bowel disease. The integration of artificial intelligence tools could potentially overcome some of the main limitations of elastosonography, such as operator dependence and low specificity, allowing for its effective integration into clinical workflow. Full article

The increase in speed, reliability, and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS), and transcriptome analysis. WXS and RNA sequencing is now being implemented as the standard of care for patients and for patients included in clinical studies. To keep track of sample relationships and analyses, a platform is needed that can unify metadata for diverse sequencing strategies with sample metadata whilst supporting automated and reproducible analyses, in essence ensuring that analyses are conducted consistently and data are Findable, Accessible, Interoperable, and Reusable (FAIR).We present “Trecode”, a framework that records both clinical and research sample (meta) data and manages computational genome analysis workflows executed for both settings, thereby achieving tight integration between analysis results and sample metadata. With complete, consistent, and FAIR (meta) data management in a single platform, stacked bioinformatic analyses are performed automatically and tracked by the database, ensuring data provenance, reproducibility, and reusability, which is key in worldwide collaborative translational research. The Trecode data model, codebooks, NGS workflows, and client programs are publicly available. In addition, the complete software stack is coded in an Ansible playbook to facilitate automated deployment and adoption of Trecode by other users. Full article