BioMedInformatics

Networks form the backbone of any healthcare system. Various databases were searched with relevant keywords, data were abstracted, and numerous papers were appraised for this synthesis. This compiled systematic review gives a comprehensive overview of various networks that are found in healthcare, with a special reference to the treatment, referral, and best-practice care of patients. Special support networks, such as Clinical decision support systems, Physician collaboration networks, Telemedicine networks, and Shared healthcare record access, are also described, as these support networks play a pivotal role in improving the quality of healthcare for patients. Full article

Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field. Full article

Breast cancer is the most prevalent form of cancer and the primary cause of cancer-related mortality among women globally. Breast cancer diagnosis involves multiple variables, making it a complex process. Therefore, the accurate estimation of features for diagnosing breast cancer is of great importance. The present study used a dataset of 21 patients with carcinoma breast cancer. Polynomial regression analysis was used to non-invasively estimate six impedance features for the diagnosis of breast cancer, including the phase angle at 500 KHz (PA500), impedance distance between spectral ends (DA), area normalized by DA (A/DA), maximum of the spectrum (Max IP), the distance between impedivity (ohm) at zero frequency and the real part of the maximum frequency point (DR), and length of the spectral curve (P). The results indicated that the polynomial degrees needed to estimate the PA500, DA, A/DA, Max IP, DR, and P features based on tumor size were 2, 2, 3, 3, 2, and 2, respectively. Additionally, we utilized a nonlinear constrained optimization (NCO) analysis to calculate the eight threshold levels for the classification of the impedance features. The deduction of eight classifications for each feature may also be an effective tool for decision-making in breast cancer. These findings may help oncologists to estimate the impedance features for breast cancer diagnosis non-invasively. Full article

The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with mutations and variants of the same. Early diagnosis is the best way to decline the mortality risk associated with it. This urges the necessity of developing new computational approaches that can analyze a large dataset and predict the disease in time. Currently, automated virus diagnosis is a major area of research for accurate and timely predictions. Artificial intelligent (AI)-based techniques such as machine learning (ML) and deep learning (DL) can be deployed for this purpose. In this, compared to traditional machine learning techniques, deep Learning approaches show prominent results. Yet it still requires optimization in terms of complex space problems. To address this issue, the proposed method combines deep learning predictive models such as convolutional neural network (CNN), long short-term memory (LSTM), auto-encoder (AE), cross-validation (CV), and synthetic minority oversampling techniques (SMOTE). This method proposes six different combinations of deep learning forecasting models such as CV-CNN, CV-LSTM+CNN, IMG-CNN, AE+CV-CNN, SMOTE-CV-LSTM, and SMOTE-CV-CNN. The performance of each model is evaluated using various metrics on the standard dataset that is approved by The Montefiore Medical Center/Albert Einstein College of Medicine Institutional Review Board. The experimental results show that the SMOTE-CV-CNN model outperforms the other models by achieving an accuracy of 98.29%. Moreover, the proposed SMOTE-CV-CNN model has been compared to existing mortality risk prediction methods based on both machine learning (ML) and deep learning (DL), and has demonstrated superior accuracy. Based on the experimental analysis, it can be inferred that the proposed SMOTE-CV-CNN model has the ability to effectively predict mortality related to COVID-19. Full article

Over the past decade, omics technologies such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics have been used in the scientific understanding of diseases. While omics technologies have provided a useful tool for the diagnosis and treatment of diseases globally, there is a dearth of literature on the use of these technologies in Africa, particularly in the diagnosis and treatment of oral cancer. This systematic scoping review aims to present the status of the omics research capacity on oral cancer in Africa. The guidelines by the Joanna Brigg’s Institute for conducting systematic scoping reviews will be adopted for this review’s methodology and it will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The literature that will be reviewed will be scooped out from PubMed, SCOPUS, Dentistry and Oral Sciences Source, AMED, CINAHL, and PsycInfo databases. In conclusion, the findings that will be obtained from this review will aid the in-depth understanding of the status of oral cancer omics research in Africa, as this knowledge is paramount for the enhancement of strategies required for capacity development and the prioritization of resources in the fight against oral cancer in Africa. Full article

Transmembrane proteins (TMPs) are a class of essential proteins for biological and therapeutic purposes. Despite an increasing number of structures, the gap with the number of available sequences remains impressive. The choice of a dedicated function to select the most probable/relevant model among hundreds is a specific problem of TMPs. Indeed, the majority of approaches are mostly focused on globular proteins. We developed an alternative methodology to evaluate the quality of TMP structural models. HPMScore took into account sequence and local structural information using the unsupervised learning approach called hybrid protein model. The methodology was extensively evaluated on very different TMP all-α proteins. Structural models with different qualities were generated, from good to bad quality. HPMScore performed better than DOPE in recognizing good comparative models over more degenerated models, with a Top 1 of 46.9% against DOPE 40.1%, both giving the same result in 13.0%. When the alignments used are higher than 35%, HPM is the best for 52%, against 36% for DOPE (12% for both). These encouraging results need further improvement particularly when the sequence identity falls below 35%. An area of enhancement would be to train on a larger training set. A dedicated web server has been implemented and provided to the scientific community. It can be used with structural models generated from comparative modeling to deep learning approaches. Full article

The restoration of osseous defects is accomplished by bone grafts and bone substitutes, which are also called biomaterials. Autogenous grafts, which are derived from the same individual, can retain the viability of cells, mainly the osteoblasts and osteoprogenitor stem cells, and they do not lead to an immunologic response, which is known as the gold standard for bone grafts. There are both different techniques and devices that can be used to obtain bone grafts according to the needs of the patients, the location, and the size of the bone defect. Here, an innovative technique is presented in which the patient’s own bone is removed from the trigone retromolar region of the mandible and is inserted into a dental alveolus after the extraction and immediate insertion of an innovative dental implant, the BKS. The first step of the technique creates the surgical alveolus; the second step perforates the BKS in the retromolar region, and shortly after, the BKS containing the bone to be grafted is removed; the third step screws the BKS bone that collects in the created surgical alveolus. Experimental studies have shown the feasibility and practicality of this new technique and the new dental implant model for autogenous transplants. Full article

The reduction of childhood mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposes a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based off of RISE (Randomized Input Sampling for Explanation of Black-box Models) was created, called Feature Alteration for explanation of Black Box Models (FAB). The findings of this novel algorithm were compared to SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). Overall, this technology allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process. Full article

Holliday junctions are the first recognized templates of legitimate recombination. Their prime physiological role is meiotic homologous recombination, resulting in rearrangements of the genetic material. In humans, recombination hotspots follow a distinct epigenetic pattern designated by the presence of PR domain-containing protein 9 (PRDM9). Repetitive DNA elements can replicate in the genome and can pair with short inverted repeats (SIRs) that form Holliday junctions in a significantly high frequency in vitro. Remarkably, PRDM9 and SIR sequence motifs, which may have the potential to act as recombination primers associated with transposable elements (TEs) and their presence, may lead to gradual spreading of recombination events in human genomes. Microdeletion and microduplication syndromes (MMSs) constitute a significant entity of genetic abnormalities, almost equal in frequency to aneuploidies. Based on our custom database, which includes all MMSs shorter than 5 Mbs in length which is the cut-off point for the standard cytogenetic resolution, we found that the majority of MMSs were present in sequences shorter than 0.5 Mbs. A high probability of TE-associated and non-TE-associated PRDM9/SIR sequence motifs was found in short and long MMSs. Significantly, following the Reactome pathway analysis, a number of affected genes have been associated with the pathophysiological pathways linked to MMSs. In conclusion, PRDM9 or SIR sequence motifs in regions spanning MMSs hotspots underlie a potential functional mechanism for MMS occurrences during recombination. Full article

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