BioMedInformatics, Volume 3, Issue 2 (June 2023) – 16 articles

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model. Full article

Background: According to GWAS, which analyzes large amounts of DNA variants in case-control strategies, the genetic differences between two human individuals do not exceed 0.5%. As a consequence, finding biological significance in GWAS results is a challenging task. We propose an alternative method for identifying disease-causing variants based on the simultaneous evaluation of genome variant data acquired from public databases and pathology epidemiological data. This method is grounded on the following premise: If a particular pathology is common in a community, genetic variants that confer susceptibility to that pathology should also be common in that population. Methods: Three groups of genes were evaluated to test this premise: variants related to depression found through GWAS, six genes unrelated to depression, and four genes already genotyped in case-control studies involving depression (TPH2, NR3C1, SLC6A2 and SLC6A3). In terms of GWAS depression-related variants, nine of the 82 SNPs evaluated showed a favorable correlation between allele frequency and epidemiological data. As anticipated, none of the 286 SNPs were correlated in the neutral group. In terms of proof of concept, two THP2 variants, 26 NR3C1 variants and four SLC6A3 variants were found to be related to depression rates and epidemiological statistics. Conclusions: Together with data from the literature involving these SNPs, these correlations support this strategy as a complementary method for identifying possible disease-causing variants. Full article

Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP. Full article

Urine output is an important indicator of renal function. In hospitals, urine is collected using a catheter connected to a urine collection bag that has volume gradation markings. This type of visual measurement has low levels of accuracy and is labor-intensive. This paper developed an Internet-of-Things enabled system that continuously monitors the urine volume collected via the urine collection system. The device is built utilizing a strain gauge load cell, an integrated circuit that contains an amplifier, analog-to-digital converter, and a WiFi-enabled microcontroller. The data is sent via wireless networking to a data collection and analysis server, which provides accurate analyses of urine output. A mobile application utilizing the Blynk.io system is used to display the data. This device and mobile application were built at a minimal cost of 26 USD. The device has been tested multiple times and reported urine output accurately, with minimal difference between actual versus measured volumes. In the future, further development of this device can provide hospitals and physicians worldwide with easy access to affordable, accurate, and real-time urine measurement, which would translate into better, life-saving medical care. Full article

DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen in several cancers. Any mutations or expression anomalies of genes encoding regulators of DNA methylation may lead to abnormal expression of critical molecules. A comprehensive genomic study encompassing all the genes related to DNA methylation regulation in relation to breast cancer is lacking. We used genomic and transcriptomic datasets from the Cancer Genome Atlas (TGCA) Pan-Cancer Atlas, Genotype-Tissue Expression (GTEx) and microarray platforms and conducted in silico analysis of all the genes related to DNA methylation with respect to writing, reading and erasing this epigenetic mark. Analysis of mutations was conducted using cBioportal, while Xena and KMPlot were utilized for expression changes and patient survival, respectively. Our study identified multiple mutations in the genes encoding regulators of DNA methylation. The expression profiling of these showed significant differences between normal and disease tissues. Moreover, deregulated expression of some of the genes, namely DNMT3B, MBD1, MBD6, BAZ2B, ZBTB38, KLF4, TET2 and TDG, was correlated with patient prognosis. The current study, to our best knowledge, is the first to provide a comprehensive molecular and genetic profile of DNA methylation machinery genes in breast cancer and identifies DNA methylation machinery as an important determinant of the disease progression. The findings of this study will advance our understanding of the etiology of the disease and may serve to identify alternative targets for novel therapeutic strategies in cancer. Full article

In this study, we propose a closed-loop insulin administration framework for multiple daily injection (MDI) treatment using a reinforcement learning (RL) agent for insulin bolus therapy. The RL agent, based on the soft actor–critic (SAC) algorithm, dynamically adjusts insulin dosages based on real-time glucose readings, meal intakes, and previous actions. We evaluated the proposed strategy on ten in silico patients with type 1 diabetes undergoing MDI therapy, considering three meal scenarios. The results show that, compared to an open-loop conventional therapy, our proposed closed-loop control strategy significantly reduces glucose variability and increases the percentage of time the glucose levels remained within the target range. In particular, the weekly mean glucose level reduced from 145.34 ± 57.26 mg/dL to 115.18 ± 7.93 mg/dL, 143.62 ± 55.72 mg/dL to 115.28 ± 8.11 mg/dL, and 171.63 ± 49.30 mg/dL to 143.94 ± 23.81 mg/dL for Scenarios A, B and C, respectively. Furthermore, the percent time in range (70–180 mg/dL) significantly improved from 63.77 ± 27.90% to 91.72 ± 9.27% (p = 0.01) in Scenario A, 64.82 ± 28.06% to 92.29 ± 9.15% (p = 0.01) in Scenario B, and 58.45 ± 27.53% to 81.45 ± 26.40% (p = 0.05) in Scenario C. The model also demonstrated robustness against meal disturbances and insulin sensitivity disturbances, achieving mean glucose levels within the target range and maintaining a low risk of hypoglycemia, which were statistically significant for Scenarios B and C. The proposed model outperformed open-loop conventional therapy in all scenarios, highlighting the potential of RL-based closed-loop insulin administration models in improving diabetes management. Full article

The recent advances in deep learning have revolutionised computer-aided diagnosis in medical imaging. However, deep learning approaches to unveil their full potential require significant amounts of data, which can be a challenging task in some scientific fields, such as musculoskeletal ultrasound imaging, in which data privacy and security reasons can lead to important limitations in the acquisition and the distribution process of patients’ data. For this reason, different generative methods have been introduced to significantly reduce the required amount of real data by generating synthetic images, almost indistinguishable from the real ones. In this study, the power of the diffusion models is incorporated for the generation of realistic data from a small set of musculoskeletal ultrasound images in four different muscles. Afterwards, the similarity of the generated and real images is assessed with different types of qualitative and quantitative metrics that correspond well with human judgement. In particular, the histograms of pixel intensities of the two sets of images have demonstrated that the two distributions are statistically similar. Additionally, the well-established LPIPS, SSIM, FID, and PSNR metrics have been used to quantify the similarity of these sets of images. The two sets of images have achieved extremely high similarity scores in all these metrics. Subsequently, high-level features are extracted from the two types of images and visualized in a two-dimensional space for inspection of their structure and to identify patterns. From this representation, the two sets of images are hard to distinguish. Finally, we perform a series of experiments to assess the impact of the generated data for training a highly efficient Attention-UNet for the important clinical application of muscle thickness measurement. Our results depict that the synthetic data play a significant role in the model’s final performance and can lead to the improvement of the deep learning systems in musculoskeletal ultrasound. Full article

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