HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy

In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.

Translational medicine in hereditary hemorrhagic telangiectasia

Scientific community have gained lots of new insights in the genetic and biochemical background of different conditions, rare diseases included, settling the basis for preclinical models that are helping to identify new biomarkers and therapeutic targets. Translational Medicine (TM) is an interdisciplinary area of biomedicine with an essential role in bench-to-bedside transition enhancement, generating a circular flow of knowledge transference between research environment and clinical setting, always centered in patient needs. Here, we present different tools used in TM and an overview of what is being done related to hereditary hemorrhagic telangiectasia (HHT), as a disease's model. This work is focused on how this combination of basic and clinical research impacts in HHT patient's daily clinical management and also looking into the future. Further randomized clinical trials with HHT patients should assess the findings of this bench-to-bedside transition. The benefits of this basic and clinical research combination, may not only be important for HHT patients but for patients with other vascular diseases sharing angiogenic disturbances.

Diagnosis support systems for rare diseases: a scoping review

INTRODUCTION

Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases.

METHODS

A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data.

RESULTS

Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts.

CONCLUSION

Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.

Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

Research on potential biomarkers in hereditary hemorrhagic telangiectasia

Hereditary hemorrhagic telangiectasia (HHT) is a genetically heterogeneous disorder, involving mutations in two predominant genes known as Endoglin (ENG; HHT1) and activin receptor-like kinase 1 (ACVRL1/ALK1; HHT2), as well as in some less frequent genes, such as MADH4/SMAD4 (JP-HHT) or BMP9/GDF2 (HHT5). The diagnosis of HHT patients currently remains at the clinical level, according to the “Curaçao criteria,” whereas the molecular diagnosis is used to confirm or rule out suspected HHT cases, especially when a well characterized index case is present in the family or in an isolated population. Unfortunately, many suspected patients do not present a clear HHT diagnosis or do not show pathogenic mutations in HHT genes, prompting the need to investigate additional biomarkers of the disease. Here, several HHT biomarkers and novel methodological approaches developed during the last years will be reviewed. On one hand, products detected in plasma or serum samples: soluble proteins (vascular endothelial growth factor, transforming growth factor β1, soluble endoglin, angiopoietin-2) and microRNA variants (miR-27a, miR-205, miR-210). On the other hand, differential HHT gene expression fingerprinting, next generation sequencing of a panel of genes involved in HHT, and infrared spectroscopy combined with artificial neural network patterns will also be reviewed. All these biomarkers might help to improve and refine HHT diagnosis by distinguishing from the non-HHT population.

Rapid screening of classic galactosemia patients: a proof-of-concept study using high-throughput FTIR analysis of plasma

FTIR as a new approach to screen a rare disease.

Shirazi F. Discrimination of Human Cell Lines by Infrared Spectroscopy and Mathematical Modeling

Variations in biochemical features are extensive among cells. Identification of marker that is specific for each cell is essential for following the differentiation of stem cell and metastatic growing. Fourier transform infrared spectroscopy (FTIR) as a biochemical analysis more focused on diagnosis of cancerous cells. In this study, commercially obtained cell lines such as Human ovarian carcinoma (A2780), Human lung adenocarcinoma (A549) and Human hepatocarcinoma (HepG2) cell lines in 20 individual samples for each cell lines were used for FTIR spectral measurements. Data dimension were reduced through principal component analysis (PCA) and then subjected to neural network and linear discrimination analysis to classify FTIR pattern in different cell lines. The results showed dramatic changes of FTIR spectra among different cell types. These appeared to be associated with changes in lipid bands from CH2 symmetric and asymmetric bands, as well as amide I and amid II bands of proteins. The PCA-ANN analysis provided over 90% accuracy for classifying the spectrum of lipid section in different cell lines. This work supports future study to establish the data bank of FTIR feature for different cells and move forward to tissues as more complex systems.