Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis.

BACKGROUND: MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation. METHODS: In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set. RESULTS: We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data. CONCLUSIONS: Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.

Automatic Choroidal Segmentation in Optical Coherence Tomography Images Based on Curvelet Transform and Graph Theory.

Background: Automatic segmentation of the choroid on optical coherence tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. Compared to manual segmentations, it is faster and is not affected by human errors. The presence of the large speckle noise in the OCT images limits the automatic segmentation and interpretation of them. To solve this problem, a new curvelet transform-based K-SVD method is proposed in this study. Furthermore, the dataset was manually segmented by a retinal ophthalmologist to draw a comparison with the proposed automatic segmentation technique. Methods: In this study, curvelet transform-based K-SVD dictionary learning and Lucy-Richardson algorithm were used to remove the speckle noise from OCT images. The Outer/Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and outer choroidal boundary was considered as the choroidal region. Results: The proposed method was evaluated on our dataset and the average dice similarity coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al. on our dataset, the mean DSC was calculated to be 55.75% ± 14.54%. Conclusions: A significant similarity was observed between automatic and manual segmentations. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review.

Choroid is one of the structural layers, playing a significant role in physiology of the eye and lying between the sclera and the retina. The segmentation of this layer could guide ophthalmologists in diagnosing most of the eye pathologies such as choroidal tumors and polypoidal choroidal vasculopathy. High signal-to-noise ratio and high speed imaging in Spectral-Domain Optical Coherence Tomography (SD-OCT) make choroidal imaging feasible. Several variables such as pre-operative axial length (AXL), time of day and age affect thickness of the choroidal vascularization and should be considered for segmentation of this layer. These days most of the eye specialists manually segment the choroidal layer which is time-consuming, tiresome and dependent on human errors. To overcome these difficulties, some studies have introduced different automatic choroidal segmentation methods. In this paper, we have conducted a comprehensive review on existing recently published methods for automatic choroidal segmentation algorithms.

Neural activity in self-identified claustrophobic individuals under in-vivo stimuli: A human electroencephalography dataset.

The electrocortical activity in claustrophobic situations is a very limited field of study and has recently caught researchers' attention. This article represents a set of electroencephalographic (EEG) data obtained from twenty-two participants. The volunteers include 9 participants with self-identified claustrophobia and 13 healthy controls under in-vivo stimuli. The EEG data were recorded using Mitsar 31-channel EEG system. Before cortical signal recording, Individuals were asked to identify themselves as healthy controls or claustrophobic participants. The EEG data collection process consisted of three experimental conditions. In all conditions, the participants were asked to stay calm and keep their eyes open. The first experimental condition was at seated resting state in a relatively large and well-lit laboratory (8m × 15m) area. In the second experimental condition, the subjects entered a moderately-lit chamber and repeated the previous resting situation. The final condition of the EEG data acquisition was performed in the same chamber but with reduced dimensions. For each experimental condition, duration of data collection was approximately 300 s. This data can be used to understand the brain's response in claustrophobic situations through various statistical or data-driven methods.

Electroencephalographic Activity in Patients with Claustrophobia: A Pilot Study.

BACKGROUND: Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population. METHOD: Two highly affected patients with claustrophobia and two healthy controls participated in this in vivo study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well-lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear-related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas. RESULTS: Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands (P < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients. CONCLUSION: The effect of in vivo exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.

Automatic classification of schizophrenia patients using resting-state EEG signals.

Schizophrenia is one of the serious mental disorders, which can suspend the patient from all aspects of life. In this paper we introduced a new method based on the adaptive neuro fuzzy inference system (ANFIS) to classify recorded electroencephalogram (EEG) signals from 14 schizophrenia patients and 14 age-matched control participants. Sixteen EEG channels from 19 main channels that had the most discriminatory information were selected. Possible artifacts of these channels were eliminated with the second-order Butterworth filter. Four features, Shannon entropy, spectral entropy, approximate entropy, and the absolute value of the highest slope of autoregressive coefficients (AVLSAC) were extracted from each selected EEG channel in 5 frequency sub-bands, Delta, Theta, Alpha, Beta, and Gamma. Forty-six features were introduced among the 640 possible ones, and the results included accuracies of near 100%, 98.89%, and 95.59% for classifiers of ANFIS, support vector machine (SVM), and artificial neural network (ANN), respectively. Also, our results show that channels of alpha of O1, theta and delta of Fz and F8, and gamma of Fp1 have the most discriminatory information between the two groups. The performance of our proposed model was also compared with the recently published approaches. This study led to presenting a new decision support system (DSS) that can receive a person's EEG signal and separates the schizophrenia patient and healthy subjects with high accuracy.

Trend in creatinine determining methods: Conventional methods to molecular-based methods.

Renal failure (RF) disease is ranked as one of the most prevalent diseases with severe morbidity and mortality. Early diagnosis of RF leads to subsequent control of disease to reduce the poor prognosis. The level of sera creatinine is considered as a significant biomarker for kidney biofunction, which is routinely detected by the Jaffe reaction. The normal range for creatinine in the blood may be 0.84-1.21 mg/dL. Low accuracy, insufficient sensitivity, explosive and toxicity of picric acid, and pseudo-interaction with nonspecific elements such as ammonium ions in the Jaffe method lead to the development of various techniques for precise detection of creatinine such as spectroscopic, electrochemical, and chromatography approaches and sensors based on enzymes, molecular imprinted polymer and nanoparticles, etc. Based on previously established results, they are trying to construct sensors with high accuracy, optimum sensitivity, acceptable linear/calibration range, and limit of detection, which are small in size and applicable by the patient him/herself (point-of-care testing). By comparing the results of research, a molecularly imprinted electrochemiluminescence-based sensor with linear/calibration range of 5-1 mMconcentration of creatinine and the detection limit of 0.5 nM has the best detectable resolution with 2 million measurable points. In this paper, we will review the recently developed methods for measuring creatinine concentration and renal biofunction.

An optimal method for measuring biomarkers: colorimetric optical image processing for determination of creatinine concentration using silver nanoparticles.

Creatinine concentration is one of the important elements in the body for diagnosing kidney failure, muscular dystrophy, glomerular filtration rate, and diabetic nephropathy. The disadvantages of recently introduced analytical techniques, such as Jaffe's, spectroscopic, colorimetric, and chromatographic methods, for quantifying creatinine in urine involve toxicity, the high cost, interference, and the complexity of the design. In this paper, we designed and fabricated a new colorimetric assay for the measurement of creatinine concentration based on color differentiation generated by mixing different concentrations of creatinine with synthesized silver nanoparticles (AgNPs) coated with polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA). An isolated box is designed for the uniform optical imaging of solutions, the captured images are processed in real time, and the quantitative and qualitative results are displayed. For colorimetric processing, a variety of color systems, such as RGB (red, green, blue), CMYK (cyan, magenta, yellow, black), and grayscale (Gr), have been evaluated, indicating that the combination of green (G) and grayscale (Gr) provides the best results for this experiment. TEM analysis and spectroscopy were used to confirm the results of the experiment. Linear range and limit of detection (LOD) were obtained for AgNPs/PVP 0.03-1 mg/dl and 0.024 mg/dl and for AgNPs/PVA 0.01-1 mg/dl and 0.014 mg/dl, respectively, indicating the superiority of our proposed method over recently introduced methods. In this experiment, the detectable resolution with AgNPs/PVP is 40, while it is 71 with AgNPs/PVA. The designed system is simple to use, small in size, and cost-effective for measuring creatinine concentration, while it can be used as a portable system.

Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans.

Heart mediastinal and epicardial fat tissues are related to several adverse metabolic effects and cardiovascular risk factors, especially coronary artery disease (CAD). The manual segmentation of those fats is that the high dependence on user intervention and time-consuming analyzes. As a result, the automated measurement of cardiac fats could be considered as one of the most important biomarkers for cardiovascular risks in imaging and medical visualization by physicians. In this paper, we validate an automatic approach for the cardiac fat segmentation in non-contrast CT images then investigate the correlation between cardiac fat volume and CAD using the association rule mining algorithm. The pre-processing step includes threshold and contrast enhancement, the feature extraction step includes Gabor filter bank based on GLCM, the cardiac fat segmentation step is predicated on pattern recognition classification algorithms, and eventually, the step of investigating the relationship between cardiac fat volume and CAD is using FP-Growth algorithm. Experimental validation using CT images of two databases points to a good performance in cardiac fat segmentation. Experiments showed that the accuracy of the designed algorithm using the ensemble classifier with the best performance over other classifiers for the cardiac fat segmentation was 99.2%, with a sensitivity of 96.3% and a specificity of 99.8%. The results of using the FP-Growth algorithm showed that the low volume of epicardial (Confidence = 0.6818, Lift = 1.0626) and mediastinal (Confidence = 0.6696, Lift = 1.0436) fat are associated with healthy individuals and the high volume of epicardial (Confidence = 0.8, Lift = 2.2326) and mediastinal (Confidence = 0.75, Lift = 2.093) fat are related to individuals of CAD. As a result, cardiac fats can be used as a reliable biomarker tool in predicting the extent of CAD stenosis.

Markerless Respiratory Tumor Motion Prediction Using an Adaptive Neuro-fuzzy Approach.

BACKGROUND: Accurate delivery of the prescribed dose to moving lung tumors is a key challenge in radiation therapy. Tumor tracking involves real-time specifying the target and correcting the geometry to compensate for the respiratory motion, that's why tracking the tumor requires caution. This study aims to develop a markerless lung tumor tracking method with a high accuracy. METHODS: In this study, four-dimensional computed tomography (4D-CT) images of 10 patients were used, and all the slices which contained the tumor were contoured for all patients. The first four phases of 4D-CT images which contained tumors were selected as input of the software, and the next six phases were considered as the output. A hybrid intelligent method, adaptive neuro-fuzzy inference system (ANFIS), was used to evaluate motion of lung tumor. The root mean square error (RMSE) was used to investigate the accuracy of ANFIS performance for tumor motion prediction. RESULTS: For predicting the positions of contoured tumors, the averages of RMSE for each patient were calculated for all the patients. The results showed that the RMSE did not have a major variation. CONCLUSIONS: The data in the 4D-CT images were used for motion tracking instead of using markers that lead to more information of tumor motion with respect to methods based on marker location.

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD.

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Apoptosis inhibition or inflammation: the role of NAIP protein expression in Hodgkin and non-Hodgkin lymphomas compared to non-neoplastic lymph node.

BACKGROUND: Inhibitors of Apoptosis (IAP) family play a critical role in apoptosis and inflammatory response. Neuronal Apoptosis Inhibitory Protein (NAIP), as a member of both IAPs and NLR families (NOD-Like Receptor), is a unique IAP harboring NOD (Nucleotide Oligomerization Domain) and LLR (Leucine Rich Repeat) motifs. Considering these motifs in NAIP, it has been suggested that the main function of NAIP is distinct from other members of IAPs. As a member of NLR, NAIP mediates the assembly of 'Inflammasome' for inflammatory caspase activation. Pathologic expression of NAIP has been reported not only in some infectious and inflammatory diseases but also in some malignancies. However, there is no report to elucidate NAIP expression in lymphomatic malignancies. METHODS: In this study, we examined NAIP protein expression in 101 Formalin-Fixed Paraffin-Embedded blocks including samples from 39 Hodgkin Lymphoma and 23 Non Hodgkin Lymphoma cases in comparison with 39 control samples (30 normal and 9 Reactive Lymphoid Hyperplasia (RLH) lymph nodes) using semi-quantitative immuno-flourecent Staining. RESULTS: NAIP expression was not statistically different in lymphoma samples neither in HL nor in NHL cases comparing to normal samples. However, we evaluated NAIP expression in normal and RLH lymph nodes. Surprisingly, we have found a statistically significant-difference between the NAIP expression in RLH (M.R of NAIP/GAPDH expression = 0.6365 ± 0.017) and normal lymph node samples (M.R of NAIP/GAPDH expression = 0.5882 ± 0.047) (P < 0.01). CONCLUSIONS: These findings show that the regulation of apoptosis could not be the main function of NAIP in the cell, so the pathologic expression of NAIP is not involved in lymphoma. But, we concluded that the over expression of NAIP has more effective role in the inflammatory response. Also, this study clarifies the NAIP expression level in lymphoma which is required for IAPs profiling in order to be used in potential translational applications of IAPs.