Journal of Medical Signals & Sensors最新文献

Background: In this study, we want to evaluate the response to Lutetium-177 (¹⁷⁷Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features.

Methods: The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm.

Results: The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response.

Conclusions: Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of ¹⁷⁷Lu-DOTATATE for patients with NETs.

Background: Cytokine storms and inflammation lead to heart failure (HF). Bioactive compounds, as complementary medicine, can be the primary source of compounds with anti-inflammatory properties. Linum usitatissimum (LiU) has antioxidant capacity and anti-inflammatory activity. Here, candidate hugeness was selected based on the in silico studies, bio-cheminformatics, and bioinformatic analysis for excremental validation.

Methods: We selected the vital genes with differential expression from the GSE26887 dataset. Based on the bioinformatics analysis, several parameters are determined to choose switchable genes involved in diabetic HF (DHF). We designed the protein-protein interactions network to consider the nodes' degree, modularity, and betweenness centrality. Hence, we selected the interleukin (IL)-6 protein as a target for drug design and discovery to reduce diabetes complications in the heart. Here, H9c2 cell lines of rat embryonic cardiomyocytes induce HF using hyperglycemic and hyperlipidemic conditions. Real-time polymerase chain reaction evaluated the relative expression of SMAD7/NRF-2/STAT3. Furthermore, we assessed the concentration of IL-6 using the enzyme-linked immunosorbent assay technique.

Results: Based on the bioinformatic analysis, we found that IL-6 with the highest network parameters score might be presented as a druggable protein in the DHF. Bioactive compounds and phytochemicals have potential strategies to manage DHF. LiUs decreased the expression level of the SMAD7 (P <0.0001) and STAT3 (P < 0.0001), and increased the expression level of the NRF2 (P < 0.0001). In addition, LiUs significantly reduced the concentration of IL-6 (P < 0.0001).

Conclusion: Our data proposed that LiUs regulated inflammation and triggered the antioxidant defense in HF. Moreover, LiUs could have potential approaches to managing and preventing DHF.

Background: Using three-dimensional conformal radiation treatment (3D-CRT) and helical tomotherapy (HT), this study examines and contrasts the dosage received by the mandible, maxilla, and teeth.

Methods: Sixteen patients with head-and-neck cancer (H and NC) were the subject of treatment planning at the Seyyed Al-Shohada Hospital in Isfahan, Iran. This study examined target coverage quality, exposure of healthy tissue, and radiation delivery effectiveness.

Results: In terms of a number of measures, including D_2%, D_50%, D_mean, V_95%, conformity index (CI), and homogeneity index (HI) for the planning target volume (PTV) and D_2%, D_98%, D_mean, V_95%, CI, and HI for the nodal PTV, HT showed considerable gains over 3D-CRT. The brainstem, D_1cc, and D_10cc received considerably lower maximum dosages in HT. Measurements of the right and left cochleas (D_mean, V55, and D_max) revealed decreases in HT, with D_mean revealing the most significant variations. The D_mean and D_max values for HT significantly decreased in constrictors as well. In terms of several HT-related indicators, the larynx, optic chiasm, optic nerves, oral cavity, mandible, thyroid, and parotid glands all showed considerable decreases.

Conclusion: The findings of the comparison of the two treatment approaches revealed that the HT method was more than 50% more effective than the 3D-CRT method in sustaining organs at risk (OARs) and the target volume dose. In general, dosimetric coverage, homogeneity, conformity indices, and the absence of cold and hot patches showed that HT produced targets with greater accuracy than 3D-CRT. In addition, HT outperformed 3D-CRT in protecting important structures (OARs). HT as a result has the potential to be a more effective method of treatment for those with H and NC and involvement of regional lymph nodes.

Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.

Monte Carlo (MC) techniques are regarded as an accurate method to simulate the dose calculation in radiotherapy for many years. The present paper aims to validate the simulated model of the 6-MV beam of OMID linear accelerator (BEHYAAR Company) by EGSnrc codes system and also investigate the effects of initial electron beam parameters (energy, radial full width at half maximum, and mean angular spread) on dose distributions. For this purpose, the comparison between the calculated and measured percentage depth dose (PDD) and lateral dose profiles was done by gamma index (GI) with 1%-1 mm acceptance criteria. MC model validating was done for 3 cm × 3 cm, 5 cm × 5 cm, 8 cm × 8 cm, 10 cm × 10 cm, and 20 cm × 20 cm field sizes. To study the sensitivity of model to beam parameters, the field size was selected as 10 cm × 10 cm and 30 cm × 30 cm. All lateral dose profiles were obtained at 10 cm. Excellent agreement was achieved with a 99.2% GI passing percentage for PDD curves and at least 93.8% GI for lateral dose profiles for investigated field sizes. Our investigation confirmed that the lateral dose profile severely depends on the considered source parameters in this study. PDD only considerably depends on the initial electron beam energy. Therefore, source parameters should not be specified independently. These results indicate that the current model of OMID 6-MV Linac is well established, and the accuracy of the simulation is high enough to be used in various applications.

Background: Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients.

Methods: This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC).

Results: Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV.

Conclusion: The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.

Background: This study explores a novel approach to detecting arousal levels through the analysis of electroencephalography (EEG) signals. Leveraging the Faller database with data from 18 healthy participants, we employ a 64-channel EEG system.

Methods: The approach we employ entails the extraction of ten frequency characteristics from every channel, culminating in a feature vector of 640 dimensions for each signal instance. To enhance classification accuracy, we employ a genetic algorithm for feature selection, treating it as a multiobjective optimization task. The approach utilizes fast bit hopping for efficiency, overcoming traditional bit-string limitations. A hybrid operator expedites algorithm convergence, and a solution selection strategy identifies the most suitable feature subset.

Results: Experimental results demonstrate the method's effectiveness in detecting arousal levels across diverse states, with improvements in accuracy, sensitivity, and specificity. In scenario one, the proposed method achieves an average accuracy, sensitivity, and specificity of 93.11%, 98.37%, and 99.14%, respectively. In scenario two, the averages stand at 81.35%, 88.65%, and 84.64%.

Conclusions: The obtained results indicate that the proposed method has a high capability of detecting arousal levels in different scenarios. In addition, the advantage of employing the proposed feature reduction method has been demonstrated.

Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.

Background: Human chorionic gonadotropin (hCG) is a polypeptide hormone synthesized during pregnancy and is also upregulated in some pathologic conditions such as certain tumors. Its measurement is essential for diagnosing pregnancy and malignancies. Despite numerous attempts to introduce an accurate method capable of detecting hCG levels, several limitations are found in previous techniques. This study aimed to address the limitations of current hCG assay methods by designing an electrochemical biosensor based on voltammetry for the rapid, selective, inexpensive, and sensitive measurement of hCG levels.

Methods: A carbon paste electrode was prepared and functionalized by para-aminobenzoic acid. The primary anti-β-hCG monoclonal antibody was immobilized on the electrode surface by activating the carboxyl groups with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide and N-hydroxysuccinimide solutions. The study also involved optimizing parameters such as the time for primary antibody fixation, the time for hCG attachment, and the pH of the hydrogen peroxide solution to maximize the biosensor response. Different concentrations of hCG hormone were prepared and loaded on the electrode surface, the secondary antibody labeled with HRP enzyme was applied, thionine in phosphate-buffered saline solution was placed on the electrode surface, and the differential pulse electrical signal was recorded.

Results: The linear range ranged from 5 to 100 mIU/ml, and the limit of detection was calculated as 0.11 mIU. The relative standard deviation was 3% and 2% for five repeated measurements of commercial standard samples with concentrations of 2 and 20 mIU/mL, respectively. The percent recovery was obtained from 98.3% to 101.5%.

Conclusion: The sensor represents a promising advancement in hCG level measurement, offering a potential solution to overcome the existing limitations in current diagnostic strategies. Simple and inexpensive design, detecting hCG in its important clinical range during early pregnancy, and successful measurement of hCG in real serum samples are the advantages of this sensor.