A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions.

Graphic abstract

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Heart Sound Classification based on Residual Shrinkage Networks

Heart sound classification is one of the non-invasive methods for early detection of the cardiovascular diseases (CVDs), the leading cause for deaths. In recent years, Computer Audition (CA) technology has become increasingly sophisticated, auxiliary diagnosis technology of heart disease based on CA has become a popular research area. This paper proposes a deep Convolutional Neural Network (CNN) model for heart sound classification. To improve the classification accuracy of heart sound, we design a classification algorithm combining classical Residual Network (ResNet) and Long Short-Term Memory (LSTM). The model performance is evaluated in the PhysioNet/CinC Challenges 2016 datasets using a 2D time-frequency feature. We extract the four features from different filter-bank coefficients, including Filterbank (Fbank), Mel-Frequency Spectral Coefficients (MFSCs), and Mel-Frequency Cepstral Coefficients (MFCCs). The experimental results show the MFSCs feature outperforms the other features in the proposed CNN model. The proposed model performs well on the test set, particularly the F1 score of 84.3 % - the accuracy of 84.4 %, the sensitivity of 84.3 %, and the specificity of 85.6 %. Compared with the classical ResNet model, an accuracy of 4.9 % improvement is observed in the proposed model.

Biomarker Discovery by Imperialist Competitive Algorithm in Mass Spectrometry Data for Ovarian Cancer Prediction

Background:

Mass spectrometry is a method for identifying proteins and could be used for distinguishing between proteins in healthy and nonhealthy samples. This study was conducted using mass spectrometry data of ovarian cancer with high resolution. Usually, diagnostic and monitoring tests are done according to sensitivity and specificity rates; thus, the aim of this study is to compare mass spectrometry of healthy and cancerous samples in order to find a set of biomarkers or indicators with a reasonable sensitivity and specificity rates.

Methods:

Therefore, combination methods were used for choosing the optimum feature set as t-test, entropy, Bhattacharya, and an imperialist competitive algorithm with K-nearest neighbors classifier. The resulting feature from each method was feed to the C5 decision tree with 10-fold cross-validation to classify data.

Results:

The most important variables using this method were identified and a set of rules were extracted. Similar to most frequent features, repetitive patterns were not obtained; the generalized rule induction method was used to identify the repetitive patterns.

Conclusion:

Finally, the resulting features were introduced as biomarkers and compared with other studies. It was found that the resulting features were very similar to other studies. In the case of the classifier, higher sensitivity and specificity rates with a lower number of features were achieved when compared with other studies.

Principal component analysis-based features generation combined with ellipse models-based classification criterion for a ventricular septal defect diagnosis system

In this study, a simple and efficient diagnostic system, which adopts a novel methodology consisting of principal component analysis (PCA)-based feature generation and ellipse models-based classification criterion, is proposed for the diagnosis of a ventricular septal defect (VSD). The three stages corresponding to the diagnostic system implementation are summarized as follows. In stage 1, the heart sound is collected by 3M-3200 electronic stethoscope and is preprocessed using the wavelet decomposition. In stage 2, the PCA-based diagnostic features, [[Formula: see text]], are generated from time-frequency feature matrix ([Formula: see text]). In the matrix TFFM, the time domain features [Formula: see text] are firstly extracted from the time domain envelope [Formula: see text] for the filtered heart sound signal [Formula: see text], and frequency domain features, [Formula: see text], are subsequently extracted from a frequency domain envelope ([Formula: see text]) for each heart sound cycle automatically segmented via the short time modified Hilbert transform (STMHT). In stage 3, support vector machines-based classification boundary curves for the dataset [Formula: see text] are first generated, and least-squares-based ellipse models are subsequently built for the classification boundary curve. Finally, based on the ellipse models, the classification criterion is defined for the diagnosis of VSD sounds. The proposed diagnostic system is validated by sounds from the internet and by sounds from clinical heart diseases. Moreover, comparative analysis to validate the usefulness of the proposed diagnostic system, mitral regurgitation and aortic stenosis sounds are used as examples for detection. As a result, the higher classification accuracy, which is achieved by this study compared to the other methods, is [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for diagnosing small VSD, moderate VSD, large VSD and normal sounds, respectively.