Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Measured and simulated mechanical properties of additively manufactured matrix-inclusion multimaterials fabricated by material jetting

Modern additive manufacturing enables the simultaneous processing of different materials during the printing process. While multimaterial 3D printing allows greater freedom in part design, the prediction of the mix-material properties becomes challenging. One type of multimaterials are matrix-inclusion composites, where one material contains inclusions of another material. Aim of this study was to develop a method to predict the uniaxial Young's modulus and Poisson's ratio of material jetted matrix-inclusion composites by a combination of simulations and experimental data.Fifty samples from commercially available materials in their pure and matrix-inclusion mixed forms, with cubic inclusions, have been fabricated using material jetting and mechanically characterized by uniaxial tensile tests. Multiple simulation approaches have been assessed and compared to the measurement results in order to find and validate a method to predict the multimaterials' properties. Optical coherence tomography and microscopy was used to characterize the size and structure of the multimaterials, compared to the design.The materials exhibited Young's moduli in the range of 1.4 GPa to 2.5 GPa. The multimaterial mixtures were never as stiff as the weighted volume average of the primary materials (up to [Formula: see text] softer for 45% RGD8530-DM inclusions in VeroClear matrix). Experimental data could be predicted by finite element simulations by considering a non-ideal contact stiffness between matrix and inclusion ([Formula: see text] for RGD8530-DM, [Formula: see text] for RGD8430-DM), and geometries of the printed inclusions that deviated from the design (rounded edge radii of [Formula: see text]m). Not considering this would lead to a difference of the estimation result of up to [Formula: see text]MPa (44%), simulating an inclusion volume fraction of 45% RGD8530-DM.Prediction of matrix-inclusion composites fabricated by multimaterial jetting printing, is possible, however, requires a priori knowledge or additional measurements to characterize non-ideal contact stiffness between the components and effective printed geometries, precluding therefore a simple multimaterial modelling.

Cell recognition based on atomic force microscopy and modified residual neural network

Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in application. The difference in mechanical properties or morphology of cells has been frequently used to detect whether cells are cancerous, but this detection method cannot be a general means for cancer cell detection, and the traditional artificial feature extraction method also has its limitations. In this work, we proposed an analytic method based on the physical properties of cells and deep learning method for recognizing cell types. The residual neural network used for recognition was modified by multi-scale convolutional fusion, attention mechanism and depthwise separable convolution, so as to optimize feature extraction and reduce operation costs. In the method, the collected cells were imaged by AFM, and the processed images were analyzed by the optimized convolutional neural network. The recognition results of two groups of cells (HL-7702 and SMMC-7721, SGC-7901 and GES-1) by this method show that the recognition rate of dataset with the combination of cell surface morphology, adhesion and Young's modulus is higher, and the recognition rate of the dataset with optimal resolution is higher. Our study indicated that the recognition of physical properties of cells using deep learning technology can serve as a universal and effective method for the automated analysis of cell information.

Metabolomics in acromegaly: a systematic review

The therapeutic response heterogeneity in acromegaly persists, despite the medical-surgical advances of recent years. Thus, personalized medicine implementation, which focuses on each patient, is justified. Metabolomics would decipher the molecular mechanisms underlying the therapeutic response heterogeneity. Identification of altered metabolic pathways would open new horizons in the therapeutic management of acromegaly. This research aimed to evaluate the metabolomic profile in acromegaly and metabolomics' contributions to understanding disease pathogenesis. A systematic review was carried out by querying four electronic databases and evaluating patients with acromegaly through metabolomic techniques. In all, 21 studies containing 362 patients were eligible. Choline, the ubiquitous metabolite identified in growth hormone (GH)-secreting pituitary adenomas (Pas) by in vivo magnetic resonance spectroscopy (MRS), negatively correlated with somatostatin receptors type 2 expression and positively correlated with magnetic resonance imaging T2 signal and Ki-67 index. Moreover, elevated choline and choline/creatine ratio differentiated between sparsely and densely granulated GH-secreting PAs. MRS detected low hepatic lipid content in active acromegaly, which increased after disease control. The panel of metabolites of acromegaly deciphered by mass spectrometry (MS)-based techniques mainly included amino acids (especially branched-chain amino acids and taurine), glyceric acid, and lipids. The most altered pathways in acromegaly were the metabolism of glucose (particularly the downregulation of the pentose phosphate pathway), linoleic acid, sphingolipids, glycerophospholipids, arginine/proline, and taurine/hypotaurine. Matrix-assisted laser desorption/ionization coupled with MS imaging confirmed the functional nature of GH-secreting PAs and accurately discriminated PAs from healthy pituitary tissue.

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks

OBJECTIVE

Despite advancements of intraoperative visualization, the difficulty to visually distinguish adenoma from adjacent pituitary gland due to textural similarities may lead to incomplete adenoma resection or impairment of pituitary function. The aim of this study was to investigate optical coherence tomography (OCT) imaging in combination with a convolutional neural network (CNN) for objectively identify pituitary adenoma tissue in an ex vivo setting.

METHODS

A prospective study was conducted to train and test a CNN algorithm to identify pituitary adenoma tissue in OCT images of adenoma and adjacent pituitary gland samples. From each sample, 500 slices of adjacent cross-sectional OCT images were used for CNN classification.

RESULTS

OCT data acquisition was feasible in 19/20 (95%) patients. The 16.000 OCT slices of 16/19 of cases were employed for creating a trained CNN algorithm (70% for training, 15% for validating the classifier). Thereafter, the classifier was tested on the paired samples of three patients (3.000 slices). The CNN correctly predicted adenoma in the 3 adenoma samples (98%, 100% and 84% respectively), and correctly predicted gland and transition zone in the 3 samples from the adjacent pituitary gland.

CONCLUSION

Trained convolutional neural network computing has the potential for fast and objective identification of pituitary adenoma tissue in OCT images with high sensitivity ex vivo. However, further investigation with larger number of samples is required.