A proposed synergistic effect of CSF1R and NMUR2 variants contributes to binge eating in hereditary diffuse leukoencephalopathy with spheroids

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists' impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice.

Construction and validation of web-based nomograms for detecting and prognosticating in prostate adenocarcinoma with bone metastasis

Bone metastasis (BM) is one of the most common sites of metastasis in prostate adenocarcinoma (PA). PA with BM can significantly diminish patients' quality of life and result in a poor prognosis. The objective of this study was to establish two web-based nomograms to estimate the risk and prognosis of BM in PA patients. From the Surveillance, Epidemiology, and End Results (SEER) database, data on 308,332 patients diagnosed with PA were retrieved retrospectively. Logistic and Cox regression, respectively, were used to determine independent risk and prognostic factors. Then, We constructed two web-based nomograms and the results were validated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) , and the Kaplan-Meier analyses. The independent risk factors for BM in PA patients included race, PSA, ISUP, T stage, N stage, brain, liver, lung metastasis, surgery, radiation and chemotherapy. The independent prognostic predictors for overall survival (OS) were age, marital status, PSA, ISUP and liver metastasis. Both nomograms could effectively predict risk and prognosis of BM in PA patients according to the results of ROC curves, calibration, and DCA in the training and validation sets. And the Kaplan-Meier analysis illustrated that the prognostic nomogram could significantly distinguish the population with different survival risks. We successfully constructed the two web-based nomograms for predicting the incidence of BM and the prognosis of PA patients with BM, which may assist clinicians in optimizing the establishment of individualized treatment programs and enhancing patients' prognoses.

Modeling CSF-1 receptor deficiency diseases - how close are we?

The role of colony-stimulating factor-1 receptor (CSF-1R) in macrophage and organismal development has been extensively studied in mouse. Within the last decade, mutations in the CSF1R have been shown to cause rare diseases of both pediatric (Brain Abnormalities, Neurodegeneration, and Dysosteosclerosis, OMIM #618476) and adult (CSF1R-related leukoencephalopathy, OMIM #221820) onset. Here we review the genetics, penetrance, and histopathological features of these diseases and discuss to what extent the animal models of Csf1r deficiency currently available provide systems in which to study the underlying mechanisms involved.

Spatiotemporal tracing of pandemic spread from infection data

COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.