Notes

Applicant kinases regarding adipogenesis as well as osteoblastogenesis from human bone tissue marrow mesenchymal stem tissues.
Protein-protein interactions (PPIs) are involved with most cellular activities at the proteomic level, making the study of PPIs necessary to comprehending any biological process. Machine learning approaches have been explored, leading to more accurate and generalized PPIs predictions. In this paper, we propose a predictive framework called StackPPI. First, we use pseudo amino acid composition, Moreau-Broto, Moran and Geary autocorrelation descriptor, amino acid composition position-specific scoring matrix, Bi-gram position-specific scoring matrix and composition, transition and distribution to encode biologically relevant features. Secondly, we employ XGBoost to reduce feature noise and perform dimensionality reduction through gradient boosting and average gain. Finally, the optimized features that result are analyzed by StackPPI, a PPIs predictor we have developed from a stacked ensemble classifier consisting of random forest, extremely randomized trees and logistic regression algorithms. Five-fold cross-validation shows StackPPI can successfully predict PPIs with an ACC of 89.27%, MCC of 0.7859, AUC of 0.9561 on Helicobacter pylori, and with an ACC of 94.64%, MCC of 0.8934, AUC of 0.9810 on Saccharomyces cerevisiae. We find StackPPI improves protein interaction prediction accuracy on independent test sets compared to the state-of-the-art models. Finally, we highlight StackPPI's ability to infer biologically significant PPI networks. StackPPI's accurate prediction of functional pathways make it the logical choice for studying the underlying mechanism of PPIs, especially as it applies to drug design. The datasets and source code used to create StackPPI are available here https//github.com/QUST-AIBBDRC/StackPPI/.
Recently, deep learning (DL) algorithms have received widespread popularity in various medical diagnostics. This study aimed to evaluate the diagnostic performance of DL models in the detection and classifying of pneumonia using chest X-ray (CXR) images.

PubMed, Embase, Scopus, Web of Science, and Google Scholar were searched in order to retrieve all studies that implemented a DL algorithm for discriminating pneumonia patients from healthy controls using CXR images. We used bivariate linear mixed models to pool diagnostic estimates including sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Also, the area under receiver operating characteristics curves (AUC) of the included studies was used to estimate the diagnostic value.

The pooled SE, SP, PLR, NLR, DOR and AUC for DL in discriminating pneumonia CXRs from controls were 0.98 (95% confidence interval (CI) 0.96-0.99), 0.94 (95% CI 0.90-0.96), 15.35 (95% CI 10.04-23.48), 0.02 (95% CI 0.01-0.04), 718.13 (95% CI 288.45-1787.93), and 0.99 (95% CI 0.98-100), respectively. The pooled SE, SP, PLR, NLR, DOR and AUC for DL in discriminating bacterial from viral pneumonia using CXR radiographs were 0.89 (95% CI 0.79-0.94), 0.89 (95% CI 0.78-0.95), 8.34 (95% CI 3.75-18.55), 0.13 (95% CI 0.06-0.26), 66.14 (95% CI 17.34-252.37), and 0.95 (0.93-0.97).

DL indicated high accuracy performance in classifying pneumonia from normal CXR radiographs and also in distinguishing bacterial from viral pneumonia. However, major methodological concerns should be addressed in future studies for translating to the clinic.
DL indicated high accuracy performance in classifying pneumonia from normal CXR radiographs and also in distinguishing bacterial from viral pneumonia. However, major methodological concerns should be addressed in future studies for translating to the clinic.The uterine electromyogram, also named Electrohysterogram (EHG), is a non-invasive technique that has been used for pregnancy and labour monitoring as well as for research work on uterine physiology. This technique is well established in this field. There is however a vast unexplored potential in the EHG that is currently the subject of interdisciplinary research work involving different scientific fields such as medicine, engineering, physics and mathematics. find more In this paper, an unsupervised clustering method is applied to a previously obtained set of frequency spectral representations of the respective EHG signal contractions that were previously automatically detected and delineated. An innovative approach using the complete spectrum projection is described, rather than a set of relevant points. The feasibility of the method is established despite the concerns of possible computational burden incurred by the processing of the whole spectrum. Given the unsupervised nature of this classification, a validation procedure was performed whereas the obtained clusters were labelled through the correlation with the common knowledge about the most relevant uterine contraction types, as described in the literature. As a result of this study, a spectral description of the Alvarez contractions was obtained where it was possible to breakdown these important events in two different types according to their spectrum. Spectral estimates of Braxton-Hicks contractions were also obtained and associated to one of the clusters. This led to a full spectral characterization of these uterine events.The optimal method for radiographic evaluation of the internal nasal valve (INV) has not been established. The objective of this study was to develop a method to assess the cross-sectional area and the angle of the INV using anatomically-accurate 3D digital nasal airway models. Axial CT images of the paranasal sinuses of twenty adult subjects with healthy nasal airways (50% female and 50% age ≥ 50) were used to create the models. Patients with significant radiographic evidence of sinonasal disease were excluded. A primary cutting plane that passed through the edge of the nasal bone, upper lateral cartilage, and the head of the inferior turbinate was defined in coronal view. This primary coronal cutting plane was then rotated in 5° increments anteriorly while ensuring the anatomic criteria for the INV were still met. The cutting plane resulting in the minimum INV area was identified as the optimal cutting plane and the total cross-sectional area of INV in this plane,198.79 ± 54.57 mm2, was significantly less than the areas obtained using the existing methods for radiographic evaluation of the INV. The angle between the optimal cutting plane and nasal dorsum was 75.00 ± 10.26°, and the corresponding INV angle was 10.77 ± 6.02°.
The main goal of this work is to develop computer-aided classification models for single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) to identify perfusion abnormalities (myocardial ischemia and/or infarction).

Two different classification models, namely, deep learning (DL)-based and knowledge-based, are proposed. The first type of model utilizes transfer learning with pre-trained deep neural networks and a support vector machine classifier with deep and shallow features extracted from those networks. The latter type of model, on the other hand, aims to transform the knowledge of expert readers to appropriate image processing techniques including particular color thresholding, segmentation, feature extraction, and some heuristics. In addition, the summed stress and rest images from 192 patients (age 26-96, average age 61.5, 38% men, and 78% coronary artery disease) were collected to constitute a new dataset. The visual assessment of two expert readers on this dataset is used as a reference standard. The performances of the proposed models were then evaluated according to this standard.

The maximum accuracy, sensitivity, and specificity values are computed as 94%, 88%, and 100% for the DL-based model and 93%, 100%, and 86% for the knowledge-based model, respectively.

The proposed models provided diagnostic performance close to the level of expert analysis. Therefore, they can aid in clinical decision making for the interpretation of SPECT MPI regarding myocardial ischemia and infarction.
The proposed models provided diagnostic performance close to the level of expert analysis. Therefore, they can aid in clinical decision making for the interpretation of SPECT MPI regarding myocardial ischemia and infarction.
Patients with severely atrophied jaws can be challenging in implantology. The All-on-4 treatment concept eliminates advanced augmentation procedures in highly resorbed ridges by preserving the relevant anatomic structures. In addition, the inclination of the distal implants enables the placement of longer implants. Hence, tilting the anterior implants allows longer implant placement, in line with the distal implants of the All-on-4 concept. This study compared the biomechanical aspects of the standard All-on-4 treatment concept with the M-4 and V-4 techniques.

A three-dimensional model of an edentulous maxilla was created to perform three-dimensional finite element analysis. Three different configurations (All-on-4, M-4, and V-4) were modeled by changing the tilt angle of the anterior implants. In each model, to simulate a foodstuff, a solid spherical material was placed on the midline of the incisors and the right first molar region, separately applying an occlusal load of 100 Newtons. The maximum principal stress and minimum principal stress values were acquired for cortical bone, and von Mises stress values were obtained for ductile materials.

According to the present study's findings, although there were no considerable differences among the models, in general, the All-on-4 group demonstrated slightly higher stresses and the M-4 and V-4 group showed lower stresses.

M-4 or V-4 configurations may be used in cases of severely atrophied anterior maxillae to achieve better primary stabilization.
M-4 or V-4 configurations may be used in cases of severely atrophied anterior maxillae to achieve better primary stabilization.
The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA).

The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segmentsf carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.Frontal vehicle crashes have been a leading cause of spinal injuries in recent years. Reconstruction of frontal crashes using computational models and spinal load analysis helps us understand the patterns of injury and load propagation during frontal crashes. By reconstructing a real crash test and using a viscoelastic crash dummy model, spinal injury patterns were analyzed. The results indicated that a moderate crash with an impact speed of 56 km/h leads to injuries in L1-L2 and L5-S1 levels (L for lumbar and S for sacral vertebrae). The largest spinal loads and injuries were mainly observed immediately after the airbag deployment when the peak of the crash acceleration transpires. Also, the effects of impulse magnitude on the spinal loads and head injury criterion (HIC) showed that HIC is more sensitive than compressive forces to the magnitude of impulse. Moreover, the effects of disc preconditioning as a major factor in the risk of injury was evaluated. The results demonstrate that as the lumbar spine is subjected to a longer preloading, it will be more vulnerable to injury; preconditioning of the discs more adversely affected the risk of injury than a 10% increase in the crash impulse.
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