11 research outputs found
Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications
In the human brain, essential iron molecules for proper neurological
functioning exist in transferrin (tf) and ferritin (Fe3) forms. However, its
unusual increment manifests iron overload, which reacts with hydrogen peroxide.
This reaction will generate hydroxyl radicals, and irons higher oxidation
states. Further, this reaction causes tissue damage or cognitive decline in the
brain and also leads to neurodegenerative diseases. The susceptibility
difference due to iron overload within the volume of interest (VOI) responsible
for field perturbation of MRI and can benefit in estimating the neural
disorder. The quantitative susceptibility mapping (QSM) technique can estimate
susceptibility alteration and assist in quantifying the local tissue
susceptibility differences. It has attracted many researchers and clinicians to
diagnose and detect neural disorders such as Parkinsons, Alzheimers, Multiple
Sclerosis, and aging. The paper presents a systematic review illustrating QSM
fundamentals and its processing steps, including phase unwrapping, background
field removal, and susceptibility inversion. Using QSM, the present work
delivers novel predictive biomarkers for various neural disorders. It can
strengthen new researchers fundamental knowledge and provides insight into its
applicability for cognitive decline disclosure. The paper discusses the future
scope of QSM processing stages and their applications in identifying new
biomarkers for neural disorders
Tubo-ovarain abscess in patient with ovarian endometriosis
Tubo-ovarian abscess (TOA) is a sequela of pelvic inflammatory disease (PID) found in 15-34% of patients, is comprised of an infectious, inflammatory complex encompassing the fallopian tube and ovary. We are presenting a case of TOA with endometriosis in a patient who underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy. Histopathological findings were compatible with endometriosis with xanthogranulomatous salpingitis and oophoritis. In our patient there was no history of any chronic infection, gynecological procedures or intra uterine device and single partner. The purpose of this case is to make aware of this condition and requirement of further studies to investigate the risk of TOA in patients with endometriosis to find out the exact cause to prevent unnecessary surgery at later stage
Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier
Structural alterations have been thoroughly investigated in the brain during
the early onset of schizophrenia (SCZ) with the development of neuroimaging
methods. The objective of the paper is an efficient classification of SCZ in 2
different classes: Cognitive Normal (CN), and SCZ using magnetic resonance
imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural
network (CNN) based framework for SCZ diagnosis using MRI images. In the
proposed model, lightweight 3D CNN is used to extract both spatial and spectral
features simultaneously from 3D volume MRI scans, and classification is done
using an ensemble bagging classifier. Ensemble bagging classifier contributes
to preventing overfitting, reduces variance, and improves the model's accuracy.
The proposed algorithm is tested on datasets taken from three benchmark
databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These
datasets have undergone preprocessing steps to register all the MRI images to
the standard template and reduce the artifacts. The model achieves the highest
accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall
94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current
state-of-the-art techniques. The performance metrics evidenced the use of this
model to assist the clinicians for automatic accurate diagnosis of SCZ
Deep Learning for Brain Age Estimation: A Systematic Review
Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the
healthy brain aging pattern are associated to the accelerated brain aging and
brain abnormalities. Hence, efficient and accurate diagnosis techniques are
required for eliciting accurate brain age estimations. Several contributions
have been reported in the past for this purpose, resorting to different
data-driven modeling methods. Recently, deep neural networks (also referred to
as deep learning) have become prevalent in manifold neuroimaging studies,
including brain age estimation. In this review, we offer a comprehensive
analysis of the literature related to the adoption of deep learning for brain
age estimation with neuroimaging data. We detail and analyze different deep
learning architectures used for this application, pausing at research works
published to date quantitatively exploring their application. We also examine
different brain age estimation frameworks, comparatively exposing their
advantages and weaknesses. Finally, the review concludes with an outlook
towards future directions that should be followed by prospective studies. The
ultimate goal of this paper is to establish a common and informed reference for
newcomers and experienced researchers willing to approach brain age estimation
by using deep learning model
Evaluation of Incidence of Gallbladder Content Spillage and Factors Leading to it During Laparoscopic Cholecystectomy
Background: Laparoscopic cholecystectomy has replaced open cholecystectomy in the treatment of cholelithiasis. However, with the increase in the number of laparoscopic operations performed, there has also been a noticeable increase in the frequency of gallbladder (GB) content spillage and its complications Objectives: Aim of this study is to evaluate the incidence of gallbladder spillage during laparoscopic cholecystectomy and factors affecting it.Materials and Methods: Cross- Sectional study conducted at General Surgery department of Saraswathi Institute of Medical Sciences, Hapur, UP. Data was collected preoperatively and intraoperatively from 126 patients who underwent laparoscopic cholecystectomy for cholelithiasis from Oct 2019 – Oct 2020.Results: Among total 126 patients, gallbladder spillage occurred in 16 patients. Dissection of gallbladder from hepatic fossa found to be major technical factor responsible for spillage. Distended gallbladder/ multiple stones/ peri GB adhesions found to be most common patient related factor responsible for spillage.Conclusion: During Laparoscopic cholecystectomy, gallbladder content spillage can occur in significant number of patients at multiple steps of the procedure due to interactive role of both patient related factors and technical factors and it can be a source of morbidity. So, every attempt should be made to prevent it rather than managing it afterwards
Investigating White Matter Abnormalities Associated with Schizophrenia Using a Deep Learning Model and Voxel-Based Morphometry
Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ’s regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model
Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research
Summary: Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds.</p