Knee osteoarthritis (OA) progression can be monitored by measuring changes in the subchondral bone structure such as area and shape from MR images as an imaging biomarker. However, measurements of these minute changes are highly dependent on the accurate segmentation of bone tissue from MR images and it is challenging task due to the complex tissue structure and inadequate image contrast/brightness. In this paper, a fully automated method for segmenting subchondral bone from knee MR images is proposed. Here, the contrast of knee MR images is enhanced using a gray-level S-curve transformation followed by automatic seed point detection using a three-dimensional multi-edge overlapping technique. Successively, bone regions are initially extracted using distance-regularized level-set evolution followed by identification and correction of leakages along the bone boundary regions using a boundary displacement technique. The performance of the developed technique is evaluated against ground truths by measuring sensitivity, specificity, dice similarity coefficient (DSC), average surface distance (AvgD) and root mean square surface distance (RMSD). An average sensitivity (91.14%), specificity (99.12%) and DSC (90.28%) with 95% confidence interval (CI) in the range 89.74-92.54%, 98.93-99.31% and 88.68-91.88% respectively is achieved for the femur bone segmentation in 8 datasets. For tibia bone, average sensitivity (90.69%), specificity (99.65%) and DSC (91.35%) with 95% CI in the range 88.59-92.79%, 99.50-99.80% and 88.68-91.88% respectively is achieved. AvgD and RMSD values for femur are 1.43 ± 0.23 (mm) and 2.10 ± 0.35 (mm) respectively while for tibia, the values are 0.95 ± 0.28 (mm) and 1.30 ± 0.42 (mm) respectively that demonstrates acceptable error between proposed method and ground truths. In conclusion, results obtained in this work demonstrate substantially significant performance with consistency and robustness that led the proposed method to be applicable for large scale and longitudinal knee OA studies in clinical settings.
Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.
Piceatannol is a naturally occurring hydroxylated resveratrol analogue that can be found in a variety of fruits and vegetables. It has been documented to have a wide range of beneficial effects, including anti-inflammatory, antioxidant, anti-aging, anti-allergic, antidiabetic, neuroprotective, cardioprotective, and chemopreventive properties. Piceatannol has significantly higher antioxidant activity than resveratrol. Piceatannol has been shown in preclinical studies to have the ability to inhibit or reduce the growth of cancers in various organs such as the brain, breast, lung, colon, cervical, liver, prostate, and skin. However, the bioavailability of Piceatannol is comparatively lower than resveratrol and other stilbenes. Several approaches have been reported in recent years to enhance its bioavailability and biological activity, and clinical trials are required to validate these findings. This review focuses on several aspects of natural stilbene Piceatannol, its chemistry, and its mechanism of action, and its promising therapeutic potential for the prevention and treatment of a wide variety of complex human diseases.
Leading pathological markers of Alzheimer's disease (AD) include Acetylcholinesterase (AChE), Butyrylcholinesterase (BuChE), Amyloid beta (Aβ) and reactive oxygen species (ROS). Indole derivatives were identified and optimized to improve the potency against AChE, BuChE, Aβ and ROS. The lead molecule IND-30 was found to be selective for AChE (selectivity ratio: 22.92) in comparison to BuChE and showed maximum inhibition potential for human AChE (IC50: 4.16 ± 0.063 μM). IND-30 was found to be safe on the SH-SY5Y cell line until the dose of 30 mM. Further, molecule IND-30 was evaluated for its ability to inhibit AChE-induced Aβ aggregation at 0.5, 10 and 20 μM doses. Approximately, 50% of AChE-induced Aβ aggregation was inhibited by IND-30. Thus, IND-30 was found to be multitargeting for AD.
Quantitative assessment of knee articular cartilage (AC) morphology using magnetic resonance (MR) imaging requires an accurate segmentation and 3D reconstruction. However, automatic AC segmentation and 3D reconstruction from hydrogen-based MR images alone is challenging because of inhomogeneous intensities, shape irregularity, and low contrast existing in the cartilage region. Thus, the objective of this research was to provide an insight into morphologic assessment of AC using multilevel data processing of multinuclear ((23)Na and (1)H) MR knee images.
Early detection of knee osteoarthritis (OA) is of great interest to orthopaedic surgeons, rheumatologists, radiologists, and researchers because it would allow physicians to provide patients with treatments and advice to slow the onset or progression of the disease. Early detection can be achieved by identifying early changes in selected features of degenerative articular cartilage (AC) using non-invasive imaging modalities. Magnetic resonance imaging (MRI) is becoming the standard for assessment of OA. The aim of this paper was to review the influence of MRI on the selection, detection, and measurement of AC features associated with early OA. Our review of the literature indicates that the changes associated with early OA are in cartilage thickness, cartilage volume, cartilage water content, and proteoglycan content that can be accurately, consistently, and non-invasively measured using MRI. Choosing an MR pulse sequence that provides the capability to assess cartilage physiology and morphology in a single acquisition and advanced multi-nuclei MRI is desirable. The results of the review indicate that using an ultra-high magnetic strength, MR imager does not affect early OA detection. In conclusion, MRI is currently the most suitable modality for early detection of knee OA, and future research should focus on the quantitative evaluation of early OA features using advances in MR hardware, software, and data processing with sophisticated image/pattern recognition techniques.
Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage.
A search for the rare η→μ^{+}μ^{-}μ^{+}μ^{-} double-Dalitz decay is performed using a sample of proton-proton collisions, collected by the CMS experiment at the CERN LHC with high-rate muon triggers during 2017 and 2018 and corresponding to an integrated luminosity of 101 fb^{-1}. A signal having a statistical significance well in excess of 5 standard deviations is observed. Using the η→μ^{+}μ^{-} decay as normalization, the branching fraction B(η→μ^{+}μ^{-}μ^{+}μ^{-})=[5.0±0.8(stat)±0.7(syst)±0.7(B_{2μ})]×10^{-9} is measured, where the last term is the uncertainty in the normalization channel branching fraction. This work achieves an improved precision of over 5 orders of magnitude compared to previous results, leading to the first measurement of this branching fraction, which is found to agree with theoretical predictions.
A search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138 fb^{-1} of proton-proton (pp) collision data at a center-of-mass energy of 13 TeV produced by the LHC in 2016-2018. No significant excess over the predicted backgrounds is observed. Upper limits are set on the product of the inelastic dark matter production cross section σ(pp→A^{'}→χ_{1}χ_{2}) and the decay branching fraction B(χ_{2}→χ_{1}μ^{+}μ^{-}), where A^{'} is a dark photon and χ_{1} and χ_{2} are states in the dark sector with near mass degeneracy. This is the first dedicated collider search for inelastic dark matter.
The first search for scalar leptoquarks produced in τ-lepton-quark collisions is presented. It is based on a set of proton-proton collision data recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138 fb^{-1}. The reconstructed final state consists of a jet, significant missing transverse momentum, and a τ lepton reconstructed through its hadronic or leptonic decays. Limits are set on the product of the leptoquark production cross section and branching fraction and interpreted as exclusions in the plane of the leptoquark mass and the leptoquark-τ-quark coupling strength.
A search is reported for near-threshold structures in the J/ψJ/ψ invariant mass spectrum produced in proton-proton collisions at sqrt[s]=13 TeV from data collected by the CMS experiment, corresponding to an integrated luminosity of 135 fb^{-1}. Three structures are found, and a model with quantum interference among these structures provides a good description of the data. A new structure is observed with a local significance above 5 standard deviations at a mass of 6638_{-38}^{+43}(stat)_{-31}^{+16}(syst) MeV. Another structure with even higher significance is found at a mass of 6847_{-28}^{+44}(stat)_{-20}^{+48}(syst) MeV, which is consistent with the X(6900) resonance reported by the LHCb experiment and confirmed by the ATLAS experiment. Evidence for another new structure, with a local significance of 4.7 standard deviations, is found at a mass of 7134_{-25}^{+48}(stat)_{-15}^{+41}(syst) MeV. Results are also reported for a model without interference, which does not fit the data as well and shows mass shifts up to 150 MeV relative to the model with interference.
The observation of WWγ production in proton-proton collisions at a center-of-mass energy of 13 TeV with an integrated luminosity of 138 fb^{-1} is presented. The observed (expected) significance is 5.6 (5.1) standard deviations. Events are selected by requiring exactly two leptons (one electron and one muon) of opposite charge, moderate missing transverse momentum, and a photon. The measured fiducial cross section for WWγ is 5.9±0.8(stat)±0.8(syst)±0.7(modeling) fb, in agreement with the next-to-leading order quantum chromodynamics prediction. The analysis is extended with a search for the associated production of the Higgs boson and a photon, which is generated by a coupling of the Higgs boson to light quarks. The result is used to constrain the Higgs boson couplings to light quarks.
Rapid population growth of major urban centres in many developing countries has created massive landfills with extraordinary heights and steep side-slopes, which are frequently surrounded by illegal low-income residential settlements developed too close to landfills. These extraordinary landfills are facing high risks of catastrophic failure with potentially large numbers of fatalities. This study presents a novel method for risk assessment of landfill slope failure, using probabilistic analysis of potential failure scenarios and associated fatalities. The conceptual framework of the method includes selecting appropriate statistical distributions for the municipal solid waste (MSW) material shear strength and rheological properties for potential failure scenario analysis. The MSW material properties for a given scenario is then used to analyse the probability of slope failure and the resulting run-out length to calculate the potential risk of fatalities. In comparison with existing methods, which are solely based on the probability of slope failure, this method provides a more accurate estimate of the risk of fatalities associated with a given landfill slope failure. The application of the new risk assessment method is demonstrated with a case study for a landfill located within a heavily populated area of New Delhi, India.
Taurine is a sulfur-containing, semi-essential amino acid that occurs naturally in the body. It alternates between inflammation and oxidative stress-mediated injury in various disease models. As part of its limiting functions, taurine also modulates endoplasmic reticulum stress, Ca2+ homeostasis, and neuronal activity at the molecular level. Taurine effectively protects against a number of neurological disorders, including stroke, epilepsy, cerebral ischemia, memory dysfunction, and spinal cord injury. Although various therapies are available, effective management of these disorders remains a global challenge. Approximately 30 million people are affected worldwide. The design of taurine formation could lead to potential drugs/supplements for the health maintenance and treatment of central nervous system disorders. The general neuroprotective effects of taurine and the various possible underlying mechanisms are discussed in this review. This article is a good resource for understanding the general effects of taurine on various diseases. Given the strong evidence for the neuropharmacological efficacy of taurine in various experimental paradigms, it is concluded that this molecule should be considered and further investigated as a potential candidate for neurotherapeutics, with emphasis on mechanism and clinical studies to determine efficacy.
The contagiosity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has startled mankind and has brought our lives to a standstill. The treatment focused mainly on repurposed immunomodulatory and antiviral agents along with the availability of a few vaccines for prophylaxis to vanquish COVID-19. This seemingly mandates a deeper understanding of the disease pathogenesis. This necessitates a plausible extrapolation of cell-based therapy to COVID-19 and is regarded equivalently significant. Recently, correlative pieces of clinical evidence reported a robust decline in lymphocyte count in severe COVID-19 patients that suggest dysregulated immune responses as a key element contributing to the pathophysiological alterations. The large granular lymphocytes also known as natural killer (NK) cells play a heterogeneous role in biological functioning wherein their frontline action defends the body against a wide array of infections and tumors. They prominently play a critical role in viral clearance and executing immuno-modulatory activities. Accumulated clinical evidence demonstrate a decrease in the number of NK cells in circulation with or without phenotypical exhaustion. These plausibly contribute to the progression of pulmonary inflammation in COVID-19 pneumonia and result in acute lung injury. In this review, we have outlined the present understanding of the immunological response of NK cells in COVID-19 infection. We have also discussed the possible use of these powerful biological cells as a therapeutic agent in view of preventing immunological harms of SARS-CoV-2 and the current challenges in advocating NK cell therapy for the same.
The coronavirus disease 2019 (COVID-19) pandemic is an issue of global significance that has taken the lives of many across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for its pathogenesis. The pulmonary manifestations of COVID-19 have been well described in the literature. Initially, it was thought to be limited to the respiratory system; however, we now recognize that COVID-19 also affects several other organs, including the nervous system. Two similar human coronaviruses (CoV) that cause severe acute respiratory syndrome (SARS-CoV-1) and Middle East respiratory syndrome (MERS-CoV) are also known to cause disease in the nervous system. The neurological manifestations of SARS-CoV-2 infection are growing rapidly, as evidenced by several reports. There are several mechanisms responsible for such manifestations in the nervous system. For instance, post-infectious immune-mediated processes, direct virus infection of the central nervous system (CNS), and virus-induced hyperinflammatory and hypercoagulable states are commonly involved. Guillain-Barré syndrome (GBS) and its variants, dysfunction of taste and smell, and muscle injury are numerous examples of COVID-19 PNS (peripheral nervous system) disease. Likewise, hemorrhagic and ischemic stroke, encephalitis, meningitis, encephalopathy acute disseminated encephalomyelitis, endothelialitis, and venous sinus thrombosis are some instances of COVID-19 CNS disease. Due to multifactorial and complicated pathogenic mechanisms, COVID-19 poses a large-scale threat to the whole nervous system. A complete understanding of SARS-CoV-2 neurological impairments is still lacking, but our knowledge base is rapidly expanding. Therefore, we anticipate that this comprehensive review will provide valuable insights and facilitate the work of neuroscientists in unfolding different neurological dimensions of COVID-19 and other CoV associated abnormalities.
Parkinson's disease is the second most common neurodegenerative condition with its prevalence projected to 8.9 million individuals globally in the year 2019. Parkinson's disease affects both motor and certain non-motor functions of an individual. Numerous research has focused on the neuroprotective effect of the glial cell line-derived neurotrophic factor (GDNF) in Parkinson's disease. Discovered in 1993, GDNF is a neurotrophic factor identified from the glial cells which was found to have selective effects on promoting survival and regeneration of certain populations of neurons including the dopaminergic nigrostriatal pathway. Given this property, recent studies have focused on the exogenous administration of GDNF for relieving Parkinson's disease-related symptoms both at a pre-clinical and a clinical level. This review will focus on enumerating the molecular connection between Parkinson's disease and GDNF and shed light on all the available drug delivery approaches to facilitate the selective delivery of GDNF into the brain paving the way as a potential therapeutic candidate for Parkinson's disease in the future.
Environmental problems with chemical and biological water pollution have become a major concern for society. Providing people with safe and affordable water is a grand challenge of the 21st century. The study investigates the photocatalytic degradation capabilities of hydrothermally prepared pure and Cu-doped ZnO nanoparticles (NPs) for the elimination of dye pollutants. A simple, cost-effective hydrothermal process is employed to synthesize the Cu-doped ZnO NPs. The photocatalytic dye degradation activity of the synthesized Cu-doped ZnO NPs is tested by using methylene blue (MB) dye. In addition, the parameters that affect photodegradation efficiency, such as catalyst concentration, starting potential of hydrogen (pH), and dye concentration, were also assessed. The dye degradation is found to be directly proportional to the irradiation time, as 94% of the MB dye is degraded in 2 hrs. Similarly, the dye degradation shows an inverse relation to the MB dye concentration, as the degradation reduced from 94% to 20% when the MB concentration increases from 5 ppm to 80 ppm. The synthesized cost-effective and environmentally friendly Cu-doped ZnO NPs exhibit improved photocatalytic activity against MB dye and can therefore be employed in wastewater treatment materials.
Aim: The present study aimed to investigate huperzine A as an anti-Alzheimer agent based on the principle that a single compound can regulate multiple proteins and associated pathways, using system biology tools. Methodology: The simplified molecular-input line-entry system of huperzine A was retrieved from the PubChem database, and its targets were predicted using SwissTargetPrediction. These targets were matched with the proteins deposited in DisGeNET for Alzheimer disease and enriched in STRING to identify the probably regulated pathways, cellular components, biological processes, and molecular function. Furthermore, huperzine A was docked against acetylcholinesterase using AutoDock Vina, and simulations were performed with the Gromacs package to take into account the dynamics of the system and its effect on the stability and function of the ligands. Results: A total of 100 targets were predicted to be targeted by huperzine A, of which 42 were regulated at a minimum probability of 0.05. Similarly, 101 Kyoto Encyclopedia of Genes and Genomes pathways were triggered, in which neuroactive ligand-receptor interactions scored the least false discovery rate. Also, huperzine A was predicted to modulate 54 cellular components, 120 molecular functions, and 873 biological processes. Furthermore, huperzine A possessed a binding affinity of -8.7 kcal/mol with AChE and interacted within the active site of AChE via H-bonds and hydrophobic interactions.