Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer.

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

Stepping towards pollen DNA metabarcoding: A breakthrough in forensic sciences.

This review is engaged in determining the capability of plant pollen as a significant source of evidence for the linkage between suspects and crime location in forensic sciences. Research and review articles were collected from Google Scholar, the Web of Science, and PubMed. Articles were searched using specific keywords such as "Forensic Palynology," "Pollen metabarcoding," "Plant forensics," and "Pollen" AND "criminal investigation." Boolean logic was also utilized to narrow the articles to be included in this review article. Through the literature and exploratory research, it has been observed in the current study that with advancements in technology, forensic palynology has found its application in creating an association between the crime scene and suspected individuals to have a link to it, as pollen DNA is a long-lasting investigative tool that can effectively help forensic investigations. Moreover, the literature shows that the DNA of pollen and spores has helped forensic scientists link suspects to crime scenes, and the introduction of pollen DNA metabarcoding tools has eased the efforts of palynologists to analyze pollen DNA. The introduction of DNA metabarcoding techniques to analyze pollen from plants has helped identify the geological locations of the plants and ultimately identify the culprit.

Investigation of mutational spectrum in cytochrome P4501B1 (CYP1B1) as the principal cause of primary congenital glaucoma.

Objective: To identify the genetic variants in the CYP1B1 gene associated with Primary Congenital Glaucoma (PCG) and to predict its pathological effect. Method: A descriptive study was conducted in the time period of nine months (September 2021-May 2022) after the ethical approval was taken from The Children Hospital and Institute of Child Health (CH & ICH). Two milliliters of the blood sample from PCG-affected individuals were collected in EDTA vacutainers and genomic DNA was extracted by a phenol-chloroform method. The semi-quantification of extracted DNA was done by agarose gel electrophoresis. PCR amplification was performed by specific primers of CYP1B1 gene then termination sequencing (di-deoxy) was done to detect the genetic variants. Different bioinformatics tools such as BLAST, Ensembl, Clustal Omega, Polyphen and SIFT were used for the further analysis of mutation causing the disease. Result: A total of 85% of patients were bilaterally affected, while 15% were unilaterally affected. Mutation analysis identified five non related known variants. Two missense mutations (c.355 G/T p.A119S and c.685G/A p.E229K) occurred in 94% patients and intragenic SNP occurred in 29% patients along with the 1% somatic (c.693C/A p.F231L) and stop gained mutation (c.840C/A p.C280*). Conclusion: Genetic analysis in the current study showed that 85% of PCG affected patients were due to the CYP1B1 mutation, and disease heterogeneity might be reduced through genetic counseling.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

An Insight into Primary Congenital Glaucoma.

Glaucoma is the second most prominent cause of impaired vision in the world. Over 60 million individuals are presently affected, and 12 million are sightless as a result. Primary congenital glaucoma (PCG) is a childhood disease that can lead to blindness in newborns and very young children. The rate of occurrence of PCG varies in different communities and across geographical boundaries, and its etiology is unknown. It is caused by genetic structural defects in the trabecular meshwork and makes its appearence in newborns and children no older than three years. PCG is most prevalent in populations with high rates of consanguineous marriages. It is categorized by inappropriate development of the eye's aqueous outflow system, causing increased intraocular pressure (IOP) and leading to swelling of the cornea, epiphora, discomfort or pain, enlargement of the eyeball (buphthalmos), corneal opacity, and optic nerve damage. PCG is classified as an autosomal recessive disorder involving four loci. The main culprit is CYP1B1, at locus GLC3A. PCG is also linked with loci GLC3B and GLC3C; however, their genetic factors have only recently been recognized. The gene LTPB2 at locus GLC3D, plays an important role in tissue healing and cell attachment. Trabeculectomy and gonioscopy are effective treatments for PCG. Additional efforts are essential to provide timely screening of children and, most important, to assign sufficient resources to allow healthcare workers to reduce the rate of avoidable blindness in developing countries.

HSV-1 Infection: Role of Viral Proteins and Cellular Receptors.

The interaction between herpes simplex virus type 1 (HSV-1) and its host starts with the attachment of the virus for entry and spreading into host cells involving viral glycoproteins and host receptors. Once entered, it remains persistent as a latent infection throughout the host's life as it cannot be cleared completely by the immune system. Viral regulatory proteins and host factors determine whether the virus will enter into the acute or latent mode of infection. Acute viral infection is usually asymptomatic and self-limiting whereas latent infection may remain in the trigeminal ganglion of oropharyngeal mucosa, where it can be activated at any time depending upon the stimulus. Host innate and adaptive immune elements play important roles in limiting HSV-1 infection by interfering with viral replication but are unable to remove the virus completely. In this review, we update how the major proteins involved in entry and pathogenesis of viruses and immune responses against infection.

fNIRS-based Neurorobotic Interface for gait rehabilitation.

BACKGROUND: In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. METHODS: fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere's primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. RESULTS: The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively. CONCLUSION: The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

Antibacterial potential of Propolis: molecular docking, simulation and toxicity analysis.

The issue of antibiotic resistance in pathogenic microbes is a global concern. This study was aimed to explore in silico and in vitro analysis of the antibacterial efficacy of different natural ligands against bacterial activity. The ligands included in the study were Propolis Neoflavanoide 1, Carvacrol, Cinnamaldehyde, Thymol, p-benzoquinone, and Ciprofloxacin (standard drug S*). The outcomes of molecular docking revealed that Propolis Neoflavaniode-1 showed a highly significant binding energy of - 7.1 and - 7.2 kcal/mol for the two gram-positive bacteria, as compared to the gram-negative bacteria. All ligands demonstrated acute toxicity (oral, dermal), except for Propolis Neoflavanoide 1 and S* drugs, with a confidence score range of 50-60%. Using a molecular dynamic simulation approach, we investigated Propolis Neoflavaniode-1's potential for therapeutic use in more detail. An MD simulation lasting 100 ns was performed using the Desmond Simulation software to examine the conformational stability and steady state of Propolis Neoflavaniode-1 in protein molecule complexes. Additionally, in vitro studies confirmed the antimicrobial activity of Propolis Neoflavaniode 1 by increasing the zone of inhibition against Gram-positive bacteria, p < 0.005 as compared to gram-negative bacteria. This study revealed the promising antibacterial efficacy of Propolis Neoflavaniode 1, demonstrated through robust in silico analyses, minimal toxicity, and confirmed in vitro antimicrobial activity, suggesting its potential as a viable alternative to combat antibiotic resistance.

Cell-free DNA as a potential diagnostic biomarker in academic stress: A case-control study in young adults.

Background: Stress is a pervasive issue in modern life, affecting both physical and mental health. Identifying biomarkers like cell-free DNA (cfDNA) could provide insights into stress response and help detect individuals at risk for stress-related disorders. Objective: The aim of this study is to investigate the potential use of cfDNA as a diagnostic biomarker in individuals experiencing stress. Methodology: A case-control analysis was conducted using convenient sampling on university participants (N = 285 cases, N = 500 controls) aged 18-24. The study assessed haematological and lipid profile parameters using the Sysmex XP-300TM automated analyzer and an automated biochemistry analyzer, and cfDNA was extracted using a standardized in house developed Phenol-Chloroform protocol and estimated using Agarose Gel Electrophoresis and Nanodrop. Statistical analysis was performed using SPSS ver. 21.0. Results: The results indicated a significant difference between stressed individuals and healthy controls in demographic, haematological and biochemical parameters. Specifically, stressed cases had significantly higher levels of cholesterol, LDL cholesterol, triglycerides, glucose, VLDL cholesterol, and lower levels of HDL compared to healthy controls. Stressed cases also showed significantly elevated levels of circulating cfDNA relative to healthy controls. Conclusion: These findings suggest that cfDNA may have potential as a diagnostic biomarker for stress.

In silico analysis of the Val66Met mutation in BDNF protein: implications for psychological stress.

The brain-derived neurotrophic factor (BDNF) involves stress regulation and psychiatric disorders. The Val66Met polymorphism in the BDNF gene has been linked to altered protein function and susceptibility to stress-related conditions. This in silico analysis aimed to predict and analyze the consequences of the Val66Met mutation in the BDNF gene of stressed individuals. Computational techniques, including ab initio, comparative, and I-TASSER modeling, were used to evaluate the functional and stability effects of the Val66Met mutation in BDNF. The accuracy and reliability of the models were validated. Sequence alignment and secondary structure analysis compared amino acid residues and structural components. The phylogenetic analysis assessed the conservation of the mutation site. Functional and stability prediction analyses provided mixed results, suggesting potential effects on protein function and stability. Structural models revealed the importance of BDNF in key biological processes. Sequence alignment analysis showed the conservation of amino acid residues across species. Secondary structure analysis indicated minor differences between the wild-type and mutant forms. Phylogenetic analysis supported the evolutionary conservation of the mutation site. This computational study suggests that the Val66Met mutation in BDNF may have implications for protein stability, structural conformation, and function. Further experimental validation is needed to confirm these findings and elucidate the precise effects of this mutation on stress-related disorders.

Silibinins and curcumin as promising ligands against mutant cystic fibrosis transmembrane regulator protein.

Cystic Fibrosis Transmembrane Regulator (CFTR) is a significant protein that is responsible for the movement of ions across cell membranes. The cystic fibrosis (CF) occur due to the mutations in the CFTR gene as it produces the dysfunctional CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands selection was performed through virtual screening and top 3 ligands choose out of 65 ligands silibinins, curcumin, demethoxycurcumin were selected with a reference drug Trikafta (R*). According to docking, ADMET analyses, the natural ligands (Silibinins and Curcumin) displayed best binding energy, pharmacokinetic and free toxicity than other natural compounds and reference drug (R*). An MD simulation for 200 ns was also established to ensure that natural ligands (Silibinins and Curcumin) attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that silibinins and curcumins have a better capacity to decrease the effect of mutant CFTR protein through improved trafficking and the restoration of original function. In conclusion, in silico studies demonstrate the potential of silibinins and curcumin as therapeutic agents for cystic fibrosis, particularly for the D614G mutated protein. Their ability to increase CFTR function while reducing cellular stress and inflammation, together with their favorable safety profile and accessibility could make them valuable additions to cystic fibrosis treatment options. Further experimental and clinical validation will be required to fully realize their potential and include them into effective therapy regimens.

In silico and in vitro analyses to investigate the effects of vitamin C on VEGF protein.

Objectives: This study was conducted to evaluate the effects of vitamin C on apoptotic and proliferative genes in injured HepG2 cells. Methods: In silico analysis was performed using molecular docking of chemical compounds with vascular endothelial growth factor (VEGF). The different computational tools used were AutoDock Vina, BIOVIA DISCOVERY studio, and PyMOL. Drug likeness and toxicity were analyzed by SWISS ADMET. Cells that were 60-70% confluent were treated with different concentrations of hydrogen peroxide (H2O2) (100-2000 µM) and ascorbic acid (30, 60, 90 µg/mL). The MTT cell proliferation assay was performed to compare the proliferative potential of HepG2 cells treated with H2O2 or ascorbic acid with untreated HepG2 cells using 96-well plates. Results: The lowest binding energy of VEGF with vitamin C -5.2 kcal/mol and L-ascorbic acid-2 glycoside -4.7 kcal/mol was observed by in silico analysis. Vitamin C was selected because it exhibited a high interaction with VEGF and fulfilled Lipinski's rule, and had better oral viability and pharmacokinetics compared to L-ascorbic acid-2 glycoside. Cell viability assays showed that vitamin C had significant apoptotic effects (P < 0.0001). After treating HepG2 cells with ascorbic acid, reduced VEGF (angiogenesis) was observed as determined by apoptotic and proliferative gene expression. Ascorbic acid treatment of HepG2 cells led to downregulation of the proliferation markers, proliferating cell nuclear antigen, Ki67, and DNA topoisomerase II alpha. Increased apoptosis after treatment with vitamin C was observed due to upregulation of p53 and annexin V. Conclusion: The results of this study showed that vitamin C inhibited the growth of cancer cells, thus protecting HepG2 cells from oxidative stress. Vitamin C exhibited antiproliferative activity as observed in silico and in vitro, as well as by the inhibited expression of genes involved in protein synthesis.

Anti-Diabetic, Anti-Cholinesterase, and Anti-Inflammatory Potential of Plant Derived Extracts and Column Semi-Purified Fractions of Ficus benghalensis.

BACKGROUND: The present study aimed to investigate the in-vitro anti-diabetic, anti-cholinesterase, and anti-inflammatory potential of extracts from different parts of Ficus benghalensis, including leaves, stem, and roots, as well as isolated column fractions (F-B-1 C, F-B-2 C, F-B-3 C, and F-B-4 C). METHODS: The extracts and subsequent fractions were evaluated for their inhibitory activity against key enzymes involved in diabetes [α-glucosidase and α-amylase], neurodegenerative diseases [acetylcholinesterase and butyrylcholinesterase], and inflammation (cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX)). RESULTS: The results showed that F. benghalensis leaf extract exhibited the highest α-glucosidase inhibitory activity (73.84%) and α-amylase inhibitory activity (76.29%) at 1000 µg/mL. The stem extract (65.50%) and F-B-2 C fraction (69.67%) also demonstrated significant α-glucosidase inhibitory activity. In terms of anti-cholinesterase activity, the extracts of roots, leaves, and stem showed promising inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with half maximal inhibitory concentration (IC50) values ranging from 50.50 to 474.83 µg/mL. The derived fractions (F-B-1 C, F-B-2 C, F-B-3 C, and F-B-4 C) also exhibited notable inhibition of AChE and BChE, with IC50 values from 91.85 to 337.94 µg/mL. Moreover, the F-B-3 C fraction demonstrated the highest COX-2 inhibitory potential (85.72%), followed by F-B-1 C (83.13%), the stem extract (80.85%), and the leaves extract (79.00%). The F-B-1 C fraction showed the highest 5-LOX inhibitory activity (87.63%), while the root extract exhibited the lowest inhibition (73.39%). CONCLUSIONS: The results demonstrated promising bioactivity, suggesting the potential of F. benghalensis as a source of natural compounds with therapeutic applications. Further studies are required to identify and isolate the active components responsible for these effects and to evaluate their in-vivo efficacy and safety.

Identification of novel natural compounds against CFTR p.Gly628Arg pathogenic variant.

Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of -6.4, -5.1, -6.6, -5.1, and - 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

Avoiding contingent incidents by quadrotors due to one or two propellers failure.

With the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures.

Efficacy of cell-free DNA as a diagnostic biomarker in breast cancer patients.

Breast cancer is the most prevalent and leading cause of mortality worldwide among women. Cell-free DNA (cfDNA) analysis is an alternative quantitative approach to conventional methods for cancer diagnosis. The current research project aimed to determine the efficacy of cfDNA as a diagnostic biomarker in breast cancer patients in Pakistan. Eighty-four female breast cancer patients were selected as cases, and 152 healthy females as controls. Immunohistochemistry was performed to identify tumor biomarkers along with clinical profiling. cfDNA was extracted from serum using the phenol-chloroform method. The cfDNA level in the serum was estimated using Agarose Gel Electrophoresis and Nanodrop. SPPS version 25.0 was used to perform statistical analyses. The results showed that the cancer biomarkers were significantly associated with breast cancer. The changes in hematological parameters were insignificant, whereas the biochemical parameter variations between the cases and controls were statistically significant. A significant association of cfDNA level with breast cancer was observed. Further cfDNA levels and cancer biomarkers were not statistically significant. A significant correlation was observed between cfDNA and biochemical parameters, except for creatinine, whereas hematological parameters showed no significant correlation.ROC analysis declared cfDNA as an authentic diagnostic marker for breast cancer. It was concluded that the level of cfDNA is significantly increased in breast cancer patients and can be utilized as a diagnostic biomarker.

Natural compound targeting BDNF V66M variant: insights from in silico docking and molecular analysis.

Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin gene family gene that encodes proteins vital for the growth, maintenance, and survival of neurons in the nervous system. The study aimed to screen natural compounds against BDNF variant (V66M), which affects memory, cognition, and mood regulation. BDNF variant (V66M) as a target structure was selected, and Vitamin D, Curcumin, Vitamin C, and Quercetin as ligands structures were taken from PubChem database. Multiple tools like AUTODOCK VINA, BIOVIA discovery studio, PyMOL, CB-dock, IMOD server, Swiss ADEMT, and Swiss predict ligands target were used to analyze binding energy, interaction, stability, toxicity, and visualize BDNF-ligand complexes. Compounds Vitamin D3, Curcumin, Vitamin C, and Quercetin with binding energies values of - 5.5, - 6.1, - 4.5, and - 6.7 kj/mol, respectively, were selected. The ligands bind to the active sites of the BDNF variant (V66M) via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analysis of the ligands revealed they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD simulation study showed that the most active ligand bound favorably and dynamically to the target protein, and protein-ligand complex stability was determined. The finding of this research could provide an excellent platform for discovering and rationalizing novel drugs against stress related to BDNF (V66M). Docking, preclinical drug testing and MD simulation results suggest Quercetin as a more potent BDNF variant (V66M) inhibitor and forming a more structurally stable complex.

Characterization of white matter alterations using diffusion kurtosis imaging in patients with amyotrophic lateral sclerosis.

BACKGROUND: To evaluate the degeneration of the corticospinal tract (CST) and corpus callosum (CC) in patients with motor neuron disease and upper motor neuron (UMN) dysfunction using diffusion kurtosis imaging (DKI). METHODS: Twenty-seven patients and 33 healthy controls underwent magnetic resonance imaging along with clinical and neuropsychological testing. Tractography of diffusion tensor images was performed to extract tracts of the bilateral CST and CC. Group mean differences both across the entire averaged tract and along each tract were assessed, including correlations between diffusion metrics and clinical measures. Tract-based spatial statistics (TBSS) was performed to evaluate the spatial distribution of whole-brain microstructural abnormalities in patients. RESULTS: In comparison to controls, patients had significantly higher mean and radial diffusivity and lower fractional anisotropy (FA), kurtosis anisotropy, mean kurtosis (MK), and radial kurtosis (RK) in the CST and CC (p < .017). Along-the-tract analysis revealed changes concentrated in the posterior limb of the internal capsule, corona radiata, and primary motor cortex (false-discovery rate p < .05). FA of the left CST correlated with disease progression rate, whereas MK of the bilateral CST correlated with UMN burden (p < .01). TBSS results corroborated along-tract analysis findings and additionally revealed reduced RK and MK in the fornix, where diffusion tensor imaging (DTI) changes were absent. CONCLUSION: DKI abnormalities in the CST and CC are present in patients with UMN dysfunction, potentially revealing complementary information to DTI regarding the pathology and microstructural alterations occurring in such patients. DKI shows promise as a potential in vivo biomarker for cerebral degeneration in amyotrophic lateral sclerosis.

Immune-related therapeutics: an update on antiviral drugs and vaccines to tackle the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic rapidly spread globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is a positive-sense single-stranded RNA virus with a reported fatality rate ranging from 1% to 7%, and people with immune-compromised conditions, children, and older adults are particularly vulnerable. Respiratory failure and cytokine storm-induced multiple organ failure are the major causes of death. This article highlights the innate and adaptive immune mechanisms of host cells activated in response to SARS-CoV-2 infection and possible therapeutic approaches against COVID-19. Some potential drugs proven to be effective for other viral diseases are under clinical trials now for use against COVID-19. Examples include inhibitors of RNA-dependent RNA polymerase (remdesivir, favipiravir, ribavirin), viral protein synthesis (ivermectin, lopinavir/ ritonavir), and fusion of the viral membrane with host cells (chloroquine, hydroxychloroquine, nitazoxanide, and umifenovir). This article also presents the intellectual groundwork for the ongoing development of vaccines in preclinical and clinical trials, explaining potential candidates (live attenuated-whole virus vaccines, inactivated vaccines, subunit vaccines, DNAbased vaccines, protein-based vaccines, nanoparticle-based vaccines, virus-like particles and mRNA-based vaccines). Designing and developing an effective vaccine (both prophylactic and therapeutic) would be a long-term solution and the most effective way to eliminate the COVID-19 pandemic.

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD).

Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.