Preclinical dose response study shows NR2E3 can attenuate retinal degeneration in the retinitis pigmentosa mouse model RhoP23H+/.

Retinitis pigmentosa (RP) is a heterogeneous disease and the main cause of vision loss within the group of inherited retinal diseases (IRDs). IRDs are a group of rare disorders caused by mutations in one or more of over 280 genes which ultimately result in blindness. Modifier genes play a key role in modulating disease phenotypes, and mutations in them can affect disease outcomes, rate of progression, and severity. Our previous studies have demonstrated that the nuclear hormone receptor 2 family e, member 3 (Nr2e3) gene reduced disease progression and loss of photoreceptor cell layers in RhoP23H-/- mice. This follow up, pharmacology study evaluates a longitudinal NR2E3 dose response in the clinically relevant heterozygous RhoP23H mouse. Reduced retinal degeneration and improved retinal morphology was observed 6 months following treatment evaluating three different NR2E3 doses. Histological and immunohistochemical analysis revealed regions of photoreceptor rescue in the treated retinas of RhoP23H+/- mice. Functional assessment by electroretinogram (ERG) showed attenuated photoreceptor degeneration with all doses. This study demonstrates the effectiveness of different doses of NR2E3 at reducing retinal degeneration and informs dose selection for clinical trials of RhoP23H-associated RP.

A comprehensive profile of SARS-CoV-2 variants spreading during the COVID-19 pandemic: a genomic characterization study from Chhattisgarh State, India.

SARS-CoV-2 has expressively changed its sequences during the COVID-19 pandemic situation by encompassing persistent evolutionary mutational changes resulting in the emergence of many clades and lineages. Evolution of these SARS-CoV-2 variants have significantly imparted fitness advantage to the virus, enhanced its transmissibility and severity of the disease. These new variants are a potential threat to the vaccine efficacy as well. It is therefore pertinent to monitor the evolution of these variants and their epidemiological and clinical impact, in a geographic setting. This study has thus looked into the geographic distribution and genetic diversity of SARS-CoV-2 variants and the evolutionary circulation of different clades in Chhattisgarh (CG) state from March 2020 to July 2023. A total of 3018 sequences were retrieved from the GISAID database, in which 558 were submitted by us. The demographic data revealed male preponderance of 56.45% versus 43.54% females, with the overall mean age of 36.5 years. SARS-CoV-2 sequences represented many variants viz., Delta (55%), Omicron (22%) and others (15%) with a small proportion of recombinant (5%), Kappa (2%), and Alpha (1%). The viral clades G was found predominant for a year from initial days of pandemic in March, 2020 to January, 2021 which then subsequently evoluted to subclade GK (Delta B.1.617.2) and remained in circulation in CG till November, 2021. From December 2021, the GRA (Omicron B.1.1.529) variant had replaced GK to become the dominant strain and continues to predominate in present time. GRA clade is however continuously encompassing new recombinant strains, having various non-synonymous mutations especially in spike protein. The non-synonymous mutation P314L in ORF1b, S84L in ORF8 and D614G in spike protein were found as the pan mutation carried over from clade G to GRA. The continuous evolution in SARS-CoV2 warrants periodical geographic genomic surveillance monitoring to timely detect any new variants having the potential of causing future outbreak.

Novel mutations found in Mycobacterium leprae DNA repair gene nth from central India.

INTRODUCTION: The importance of DNA repair enzymes in maintaining genomic integrity is highlighted by the hypothesis that DNA damage by reactive oxygen/nitrogen species produced inside the host cell is essential for the mutagenesis process. Endonuclease III (Nth), formamidopyrimide (Fpg) and endonuclease VIII (Nei) DNA glycosylases are essential components of the bacterial base excision repair process. Mycobacterium leprae lost both fpg/nei genes during the reductive evolution event and only has the nth (ML2301) gene. This study aims to characterize the mutations in the nth gene of M. leprae strains and explore its correlation with drug-resistance. METHOD: A total of 91 M. leprae positive DNA samples extracted from skin biopsy samples of newly diagnosed leprosy patients from NSCB Hospital Jabalpur were assessed for the nth gene as well as drug resistance-associated loci of the rpoB, gyrA and folP1 genes through PCR followed by Sanger sequencing. RESULTS: Of these 91 patients, a total of two insertion frameshift mutations, two synonymous and seven nonsynonymous mutations were found in nth in seven samples. Sixteen samples were found to be resistant to ofloxacin and one was found to be dapsone resistant as per the known DRDR mutations. No mutations were found in the rpoB region. Interestingly, none of the nth mutations were identified in the drug-resistant associated samples. CONCLUSION: The in-silico structural analysis of the non-synonymous mutations in the Nth predicted five of them were to be deleterious. Our results suggest that the mutations in the nth gene may be potential markers for phylogenetic and epidemiological studies.

Genetic diversity of Mycobacterium leprae: Need to move towards genome-wide approaches.

ABSTRACT: Leprosy, an ancient disease, continues to be a public health concern as it remains endemic in several countries. After reaching the elimination target (1/10,000) as a public health problem in 2005 in India, around 1.2 lakh cases have been detected every year over the last decade indicating active transmission of leprosy bacillus (Mycobacterium leprae). Single-nucleotide polymorphisms (SNPs), genomic insertions/deletions and variable-number tandem repeats (VNTRs) have been identified as genetic markers for tracking M. leprae transmission. As the leprosy bacilli cannot be cultured in vitro, molecular testing of M. leprae genotypes is done by polymerase chain reaction-based sequencing which provides a practical alternative for the identification of strains as well as drug resistance-associated mutations. Whole-genome sequencing (WGS) of M. leprae directly from clinical samples has also proven to be an effective tool for identifying genetic variations which can further help refine the molecular epidemiological schemes based on SNPs and VNTRs. However, the WGS data of M. leprae strains from India are scarce, being responsible for a gross under-representation of the genetic diversity of M. leprae strains present in India and need to be addressed suitably. Molecular studies of leprosy can provide better insight into phylogeographic markers to monitor the transmission dynamics and emergence of antimicrobial resistance. An improved understanding of M. leprae transmission is essential to guide efficient leprosy control strategies. Therefore, this review compiles and discusses the current status of molecular epidemiology, genotyping and the potential of genome-wide analysis of M. leprae strains in the Indian context.

Exploring the feasibility, acceptability and preliminary effects of a nurse delivered mhealth intervention for women living with HIV in South India: a pilot randomized controlled trial.

PURPOSE: We evaluated the feasibility, acceptability and preliminary efficacy of a standardized nurse delivered mobile phone intervention to improve adherence to antiretroviral treatment and clinical outcomes. METHODS: Feasibility and acceptability of the phone intervention was assessed with rates of eligibility, completed visits, and attritions. Intervention fidelity was assessed by checking recorded calls and feedback. Efficacy was assessed using a randomized controlled trial in which 120 women living with HIV and psychosocial vulnerabilities, were randomized to Treatment as Usual (TAU = 60) or TAU plus the mobile phone intervention (N = 60). Trained basic nurses delivered the theory-guided, standardized mobile phone intervention for mental health issues and psychosocial risk factors to improve antiretroviral treatment (ART) adherence and retention in care and improve clinical outcomes. Blind raters performed the assessments at 6, 12 and 24 weeks post-randomization. RESULTS: Adherence diminished over time in the TAU only group, while it was sustained in the TAU Plus group, only dropping at 24 weeks after the intervention had been discontinued. Among participants with depressive symptoms (CESD ≥ 16), the intervention had significant improvement in adherence rates (p < 0.01), psychological quality of life (p < 0.05) and illness perception (p < 0.05) compared to those in the TAU only group. Greater improvements of quality of life subscales were observed in the TAU Plus group among participants with less psychological vulnerability (PSV < 2). HIV RNA was not significantly different between the groups at week 24. CONCLUSIONS: The mobile-delivered counseling intervention was feasible and acceptable and shows promise among women living with HIV and psychosocial vulnerabilities in rural South India. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02319330 [Registered on: December 18, 2014].

Exploring a new frontier in cardiac diagnosis: ECG analysis enhanced by machine learning and parametric quartic spline modeling.

The heart's study holds paramount importance in human physiology, driving valuable research in cardiovascular health. However, assessing Electrocardiogram (ECG) analysis techniques poses challenges due to noise and artifacts in authentic recordings. The advent of machine learning systems for automated diagnosis has heightened the demand for extensive data, yet accessing medical data is hindered by privacy concerns. Consequently, generating artificial ECG signals faithful to real ones is a formidable task in biomedical signal processing. This paper introduces a method for ECG signal modeling using parametric quartic splines and generating a new dataset based on the modeled signals. Additionally, it explores ECG classification using three machine learning techniques facilitated by Orange software, addressing both normal and abnormal sinus rhythms. The classification enables early detection and prediction of heart-related ailments, facilitating timely clinical interventions and improving patient outcomes. The assessment of synthetic signal quality is conducted through power spectrum analysis and cross-correlation analysis, power spectrum analysis of both real and synthetic ECG waves provides a quantitative assessment of their frequency content, aiding in the validation and evaluation of synthetic ECG signal generation techniques. Cross-correlation analysis revealing a robust correlation coefficient of 0.974 and precise alignment with a negligible time lag of 0.000 s between the synthetic and real ECG signals. Overall, the adoption of quartic spline interpolation in ECG modeling enhances the precision, smoothness, and fidelity of signal representation, thereby improving the effectiveness of diagnostic and analytical tasks in cardiology. Three prominent machine learning algorithms, namely Decision Tree, Logistic Regression, and Gradient Boosting, effectively classify the modeled ECG signals with classification accuracies of 0.98620, 0.98965, and 0.99137, respectively. Notably, all models exhibit robust performance, characterized by high AUC values and classification accuracy. While Gradient Boosting and Logistic Regression demonstrate marginally superior performance compared to the Decision Tree model across most metrics, all models showcase commendable efficacy in ECG signal classification. The study underscores the significance of accurate ECG modeling in health sciences and biomedical technology, offering enhanced accuracy and flexibility for improved cardiovascular health understanding and diagnostic tools.

Genomic characterization unravelling the causative role of SARS-CoV-2 Delta variant of lineage B.1.617.2 in 2nd wave of COVID-19 pandemic in Chhattisgarh, India.

COVID-19 pandemic 2nd wave catastrophic effect in the state of Chhattisgarh, India, from where no exclusive genomic data yet published, has prompted us to undertake this study to unearth the causative variant. Whole-genome sequencing of SARS-CoV-2 isolated from COVID-19 infected nine vaccinated healthcare workers (HCW), thirty mild/moderate, seventeen severe, and twenty-seven deceased patients, was performed. The significant predominance of the SARS-CoV-2 variant of concern (VOC), Delta (lineage B.1.617.2) identified in sixty-four (77.1%) cases in contrast to B.1 and its sublineage in eleven (13.2%), variant under monitoring (VUM), Kappa (lineage B.1.617.1) in five (6.0%) and another VOC Alpha (lineage B.1.1.7) in three (3.6%) cases respectively (p < 0.05, χ2 = 162.49). 88.8% vaccine breakthrough, 60% mild/moderate, 94.4% severe and 81.5% dead patients were infected by Delta. Kappa presents exclusively in mild/moderate, Alpha in vaccine breakthrough, mild/moderate, and dead patient and B.1 and its sublineages in mild, severe, and dead patient categories. Delta variant spike mutation of T19R, G142D, E156G, L452R, and deletion (F157 and R158) helps in escaping antibody response, T478K and D614G enhance viral affinity with ACE2 receptor while P681R and D950N result in higher replication and transmissibility by cleaving S1/S2 at furin site. We conclude that Delta variant predominant role along with co-occurrence of Kappa, Alpha, and B.1 variant during COVID-19 2nd wave pandemic in Chhattisgarh may pose a potential threat of future outbreak through hybrid variant evolution. Thus, intensive genomic surveillance for monitoring variant evolution and a more efficacious vaccine against the Delta and Alpha variants are required.

Epidemiological scenario of leprosy in marginalized communities of India: Focus on scheduled tribes.

The Scheduled Tribes (STs) are designated among the most disadvantaged social groups in India. Until the year 2005 (pre-elimination era of leprosy in India), several leprosy-specific control field programmes were implemented, which have been discontinued subsequently. Since then, leprosy diagnosis and treatment have been integrated with General Health Services. Thereafter, specialized expertise for the early diagnosis of leprosy has been gradually diminishing, especially at the peripheral clinics in remote areas. Hence, leprosy cases usually remain undetected for a long time and persist as endemic reservoirs. The tribal population of India accounts for just 8.6 per cent of the overall population. However, 18.5 per cent of the new leprosy cases were detected within the tribal community in the year 2020, indicating a disproportionately high burden of leprosy among the tribal population. Recent data suggest that these health disparities can be mainly related to the increased marginalization of STs as compared to other communities. This shows the need to further explore the current situation of leprosy in STs so that suitable interventions can address the contributing factors, leading to health inequalities in disadvantaged socio-economic groups. Therefore, this review aims to present the current distribution of leprosy in marginalized communities with a special emphasis on STs. Further, this review discusses how resources might be mobilized for such communities to find and treat undetected leprosy patients in STs to enable effective control of leprosy through early detection and timely treatment.

An automated detection of atrial fibrillation from single­lead ECG using HRV features and machine learning.

BACKGROUND: Atrial fibrillation (AF) is a disorder of the heart rhythm where irregular and rapid heartbeats are observed. This supraventricular arrhythmia may increase the risk of blood clots, stroke, heart failure, and other serious heart complications. Automatic analysis of AF that is based on machine learning (ML) plays an important role in detecting this heart disease. METHODS: A new approach for automated AF detection is presented using heart rate variability (HRV) features and machine learning. A set of time-domain, frequency-domain and nonlinear features are extracted from the R-R intervals. A new method for frequency-domain analysis of R-R intervals using the Fourier Decomposition Method is presented, which provides promising results as compared to the usual method of power spectral density estimation. We train the algorithm on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) atrial fibrillation database and perform a comprehensive analysis using statistical tests to obtain the results without any intra-patient bias. RESULTS: The proposed method is able to achieve average result of 95.16% sensitivity, 92.46% specificity and 94.43% accuracy and its performance is better than the existing approaches. Furthermore, the efficacy of the proposed algorithm is tested on eight records from a previously unseen MIT-BIH Arrhythmia Database. CONCLUSION: This work shows that the proposed HRV features and ML approach can be effectively used for the analysis, detection, and classification of AF.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Mosaic Recombination Inflicted Various SARS-CoV-2 Lineages to Emerge into Novel Virus Variants: a Review Update.

Human Coronaviruses (hCoVs) belongs to the enormous and dissimilar family of positive-sense, non-segmented, single-stranded RNA viruses. The RNA viruses are prone to high rates of mutational recombination resulting in emergence of evolutionary variant to alter various features including transmissibility and severity. The evolutionary changes affect the immune escape and reduce effectiveness of diagnostic and therapeutic measures by becoming undetectable by the currently available diagnostics and refractory to therapeutics and vaccines. Whole genome sequencing studies from various countries have adequately reported mosaic recombination between different lineage strain of SARS-CoV-2 whereby RNA dependent RNA polymerase (RdRp) gene reconnects with a homologous RNA strand at diverse position. This all lead to evolutionary emergence of new variant/ lineage as evident with the emergence of XBB in India at the time of writing this review. The continuous periodical genomic surveillance is utmost required for understanding the various lineages involved in recombination to emerge into hybrid variant. This may further help in assessing virus transmission dynamics, virulence and severity factor to help health authorities take appropriate timely action for prevention and control of any future COVID-19 outbreak.

Electrophysiology using coaxial atom probe array: live imaging reveals hidden circuits of a hippocampal neural network.

Since the 1960s, it is held that when a neuron fires, a nerve spike passes only through the selective branches, the calculated choice is a key to learning by rewiring. It is argued by chemically estimating the membrane's ion channel density that different axonal branches get active to pass the spike-branches blink at firing at different time domains. Here, using a new time-lapse dielectric imaging, we visualize the classic branch selection process; thenceforth, hidden circuits operating at different time domains become visible. The fractal grid of coaxial probes captures wireless snapshots of material's vibration at various depths below the membrane by setting a suitable frequency. Thus far, branch selection observed emitted energy or particle but never the emitters, what they do. As each dielectric material transmits and reflects signals of different frequencies, we image live how filaments search for many branch-made circuits, choose a unique pathway 103 times faster than a single nerve spike. It reveals that neural branches and circuit visible in a microscope are not absolute, there coexist many circuits each operating in different dime domains, operating at a time.NEW & NOTEWORTHY Using dielectric resonance scanner, we show electromagnetic field connections between physically separated neurons. Electromagnetic field creates field lines that pass through gap junctions, connect Axon initial segment with the dendrites through Soma, and connect axonal or dendritic branches even if there is no synaptic junction. Consequently, many distinct loops connecting various branches form coexisting circuits. Our discovery suggests that physically appearing neural circuit is a fractional view of many simultaneously operating circuits in different time domains in a neural network.

Direct Arylation of Distal and Proximal C(sp3)-H Bonds of t-Amines with Aryl Diazonium Tetrafluoroborates via Photoredox Catalysis.

A visible light-mediated arylation protocol for t-amines has been reported through the coupling of γ- and α-amino alkyl radicals with different aryl diazonium salts using Ru(bpy)3Cl2·6H2O as a photocatalyst. Structurally different 9-aryl-9,10-dihydroacridine, 1-aryl tetrahydroisoquinoline, hexahydropyrrolo[2,1-a]isoquinoline, and hexahydro-2H-pyrido[2,1-a]isoquinoline frameworks with different substitution patterns have been synthesized in good yield using this methodology.

Filaments and four ordered structures inside a neuron fire a thousand times faster than the membrane: theory and experiment.

The current action potential paradigm considers that all components beneath the neuron membrane are inconsequential. Filamentary communication is less known to the ionic signal transmission; recently, we have proposed that the two are intimately linked through time domains. We modified the atom probe-connected dielectric resonance scanner to operate in two-time domains, milliseconds and microseconds simultaneously for the first time. We resonate the ions for imaging rather than neutralizing them as patch clamps do; resonant transmission images the ion flow 103 times faster than the existing methods. We revisited action potential-related events by scanning in and around the axon initial segment (AIS). Four ordered structures in the cytoskeletal filaments exchange energy ~250 µs before a neuron fires, editing spike-time-gap-key to the brain's cognition. We could stop firing above a threshold or initiate a fire by wirelessly pumping electromagnetic signals. We theoretically built AIS, whose simulated electromagnetic energy exchange matched the experiment. Thus far, the scanner could detect & link uncorrelated biological events unfolding over 106 orders in the time scale simultaneously. Our experimental findings support a new dielectric resonator model of neuron functioning in various time domains, thus suggesting the dynamic anatomy of electrical activity as information-rich.

Isolation of Mycobacterium lepromatosis and Development of Molecular Diagnostic Assays to Distinguish Mycobacterium leprae and M. lepromatosis.

BACKGROUND: Mycobacterium leprae was thought to be the exclusive causative agent of leprosy until Mycobacterium lepromatosis was identified in a rare form of leprosy known as diffuse lepromatous leprosy (DLL). METHODS: We isolated M. lepromatosis from a patient with DLL and propagated it in athymic nude mouse footpads. Genomic analysis of this strain (NHDP-385) identified a unique repetitive element, RLPM, on which a specific real-time quantitative polymerase chain reaction assay was developed. The RLPM assay, and a previously developed RLEP quantitative polymerase chain reaction assay for M. leprae, were validated as clinical diagnostic assays according to Clinical Laboratory Improvement Amendments guidelines. We tested DNA from archived histological sections, patient specimens from the United States, Philippines, and Mexico, and US wild armadillos. RESULTS: The limit of detection for the RLEP and RLPM assays is 30 M. leprae per specimen (0.76 bacilli per reaction; coefficient of variation, 0.65%-2.44%) and 122 M. lepromatosis per specimen (3.05 bacilli per reaction; 0.84%-2.9%), respectively. In histological sections (n = 10), 1 lepromatous leprosy (LL), 1 DLL, and 3 Lucio reactions contained M. lepromatosis; 2 LL and 2 Lucio reactions contained M. leprae; and 1 LL reaction contained both species. M. lepromatosis was detected in 3 of 218 US biopsy specimens (1.38%). All Philippines specimens (n = 180) were M. lepromatosis negative and M. leprae positive. Conversely, 15 of 47 Mexican specimens (31.91%) were positive for M. lepromatosis, 19 of 47 (40.43%) were positive for M. leprae, and 2 of 47 (4.26%) contained both organisms. All armadillos were M. lepromatosis negative. CONCLUSIONS: The RLPM and RLEP assays will aid healthcare providers in the clinical diagnosis and surveillance of leprosy.

Biophysical characterization of polydisperse liposomal adjuvant formulations.

Army Liposome Formulations (ALF) are potent adjuvants, of which there are two primary forms, lyophilized ALF (ALFlyo) containing monophosphoryl lipid A (MPLA) and ALF containing MPLA and QS21 (ALFQ). ALFlyo and ALFQ adjuvants are essential constituents of candidate vaccines for bacterial, viral, and parasitic diseases. They have been widely used in preclinical immunogenicity studies in small animals and non-human primates and are progressing to phase I/IIa clinical trials. ALFQ was prepared by adding saponin QS21 to small unilamellar liposome vesicles (SUVs) of ALF55 that contain 55 mol% cholesterol, whereas ALFlyo was created by reconstituting lyophilized SUVs of ALF43, consisting of 43 mol% cholesterol, in aqueous buffer solution. These formulations display heterogenous particle size distribution. Since biophysical characteristics of liposomes may impact their adjuvant potential, we characterized the particle size distribution and lamellarity of the individual liposome particles in ALFlyo and ALFQ formulations using cryo-electron microscopy and a newly developed MANTA technology. ALFlyo and ALFQ exhibited similar particle size distributions with liposomes ranging from 50 nm to several µm. However, fundamental differences were observed in the lamellar structures of the liposomes. ALFlyo displayed a greater number of multilamellar and multivesicular liposome particles, as compared to that in ALFQ, which was predominately unilamellar.

Ancient genomes reveal a high diversity of Mycobacterium leprae in medieval Europe.

Studying ancient DNA allows us to retrace the evolutionary history of human pathogens, such as Mycobacterium leprae, the main causative agent of leprosy. Leprosy is one of the oldest recorded and most stigmatizing diseases in human history. The disease was prevalent in Europe until the 16th century and is still endemic in many countries with over 200,000 new cases reported annually. Previous worldwide studies on modern and European medieval M. leprae genomes revealed that they cluster into several distinct branches of which two were present in medieval Northwestern Europe. In this study, we analyzed 10 new medieval M. leprae genomes including the so far oldest M. leprae genome from one of the earliest known cases of leprosy in the United Kingdom-a skeleton from the Great Chesterford cemetery with a calibrated age of 415-545 C.E. This dataset provides a genetic time transect of M. leprae diversity in Europe over the past 1500 years. We find M. leprae strains from four distinct branches to be present in the Early Medieval Period, and strains from three different branches were detected within a single cemetery from the High Medieval Period. Altogether these findings suggest a higher genetic diversity of M. leprae strains in medieval Europe at various time points than previously assumed. The resulting more complex picture of the past phylogeography of leprosy in Europe impacts current phylogeographical models of M. leprae dissemination. It suggests alternative models for the past spread of leprosy such as a wide spread prevalence of strains from different branches in Eurasia already in Antiquity or maybe even an origin in Western Eurasia. Furthermore, these results highlight how studying ancient M. leprae strains improves understanding the history of leprosy worldwide.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

Potential Inhibitors for SARS-CoV-2 and Functional Food Components as Nutritional Supplement for COVID-19: A Review.

The severe acute respiratory syndrome is a viral respiratory infection and commonly called as COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). It widely transmitted through direct or indirect contact. Currently, no specific treatment against SARS-CoV-2 are available; only prevention and supportive strategy are the preventive measures. The present review emphasizes the latest research related to COVID-19 and SARS-CoV-2 virus as well as the current status of potential inhibitors identified. Recent interest in SARS-CoV-2 has focused on transmission, symptoms, structure, and its structural proteins that exhibit promising therapeutics targets for rapid identification of potential inhibitors. The quick identification of potential inhibitors and immune-boosting functional food ingredients are crucial to combat this pandemic disease. We also tried to give an overview of the functional food components as a nutritional supplement, which helps in boosting our immune system and could be useful in preventing the COVID-19 and/or to improve the outcome during therapy.

Saturated phospholipids are required for nano- to micron-size transformation of cholesterol-containing liposomes upon QS21 addition.

Liposomes containing cholesterol and monophosphoryl lipid A (such as ALFQ and AS01B) are vaccine adjuvants. During construction of the formulations, addition of QS21 to nano-size (50-100 nm) liposomes resulted in extremely large (up to ∼30 µm) liposomes in ALFQ, but AS01B liposomes remained small nano-vesicles. Here, we show that saturation of phospholipid chains is essential for production of large liposomes by QS21.