M gene targeted qRT-PCR approach for SARS-CoV-2 virus detection.

Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) is the gold standard method for SARS-CoV-2 detection, and several qRT-PCR kits have been established targeting different genes of the virus. Due to the high mutation rate of these genes, false negative results arise thus complicating the interpretation of the diagnosis and increasing the need of alternative targets. In this study, an alternative approach for the detection of SARS-CoV-2 viral RNA targeting the membrane (M) gene of the virus using qRT-PCR was described. Performance evaluation of this newly developed in-house assay against commercial qRT-PCR kits was done using clinical oropharyngeal specimens of COVID-19 positive patients. The limit of detection was determined using successive dilutions of known copies of SARS-CoV-2 pseudovirus. The M gene based assay was able to detect a minimum of 100 copies of virus/mL indicating its capacity to detect low viral load. The assay showed comparable accuracy, sensitivity and specificity with commercially available kits while detecting all the variants efficiently. The study concluded that the in-house M gene based assay might be an effective alternative for the currently available commercial qRT-PCR kits.

Mental Health Status of Indian Migrant Workers in the United Arab Emirates during the COVID-19 Pandemic.

Migration has become a de facto phenomenon in the contemporary globalized world and India is not untouched. Indian labourers from the states of Bihar and Uttar Pradesh migrated to the UAE in search of better jobs and prospects. They migrated alone and left behind their families. The distance between them and their family can also create mental disorders; therefore, it becomes necessary to analyze the mental health of the migrant workers during the COVID-19 pandemic. The current study is quantitative and based on a sample survey approach. The researchers collected 416 samples through a structured questionnaire and used the snowball sampling technique. Descriptive statistics, Pearson's correlation coefficient, chi-square test and logistic regression were utilized to analyze and interpret the results. The outbreak of coronavirus disturbed their livelihood resulting in a cut to their salary or earnings; in total, 83% of migrants were affected by the COVID-19 outbreak in terms of loss of their income, out of which 76% were affected by less than AED 1000. The respondents' mental health was worrisome, but they were hopeful for the future. In total, 73.5% of respondents felt nervous, 62% felt depressed, 77% felt lonely, 63.4% had a hard time sleeping, and 63% had difficulties concentrating. The findings of the study draw attention to the policymakers to carry out necessary provisions to the targeted psychologically affected community. The findings also suggest creating awareness among the people by using social networking sites and diagnosing mental disorders on an urgent basis.

Concept of dementia (Nisy a n) in Unani system of medicine and scientific validation of an important Unani pharmacopoeial preparation 'Majoon Vaj' for its management: a review.

OBJECTIVES: This review focused on the concept of dementia in the Unani system of medicine and comprehensive, updated information on Majoon Vaj about the phytochemistry, nootropic, CNS activities and provide insights into potential opportunities for future research. METHODS: The classical literature on Majoon Vaj for its anti-dementic properties, and therapeutic uses were gathered from nearly thirteen classical Unani books including Unani Pharmacopoeia. The information of pharmacognosy, phytochemical and pharmacological activities of Majoon Vaj and its ingredient was collected by browsing the Internet (PubMed, ScienceDirect, Wiley online library, Google Scholar, ResearchGate). The relevant primary sources were probed, analysed, and included in this review. The keywords used to browse were Majoon Vaj, Dementia, Nootropic, Acorus calamus, Piper nigram, Zingiber officinalis, Nigella sativa, Carum carvi, Plumbago zeylanica, and ß-asarone. Relevant Sources were gathered up to July 2021, and the chemical structures were drawn using ACD/ChemSketch software. The species name and synonyms were checked with WFO (2021): World Flora online (http://www.worldfloraonline.org) an updated version of 'The Plant List.' RESULTS: Majoon Vaj contains an excess of bioactive compounds e.g., alkaloids, phenols, flavonoids, tannins, diterpenes, coumarins, carbohydrates, and fixed oils and its ingredients possess broad pharmacological properties, including cognitive-enhancing, neuroprotective, anti-inflammatory, antioxidant and antimicrobial properties. CONCLUSIONS: The literature of Unani medicine is quite rich in discussing the pathophysiological basis of memory disorders. It argues that memory, retention, and retrieval are regulated by a complex process involving various faculties. Majoon Vaj seems to have great potential for therapeutic applications in the treatment of dementia and thus encourage more preclinical and clinical trials in this field.

Genomic and evolutionary study from SARS-CoV-2 virus isolates from Bangladesh during the early stage of pandemic strongly correlate with European origin and not with China.

The goal of this study was to identify the genomic variants and determine molecular epidemiology of SARS-CoV-2 virus during the early pandemic stage in Bangladesh. Viral RNA was extracted, converted to cDNA, and amplified using Ion AmpliSeq™ SARS-CoV-2 Research Panel. 413 unique mutants from 151 viral isolates were identified. 80% of cases belongs to 8 mutants: 241C toT, 1163A toT, 3037C toT, 14408C toT, 23403A toG, 28881G toA, 28,882 G toA, and 28883G toC. Observed dominance of GR clade variants that have strong presence in Europe, suggesting European channel a possible entry route. Among 37 genomic mutants significantly associated with clinical symptoms, 3916CtoT (associated with sore-throat), 14408C to T (associated with cough-protection), 28881G to A, 28882G to A, and 28883G to C (associated with chest pain) were notable. These findings may inform future research platforms for disease management and epidemiological study.

Mechanistic insight into deep holes from interband transitions in Palladium nanoparticle photocatalysts.

Utilizing hot electrons generated from localized surface plasmon resonance is of widespread interest in the photocatalysis of metallic nanoparticles. However, hot holes, especially generated from interband transitions, have not been fully explored for photocatalysis yet. In this study, a photocatalyzed Suzuki-Miyaura reaction using mesoporous Pd nanoparticle photocatalyst served as a model to study the role of hot holes. Quantum yields of the photocatalysts increase under shorter wavelength excitations and correlate to "deeper" energy of the holes from the Fermi level. This work suggests that deeper holes in the d-band catalyze the oxidative addition of aryl halide R-X onto Pd0 at the nanoparticles' surface to form R-PdII-X complex, thus accelerating the rate-determining step of the catalytic cycle. The hot electrons do not play a decisive role. In the future, catalytic mechanisms induced by deep holes should deserve as much attention as the well-known hot electron transfer mechanism.

Machine learning-based modeling in food processing applications: State of the art.

Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better-quality products. The development of a machine learning (ML)-based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better-quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML-based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step-by-step procedure to develop an ML-based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML-based techniques to hybrid food processing operations. The potential of physics-informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML-based technology for food processing applications.

Sustainable food drying technologies based on renewable energy sources.

Waste in the food supply chain is estimated to be about 30-40% of the total food production, which aggravates the world hunger and increases waste management burden and environmental impact. Despite the dire food scarcity, majority of this food waste takes place in developing countries because of the lack of appropriate and affordable preservation techniques. Traditional open sun drying is the most popular food-reservation technique to the local farmers due to near-zero capital cost and cheap labor cost. However, this method is highly energy intensive, unhygienic, and time demanding. The high energy consumption resulting from uncontrolled simultaneous heat, mass, and momentum transfer processes in traditional drying systems highlights the necessity of pursuing sustainability in drying process targeting reduced energy consumption, environmental and social impacts. This paper presents a comprehensive review on the sustainable food drying technologies based on renewable energy sources, with emphasis on the developing countries. It was observed that the integration of thermal energy storage with heat pump makes the integrated drying system more efficient, and dries food with better quality. Likewise, advanced integrated drying systems, such as, solar with microwave, and heat pump with microwave make the drying process more cost and quality competent. Finally, impact of resource distribution and governmental incentives for renewable energy use in sustainable drying is discussed.

The Economic and Psychological Impacts of COVID-19 Pandemic on Indian Migrant Workers in the Kingdom of Saudi Arabia.

The ongoing Coronavirus disease 2019 (COVID-19) pandemic has changed the working environment, occupation, and living style of billions of people around the world. The severest impact of the coronavirus is on migrant communities; hence, it is relevant to assess the economic impact and mental status of the Indian migrants. This study is quantitative in nature and based on a sample survey of 180 migrant workers. Descriptive statistics, chi-square test, dependent sample t-test, and Pearson's correlation coefficient were utilized to analyze the surveyed data. The findings of the study reveal, through the working experience of the migrants, that new international migration has reduced due to lockdown and international travel restrictions. It was also reported that the majority of the migrants worked less than the normal working hours during the lockdown, causing a reduction of salary and remittances. Chi-square test confirms that the perceptions of migrants towards the COVID-19 management by the government were significantly different in opinion by different occupation/profession. Majority of the sampled migrants reported the problem of nervousness, anxiety, and depression; however, they were also hopeful about the future. The psychological problem was severe for the migrants above the age of 40, not educated, and with a higher number of family members. Subsequently, the policy implications from the findings of the research can draw attention of the policy makers towards protective measures which need to be implemented to support migrants during the ongoing pandemic. The government should take some necessary steps, such as a financial benefit scheme, to overcome the problems in the reduction of migrant earnings and remittances. The government should not focus only on vaccination and physical fitness of the migrants but also need to find out the cure of the psychological impact arising during the pandemic.

Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.

Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

Long-Range Proton Transport in Films from a Reflectin-Derived Polypeptide.

Protein- and peptide-based proton conductors have been extensively studied because of their important roles in biological processes and established potential for bioelectronic device applications. However, despite much progress, the demonstration of long-range proton transport for such materials has remained relatively rare. Herein, we fabricate, electrically interrogate, and physically characterize films from a reflectin-derived polypeptide. The electrical measurements indicate that device-integrated films exhibit proton conductivities with values of ∼0.4 mS/cm and sustain proton transport over distances of ∼1 mm. The accompanying physical characterization indicates that the polypeptide possesses characteristics analogous to those of the parent protein class and furnishes insight into the relationship between the polypeptide's electrical functionality and structure in the solid state. When considered together, our findings hold significance for the continued development and engineering of not only reflectin-based materials but also other bioinspired proton conductors.

Efficacy of Khar-i-khasak (Tribulus terrestris Linn.) in prehypertension: a randomized, double-blind, placebo-controlled trial.

BACKGROUND: Prehypertension is a state of above-normal blood pressure that does not meet the criteria for the diagnosis of hypertension and its prevalence estimated in population-based samples ranges from 22 to 52%. It conveys potentially many deleterious consequences such as high risk of progression to hypertension and cardiovascular disease later in life. OBJECTIVES: The present study was conducted to evaluate the blood pressure-lowering effect of Khar-i-khasak (Tribulus terrestris Linn.) in prehypertensive individuals. METHODS: This randomized, double-blind, placebo-controlled, clinical trial was conducted at the National Institute of Unani Medicine, Hospital, Bengaluru, after approval by the Institutional Ethics Committee. Prehypertensive individuals over 18 years of age were enrolled after obtaining their written informed consent and were randomly allocated to the test or placebo group. The test and placebo groups were administered powdered dried fruits of Khar-i-khasak (6g) and matched placebo (6g) in three divided doses for two months respectively. The efficacy assessment was determined by changes in systolic and diastolic blood pressures. RESULTS: Both systolic and diastolic blood pressure showed a significant decline in the test group (p<0.001) as compared to the placebo group. The average decline in systolic/diastolic blood pressure was -7.7/5.5 mmHg in the test group and -1.9/0.2 mmHg in the placebo group. During the post-therapy follow-up period, no prehypertensive developed full-blown hypertension in either group. Safety parameters were found to be within normal limits. CONCLUSIONS: The test drug Khar-i-khasak (T. terrestris Linn.) was found to be effective and safe in lowering blood pressure compared to placebo in prehypertensive individuals.

Ag@ZnO Nanoparticles Induce Antimicrobial Peptides and Promote Migration and Antibacterial Activity of Keratinocytes.

Antibacterial activity of silver nanoparticles is often associated with toxicity to the host. We here report that noncytotoxic doses of silver nanoparticles coated with zinc oxide, Ag@ZnO, can stimulate proliferation and migration of human keratinocytes, HaCaT, with increased expression of Ki67 and vinculin at the leading edge of wounds. Interestingly, Ag@ZnO stimulates keratinocytes to produce the antimicrobial peptides hBD2 and RNase7, promoting antibacterial activity against both extracellular and intracellular Staphylococcus aureus isolated from wounds. Overall, these results suggest that Ag@ZnO has the potential to significantly improve treatment outcomes in clearing wound infection.

Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.

With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.

Cytotoxic Potential of Biogenic Zinc Oxide Nanoparticles Synthesized From Swertia chirayita Leaf Extract on Colorectal Cancer Cells.

Chemotherapy side effects, medication resistance, and tumor metastasis impede the advancement of cancer treatments, resulting in a poor prognosis for cancer patients. In the last decade, nanoparticles (NPs) have emerged as a promising drug delivery system. Swertia chirayita has long been used as a treatment option to treat a variety of ailments. Zinc oxide nanoparticles (ZnO-NPs) were synthesized from ethanolic and methanolic extract of S. chirayita leaves. ZnO-NPs were characterized using UV-visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), scanning electron Microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and X-ray diffraction (XRD). Its anti-cancer activities were analyzed using cytotoxicity assays [MTT assay and acridine orange (AO) staining] and quantitative real-time PCR (qRT-PCR) using colorectal cancer (CRC) cells (HCT-116 and Caco-2) and control cells (HEK-293). The ZnO-NPs synthesized from the ethanolic extract of S. chirayita have an average size of 24.67 nm, whereas those from methanolic extract have an average size of 22.95 nm with a spherical shape. MTT assay showed NPs' cytotoxic potential on cancer cells (HCT-116 and Caco-2) when compared to control cells (HEK-293). The IC50 values of ethanolic and methanolic extract ZnO-NPs for HCT-116, Caco-2, and HEK-293 were 34.356 ± 2.71 and 32.856 ± 2.99 µg/ml, 52.15 ± 8.23 and 63.1 ± 12.09 µg/ml, and 582.84 ± 5.26 and 615.35 ± 4.74 µg/ml, respectively. Acridine orange staining confirmed the ability of ZnO-NPs to induce apoptosis. qRT-PCR analysis revealed significantly enhanced expression of E-cadherin whereas a reduced expression of vimentin and CDK-1. Altogether, these results suggested anti-cancer properties of synthesized ZnO-NPs in CRC.

Modeling broadband cloaking using 3D nano-assembled plasmonic meta-structures.

The concept of "cloaking" an object is a very attractive one, especially in the visible (VIS) and near infra-red (NIR) regions of the electromagnetic spectrum, as that would reduce the visibility of an object to the eye. One possible route to achieving this goal is by leveraging the plasmonic property of metallic nanoparticles (NPs). We model and simulate light in the VIS and NIR scattered by a core of a homogeneous medium, covered by plasmonic cloak that is a spherical shell composed of gold nanoparticles (AuNPs). To consider realistic, scalable, and robust plasmonic cloaks that are comparable, or larger, in size to the wavelength, we introduce a multiscale simulation platform. This model uses the multiple scattering theory of Foldy and Lax to model interactions of light with AuNPs combined with the method of fundamental solutions to model interactions with the core. Numerical results of our simulations for the scattering cross-sections of core-shell composite indicate significant scattering suppression of up to 50% over a substantial portion of the desired spectral range (400 - 600 nm) for cores as large as 900 nm in diameter by a suitable combination of AuNP sizes and filling fractions of AuNPs in the shell.

Suramin derivatives play an important role in blocking the interaction between FGF1 and FGFRD2 to inhibit cell proliferation.

The inhibition of protein function by small compounds plays a critical role in controlling cell proliferation. We report on a new class of small molecule (NCTU-Alan-2026) inhibitors for cell proliferation. NCTU-Alan-2026 blocks the interaction between FGF1 and its receptor FGF1R2D2. Extensive NMR studies combined with fluorescence experiments provided the specific mechanism of how NCTU-Alan-2026 could inhibit cell proliferation. We describe an innovative therapeutic approach for anti-proliferation and demonstrate an example of inhibition of small molecules by blocking the protein-protein interaction. We found that the compound NCTU-Alan-2026 blocked the interaction between the two proteins FGF1 and FGF1R2D2 and inhibited cell proliferation. The toxicity of NCTU-Alan-2026 is lower than that of suramin. Thus, NCTU-Alan-2026 could be a better drug than suramin in the treatment of cancer.

Biogenic Ag/CaO nanocomposites kill Staphylococcus aureus with reduced toxicity towards mammalian cells.

Rapid increase in the case of bacterial infections is considered as a major public health concern and hence exploration of alternative treatment procedures including development of nanomaterials based therapeutic strategies is receiving much attention. In this aspect, here we investigated the antibacterial efficacy of a simple and potential metal/metal oxide nanocomposite system. Biogenic synthetic protocol was designed for processing of Ag/CaO nanocomposites (NCs). Structural features and morphology of the synthesized nanomaterials were investigated by X-ray diffraction (XRD) and electron microscopy techniques respectively. Optical properties of the nanomaterials were analyzed by UV-vis spectrophotometer. Presence of water and possible impurity molecules on the materials surface was examined by Fourier-transform infrared spectroscopy (FTIR). Effective antibacterial activity of the NCs was observed (within a range of 25-150 µg/mL of NCs) against Staphylococcus aureus (S. aureus) and Methicillin-resistant Staphylococcus aureus (MRSA). The potential anti-biofilm effect of as synthesized NCs was tested against S. aureus. Experimental results suggest that the antibacterial action of the NCs could be due to the induction of reactive oxygen species (ROS). DNA degradation and change in the bacterial cell membrane has further indicated the complete disinfection of the target bacterial system. The cytotoxicity evaluation has confirmed that the formation of NCs has maintained the antibacterial efficacy of Ag NPs but reduced its toxicity towards mammalian cells.

Lysozyme as the anti-proliferative agent to block the interaction between S100A6 and the RAGE V domain.

In this report, using NMR and molecular modeling, we have studied the structure of lysozyme-S100A6 complex and the influence of tranilast [N-(3, 4-dimethoxycinnamoyl) anthranilic acid], an antiallergic drug which binds to lysozyme, on lysozyme-S100A6 and S100A6-RAGE complex formation and, finally, on cell proliferation. We have found that tranilast may block the S100A6-lysozyme interaction and enhance binding of S100A6 to RAGE. Using WST1 assay, we have found that lysozyme, most probably by blocking the interaction between S100A6 and RAGE, inhibits cell proliferation while tranilast may reverse this effect by binding to lysozyme. In conclusion, studies presented in this work, describing the protein-protein/-drug interactions, are of great importance for designing new therapies to treat diseases associated with cell proliferation such as cancers.

S100A4 inhibits cell proliferation by interfering with the S100A1-RAGE V domain.

The Ca2+-dependent human S100A4 (Mts1) protein is part of the S100 family. Here, we studied the interactions of S100A4 with S100A1 using nuclear magnetic resonance (NMR) spectroscopy. We used the chemical shift perturbed residues from HSQC to model S100A4 and S100A1 complex with HADDOCK software. We observed that S100A1 and the RAGE V domain have an analogous binding area in S100A4. We discovered that S100A4 acts as an antagonist among the RAGE V domain and S100A1, which inhibits tumorigenesis and cell proliferation. We used a WST-1 assay to examine the bioactivity of S100A1 and S100A4. This study could possibly be beneficial for evaluating new proteins for the treatment of diseases.