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.

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.

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.

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.

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.

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.

Biogenic Au@ZnO core-shell nanocomposites kill Staphylococcus aureus without provoking nuclear damage and cytotoxicity in mouse fibroblasts cells under hyperglycemic condition with enhanced wound healing proficiency.

The aim of the present study is focused on the synthesis of Au@ZnO core-shell nanocomposites, where zinc oxide is overlaid on biogenic gold nanoparticles obtained from Hibiscus Sabdariffa plant extract. Optical property of nanocomposites is investigated using UV-visible spectroscopy and crystal structure has been determined using X-ray crystallography (XRD) technique. The presence of functional groups on the surface of Au@ZnO core-shell nanocomposites has been observed by Fourier transforms infrared (FTIR) spectroscopy. Electron microscopy studies revealed the morphology of the above core-shell nanocomposites. The synthesized nanocomposite material has shown antimicrobial and anti-biofilm activity against Staphylococcus aureus and Methicillin Resistant Staphylococcus haemolyticus (MRSH). The microbes are notorious cross contaminant and are known to cause infection in open wounds. The possible antimicrobial mechanism of as synthesized nanomaterials has been investigated against Staphylococcus aureus and obtained data suggests that the antimicrobial activity could be due to release of reactive oxygen species (ROS). Present study has revealed that surface varnishing of biosynthesized gold nanoparticles through zinc oxide has improved its antibacterial proficiency against Staphylococcus aureus, whereas reducing its toxic effect towards mouse fibroblast cells under normal and hyperglycaemic condition. Further studies have been performed in mice model to understand the wound healing efficiency of Au@ZnO nanocomposites. The results obtained suggest the possible and effective use of as synthesized core shell nanocomposites in wound healing.

Quality of plant-based food materials and its prediction during intermittent drying.

In most drying processes, several physical, chemical and nutritional modifications take place in food products. Innovative drying techniques such as intermittent drying can enhance the quality of dehydrated products effectively and efficiently. Intermittent drying is a technique where drying conditions are changed through varying the drying air temperature, humidity, velocity, pressure, or even mode of heat input. This drying technique has been successfully applied to overcome the problems of conventional drying systems such as longer time consumption, case hardening, lower energy efficiency and poor-quality attributes. However, as the effect of intermittent drying on food quality is not yet well understood, a comprehensive study of quality change during intermittent drying is crucial. The main aim of this paper is to present a thorough review of the potential effect of intermittent drying methods on physical, chemical, nutritional, and stability characteristics of plant-based food material. It is found that application of intermittency using different drying systems has a significant effect on product quality and its stability. In addition, a comprehensive review on existing models of physio/biochemical kinetics for food drying is presented. Finally, the paper is concluded with the discussion of the current challenges and future directions of intermittent drying for producing high-quality dried food products.

Multiphase porous media modelling: A novel approach to predicting food processing performance.

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Shrinkage of Food Materials During Drying: Current Status and Challenges.

The structural heterogeneities of fruits and vegetables intensify the complexity to comprehend the interrelated physicochemical changes that occur during drying. Shrinkage of food materials during drying is a common physical phenomenon which affects the textural quality and taste of the dried product. The shrinkage of food material depends on many factors including material characteristics, microstructure, mechanical properties, and process conditions. Understanding the effect of these influencing factors on deformation of fruits and vegetables during drying is crucial to obtain better-quality product. The majority of the previous studies regarding shrinkage are either experimental or empirical; however, such studies cannot provide a realistic understanding of the physical phenomena behind the material shrinkage. In contrast, theoretical modeling can provide better insights into the shrinkage that accompanies simultaneous heat and mass transfer during drying. However, limited studies have been conducted on the theoretical modeling of shrinkage of fruits and vegetables. Therefore, the main aim of this paper is to critically review the existing theoretical shrinkage models and present a framework for a theoretical model for the shrinkage mechanism. This paper also describes the effect of different drying conditions on material shrinkage. Discussions on how the diverse characteristics of fruits and vegetables affect shrinkage propagation is presented. Moreover, a comprehensive review of formulation techniques of shrinking models and their results are also presented. Finally, the challenges in developing a physics-based shrinkage model are discussed.

Multiscale Modeling for Food Drying: State of the Art.

Plant-based food materials are mostly porous in nature and heterogeneous in structure with huge diversity in cellular orientation. Different cellular environments of plant-based food materials, such as intercellular, intracellular, and cell wall environments, hold different proportions of water with different characteristics. Due to this structural heterogeneity, it is very difficult to understand the drying process and associated morphological changes during drying. Transport processes and morphological changes that take place during drying are mainly governed by the characteristics of and the changes in the cells. Therefore, to predict the actual heat and mass transfer process that occurs in the drying process and associated morphological changes, development of multiscale modeling is crucial. Multiscale modeling is a powerful approach with the ability to incorporate this cellular structural heterogeneity with microscale heat and mass transfer during drying. However, due to the huge complexity involved in developing such a model for plant-based food materials, the studies regarding this issue are very limited. Therefore, we aim in this article to develop a critical conceptual understanding of multiscale modeling frameworks for heterogeneous food materials through an extensive literature review. We present a critical review on the multiscale model formulation and solution techniques with their spatial and temporal coupling options. Food structure, scale definition, and the current status of multiscale modeling are also presented, along with other key factors that are critical to understanding and developing an accurate multiscale framework. We conclude by presenting the main challenges for developing an accurate multiscale modeling framework for food drying.

Retraction Note to: influencing micropropagation in Clitoria ternatea L. through the manipulation of TDZ levels and use of different explant types.

[This retracts the article DOI: 10.1007/s12298-012-0136-4.].

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.

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.

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.

Influencing micropropagation in Clitoria ternatea L. through the manipulation of TDZ levels and use of different explant types.

A comparative performance of two explants types (CN and Nodal) for their efficiency to induce multiple shoot regeneration in Clitoria ternatea has been carried out. Thidiazuron (TDZ) in different concentrations (0.05-2.5 µM) was used as a supplement to the Murashige and Skoog's (MS) basal media. Explant type apart, two factors viz. concentration and exposure duration to TDZ played an important role in affecting multiple shoot regeneration. Cotyledonary node explants produced the best results at 0.1 µM TDZ, while in nodal explants the highest rate of shoot formation was achieved on MS medium supplemented with 1.0 µM TDZ. In both the explants, shoot multiplication increased when the regenerated shoots were subcultured on hormone free MS medium after 4 weeks of exposure to TDZ. Among the two, cotyledonary node explants produced considerably higher number of shoots at a comparatively lower concentration of TDZ than nodal explants. The regenerated shoots rooted best on MS medium containing 1.0 µM indole-3-butyric acid (IBA) and were successfully established in pots containing garden soil with 88 % survival rate. All the regenerated plants showed normal morphology and growth characteristics.

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.

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.

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.

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.