Nutraceuticals and COVID‐19: A mechanistic approach toward attenuating the disease complications

Nutraceuticals have emerged as potential compounds to attenuate the COVID‐19 complications. Precisely, these food additives strengthen the overall COVID treatment and enhance the immunity of a person. Such compounds have been used at a large scale, in almost every household due to their better affordability and easy access. Therefore, current research is focused on developing newer advanced formulations from potential drug candidates including nutraceuticals with desirable properties viz, affordability, ease of availability, ease of administration, stability under room temperature, and potentially longer shelf‐lives. As such, various nutraceutical‐based products such as compounds could be promising agents for effectively managing COVID‐19 symptoms and complications. Most importantly, regular consumption of such nutraceuticals has been shown to boost the immune system and prevent viral infections. Nutraceuticals such as vitamins, amino acids, flavonoids like curcumin, and probiotics have been studied for their role in the prevention of COVID‐19 symptoms such as fever, pain, malaise, and dry cough. In this review, we have critically reviewed the potential of various nutraceutical‐based therapeutics for the management of COVID‐19. We searched the information relevant to our topic from search engines such as PubMed and Scopus using COVID‐19, nutraceuticals, probiotics, and vitamins as a keyword. Any scientific literature published in a language other than English was excluded. PRACTICAL APPLICATIONS: Nutraceuticals possess both nutritional values and medicinal properties. They can aid in the prevention and treatment of diseases, as well as promote physical health and the immune system, normalizing body functions, and improving longevity. Recently, nutraceuticals such as probiotics, vitamins, polyunsaturated fatty acids, trace minerals, and medicinal plants have attracted considerable attention and are widely regarded as potential alternatives to current therapeutic options for the effective management of various diseases, including COVID‐19.

Synergism of CD28 Immune Molecule in Late Immunosuppressive Phase of COVID-19: Effectiveness in Vaccinated Individuals.

CONTEXT: Lymphopenia has been frequently documented and linked to coronavirus disease 2019 (COVID-19) in a severe acute respiratory syndrome (SARS)-coronavirus 2 (CoV-2) attack. A decrease in the T-lymphocyte count has shown promise as a clinical indicator and predictor of COVID-19 severity. OBJECTIVE: The review intended to examine the relationship of COVID-19 infections in individuals to lost expression of CD28 on naive CD4+/CD8+-mediated, vaccine-specific, neutralizing antibody responses. DESIGN: The research team performed a narrative review by searching eight databases: Medline, Elsevier, Cochrane, PubMed, Google Scholar, Mendeley, and Springer Nature. The search used the following key terms: SARS CoV-2, clinical aspects and pathology of SARS CoV-2, involvement of viral spike (S) protein in SARS CoV-2, immunological changes in COVID-19 infection, basic overview of CD28 immuno-molecule ligand, reduction of vaccine therapeutic efficacy in COVID-19 infection, and immunomodulatory response of lost CD28 ligand. SETTING: This study was done in a Maharishi Arvind College of Pharmacy, Jaipur, India. RESULTS: In COVID-19 patients, particularly those with severe disease, had increased levels of IL-2 or IL-2R. Given IL-2's supportive role in the expansion and differentiation of T cells, the authors exhibiting that lymphopenia, particularly in severe COVID-19, could be attributed to nonfunctional and dysfunctional differentiation of CD4+ and CD8+ T cells as a result of low CD28 immuno-molecule expression on naive T cells. CONCLUSIONS: The literature review found that independent, early immunological prognostic markers for a poor prognosis, in addition to higher levels of IL-6, include a substantial proportion of large inflammatory monocytes and a small proportion of chronic CD28+ CD4+T cells. The current findings suggest that a combination of COVID-19 vaccination with SARS CoV-2-reactive naive T cells with the CD28 immune-molecule may be a viable method for establishing T-cell-based, adaptive cellular immunotherapy against COVID-19 infection. Further research is needed, especially larger studies to confirm the current findings, to improve early clinical treatment.

A study of human resource digital transformation (HRDT): A phenomenon of innovation capability led by digital and individual factors

Surviving in a digitally disrupted world required organizations to continuously innovate and digitally transform their work practices, especially HR processes. Scholarly failure to define and conceptualize the construct of human resource digital transformation (HRDT) suggests the lack of an integrated approach to understanding HRDT as a discipline. This study presents an overarching and integrated conceptual framework of HRDT, grounded in robust qualitative research, to describe factors in successfully implementing HRDT. Drawing insights from 20 senior HR professionals in multinational organizations, this study defines HRDT as a multidimensional construct resulting from successful integration of digital and individual factors into the innovation capability of organizations. Expanding the dynamic-capabilities perspective, this study proposes that in uncertain and turbulent times, such as COVID-19, businesses must constantly upgrade organizational capabilities, manifested in the innovation capability with enablers such as- digital infrastructure, architecture, and individual capability and creativity. This research further elaborates on HRDT indicators. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Advances and opportunities in nanoimaging agents for the diagnosis of inflammatory lung diseases.

The development of rapid, noninvasive diagnostics to detect lung diseases is a great need after the COVID-2019 outbreak. The nanotechnology-based approach has improved imaging and facilitates the early diagnosis of inflammatory lung diseases. The multifunctional properties of nanoprobes enable better spatial-temporal resolution and a high signal-to-noise ratio in imaging. Targeted nanoimaging agents have been used to bind specific tissues in inflammatory lungs for early-stage diagnosis. However, nanobased imaging approaches for inflammatory lung diseases are still in their infancy. This review provides a solution-focused approach to exploring medical imaging technologies and nanoprobes for the detection of inflammatory lung diseases. Prospects for the development of contrast agents for lung disease detection are also discussed.

Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19.

In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains, especially in an uncertain environment. In this context, this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19, and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry; this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings, the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability, demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support, purchasing process planning and supply chain traceability, respectively.

Short Chain Fatty Acids: Fundamental mediators of the gut-lung axis and their involvement in pulmonary diseases.

The human microbiota is fundamental to correct immune system development and balance. Dysbiosis, or microbial content alteration in the gut and respiratory tract, is associated with immune system dysfunction and lung disease development. The microbiota's influence on human health and disease is exerted through the abundance of metabolites produced by resident microorganisms, where short-chain fatty acids (SCFAs) represent the fundamental class. SCFAs are mainly produced by the gut microbiota through anaerobic fermentation of dietary fibers, and are known to influence the homeostasis, susceptibility to and outcome of many lung diseases. This article explores the microbial species found in healthy human gastrointestinal and respiratory tracts. We investigate factors contributing to dysbiosis in lung illness, and the gut-lung axis and its association with lung diseases, with a particular focus on the functions and mechanistic roles of SCFAs in these processes. The key focus of this review is a discussion of the main metabolites of the intestinal microbiota that contribute to host-pathogen interactions: SCFAs, which are formed by anaerobic fermentation. These metabolites include propionate, acetate, and butyrate, and are crucial for the preservation of immune homeostasis. Evidence suggests that SCFAs prevent infections by directly affecting host immune signaling. This review covers the various and intricate ways through which SCFAs affect the immune system's response to infections, with a focus on pulmonary diseases including chronic obstructive pulmonary diseases, asthma, lung cystic fibrosis, and tuberculosis. The findings reviewed suggest that the immunological state of the lung may be indirectly influenced by elements produced by the gut microbiota. SCFAs represent valuable potential therapeutic candidates in this context.

Biological Sequence Classification Using Deep Learning Architectures

Finding similar biological sequences to categorize into respective families is an important task. The present works attempt to use machine learning-based approaches to find the family of a given sequence. The first task in this direction is to convert the sequences to vector representations and then train a model using a suitable machine learning architecture. The second task is to find which family the sequence belongs to. In this work, deep learning-based architectures are proposed to do the task. A comparative study on how effective various deep learning architectures for this problem is also discussed in this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Vitamin D-A prominent immunomodulator to prevent COVID-19 infection.

COVID-19 remains a life-threatening infectious disease worldwide. Several bio-active agents have been tested and evaluated in an effort to contain this disease. Unfortunately, none of the therapies have been successful, owing to their safety concerns and the presence of various adverse effects. Various countries have developed vaccines as a preventive measure; however, they have not been widely accepted as effective strategies. The virus has proven to be exceedingly contagious and lethal, so finding an effective treatment strategy has been a top priority in medical research. The significance of vitamin D in influencing many components of the innate and adaptive immune systems is examined in this study. This review aims to summarize the research on the use of vitamin D for COVID-19 treatment and prevention. Vitamin D supplementation has now become an efficient option to boost the immune response for all ages in preventing the spread of infection. Vitamin D is an immunomodulator that treats infected lung tissue by improving innate and adaptive immune responses and downregulating the inflammatory cascades. The preventive action exerted by vitamin D supplementation (at a specific dose) has been accepted by several observational research investigations and clinical trials on the avoidance of viral and acute respiratory dysfunctions. To assess the existing consensus about vitamin D supplementation as a strategy to treat and prevent the development and progression of COVID-19 disease, this review intends to synthesize the evidence around vitamin D in relation to COVID-19 infection.

The Impact of Coronavirus Disease of 2019 (COVID-19) Lockdown Restrictions on the Criteria Pollutants

Air pollution is accountable for various long-term and short-term respiratory diseases and even deaths. Air pollution is normally associated with a decreasing life expectancy. Governments have been implementing strategies to improve air quality. However, natural events have always played an important role in the concentration of air pollutants. In Australia, the lockdown period followed the Black Summer of 2019-2020 and coincided with the season of prescribed burns. This paper investigates the changes in the concentration of criteria pollutants such as particulate matter, nitrogen dioxide, ozone, and sulphur dioxide. The air quality data for the lockdown period in 2020 was compared with the pre-lockdown period in 2020 and with corresponding periods of previous years from 2016 to 2019. The results were also compared with the post-lockdown scenario of 2020 and 2021 to understand how the concentration levels changed due to behavioural changes and a lack of background events. The results revealed that the COVID-19 restrictions had some impact on the concentration of pollutants;however, the location of monitoring stations played an important role.

A study of human resource digital transformation (HRDT): A phenomenon of innovation capability led by digital and individual factors

Surviving in a digitally disrupted world required organizations to continuously innovate and digitally transform their work practices, especially HR processes. Scholarly failure to define and conceptualize the construct of human resource digital transformation (HRDT) suggests the lack of an integrated approach to understanding HRDT as a discipline. This study presents an overarching and integrated conceptual framework of HRDT, grounded in robust qualitative research, to describe factors in successfully implementing HRDT. Drawing insights from 20 senior HR professionals in multinational organizations, this study defines HRDT as a multidimensional construct resulting from successful integration of digital and individual factors into the innovation capability of organizations. Expanding the dynamic-capabilities perspective, this study proposes that in uncertain and turbulent times, such as COVID-19, businesses must constantly upgrade organizational capabilities, manifested in the innovation capability with enablers such as- digital infrastructure, architecture, and individual capability and creativity. This research further elaborates on HRDT indicators.

Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China.

COVID-19 has negative impacts on supply chain operations between countries. The novelty of the study is to evaluate the sectoral effects of COVID-19 on global supply chains in the example of Turkey and China, considering detailed parameters, thanks to the developed System Dynamics (SD) model. During COVID-19 spread, most of the countries decided long period of lockdowns which impacted the production and supply chains. This had also caused decrease in capacity utilizations and industrial productions in many countries which resulted with imbalance of maritime trade between countries that increased the freight costs. In this study, cause and effect relations of trade parameters, supply chain parameters, demographic data and logistics data on disruptions of global supply chains have been depicted for specifically Turkey and China since China is the biggest importer of Turkey. Due to this disruption, mainly exports from Turkey to China has been impacted in food, chemical and mining sectors. This study is helpful to plan in which sectors; the actions should be taken by the government bodies or managers. Based on findings of this study, new policies such as onshore activities should consider to overcome the logistics and supply chain disruptions in global supply chains. This study has been presented beneficial implications for the government, policymakers and academia.

Elective surgery scheduling under uncertainty in demand for intensive care unit and inpatient beds during epidemic outbreaks.

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

COMPARISON OF CURB-65 SCORES IN NON-DIABETIC AND DIABETIC SUBJECTS HOSPITALIZED FOR COVID-19 INFECTION

Background: Diabetes mellitus has been established as a contributory factor for comorbidity in subjects with COVID-19 owing to diabetics being at high infection susceptibility from different bacteria and viruses including those of the respiratory tract. CURB 65 scores are an easier system among the various scoring systems developed to assess CAP risk. Aim: To record and comparatively analyze the CURB-65 scores in non-diabetic and diabetic subjects hospitalized for COVID-19 infection in an Indian health care center. Methods: In 280 subjects admitted for COVID-19 infection, glycemic state and CURB-65 scores were evaluated. The subjects were grouped as having mild, moderate, or severe illnesses based on the CURB-65 scoring. Also, ICU admission, the requirement of a ventilator, hospitalization duration, and mortality rates were assessed. All subjects were followed till discharge or death, whichever was early. Results: Mild CURB-65 was seen for 65.21% (n=90) diabetic subjects and 97.18% (n=138) non-diabetic subjects. 30.43% (n=42) diabetic subjects and 2.81% (n=4) non-diabetic subjects had CURB-65 scores as moderate. ICU admission was needed in 24.63% (n=34) diabetic subjects and in 5.63% (n=8) non-diabetic subjects (p=0.002). Ventilatory support was needed in 18.84% (n=26) diabetic subjects in the study and in 4.22% (n=6) non-diabetic subjects. This difference was statistically significant with p=0.007. 24.63% (n=34) diabetic subjects died and in non-diabetic (p<0.0001). The mean duration of hospital stay was 9.23±5.2 days in diabetic subjects and 7.03±4.28 days in nondiabetic subjects (p=0.005). Conclusion: Increased and higher values of CURB-65 scores were seen for subjects having diabetes mellitus and COVID-19 infection compared to non-diabetic subjects with COVID-19 infection. Also, the disease severity was more in subjects with diabetes mellitus and COVID-19 compared to non-diabetics. [ FROM AUTHOR]

Using network data envelopment analysis to assess the sustainability and resilience of healthcare supply chains in response to the COVID-19 pandemic.

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.

Nutraceuticals and COVID-19: A mechanistic approach toward attenuating the disease complications.

Nutraceuticals have emerged as potential compounds to attenuate the COVID-19 complications. Precisely, these food additives strengthen the overall COVID treatment and enhance the immunity of a person. Such compounds have been used at a large scale, in almost every household due to their better affordability and easy access. Therefore, current research is focused on developing newer advanced formulations from potential drug candidates including nutraceuticals with desirable properties viz, affordability, ease of availability, ease of administration, stability under room temperature, and potentially longer shelf-lives. As such, various nutraceutical-based products such as compounds could be promising agents for effectively managing COVID-19 symptoms and complications. Most importantly, regular consumption of such nutraceuticals has been shown to boost the immune system and prevent viral infections. Nutraceuticals such as vitamins, amino acids, flavonoids like curcumin, and probiotics have been studied for their role in the prevention of COVID-19 symptoms such as fever, pain, malaise, and dry cough. In this review, we have critically reviewed the potential of various nutraceutical-based therapeutics for the management of COVID-19. We searched the information relevant to our topic from search engines such as PubMed and Scopus using COVID-19, nutraceuticals, probiotics, and vitamins as a keyword. Any scientific literature published in a language other than English was excluded. PRACTICAL APPLICATIONS: Nutraceuticals possess both nutritional values and medicinal properties. They can aid in the prevention and treatment of diseases, as well as promote physical health and the immune system, normalizing body functions, and improving longevity. Recently, nutraceuticals such as probiotics, vitamins, polyunsaturated fatty acids, trace minerals, and medicinal plants have attracted considerable attention and are widely regarded as potential alternatives to current therapeutic options for the effective management of various diseases, including COVID-19.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.