Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: an algorithm-based method for overcoming resistance to antiviral agents

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

A Feasibility Open-Labeled Clinical Trial Using a Second-Generation Artificial-Intelligence-Based Therapeutic Regimen in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy

Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme β-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings.

Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stable vs. unstable PA ; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stable vs. unstable PA ; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stable vs. unstable PA ; P=0.02; adjusted P=0.04) and improved short-term memory (score change: -0.28 vs. +0.29, stable vs. unstable PA ; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them.IMPORTANCE SECTIONThe paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.Key messagesAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents.We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

INTRODUCTION

Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens.

METHODS

Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function.

RESULTS

The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations.

SUMMARY

The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.

Making use of noise in biological systems

Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.

Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function

Variability characterizes the complexity of biological systems and is essential for their function. Microtubules (MTs) play a role in structural integrity, cell motility, material transport, and force generation during mitosis, and dynamic instability exemplifies the variability in the proper function of MTs. MTs are a platform for energy transfer in cells. The dynamic instability of MTs manifests itself by the coexistence of growth and shortening, or polymerization and depolymerization. It results from a balance between attractive and repulsive forces between tubulin dimers. The paper reviews the current data on MTs and their potential roles as energy-transfer cellular structures and presents how variability can improve the function of biological systems in an individualized manner. The paper presents the option for targeting MTs to trigger dynamic improvement in cell plasticity, regulate energy transfer, and possibly control quantum effects in biological systems. The described system quantifies MT-dependent variability patterns combined with additional personalized signatures to improve organ function in a subject-tailored manner. The platform can regulate the use of MT-targeting drugs to improve the response to chronic therapies. Ongoing trials test the effects of this platform on various disorders.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

Aging is a complex biological process with multifactorial nature underlined by genetic, environmental, and social factors. In the present paper, we review several mechanisms of aging and the pre-clinically and clinically studied anti-aging therapies. Variability characterizes biological processes from the genome to cellular organelles, biochemical processes, and whole organs' function. Aging is associated with alterations in the degrees of variability and complexity of systems. The constrained disorder principle defines living organisms based on their inherent disorder within arbitrary boundaries and defines aging as having a lower variability or moving outside the boundaries of variability. We focus on associations between variability and hallmarks of aging and discuss the roles of disorder and variability of systems in the pathogenesis of aging. The paper presents the concept of implementing the constrained disease principle-based second-generation artificial intelligence systems for improving anti-aging modalities. The platform uses constrained noise to enhance systems' efficiency and slow the aging process. Described is the potential use of second-generation artificial intelligence systems in patients with chronic disease and its implications for the aged population.

COVID-19 Vaccines, Effectiveness, and Immune Responses

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has captivated the globe's attention since its emergence in 2019. This highly infectious, spreadable, and dangerous pathogen has caused health, social, and economic crises. Therefore, a worldwide collaborative effort was made to find an efficient strategy to overcome and develop vaccines. The new vaccines provide an effective immune response that safeguards the community from the virus' severity. WHO has approved nine vaccines for emergency use based on safety and efficacy data collected from various conducted clinical trials. Herein, we review the safety and effectiveness of the WHO-approved COVID-19 vaccines and associated immune responses, and their impact on improving the public's health. Several immunological studies have demonstrated that vaccination dramatically enhances the immune response and reduces the likelihood of future infections in previously infected individuals. However, the type of vaccination and individual health status can significantly affect immune responses. Exposure of healthy individuals to adenovirus vectors or mRNA vaccines causes the early production of antibodies from B and T cells. On the other hand, unhealthy individuals were more likely to experience harmful events due to relapses in their existing conditions. Taken together, aligning with the proper vaccination to a patient's case can result in better outcomes.

Codivir suppresses SARS-Cov-2 viral replication and stabilizes clinical outcome: In vitro and Phase I clinical trial results

BACKGROUND

Treatment of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) remains a significant challenge in the face of increased worldwide morbidity and mortality. The acute illness caused by SARS-CoV-2 is initiated by a viral phase, followed by an inflammatory phase. Numerous anti-inflammatory and anti-viral therapies, with a relatively minor clinical effect, have been applied. Developing a safe and efficient direct anti-viral treatment is essential as it can block disease progression before significant complications ensue and potentially prevent transmission.

AIM

The present phase 1 study aimed to determine the safety of Codivir, a newly developed anti-viral agent, and to preliminarily assess its anti-viral activity in patients infected by COVID-19.

METHODS

In vitro studies were conducted to determine the direct anti-viral effect of Codivir using an immunofluorescence-based assay and to assess its cytotoxic effect by tetrazolium assay (MTT). In a phase I clinical trial, Codivir was administered for ten days in 12 patients who were followed for its safety. Patients were followed for clinical manifestations during administration. Sequential nasal viral PCR titers (Cycle Threshold, CT) were determined preceding and during treatment.

RESULTS

In vitro, Codivir showed activity against SARS-CoV-2 with 90% viral replication suppression and minimal cytotoxicity. The anti-viral activity was demonstrated at the early stages of infection, post-entry of the virus in the cell. Codivir was safe in all 12 patients in phase I clinical trial and significantly suppressed viral replication in 5/7 fully assessed patients, with an anti-viral effect noted as early as three days.

SUMMARY

The present study's data support the safety of Codivir administration in humans and suggest its significant anti-COVID-19 effect. These results support the testing of the drug in more extensive controlled trials in patients with SARS-CoV-2.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Personalized-Inherent Variability in a Time-Dependent Immune Response: A Look into the Fifth Dimension in Biology

Introduction

Individualized response to the immune triggers influences the course of immune-mediated diseases and the response to immunotherapies. Both inter- and intra-subject variations occur in time-dependent dynamics of biological systems. The present study aimed to establish a model for inherent personalized-time-dependent variability in response to immune triggers.

Methods

Male C57BL/6 mice were administered concanavalin A (ConA) and followed every 2 h for 10 h and at 24 h for serum alanine aminotransferase (ALT) levels.

Results

A marked intragroup variability was noted for both the timing of the effect of ConA, the magnitude of the increase in ALT levels, and the time to peak. While in some mice, a peak level was achieved, whereas a continuous increase in liver damage was noted in others. Four mice died at different time points during the study irrespective of their liver damage, further supporting the individualized-based response to the trigger.

Conclusions

This feasibility study established a model for determining the personalized-inherent variability in a time-dependent response to the immune triggers. These results highlight the importance of considering both the time and the wide range of individualized variability in immune responses while designing personalized-based immunotherapies.

The role of bacterial translocation in sepsis: a new target for therapy

Sepsis is a leading cause of death in critically ill patients, primarily due to multiple organ failures. It is associated with a systemic inflammatory response that plays a role in the pathogenesis of the disease. Intestinal barrier dysfunction and bacterial translocation (BT) play pivotal roles in the pathogenesis of sepsis and associated organ failure. In this review, we describe recent advances in understanding the mechanisms by which the gut microbiome and BT contribute to the pathogenesis of sepsis. We also discuss several potential treatment modalities that target the microbiome as therapeutic tools for patients with sepsis.

Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects

Opioids remain an essential part of the treatment of chronic pain. However, their use and increasing rates of misuse are associated with high morbidity and mortality. The development of tolerance to opioids and analgesics further complicates dosing and the need to reduce side effects. First-generation digital systems were developed to improve analgesics but are not always capable of making clinically relevant associations and do not necessarily lead to better clinical efficacy. A lack of improved clinical outcomes makes these systems less applicable for adoption by clinicians and patients. There is a need to enhance the therapeutic regimens of opioids. In the present paper, we present the use of a digital analgesic that consists of an analgesic administered under the control of a second-generation artificial intelligence system. Second-generation systems focus on improved patient outcomes measured based on clinical response and reduced side effects in a single subject. The algorithm regulates the administration of analgesics in a personalized manner. The digital analgesic provides advantages for both users and providers. The system enables dose optimization, improving effectiveness, and minimizing side effects while increasing adherence to beneficial therapeutic regimens. The algorithm improves the clinicians’ experience and assists them in managing chronic pain. The system reduces the financial burden on healthcare providers by lowering opioid-related morbidity and provides a market disruptor for pharma companies.

Highlight of potential impact of new viral genotypes of SARS-CoV-2 on vaccines and anti-viral therapeutics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of the coronavirus disease (COVID-19) pandemic, has infected millions of people globally. Genetic variation and selective pressures lead to the accumulation of single nucleotide polymorphism (SNP) within the viral genome that may affect virulence, transmission rate, viral recognition and the efficacy of prophylactic and interventional measures. To address these concerns at the genomic level, we assessed the phylogeny and SNPs of the SARS-CoV-2 mutant population collected to date in Iran in relation to globally reported variants. Phylogenetic analysis of mutant strains revealed the occurrence of the variants known as B.1.1.7 (Alpha), B.1.525 (Eta), and B.1.617 (Delta) that appear to have delineated independently in Iran. SNP analysis of the Iranian sequences revealed that the mutations were predominantly positioned within the S protein-coding region, with most SNPs localizing to the S1 subunit. Seventeen S1-localizing SNPs occurred in the RNA binding domain that interacts with ACE2 of the host cell. Importantly, many of these SNPs are predicted to influence the binding of antibodies and anti-viral therapeutics, indicating that the adaptive host response appears to be imposing a selective pressure that is driving the evolution of the virus in this closed population through enhancing virulence. The SNPs detected within these mutant cohorts are addressed with respect to current prophylactic measures and therapeutic interventions.

A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review

The plateau effect in training is a significant obstacle for professional athletes and average subjects. It evolves from both the muscle-nerve-axis-associated performance and various cardiorespiratory parameters. Compensatory adaptation mechanisms contribute to a lack of continuous improvement with most exercise regimens. Attempts to overcome this plateau in exercise have been only partially successful, and it remains a significant unmet need in both healthy subjects and those suffering from chronic neuromuscular, cardiopulmonary, and metabolic diseases. Variability patterns characterize many biological processes, from cellular to organ levels. The present review discusses the significant obstacles in overcoming the plateau in training and establishes a platform to implement subject-tailored variability patterns to prevent and overcome this plateau in muscle and cardiorespiratory performance.

Structural biology of SARS-CoV-2: open the door for novel therapies

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the causative agent of the pandemic disease COVID-19, which is so far without efficacious treatment. The discovery of therapy reagents for treating COVID-19 are urgently needed, and the structures of the potential drug-target proteins in the viral life cycle are particularly important. SARS-CoV-2, a member of the Orthocoronavirinae subfamily containing the largest RNA genome, encodes 29 proteins including nonstructural, structural and accessory proteins which are involved in viral adsorption, entry and uncoating, nucleic acid replication and transcription, assembly and release, etc. These proteins individually act as a partner of the replication machinery or involved in forming the complexes with host cellular factors to participate in the essential physiological activities. This review summarizes the representative structures and typically potential therapy agents that target SARS-CoV-2 or some critical proteins for viral pathogenesis, providing insights into the mechanisms underlying viral infection, prevention of infection, and treatment. Indeed, these studies open the door for COVID therapies, leading to ways to prevent and treat COVID-19, especially, treatment of the disease caused by the viral variants are imperative.

Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response

Legalized use of cannabis products and the rising interest in their therapeutic benefits have opened up new opportunities for therapy and marketing. However, the marked variability in formulations, administration modes, therapeutic regimens, and inter- and intra-subject responses make the standardization of medical cannabis-based regimens difficult. Legalization has made the cannabis market highly competitive and lowered the revenue margins. This study reviews some of the challenges in medical cannabis use and difficulties in standardizing its therapeutic regimens that hinder maximizing its beneficial effects. The development of tolerance toward cannabis and low adherence to chronic administration further impair its long-term beneficial effects. Digital medical cannabis is a cannabis product controlled by a second-generation artificial intelligence (AI) system that improves patient responses by increasing adherence and dealing with tolerance. Second-generation AI systems focus on a single patient's outcome and deal with the inter- and intra-subject variability in responses. The use of digital medical cannabis is expected to improve product standardization, maximize therapeutic benefits, reduce health care costs, and increase the revenue of companies. Digital medical cannabis offers several market differentiators for cannabis companies. This study presents a model for promoting the use of digital medical cannabis and presents its advantages for patients, clinicians, health care authorities, insurance companies, and cannabis manufacturers. Ongoing trials and real-world data on the use of these systems further support the use of digital medical cannabis for improved global health.

A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage

Coronavirus disease 2019 (COVID-19), which is a respiratory illness associated with high mortality, has been classified as a pandemic. The major obstacles for the clinicians to contain the disease are limited information availability, difficulty in disease diagnosis, predicting disease prognosis, and lack of disease monitoring tools. Additionally, the lack of valid therapies has further contributed to the difficulties in containing the pandemic. Recent studies have reported that the dysregulation of the immune system leads to an ineffective antiviral response and promotes pathological immune response, which manifests as ARDS, myocarditis, and hepatitis. In this study, a novel platform has been described for disseminating information to physicians for the diagnosis and monitoring of patients with COVID-19. An adjuvant approach using compounds that can potentiate antiviral immune response and mitigate COVID-19-induced immune-mediated target organ damage has been presented. A prolonged beneficial effect is achieved by implementing algorithm-based individualized variability measures in the treatment regimen.

A Review of Human Coronaviruses' Receptors: The Host-Cell Targets for the Crown Bearing Viruses

A novel human coronavirus prompted considerable worry at the end of the year 2019. Now, it represents a significant global health and economic burden. The newly emerged coronavirus disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the primary reason for the COVID-19 global pandemic. According to recent global figures, COVID-19 has caused approximately 243.3 million illnesses and 4.9 million deaths. Several human cell receptors are involved in the virus identification of the host cells and entering them. Hence, understanding how the virus binds to host-cell receptors is crucial for developing antiviral treatments and vaccines. The current work aimed to determine the multiple host-cell receptors that bind with SARS-CoV-2 and other human coronaviruses for the purpose of cell entry. Extensive research is needed using neutralizing antibodies, natural chemicals, and therapeutic peptides to target those host-cell receptors in extremely susceptible individuals. More research is needed to map SARS-CoV-2 cell entry pathways in order to identify potential viral inhibitors.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

Augmented antiviral T cell immunity by oral administration of IMM-124E in preclinical models and a phase I/IIa clinical trial: A method for the prevention and treatment of COVID-19

Biological adjuvants that target the gut immune system are being developed for modulating the immune system. Hyperimmune bovine colostrum (HBC), produced by harvesting the bovine colostrum of dairy cows immunized to exogenous antigens, has been shown to modulate the immune responses and alleviate immune‐mediated organ damages. The aim of the present study was to determine the ability of HBC to promote antiviral interferonγ (IFNγ) T cell responses. In a preclinical study, mice were orally administered with HBC for 5 days and tested for the number of T cell clones secreting IFNγ in response to viral antigens of the swine flu, New Caledonia influenza, and cytomegalovirus. In a phase I/IIa clinical trial, five healthy volunteers were treated for 5 days with HBC followed by testing the anti‐coronavirus disease (COVID‐19) immunity. In the preclinical study, oral administration of HBC augmented the number of T cell clones secreting IFNγ in response to viral antigens. In the clinical trial, oral administration of HBC to healthy males significantly increased the number of anti‐COVID‐19 spike protein IFNγ positive T cell clones. Oral administration of HBC provides a novel method for augmenting antiviral responses. Its high‐safety profile makes it ideal for all disease stages and for pre‐emptive therapy among medical personnel and other workers who are at a high risk of exposure to infections. The relatively low cost of HBC is expected to minimize care provider burdens, costs, and enable its global application.

Role of circadian rhythm and autonomic nervous system in liver function: a hypothetical basis for improving the management of hepatic encephalopathy

Hepatic encephalopathy (HE) is a common, incapacitating complication of cirrhosis that affects many patients with cirrhosis. Although several therapies have proven effective in the treatment and prevention of this condition, several patients continue to suffer from covert disease or episodes of relapse. The circadian rhythm has been demonstrated to be pivotal for many body functions, including those of the liver. Here, we explore the impact of circadian rhythm-dependent signaling on the liver and discuss the evidence of its impact on liver pathology and metabolism. We describe the various pathways through which circadian influences are mediated. Finally, we introduce a novel method for improving patient response to drugs aimed at treating HE by utilizing the circadian rhythm. A digital system that introduces a customization-based technique for improving the response to therapies is presented as a hypothetical approach for improving the effectiveness of current medications used for the treatment of recurrent and persistent hepatic encephalopathy.

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Heart failure is a major public health problem, which is associated with significant mortality, morbidity, and healthcare expenditures. A substantial amount of the morbidity is attributed to volume overload, for which loop diuretics are a mandatory treatment. However, the variability in response to diuretics and development of diuretic resistance adversely affect the clinical outcomes. Morevoer, there exists a marked intra- and inter-patient variability in response to diuretics that affects the clinical course and related adverse outcomes. In the present article, we review the mechanisms underlying the development of diuretic resistance. The role of the autonomic nervous system and chronobiology in the pathogenesis of congestive heart failure and response to therapy are also discussed. Establishing a novel model for overcoming diuretic resistance is presented based on a patient-tailored variability and chronotherapy-guided machine learning algorithm that comprises clinical, laboratory, and sensor-derived inputs, including inputs from pulmonary artery measurements. Inter- and intra-patient signatures of variabilities, alterations of biological clock, and autonomic nervous system responses are embedded into the algorithm; thus, it may enable a tailored dose regimen in a continuous manner that accommodates the highly dynamic complex system.

Phyllanthin and hypophyllanthin, the isolated compounds of Phyllanthus niruri inhibit protein receptor of corona virus (COVID-19) through in silico approach

OBJECTIVES

Phyllanthus niruri has been known as an immunomodulator and also reported to possess an antiviral activity against several RNA viruses, such as hepatitis B virus and hepatitis C virus by inhibiting viral entry and replication. Since the current situation of Coronavirus Disease 2019 (COVID-19) which infected among the world and caused severe disease and high morbidity, it urgently needed to find new agents against COVID-19. Therefore, in silico screening against COVID-19 receptors is carried out as an initial stage of drug discovery by evaluating the activity of phyllanthin and hypophyllanthin, an isolated from Phyllanthus niruri, in inhibiting spike glycoprotein (6LZG) and main protease (5R7Y) which play as target receptors of COVID-19.

METHODS

Molegro Virtual Docker 6.0 used to determine the best binding energy through the rerank score which shows the total energy bonds calculation.

RESULTS

Phyllanthin and hypophyllanthin demonstrated to possess greater binding affinity toward the COVID-19 inhibition sites than their native ligand. The rerank score of phyllanthin and hypophyllanthin are lower than the native ligands 6LZG and 5R7Y. This result indicated that phyllanthin and hypophyllanthin have a stronger interaction than the native ligands both in spike glycoprotein (entry inhibitor) and main protease (translation and replication inhibitor).

CONCLUSIONS

In conclusion, phyllanthin and hypophyllanthin are predicted to have strong activity against COVID-19 through inhibiting spike glycoprotein and main protease under in silico study. Further research is needed to support the development of P. niruri as inhibitor agents of COVID-19 through bioassay studies.

Role of Serine Proteases and Host Cell Receptors Involved in Proteolytic Activation, Entry of SARS-CoV-2 and Its Current Therapeutic Options

The current global pandemic of a novel severe acute respiratory syndrome coronavirus-2 continues with its public health disaster beginning from late December 2019 in Wuhan, Hubei province, China. The scientific community has tried to fight against this novel coronavirus through vaccine development and designing different candidate drugs. However, there is no well-defined therapy to prevent 2019-nCov infection, thus complete prevention of the virus remains difficult. Therefore, it is a critical factor for death of millions worldwide. Many clinical trials and insights are ongoing in the struggle with this pandemic of SARS-CoV-2. SARS-CoV-2 entry into the host cell requires host cell angiotensin-converting enzyme-2 (ACE2) and glucose regulated protein 78 (GRP78). On the other hand, proteolytic activation of the viral spike protein (S protein) needs the host cell serine proteases, including transmembrane serine protease 2 (TMPRSS2), cathepsins, trypsin and furin. This review focuses on the protein involved in the mechanism of entry, and proteolytic activation. In addition, it looks at current therapeutic options for SARS-CoV-2.

Drug Repurposing Approach, Potential Drugs, and Novel Drug Targets for COVID-19 Treatment

Novel coronavirus first appeared in Wuhan, China, in December 2019, and it speedily expanded globally. Some medications which are used to treat other diseases seem to be effective in treating COVID-19 even without explicit support. The existing drugs that are summarized in this review primarily focused on therapeutic agents that possessed activity against other RNA viruses such as MERS-CoV and SARS-CoV. Drug repurposing or repositioning is a promising field in drug discovery that identifies new therapeutic opportunities for existing drugs such as corticosteroids, RNA-dependent RNA polymerase inhibitors, interferons, protease inhibitors, ivermectin, melatonin, teicoplanin, and some others. A search for new drug/drug targets is underway. Thus, blocking coronavirus structural protein, targeting viral enzyme, dipeptidyl peptidase 4, and membrane fusion blocker (angiotensin-converting enzyme 2 and CD147 inhibitor) are major sites based on molecular targets for the management of COVID-19 infection. The possible impact of biologics for the management of COVID19 is promising and includes a wide variety of options such as cytokines, nucleic acid-based therapies targeting virus gene expression, bioengineered and vectored antibodies, and various types of vaccines. This review demonstrates that the available data are not sufficient to suggest any treatment for the eradication of COVID-19 to be used at the clinical level. This article aims to review the roles of existing drugs and drug targets for COVID-19 treatment.

Prediction of Anti-COVID 19 Therapeutic Power of Medicinal Moroccan Plants Using Molecular Docking

The emerging pathogen SARS-CoV2 causing coronavirus disease 2019 (COVID-19) is a global public health challenge. To the present day, COVID-19 had affected more than 40 million people worldwide. The exploration and the development of new bioactive compounds with cost-effective and specific anti-COVID 19 therapeutic power is the prime focus of the current medical research. Thus, the exploitation of the molecular docking technique has become essential in the discovery and development of new drugs, to better understand drug-target interactions in their original environment. This work consists of studying the binding affinity and the type of interactions, through molecular docking, between 54 compounds from Moroccan medicinal plants, dextran sulfate and heparin (compounds not derived from medicinal plants), and 3CLpro-SARS-CoV-2, ACE2, and the post fusion core of 2019-nCoV S2 subunit. The PDB files of the target proteins and prepared herbal compounds (ligands) were subjected for docking to AutoDock Vina using UCSF Chimera, which provides a list of potential complexes based on the criteria of form complementarity of the natural compound with their binding affinities. The results of molecular docking revealed that Taxol, Rutin, Genkwanine, and Luteolin-glucoside have a high affinity with ACE2 and 3CLpro. Therefore, these natural compounds can have 2 effects at once, inhibiting 3CLpro and preventing recognition between the virus and ACE2. These compounds may have a potential therapeutic effect against SARS-CoV2, and therefore natural anti-COVID-19 compounds.

Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity

Prevention of weight regain following successful weight loss is a major challenge in the treatment of obesity, irrespective of the weight reduction method used. The majority of individuals regain the lost weight over time; thus, achieving long-term sustainability in weight loss remains an unresolved issue. A compensatory adaptation to the weight loss methods occurs in several body organs and partly explains the lack of sustainable effect. Variability is inherent in many biological systems, and patterns of variability constitute a body mechanism that is active at several levels, starting from the genes and cellular pathways through to the whole-organ level. This study aimed to describe a platform that introduces individually tailored variability in vagal nerve stimulation and dietary regimen to ensure prolonged and sustainable weight loss and prevent weight regain. The platform is intended to provide a method that can overcome the body's compensatory adaptation mechanisms while ensuring a prolonged beneficial effect.

Why scientists, academic institutions, and investors fail in bringing more products to the bedside: the Active Compass model for overcoming the innovation paradox

The vast majority of good science and excellent ideas do not translate into products. Many good products that have the potential to assist in diagnosis and therapy do not mature into everyday care. This often becomes a source of frustration for innovators, academic institutions, companies both small and large, and investors. The "innovation paradox" , wherein excellent ideas and good science fail to reach the bedside, is a major challenge. This study presents the Active Compass model as a way to overcome this obstacle. The model is designed to assist projects at early stages by redirecting and reshaping them in a way that increases their chances of reaching the markets. The model is based on the use of next-generation translational research and on creating differentiators at the early stages of development. The proposed model's implementation by innovators, scientists, technology transfer offices, academic institutions, analysts, and investors can help move forward high-potential projects to improve the quality of life and alleviate the burdens of disease.

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Overview of Targets and Potential Drugs of SARS-CoV-2 According to the Viral Replication

Since the novel coronavirus pandemic, people around the world have been touched in varying degrees, and this pandemic has raised a major global health concern. As there is no effective drug or vaccine, it is urgent to find therapeutic drugs that can serve to deal with the current epidemic situation in all countries and regions. We searched drugs and response measures for SARS-CoV-2 in the PubMed database, and then updated the potential targets and therapeutic drugs from the perspective of the viral replication cycle. The drug research studies of the viral replication cycle are predominantly focused on the process of the virus entering cells, proteases, and RdRp. The inhibitors of the virus entry to cells and RdRp, such as Arbidol, remdesivir, favipiravir, EIDD-2081, and ribavirin, are in clinical trials, while most of the protease inhibitors are mainly calculated by molecular docking technology, which needs in vivo and in vitro experiments to prove the effect for SARS-CoV-2. This review summarizes the drugs targeting the viral replication process and provides a basis and directions for future drug development and reuse on the protein level of COVID-19.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Selective Pressure-Free Treatments for COVID-19

The new severe acute respiratory syndrome coronavirus (SARS-CoV-2) has caused more than 40 million human infections since December 2019, when a cluster of unexplained pneumonia cases was first reported in Wuhan, China. Just a few days after the coronavirus was officially recognized, it was identified as the causative agent of this mysterious pneumonia. This paper discusses the pros and cons of antiviral drugs from the selective pressure and possible drug resistance point of view. We also address the key advantages of potential selective pressure-free treatment methods such as the use of sparsely and densely ionizing low-dose radiation (LDR). It is known that LDR has the capacity to modulate excessive inflammatory responses, regulate lymphocyte counts and control bacterial co-infections in patients with COVID-19 and different modalities. Substantial evidence shows that viruses are constantly mutating and evolving. When an antiviral immune response is unable to eliminate a virus, viral evolution is promoted. Therefore, it is of crucial importance to limit the use of antivirals/vaccines against SARS-CoV-2 when their effects on viral fitness are not fully understood. Furthermore, to limit the spread of the virus, it is essential to develop a vaccine that is available for as many people as possible. However, with the advent of vaccines or new therapies, the new situation may force the virus to evolve. Given this consideration, selective pressure-free treatments for COVID-19 are of great importance.

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance

Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.

Covid-19 pandemic and pregnancy

At the end of 2019, a new coronavirus disease, COVID-19, emerged and quickly spread around the world. Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), the causative virus of this disease, belongs to the β-coronavirus family, together with SARS and middle east respiratory syndrome, and has similar biological characteristics to these viruses. For obstetricians, the susceptibility and prognoses of pregnant women and the effects of the infection on the fetus have been the focus of attention; however, at present, the seriousness of the disease in pregnant women is not apparent, and COVID-19 does not increase the rate of miscarriage, stillbirth, preterm labor or teratogenicity. Even so, carriers might transmit SARS-CoV-2 to pregnant women. Thus, we must keep in mind that all medical personnel must understand and maintain standard precautions in their clinical and laboratory practices.

Antibody response to SARS-Co-V-2, diagnostic and therapeutic implications

The immune response against SARS-CoV-2 is comprised of both cellular and humoral arms. While current diagnostic methods are mainly based on PCR, they suffer from insensitivity. Therefore, antibody-based serological tests are being developed to achieve higher sensitivity and specificity. Current efforts in treating SARS-CoV-2 infection include blocking of viral entry into the host cells, prohibiting viral replication and survival in the host cells, or reducing the exaggerated host immune response. Administration of convalescent plasma containing anti-viral antibodies was proposed to improve the outcome in severe cases. In this paper, we review some of the aspects associated with the development of antibodies against SARS-CoV-2 and their potential use for improved diagnosis and therapy.

Natural and semisynthetic candidate molecules for COVID-19 prophylaxis and treatment

Coronaviruses (CoVs) represent a family of viruses that have numerous animal hosts, and they cause severe respiratory, as well as systemic and enteric infections, in humans. Currently, there are limited antiviral strategies for treating patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The lack of specific antiviral medicines and SARS-CoV-2 vaccines continues to aggravate the situation. Natural product-based antiviral drugs have been used in the two previous CoV outbreaks: Middle East respiratory syndrome coronavirus (MERS-CoV) and the first SARS-CoV. This review emphasizes the role of natural and semisynthetic candidate molecules for coronavirus disease 2019 (COVID-19) prophylaxis and treatment. The experimental evidence suggests that nature could offer huge possibilities for treatment of the COVID-19 pandemic.