A Systematic Review of Nonclinical Studies on the Effect of Curcumin in Chemotherapy- induced Cardiotoxicity.

BACKGROUND: Various anticancer drugs are effective therapeutic agents for cancer treatment; however, they cause severe toxicity in body organs. Cardiotoxicity is one of the most critical side effects of these drugs. Based on various findings, turmeric extract has positive effects on cardiac cells. OBJECTIVE: This study aims to evaluate how curcumin, as the main component of turmeric, may affect chemotherapy- induced cardiotoxicity. METHODS: A database search was performed up to April 2021 using "curcumin OR turmeric OR Curcuma longa" and "chemotherapy-induced cardiac disease", including their equivalents and similar terms. After screening the total articles obtained from the electronic databases, 25 relevant articles were included in this systematic review. RESULTS: The studies demonstrate lower body weight and increased mortality rates due to doxorubicin administration. Besides, cancer therapeutic agents induced various morphological and biochemical abnormalities compared to the non-treated groups. Based on most of the obtained results, curcumin at nontoxic doses can protect the cardiac cells mainly through modulating antioxidant capacity, regulation of cell death, and antiinflammatory effects. Nevertheless, according to a minority of findings, curcumin increases the susceptibility of the rat cardiomyoblast cell line (H9C2) to apoptosis triggered by doxorubicin. CONCLUSION: According to most nonclinical studies, curcumin could potentially have cardioprotective effects against chemotherapy-induced cardiotoxicity. However, based on limited, contradictory findings demonstrating the function of curcumin in potentiating doxorubicin-induced cardiotoxicity, well-designed studies are needed to evaluate the safety and effectiveness of treatment with new formulations of this compound during cancer therapy.

Achieving Equitable Access to Medicines and Health Services: A COVID-19-time Recalled Matter.

In the 21st century, while some people seek to use artificial intelligence for health services delivery, others have to surrender their health rights to meet basic needs. The gradient in health has become more pronounced in the COVID-19 crisis considering discrepancies in disease prevalence, geographical accessibility, availability, affordability, quality/safety of health services, and human resources. Through PubMed, GoogleScholar, Scopus, WHO, OECD, and UN databases, the English documents and global statistics were collected. Determining the role of health equity-related factors and introducing mechanisms to maintain regional and international justice in health, specifically during the COVID-19 pandemic, were among the core concepts of this paper. Social determinants of health (SDH), interregional and intraregional bodies are the main drivers of discrimination in health services. Governments should relish chief health strategists' role in possessing legitimacy, accountability, direction, transparent performance, fairness, and good governance in one word. Improving health literacy and telemedicine, providing income support, and reforming insurance where needed, are other national mechanisms to amend inequity. Among interregional issues, what is concerning is the matter of sanctions on access to health services, which is against the Universal Declaration of Human Rights. Shortage of vital medications, ventilators, test kits, COVID-19 vaccines, pharmaceutical raw materials, foreign currency, decreased national currency value, purchasing power parity, and quality/safety of health services resulted from such oppression. The article also provides practical suggestions, paving the way for re-establishing global solidarity and developing health justice in deprived regions.

Evaluation of the effect of sofosbuvir and daclatasvir in hospitalized COVID-19 patients: a randomized double-blind clinical trial (DISCOVER).

BACKGROUND: The combination of sofosbuvir and daclatasvir has shown preliminary efficacy for hospitalized patients with COVID-19 in four open-label studies with small sample sizes. This larger trial aimed to assess if the addition of sofosbuvir/daclatasvir to standard care improved clinical outcomes in hospitalized patients with COVID-19. METHODS: This was a placebo-controlled, double-blind, randomized clinical trial in adults hospitalized with COVID-19 at 19 hospitals in Iran. Patients were randomized to oral sofosbuvir/daclatasvir 400/60 mg once-daily or placebo in addition to standard of care. Patients were included if they had positive PCR or diagnostic chest CT, O2 saturation <95% and compatible symptoms. The primary outcome was hospital discharge within 10 days of randomization. Secondary outcomes included mortality and time to clinical events. The trial is registered on the Iran Registry of Clinical Trials under IRCT20200624047908N1. RESULTS: Between July and October 2020, 1083 patients were randomized to either the sofosbuvir/daclatasvir arm (n = 541) or the placebo arm (n = 542). No significant difference was observed in the primary outcome of hospital discharge within 10 days, which was achieved by 415/541 (77%) in the sofosbuvir/daclatasvir arm and 411/542 (76%) in the placebo arm [risk ratio (RR) 1.01, 95% CI 0.95-1.08, P = 0.734]. In-hospital mortality was 60/541 (11%) in the sofosbuvir/daclatasvir arm versus 55/542 (10%) in the placebo arm (RR 1.09, 95% CI 0.77-1.54, P = 0.615). No differences were observed in time to hospital discharge or time to in-hospital mortality. CONCLUSIONS: We observed no significant effect of sofosbuvir/daclatasvir versus placebo on hospital discharge or survival in hospitalized COVID-19 patients.

Assessment of cloth masks ability to limit Covid-19 particles spread: a systematic review.

After the spread of Covid 19 worldwide, the use of cloth masks increased significantly due to a shortage of medical masks. Meanwhile, there were different opinions about the effectiveness of these masks and, so far, no study has been done to find the best fabric masks. This study reviews and summarizes all studies related to fabric masks' effectiveness and various fabrics against coronavirus. This systematic review is based on PRISMA rules. Two researchers separately examined three databases: PubMed, Scopus, and Web of Science. Laboratory and clinical studies were included. After extracting the articles, their quality was assessed with the Joanna Briggs Institute (JBI) tool. In addition to efficacy, other factors, including the penetration of masks, pressure drop, and quality factor, were examined to select the best fabrics. Of the 42 studies selected, 39 were laboratory studies, and 3 were clinical studies. Among the various fabrics examined, cotton quilt 120 thread per inch (TPI), copy paper (bonded), hybrid of cotton with chiffon/ silk, and flannel filtration were found to have over 90% effectiveness in the particle size range of Covid-19. The results and comparison of different factors (pressure drop, filtration efficacy, penetration, filtration quality, and fit factor have been evaluated) showed that among different fabrics, hybrid masks, 2-layered cotton quilt, 2-layered 100% cotton, cotton flannel, and hairy tea towel + fleece sweater had the best performance. Clinical studies have not explicitly examined cloth masks' effectiveness in Covid-19, so the effectiveness of these types of masks for Covid 19 is questionable, and more studies are needed.

Interdisciplinary Approaches to COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has been a significant concern worldwide. The pandemic has demonstrated that public health issues are not merely a health concern but also affect society as a whole. In this chapter, we address the importance of bringing together the world's scientists to find appropriate solutions for controlling and managing the COVID-19 pandemic. Interdisciplinary cooperation, through modern scientific methods, could help to handle the consequences of the pandemic and to avoid the recurrence of future pandemics.

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.

OBJECTIVE: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). CONCLUSION: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

The mechanisms of cellular crosstalk between mesenchymal stem cells and natural killer cells: Therapeutic implications.

Mesenchymal stem cells (MSCs) are mesenchymal precursors of various origins, with well-known immunomodulatory effects. Natural killer (NK) cells, the major cells of the innate immune system, are critical for the antitumor and antiviral defenses; however, in certain cases, they may be the main culprits in the pathogenesis of some NK-related conditions such as autoimmunities and hematological malignancies. On the other hand, these cells seem to be the major responders in beneficial phenomena like graft versus leukemia. Substantial data suggest that MSCs can variably affect NK cells and can be affected by these cells. Accordingly, acquiring a profound understanding of the crosstalk between MSCs and NK cells and the involved mechanisms seems to be a necessity to develop therapeutic approaches based on such interactions. Therefore, in this study, we made a thorough review of the existing literature on the interactions between MSCs and NK cells with a focus on the underlying mechanisms. The current knowledge herein suggests that MSCs possess a great potential to be used as tools for therapeutic targeting of NK cells in disease context and that preconditioning of MSCs, as well as their genetic manipulation before administration, may provide a wider variety of options in terms of eliciting more specific and desirable therapeutic outcomes. Nevertheless, our knowledge regarding the effects of MSCs on NK cells is still in its infancy, and further studies with well-defined conditions are warranted herein.

Clinical characteristics and outcomes of diabetics hospitalized for COVID-19 infection: a single-centered, retrospective, observational study.

Some debates exist regarding the association of diabetes mellitus (DM) with COVID-19 infection severity and mortality. In this study, we aimed to describe and compare the clinical characteristics and outcomes of hospitalized COVID-19 patients with and without DM. In this single-centered, retrospective, observational study, we enrolled adult patients with COVID-19 who were admitted to the Shariati hospital, Tehran, Iran, from February 25, 2020, to April 21, 2020. The clinical and paraclinical information as well as the clinical outcomes of patients were collected from inpatient medical records. A total of 353 cases were included (mean age, 61.67 years; 57.51 % male), of whom 111 patients were diabetics (mean age, 63.66 years; 55.86 % male). In comparison to those without DM, diabetic patients with COVID-19 were more likely to have other comorbidities, elevated systolic blood pressure (SBP), elevated blood sugar (BS), lower estimated glomerular filtration rate (eGFR) and elevated blood urea nitrogen (BUN). The association of DM with severe outcomes of COVID-19 infection (i.e. mechanical ventilation, median length of hospital stay and mortality) remained non-significant before and after adjustments for several factors including age, sex, body mass index (BMI), smoking status, and comorbidities. Based on our results DM has not been associated with worse outcomes in hospitalized patients for COVID-19 infection.

Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study.

BACKGROUND: COVID-19 has caused great concern for patients with underlying medical conditions. We aimed to determine the prognosis of patients with current or previous cancer with either a PCR-confirmed COVID-19 infection or a probable diagnosis according to chest CT scan. METHODS: We conducted a case control study in a referral hospital on confirmed COVID-19 adult patients with and without a history of cancer from February25th to April21st, 2020. Patients were matched according to age, gender, and underlying diseases including ischemic heart disease (IHD), diabetes mellitus (DM), and hypertension (HTN). Demographic features, clinical data, comorbidities, symptoms, vital signs, laboratory findings, and chest computed tomography (CT) images have been extracted from patients' medical records. Multivariable logistic regression was used to estimate odd ratios and 95% confidence intervals of each factor of interest with outcomes. RESULTS: Fifty-three confirmed COVID-19 patients with history of cancer were recruited and compared with 106 non-cancerous COVID-19 patients as controls. Male to female ratio was 1.33 and 45% were older than 65. Dyspnea and fever were the most common presenting symptoms in our population with 57.86 and 52.83% respectively. Moreover, dyspnea was significantly associated with an increased rate of mortality in the cancer subgroup (p = 0.013). Twenty-six patients (49%) survived among the cancer group while 89 patients (84%) survived in control (p = 0.000). in cancer group, patients with hematologic cancer had 63% mortality while patients with solid tumors had 37%. multivariate analysis model for survival prediction showed that history of cancer, impaired consciousness level, tachypnea, tachycardia, leukocytosis and thrombocytopenia were associated with an increased risk of death. CONCLUSION: In our study, cancer increased the mortality rate and hospital stay of COVID-19 patients and this effect remains significant after adjustment of confounders. Compared to solid tumors, hematologic malignancies have been associated with worse consequences and higher mortality rate. Clinical and para-clinical indicators were not appropriate to predict death in these patients.

Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia.

BACKGROUND: Chest computed tomography (CT) scan is frequently used in the diagnosis of COVID-19 pneumonia. OBJECTIVES: This study investigates the predictive value of CT severity score (CSS) for length-of-stay (LOS) in hospital, initial disease severity, ICU admission, intubation, and mortality. METHODS: In this retrospective study, initial CT scans of consecutively admitted patients with COVID-19 pneumonia were reviewed in a tertiary hospital. The association of CSS with the severity of disease upon admission and the final adverse outcomes was assessed using Pearson's correlation test and logistic regression, respectively. RESULTS: Total of 121 patients (60±16 years), including 54 women and 67 men, with positive RT-PCR tests were enrolled. We found a significant but weak correlation between CSS and qSOFA, as a measure of disease severity (r: 0.261, p = 0.003). No significant association was demonstrated between CSS and LOS. Patients with CSS>8 had at least three-fold higher risk of ICU admission, intubation, and mortality. CONCLUSIONS: CSS in baseline CT scan of patients with COVID-19 pneumonia can predict adverse outcomes and is weakly correlated with initial disease severity.

Beneficial Effects of Anti-Oxidative Herbal Medicines in Diabetic Patients Infected with COVID-19: A Hypothesis.

During the pandemic of Coronavirus Disease 2019 (COVID-19), it is critical to introduce potential medical treatments. Anti-oxidative herbal medicines with evidence-based beneficial impacts in the treatment of diabetes mellitus can be suggested as an adjuvant therapy to its conventional treatments in patients infected with COVID-19.

The under-reported role of toxic substance exposures in the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) and previous pandemics have been viewed almost exclusively as virology problems, with toxicology problems mostly being ignored. This perspective is not supported by the evolution of COVID-19, where the impact of real-life exposures to multiple toxic stressors degrading the immune system is followed by the SARS-CoV-2 virus exploiting the degraded immune system to trigger a chain of events ultimately leading to COVID-19. This immune system degradation from multiple toxic stressors (chemical, physical, biological, psychosocial stressors) means that attribution of serious consequences from COVID-19 should be made to the virus-toxic stressors nexus, not to any of the nexus constituents in isolation. The leading toxic stressors (identified in this study as contributing to COVID-19) are pervasive, contributing to myriad chronic diseases as well as immune system degradation. They increase the likelihood for comorbidities and mortality associated with COVID-19. For the short-term, tactical/reactive virology-focused treatments are of higher priority than strategic/proactive toxicology-focused treatments, although both could be implemented in parallel to reinforce each other. However, for long-term pandemic prevention, toxicology-based approaches should be given higher priority than virology-based approaches. Since current COVID-19 treatments globally ignore the toxicology component almost completely, only limited benefits can be expected from these treatments.