Assessment of prevalence and risk factors of isolated coronary artery ectasia: A 5-year double-center retrospective study in Yazd, Iran.

Background: The prevalence of Coronary artery ectasia (CAE) varies from 0.|3 to 5% in different countries. The prevalence of CAE has varied in different parts of the world and the study of risk factors can be effective in the process of diagnosis and treatment of patients, we reviewed patients who underwent coronary angiography for 5 years to determine the prevalence of isolated CAE and its associated risk factors. Methods: A retrospective analysis was conducted on 16600 patients who underwent coronary angiography at Shahid Sadoughi and Afshar hospitals between March 2015 to April 2020. Diagnosis and confirmation of CAE was defined as a vessel diameter greater than 1.5 times the normal diameter of the vessel, which must be confirmed by at least two cardiologists. Demographic variables, angiography and echocardiography reports were included in our final analysis. Results: Isolated CAE was diagnosed in 287 (1.7%) patients. After triple-vessel disease (53%), the left anterior descending artery (LAD) was the commonest affected vessel by ectasia 16% (46 cases). Diffuse isolated CAE was diagnosed in 52% of LAD, 76.6% of Right coronary artery (RCA), and 74.1% of left circumflex artery. A significant association was seen between the vessel involved and the nature of ectasia (p<0.001). Conclusion: In our study, the occurrence of isolated CAE was similar to other studies. This condition often affects all three major vessels of the coronary arteries, and is commonly categorized as type 1, which involves diffuse involvement of the arteries based on the Markis and Harikrishnan Classification.

Conceptual Approach to Body Fluids and Edema, Education Determines Clinical Outcomes in Heart Failure.

In this brief communication, we reemphasize the importance of critical thinking in clinical practice using the example of edema. The common practice of thinking and inquiry by practicing clinicians has beneficial implications for healthcare by improving outcomes and patient care while alleviating the burden of misconceptions in practice. We provide an in-depth and interactive investigation of physiological concepts as a foundation for understanding body fluid dynamics. Finally, we offer a new classification of symptoms of heart failure. DOI: 10.52547/ijkd.8171.

Synergistic anticancer effects of co-delivery of linc-RoR siRNA and curcumin using polyamidoamine dendrimers against breast cancer.

Breast cancer resistance to chemotherapy necessitates novel combination therapeutic approaches. Linc-RoR is a long intergenic noncoding RNA that regulates stem cell differentiation and promotes metastasis and invasion in breast cancer. Herein, we report a dual delivery system employing polyamidoamine dendrimers to co-administer the natural compound curcumin and linc-RoR siRNA for breast cancer treatment. Polyamidoamine dendrimers efficiently encapsulated curcumin and formed complexes with linc-RoR siRNA at an optimal N/P ratio. In MCF-7 breast cancer cells, the dendriplexes were effectively internalized and the combination treatment synergistically enhanced cytotoxicity, arresting the cell cycle at the G1 phase and inducing apoptosis. Linc-RoR gene expression was also significantly downregulated. Individual treatments showed lower efficacy, indicating synergism between components. Mechanistic studies are warranted to define the molecular underpinnings of this synergistic interaction. Our findings suggest dual delivery of linc-RoR siRNA and curcumin via dendrimers merits further exploration as a personalized therapeutic approach for overcoming breast cancer resistance.

Nanoparticles Containing Oxaliplatin and the Treatment of Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is a highly widespread malignancy and ranks as the second most common cause of cancer-related mortality. OBJECTIVE: Cancer patients, including those with CRC, who undergo chemotherapy, are often treated with platinum- based anticancer drugs such as oxaliplatin (OXA). Nevertheless, the administration of OXA is associated with a range of gastrointestinal problems, neuropathy, and respiratory tract infections. Hence, it is necessary to devise a potential strategy that can effectively tackle these aforementioned challenges. The use of nanocarriers has shown great potential in cancer treatment due to their ability to minimize side effects, target drugs directly to cancer cells, and improve drug efficacy. Furthermore, numerous studies have been published regarding the therapeutic efficacy of nanoparticles in the management of colorectal cancer. METHODS: In this review, we present the most relevant nanostructures used for OXA encapsulation in recent years, such as solid lipid nanoparticles, liposomes, polysaccharides, proteins, silica nanoparticles, metal nanoparticles, and synthetic polymer-carriers. Additionally, the paper provides a summary of the disadvantages and limits associated with nanoparticles. RESULTS: The use of different carriers for the delivery of oxaliplatin increased the efficiency and reduced the side effects of the drug. It has been observed that the majority of research investigations have focused on liposomes and polysaccharides. CONCLUSION: This potentially auspicious method has the potential to enhance results and enhance the quality of life for cancer patients undergoing chemotherapy. However, additional investigation is required to ascertain the most suitable medium for the transportation of oxaliplatin and to assess its efficacy through clinical trials.

An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit.

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data.

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention. In this study, we introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision-making process made by machines specifically for medical applications. More specifically, the framework comprises two phases: In the first phase, referred to as the "clinician-guided design" phase, the dataset is preprocessed using explainable AI and domain expert input. To better demonstrate this phase, we prepared a benchmark dataset of carefully curated clinical and biochemical markers based on clinician assessments for survival and kidney injury prediction in COVID-19 patients. This dataset was selected from a patient cohort of 1366 individuals at Stony Brook University. Moreover, we designed and trained a diverse collection of machine learning models, encompassing gradient-based boosting tree architectures and deep transformer architectures, specifically for survival and kidney injury prediction based on the selected markers. In the second phase, called the "explainability-driven design refinement" phase, the proposed framework employs explainability methods to not only gain a deeper understanding of each model's decision-making process but also to identify the overall impact of individual clinical and biochemical markers for bias identification. In this context, we used the models constructed in the previous phase for the prediction task and analyzed the explainability outcomes alongside a clinician with over 8 years of experience to gain a deeper understanding of the clinical validity of the decisions made. The explainability-driven insights obtained, in conjunction with the associated clinical feedback, are then utilized to guide and refine the training policies and architectural design iteratively. This process aims to enhance not only the prediction performance but also the clinical validity and trustworthiness of the final machine learning models. Employing the proposed explainability-driven framework, we attained 93.55% accuracy in survival prediction and 88.05% accuracy in predicting kidney injury complications. The models have been made available through an open-source platform. Although not a production-ready solution, this study aims to serve as a catalyst for clinical scientists, machine learning researchers, and citizen scientists to develop innovative and trustworthy clinical decision support solutions, ultimately assisting clinicians worldwide in managing pandemic outcomes.

Arterial Versus Venous Blood Gas Analysis Comparisons, Appropriateness, and Alternatives in Different Acid/Base Clinical Settings: A Systematic Review.

Arterial blood gases (ABGs) are routinely done in critical clinical settings to ascertain acid-base status. Due to difficulties and the potential side effects following arterial blood sampling, much research has been done to find the possibility of using venous samples as an alternative. However, this comparison needs to be evaluated in various contexts. Hence, this systematic review aims to explore the differences, appropriateness, and alternatives of arterial versus venous blood gas (VBG) analysis in different acid-base states. A comprehensive literature search was conducted through electronic databases using the terms "ABG," "VBG," "Arterial Blood Gas," "Venous Blood Gas," and "Gas analysis." Studies' qualities were assessed by using Newcastle - Ottawa Quality Assessment Scale. Of 531 articles, 22 were included in the study after title, abstract, and full-text screening. Based on the Newcastle - Ottawa Quality Assessment Scale, 23% of the studies had good quality (score ≥ 7), 77% fair quality (score 2-6), and none of the studies had poor quality (score ≤ 1). Moreover, 22.5% of the included articles found a strong correlation between ABG and VBG. 73% compared arterial and VBG parameters among patients with any clinical contexts, 22.5% in respiratory diseases, and 4.5% in metabolic conditions, and their results had a significant disparity. There was a considerable discrepancy among authors about the appropriateness and utilization of VBG as an alternative to ABG. Our findings suggest that those studies did not consider physiological differences between venous and arterial blood values and obviated the significance of sampling procedures.

Fascioliasis associated with chronic cholecystitis in a woman from Sistan and Baluchestan province, a non-endemic region in Southeastern Iran.

BACKGROUND: Fascioliasis, caused by Fasciola hepatica, is a neglected zoonotic food-borne trematodiasis. The Caspian littoral in northern Iran is endemic for the disease, and human fascioliasis is well-known in that region. In the present study, we report the diagnosis, identification, and clinical management of a human case of fascioliasis associated with common bile duct (CBD) obstruction from a non-endemic remote area in southeastern Iran. CASE PRESENTATION: A 42-year-old female was admitted to Afzalipour Medical Center hepatobiliary surgery ward in Kerman with abdominal pain for the past three months. Dilated biliary tract and an ill-defined mass in CBD were reported in abdominal ultrasonography and magnetic resonance cholangiopancreatography, respectively. During distal CBD operation, nine leaf-like motile flatworms were isolated. A morphological study confirmed all the isolates as Fasciola, and further molecular investigations, identified the flukes as F. hepatica using both pepck multiplex PCR and cox1 sequencing. CONCLUSION: Molecular and morphological findings of the study indicated the presence of human fascioliasis in the southeastern province of Sistan and Baluchestan in Iran. Fascioliasis is among the etiologies of chronic cholecystitis, and physicians should consider chronic cholecystitis associated with fascioliasis in the differential diagnosis. In the present report, endoscopic ultrasound was usefully applied for the accurate diagnosis of biliary fasciolosis.

Upregulation of the c-MYC oncogene and adjacent long noncoding RNAs PVT1 and CCAT1 in esophageal squamous cell carcinoma.

BACKGROUND: All cell types express long non-coding RNAs (lncRNAs), which have the potential to play a role in carcinogenesis by altering the levels of their expression. Squamous cell carcinoma of the esophagus (ESCC) is a deadly disease with a poor prognosis and a high frequency of lymphatic metastases. Understanding the functional role and signaling pathways of two neighboring lncRNAs, CCAT1 and PVT1, in this oncogene's pathogenesis may help us determine ESCC. Furthermore, it is still unclear whether these lncRNAs are linked to the clinicopathological characteristics of patients with ESCC. METHODS: For this study, we used biopsy from the Imam Khomeini Cancer Institute's tumor bank in Tehran, Iran to obtain 40 ESCC tumor samples and their normal margin counterparts. The expression levels of the CCAT1, PVT1, and c-MYC genes were assessed using quantitative Real-Time RT-PCR. Additionally, demographic data and clinical-pathologic characteristics, such as tumor grade, tumor stage, lymph node, and metastasis, were taken into consideration. Graphpad prism version 8 was used for bioinformatics analyses. RESULTS: Comparing ESCC tissues to non-tumor tissues, we found significant upregulation of PVT1, CCAT1, and c-MYC. Patients with ESCC who had increased PVT1 expression also had higher rates of advanced stage and lymph node metastasis, whereas increased CCAT1 expression was only linked to advanced stage and wasn't associated with lymph node metastasis. In predicting ESCC, CCAT1 (p < 0.05) was found to be an important factor. Overall survival was reduced by c-MYC and PVT1 overexpression (p < 0.001), according to Kaplan-Meier analysis. PVT1, CCAT1, and c-MYC were found to interact with 23 miRNAs with high and medium score classes, as shown in a bioinformatics study. We summarized the experimentally proven interactions between c-MYC, PVT1, and CCAT1 and other miRNAs, lncRNAs, and proteins. CONCLUSION: This is the first report that CCAT1, PVT1 and c-MYC have been found to be up-regulated simultaneously in ESCC. It is possible that these genes may be involved in ESCC as a result of these findings. Therefore, as consequence, more research is needed to determine whether or not these lncRNAs play an oncogenic role in ESCC development and progression, as well as the regulatory mechanisms that control them.

A survey on the effect of adding aspirin to anti-migraine drugs on the severity of headache in patients with chronic migraine headaches with lateral venous sinus stenosis in MRV.

Introduction: One of the probable etiologies raised in patients with chronic migraine headaches is stenosis of the lateral venous sinuses of the brain, which is detectable using magnetic resonance venography (MRV). In this study, we decided to observe the effect of adding aspirin to anti-migraine medicines on the severity of headache in patients with chronic migraine headaches with lateral venous sinus stenosis in MRV. Methods: The study was a double-blind randomized clinical trial. Patients were included in the study in two groups including 30 people. The first group was treated with propranolol and nortriptyline, and the second group was treated with propranolol, nortriptyline, and aspirin. The severity of headache, number of headaches during one month, and duration of a headache before treatment and one, two, and three months after treatment were examined. Data were analyzed utilizing SPSS software version 19 and statistical tests like t-test, Chi-squared test, Paired t-test, and repeated measure. Results: The results showed that the mean severity of headache in the second group was significantly lower than the first group two months after treatment (P = 0.003) and three months after treatment (P = 0.002). Additionally, the number of headaches (P = 0.001) and duration of headache (P = 0.043) were significantly lower in the second group than the first group in the first three months after treatment. No statistically significant difference was observed between the frequency distribution of nausea/vomiting in the two groups. Conclusion: The addition of aspirin to anti-migraine medicines is effective in improving the severity of headache in patients with migraine with lateral venous sinus stenosis of the brain.

Social equity-based distribution networks design for the COVID-19 vaccine.

This study aims to investigate the role of social equity in vaccine distribution network design problems. Inspired by the current COVID-19 vaccine allocation in-country context, we capture social equity-based distribution by modeling three theories: Rawls' theory, Sadr's theory, and utilitarianism. We consider various social groups based on degree of urbanization, including inhabitants of cities, towns and suburbs, and rural areas. The distribution problem is subject to, on the one hand, demand-side uncertainty characterized by the daily contamination rate and its space-time propagation that anticipate the in-need population. On the other hand, supply-side uncertainty characterized by the stochastic arrival of vaccine doses for the supply period. To tackle this problem, we propose a novel bi-objective two-stage stochastic programming model using the sample average approximation (SAA) method. We also develop a lexicographic goal programming approach where the social equity objective is prioritized, thereafter reaching an efficiency level. Using publicly available data on COVID-19 in-country propagation and the case of two major provinces in Iran as example of middle-income country, we provide evidence of the benefits of considering social equity in a model-based decision-making approach. The findings suggest that the design solution produced by each social equity theory matches its essence in social science, differing considerably from the cost-based design solution. According to the general results, we can infer that each social equity theory has its own merits. Implementing Rawls' theory brings about a greater coverage percentage in rural areas, while utilitarianism results in a higher allocation of vaccine doses to social groups compared to the Sadr and Rawls theories. Finally, Sadr's theory outperforms Rawls' in terms of both the allocation and cost perspective. These insights would help decision-makers leverage the right equity approach in the COVID-19 vaccine context, and be better prepared for any pandemic crisis that the future may unfold.

Differentiation of Human Wharton Jelly Mesenchymal Stem Cells into Germ-Like Cells; emphasis on evaluation of Germ-long non-coding RNAs.

BACKGROUND: The proliferation and differentiation of stem cells into Germ-Like Cells (GLCs) is mediated by several growth factors and specific genes, of which some are related to long non-coding RNAs (lncRNAs). We have developed a modified differentiation process and identified a panel of GermlncRNAs related to GLCs. METHODS: Human Wharton Jelly Mesenchymal Stem Cells were treated with 25 ng/ml Bone Morphogenetic Protein (BMP)-4 and 10- 5 M all-trans retinoic acid to differentiate them into germ-like cells. To confirm the differentiation, changes in the expression of Oct-4, C-kit, Stella, and Vasa genes were assessed using quantitative Real-Time PCR (qPCR) and immunocytochemistry. QPCR was also used before and after differentiation to evaluate the changes in a lncRNA panel, using a 96-well array. Statistical analysis of the data was performed by SPSS 21. RESULTS: After 21 days of induction, the HWJ-MSCs derived germ-like cells were formed. Also, qPCR and immunocytochemistry showed that the pluripotent Oct4 marker was expressed in the undifferentiated HWJ-MSCs, but its expression gradually decreased in the differentiated cells. C-kit was expressed on days 7, 14, and 21 of differentiation. Both GLC markers of Stella and Vasa genes/proteins were present only in differentiated cells. Of the 44 lncRNA genes array, 36 of them showed an increase and eight genes showed a decrease. CONCLUSION: Our study showed that BMP4 and RA are effective in inducing HWJ-MSCs differentiation into GLCs. In addition, our study for the first time showed changes in the lncRNAs expression during the differentiation of HWJ-MSCs into GLCs by using BMP4 and RA.

A causality analysis of risks to perishable product supply chain networks during the COVID-19 outbreak era: An extended DEMATEL method under Pythagorean fuzzy environment.

In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts' judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.

Perioperative Hypertension Etiologies in Patients Undergoing Noncardiac Surgery in University Health Network Hospitals-Canada from 2015-2020.

Introduction: Perioperative hypertension, defined as increased blood pressure around the surgery, is a known risk factor for perioperative complications, including cardiovascular events. Identifying reasons associated with hypertension in each period is of great help in preventing and better managing perioperative hypertension. Objective: The aim of the study was to explore common etiologies of hypertension during the perioperative period (pre, intra, and post-operation) in patients who underwent noncardiac surgeries in University Health Network (UHN) hospitals, Canada, from 2015 to 2020. Patients and Methods: We retrospectively analyzed the medical records of 174 patients undergoing noncardiac surgeries who experienced perioperative hypertension. We assessed the prevalence of 10 reasons for perioperative hypertension as a whole and also each period separately according to the physicians' notes in patients' medical records. Two-way measurements ANOVA was used to determine the change of mean hypertension among patients for specific etiology. Results: The common etiologies of perioperative hypertension were poorly controlled hypertension (21.8%), excessive fluid therapy (19.5%), excessive vasopressor (18.4%), and medication withdrawal (13.7%). Regarding each period separately, the most common reasons were poorly controlled hypertension for pre (42.9%) and intraoperative period (22.7%) and fluid overload for the postoperative period (20.1%). Poor control of hypertension showed both within-subject statistical significance for systolic and between-subject statistical significance for diastolic blood pressure. Conclusion: Poorly controlled hypertension is the most significant etiology of perioperative hypertension in patients undergoing noncardiac surgeries. Apart from poorly controlled hypertension, as a patient-related factor, iatrogenic factors such as excessive vasopressor therapy, aggressive fluid replacement and poor management of antihypertensive medications can also cause perioperative hypertension.

Low expression of LncRNA-CAF attributed to the high expression of HIF1A in esophageal squamous cell carcinoma and gastric cancer patients.

PURPOSE: Cancer-associated fibroblasts (CAFs) are major components of tumor microenvironment that stimulate ESCC and GC progression. The LncRNA-CAF, FLJ22447, is located in the vicinity of HIF1A, while their association remains unclear. This study aims to assess the FLJ22447 expression in the ESCC and GC patients and evaluate its association with the HIF1A gene. METHODS: Fresh ESCC and GC tumor samples and their adjacent non-tumor tissues were collected from patients who underwent surgery in Imam Khomeini Hospital, Tehran, Iran. The expression of FLJ22447, HIF1A, and VEGF was evaluated using qRT-PCR test. The association of their expression with tumor clinicopathological features in ESCC patients was assessed. System biology tools were then applied for the possible biological subsequences of the FLJ22447. RESULTS: A significant reduction in FLJ22447 expression was observed in ESCC and GC tissues than adjacent non-tumor tissues, while, the expression of HIF1A and VEGF were increased. Low expression of FLJ22447 was significantly correlated with HIF1A (P = 2.4e-73, R = 0.63) and VEGF (P = 0.00019, R = 0.15) expression. A significant relationship was detected between the high expression of HIF1A and tumor stages (I-II) and it was related to the reduced survival of ESCC patients. Conversely, increased VEGF expression was linked to the advanced stages (III-IV) and metastasis in ESCC. The analysis of FLJ22447-interacted proteins showed that MYC, JUN, SMRCA4, PPARG, AR, FOS, and CEBPA are the hub genes. These proteins were implicated in the cancer related pathways. Among them, SPI1, E2F1, TCF7L2, and STAT1 were significantly expressed in esophageal and gastric cancers that were functionally involved in the proliferation, apoptosis, and angiogenesis pathways in cancer. CONCLUSION: The results suggested that FLJ22447 may have a regulatory function on the HIF1A expression. We identified the FLJ22447-interacted proteins and their molecular function in cancer pathogenesis. Further research emphasis is to realize the association of FLJ22447 with its protein partners in progression of cancer. These may provide an insight into the FLJ22447 activity that could introduce it as a potential value in tumor gene therapy.

Diagnostic and prognostic value of Sepsis-Induced coagulopathy and International Society on Thrombosis and Hemostasis scoring systems in COVID-19-associated disseminated intravascular coagulopathy.

BACKGROUND: The coronavirus disease 2019 (COVID-19) presents various phenotypes from asymptomatic involvement to death. Disseminated intravascular coagulopathy (DIC) is among the poor prognostic complications frequently observed in critical illness. To improve mortality, a timely diagnosis of DIC is essential. The International Society on Thrombosis and Hemostasis (ISTH) introduced a scoring system to detect overt DIC (score ≥5) and another category called sepsis-induced coagulopathy (SIC) to identify the initial stages of DIC (score ≥4). This study aimed to determine whether clinicians used these scoring systems while assessing COVID-19 patients and the role of relevant biomarkers in disease severity and outcome. MATERIALS AND METHODS: An exhaustive search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, using Medline, Embase, Cochrane, CINAHL, and PubMed until August 2020. Studies considering disease severity or outcome with at least two relevant biomarkers were included. For all studies, the definite, maximum, and minimum ISTH/SIC scores were calculated. RESULTS: A total of 37 papers and 12,463 cases were reviewed. Studies considering ISTH/SIC criteria to detect DIC suggested a higher rate of ISTH ≥5 and SIC ≥4 in severe cases and nonsurvivors compared with nonsevere cases and survivors. The calculated ISTH scores were dominantly higher in severe infections and nonsurvivors. Elevated D-dimer was the most consistent abnormality on admission. CONCLUSION: Higher ISTH and SIC scores positively correlate with disease severity and death. In addition, more patients with severe disease and nonsurvivors met the ISTH and SIC scores for DIC. Given the high prevalence of coagulopathy in COVID-19 infection, dynamic monitoring of relevant biomarkers in the form of ISTH and SIC scoring systems is of great importance to timely detect DIC in suspicious patients.

Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices.

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

Anticoagulation therapy in COVID-19 patients with chronic kidney disease.

Coagulopathy and derangements in the coagulation parameters are significant features of COVID-19 infection, which increases the risk of disseminated intravascular coagulation, thrombosis, and hemorrhage in these patients, resulting in increased morbidity and mortality. In times of COVID-19, special consideration should be given to patients with concurrent chronic kidney disease (CKD) and COVID-19 (CKD/COVID-19 patients) as renal dysfunction increases their risk of thrombosis and hemorrhage, and falsely affects some of the coagulation factors, which are currently utilized to assess thrombosis risk in patients with COVID-19. Hence, we believe extra attention should be given to determining the risk of thrombosis and bleeding and optimizing the timing and dosage of anticoagulant therapy in this unique population of patients. CKD/COVID-19 patients are considered a high-risk population for thrombotic events and hemorrhage. Furthermore, effects of renal function on paraclinical and clinical data should be considered during the evaluation and interpretation of thrombosis risk stratification. Individualized evaluation of clinical status and kidney function is necessary to determine the best approach and management for anticoagulant therapy, whereas there is a lack of studies about the population of CKD/COVID-19 patients who need anticoagulant therapy now.

Dysregulation of Key Proteinases in Aspergillus fumigatus Induced by Blood Platelets.

BACKGROUND: Aspergillus fumigatus is the most common species causing invasive aspergillosis (IA), a life-threatening infection with more than 80% mortality. Interactions between A. fumigatus and human blood platelets lead to intravascular thrombosis and localized infarcts. To better understand A. fumigatus pathogenesis, we aimed to analyze the genetic basis of interactions between the pathogen and blood platelets. METHODS: A bioinformatic pipeline on microarray gene expression dataset, including analysis of differentially expressed genes (DEGs) using Limma R package and their molecular function, as well as biological pathways identification, was conducted to find the effective genes involved in IA. In the wet phase, the gene expression patterns following fungal exposure to blood platelets at 15, 30, 60, and 180 min were evaluated by quantitative reverse transcriptase-PCR analysis. RESULTS: Three genes encoding aspartic endopeptidases including (Pep1), (Asp f 13), and (ß-glucanase) were the standing candidates. The invasion-promoting fungal proteinase-encoding genes were down-regulated after 30 min of hyphal incubation with blood platelets, and then up-regulated at 60 and 180 min, although only Pep1 was greater than the control at the 60and 180 min time points. Also, the same genes were downregulated in more the clinical isolates relative to the standard strain CBS 144.89. CONCLUSION: Our findings delineate the possible induction of fungal-encoded proteinases by blood platelets. This provides a new research line into A. fumigatus' molecular pathogenesis. Such insight into IA pathogenesis might also guide researchers toward novel platelet-based therapies that involve molecular interventions, especially in IA patients.

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection.

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model's latency show that the OutlierNet architectures can achieve as much as 30× lower latency than published networks.