Health Care Systems In World Research Articles

Taiwan renal data report system: Framework, function, and development.

The world healthcare system is actively seeking possible solutions for the rapid growth of kidney disease threats. The Taiwan Renal Data System (TWRDS) was central in assisting kidney health and care policymaking to reduce end-stage kidney disease incidence and mortality. This article summarizes the TWRDS framework, recent applications, and developments to provide new insights for some international researchers to promote planetary kidney health. The TWRDS originated in 1987 for the accreditation and quality monitoring of dialysis units and was connected with enriched health claim databases after the implementation of universal national health insurance in Taiwan in 1995. As a healthcare information centre, TWRDS has published annual reports forming indispensable instructions for renal care improvement since 2014. The TWRDS possesses three main functions: (1) kidney disease surveillance; (2) offering rich materials for research purposes; (3) achieving precision prevention and care through complex algorithms. In the new era, TWRDS can help build a more resilient society against communicable disease threats by integrating remote sensor techniques for developing future remote healthcare structures, as well as identifying kidney health inequity populations and promoting healthcare resources distributed equity. The global healthcare system is facing escalating burdens of non-communicable disease care due to the rapidly growing elderly population. Therefore, a considerable-scale data system is an essential decision-supportive tool in promoting an evidence-based, resilient, sustainable, equity care environment. Undoubtedly, TWRDS experience is a practical example of leveraging healthcare providers' decisions, care outcomes, and renovation.

Using m-health and wearable technologies to promote self-management and treatment adherence among cardiovascular disease patients: a clinical trial protocol

Abstract Funding Acknowledgements Type of funding sources: None. Background Cardiovascular diseases (CVD) are the leading cause of death globally, taking an estimated 17.9 million lives each year. Cardiac rehabilitation (CR) is shown to reduce mortality and hospital readmissions, while improving physical fitness and quality of life. Despite the recommendations and proven benefits, acceptance and adherence remain low. Mobile health (mHealth) solutions may contribute to more personalized and tailored patient recommendations according to their specific needs. Purpose The main goal of this research is to determine the effectiveness of an e-therapeutic intervention in improving the adherence to treatment and self-management of cardiovascular outpatients, and on the reduction of recurrent cardiovascular events. Methods The study follows a multicentre randomized controlled design involving two cardiology units in the Centre Region of Portugal. Participants will be cardiovascular outpatients older than 18 years of age, after the onset of acute cardiac event or those who are engaged in a structured CR program). Sample size was calculated and we estimate to recruit a sample of 330 patients, per arm. The intervention length will be 6 months, with the direct support of an interdisciplinary team. There will be two groups of treatment: control group and the experimental group (standard outpatient protocol plus the e-therapeutic program). The e-therapeutic kit includes mobile apps and wearables according to the patient needs and profile, such as heart rate, blood pressure, peripheral oxygen saturation (SpO2), sleep and step trackers, symptoms, lifestyle self-monitoring tools, medication reminders or motivational resources. Outcomes: Outcome measurements will be carried out at the 3-month (T1) and the 6-month interventions (T2). The primary outcome will be the reduction of recurrent cardiovascular events, which is a composite of (i) cardiovascular rehospitalization or urgent visit; or (ii) unplanned revascularization; (iii) cardiovascular mortality; or (iv) worsening heart failure. Secondary outcomes measures will include: a) Quality of Life; b) Adherence to treatment; c) Nutrition and Physical Activity; d) Anthropometric measures; e) cardiovascular risk (SCORE). Conclusion The success of the e-therapeutic intervention will be a step toward the use of technological interfaces as an integral part of CR programs. Altogether, these programmes are likely to facilitate the management of resources for healthcare professionals and reduce inequality of access to healthcare for CVD patients. In the western world healthcare systems, the complementarity of mHealth solutions to the treatment, care and rehabilitation along the chronic diseases pathway is mandatory if aiming for sustainable, integrated healthcare systems that endorse patient’s willingness to be part of their treatment journey, while assisting them at distance.

Literature Review-Transthoracic Echocardiography, Computed Tomography Angiography, and Their Value in Clinical Decision Making and Outcome Predictions in Patients with COVID-19 Associated Cardiovascular Complications.

The sudden outbreak of the COVID-19 pandemic posed a great threat to the world's healthcare systems. It resulted in the development of new methods and algorithms for the diagnosis and treatment of both COVID-19 and its complications. Diagnostic imaging played a crucial role in both cases. Among the most widely used examinations are transthoracic echocardiography (TTE) and computed tomography angiography (CTA). Cardiovascular complications in COVID-19 are frequently associated with a severe inflammatory response, which results in acute respiratory failure, further leading to severe complications of the cardiovascular system. Our review aims to discuss the value of TTE and CTA in clinical decision making and outcome prediction in patients with COVID-19-associated cardiovascular complications. Our review revealed the high clinical value of various TTE findings and their association with mortality and the prediction of patients' clinical outcomes, especially when used with other laboratory parameters. The strongest association between increased mortality and findings in TTE was observed for tachycardia and decreased left ventricular ejection fraction (odds ratio (OR) 24.06) and tricuspid annular plane systolic excursion/pulmonary artery systolic pressure ratio (TAPSE/PASP ratio) < 0.31 mm/mmHg (OR 17.80). CTA is a valuable tool in diagnosing COVID-19-associated pulmonary embolism, but its association with mortality and its predictive role should always be combined with laboratory findings and patients' medical history. D-dimers > 3000 ng/mL were found as the strongest predictors of pulmonary embolism (PE) (OR 7.494). Our review indicates the necessity for an active search for cardiovascular complications in patients with severe COVID-19, as they are linked with an increased probability of fatal outcomes.

Diabetes Prediction Using Machine Learning

Chronic diseases like diabetes have the potential to wreck the world's health care system. According to the International Diabetes Federation, there are 382 million diabetics worldwide. By 2035, this will increase to 592 million. Diabetes is a disease characterised by high blood glucose levels. The signs of this raised blood sugar level include increased thirst, appetite, and frequency of urinating. Diabetes is a significant contributing factor to heart failure, stroke, kidney failure, amputations, blindness, and kidney failure. When we eat, our bodies turn the food we consume into sugars like glucose. Then, we anticipate insulin to be released from our pancreas. Insulin functions as a key to unlock our cells, allowing glucose to enter and be used as fuel by us. However, in diabetes, this mechanism does not work. The most common types of diabetes are type 1 and type 2, but there are others, including gestational diabetes, which develops during pregnancy. Machine learning is a new area in data science that investigates how machines learn from experience. The purpose of this work is to develop a system that, by combining the findings of different machine learning algorithms, can more correctly identify early diabetes in a patient. Some of the approaches used include Logistic Regression, Random Forest, Support Vector Machine, and the Nave Bayes Algorithm. The accuracy of each algorithm is computed alongside.

Prediction of Diabetes using Machine Learning

Abstract: Diabetes is a chronic condition that could lead to a catastrophe in the world's healthcare system. 537 million people worldwide have diabetes, according to the International Diabetes Federation. By 2045, this number is anticipated to reach 783 million. A rise in blood glucose levels can result in diabetes. Numerous symptoms, which includes frequent urination, increased thirst, and increased appetite, are brought on by this raised blood glucose level. It is a significant contributor to heart failure, stroke, kidney failure, blindness, and amputations. The objective of the given study mainly is to develop the single collective system that combines result of many different types machine learning techniques, including Logistic Regression, Linear Regression, Support Vector Machine, and Random Forest, to more accurately predict diabetes in a patient. It collects the patient's records based on their pregnancy, blood sugar levels, blood pressure, insulin levels, body mass index, and many other factors. Each of the strategies will be used to determine the model's correctness, however, we found that the Support vector machine method had the highest accuracy (77%). The diabetes forecast is then made using the model with the highest accuracy.

MICaFVi: A Novel Magnetic Immuno-Capture Flow Virometry Nano-Based Diagnostic Tool for Detection of Coronaviruses.

COVID-19 has resulted in a pandemic that aggravated the world's healthcare systems, economies, and education, and caused millions of global deaths. Until now, there has been no specific, reliable, and effective treatment to combat the virus and its variants. The current standard tedious PCR-based tests have limitations in terms of sensitivity, specificity, turnaround time, and false negative results. Thus, an alternative, rapid, accurate, and sensitive diagnostic tool that can detect viral particles, without the need for amplification or viral replication, is central to infectious disease surveillance. Here, we report MICaFVi (Magnetic Immuno-Capture Flow Virometry), a novel precise nano-biosensor diagnostic assay for coronavirus detection which combines the MNP-based immuno-capture of viruses for enrichment followed by flow-virometry analysis, enabling the sensitive detection of viral particles and pseudoviruses. As proof of concept, virus-mimicking spike-protein-coated silica particles (VM-SPs) were captured using anti-spike-antibody-conjugated MNPs (AS-MNPs) followed by detection using flow cytometry. Our results showed that MICaFVi can successfully detect viral MERS-CoV/SARS-CoV-2-mimicking particles as well as MERS-CoV pseudoviral particles (MERSpp) with high specificity and sensitivity, where a limit of detection (LOD) of 3.9 µg/mL (20 pmol/mL) was achieved. The proposed method has great potential for designing practical, specific, and point-of-care testing for rapid and sensitive diagnoses of coronavirus and other infectious diseases.

Precision Medicine in Oral Health and Diseases: A Systematic Review.

Precision medicine (PM) is personalized medicine that can develop targeted medical therapies for the individual patient, in which "omics" sciences lead to an integration of data that leads to highly predictive models of the functioning of the individual biological system. They enable rapid diagnosis, assessment of disease dynamics, identification of targeted treatment protocols, and reduction of costs and psychological stress. "Precision dentistry" (DP) is one promising application that need further investigation; the purpose of this paper is therefore to give physicians an overview of the knowledge they need to enhance treatment planning and patient response to therapy. A systematic literature review was conducted on the PubMed, Scopus, and Web of Science databases by analyzing the articles examining the role of precision medicine in dentistry. PM aims to shed light on cancer prevention strategies, by identifying risk factors, and on malformations such as orofacial cleft. Another application is pain management by repurposing drugs created for other diseases to target biochemical mechanisms. The significant heritability of traits regulating bacterial colonization and local inflammatory responses is another result of genomic research, and is useful for DP in the field of caries and periodontitis. This approach may also be useful in the field of orthodontics and regenerative dentistry. The possibility of creating an international network of databases will lead to the diagnosis, prediction, and prevention of disease outbreaks, providing significant economic savings for the world's health care systems.

Authentication of Lung CT-Scan Data in NIfTI Format Using LWT, Hessenberg Decomposition, and Affine Transform

Technological advancement in digital medical imaging changes the world health care system because various diseases are diagnosed through these technologies. In the Covid-19 phase, telemedicine played a tremendous role in providing remote medical consultation in rural areas. But in remote consultation, various medical images send to a radiologist for diagnosis through the internet. Worldwide has seen a significant surge in digital media attacks that replicate and tamper with the digital image, resulting in a breach of authenticity and ownership. A robust and safe watermarking scheme for NIfTI images has been proposed in this paper. This novel method entails meticulously integrating a watermark in the slice of the NIfTI image. We aim to correctly incorporate the watermark with minimal distortion and retain the medical information of the selected image slice. The proposed method uses LWT transform to transform the image, allowing for surprisingly good modification during insertion. Furthermore, Hessenberg matrix decomposition is applied on the LL sab bands with the image's maximal energy to be retained. Scrambling the watermark before embedding it in the slice is accomplished using the Affine transform. A thorough study of the trade-off between security, imperceptibility, and robustness utilizing performance measures viz. NC, PSNR, SNR, and SSIM have been given. The simulation findings have been validated against image processing threats.

Outpatient therapies for COVID-19.

The world's healthcare systems were forced to evaluate evolving evidence regarding the COVID-19 pandemic quickly. The technology used to improve patient outcomes and survival is rapidly evolving along with the virus. This article discusses new therapies developed to treat and prevent COVID-19 in qualifying patients.

Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2.

The pandemic caused by the coronavirus SARS-CoV-2 led to a global crisis in the world healthcare system. Despite some progress in the creation of antiviral vaccines and mass vaccination of the population, the number of patients continues to grow because of the spread of new SARS-CoV-2 mutations. There is an urgent need for direct-acting drugs capable of suppressing or stopping the main mechanisms of reproduction of the coronavirus SARS-CoV-2. Several studies have shown that the successful replication of the virus in the cell requires proteolytic cleavage of the protein structures of the virus. Two proteases are crucial in replicating SARS-CoV-2 and other coronaviruses: the main protease (Mpro) and the papain-like protease (PLpro). In this review, we summarize the essential viral proteins of SARS-CoV-2 required for its viral life cycle as targets for chemotherapy of coronavirus infection and provide a critical summary of the development of drugs against COVID-19 from the drug repurposing strategy up to the molecular design of novel covalent and non-covalent agents capable of inhibiting virus replication. We overview the main antiviral strategy and the choice of SARS-CoV-2 Mpro and PLpro proteases as promising targets for pharmacological impact on the coronavirus life cycle.

Artificial intelligence in the healthcare sector

A subfield of artificial intelligence called "machine learning" enables computers to learn from data without explicit human programming. In a broad sense, artificial intelligence (AI) refers to any computer or system behavior that resembles human behavior. One of the most significant contemporary trends in global healthcare is the use of artificial intelligence (AI) technologies in medicine. Technologies based on artificial intelligence are profoundly transforming the world's healthcare system, enabling a dramatic reconstruction of the medical diagnostics system while simultaneously lowering healthcare expenditures. Identifying the class of diseases to which a disease belongs is crucial before treating it. It is feasible to categorize the type of disease based on the feature space of the condition. Algorithms for machine learning can address this issue.

Immediate effect of the COVID-19 pandemic on governance: The case of México

COVID-19 outbreak exposed the governance and governability limitations of world health care systems, and México was no exception. With data from the Mexican Ministry of Health, two Gompertz models were estimated with a time horizon of the first three months of the pandemic. Factors such as chronic diseases, age groups, and sex were related. The probability of dying increased with age &gt;51 years and in men. Comorbidities increased the probability of death: Diabetes at 70.1%, renal failure at 50.8%, and obesity at 42.7%. Compared to other countries, México was unable during the initial stage of the Covid-19 outbreak to establish a coherent policy appropriate to the epidemiological emergency.

A culture-free biphasic approach for sensitive and rapid detection of pathogens in dried whole-blood matrix

Blood stream infections (BSIs) cause high mortality, and their rapid detection remains a significant diagnostic challenge. Timely and informed administration of antibiotics can significantly improve patient outcomes. However, blood culture, which takes up to 5 d for a negative result, followed by PCR remains the gold standard in diagnosing BSI. Here, we introduce a new approach to blood-based diagnostics where large blood volumes can be rapidly dried, resulting in inactivation of the inhibitory components in blood. Further thermal treatments then generate a physical microscale and nanoscale fluidic network inside the dried matrix to allow access to target nucleic acid. The amplification enzymes and primers initiate the reaction within the dried blood matrix through these networks, precluding any need for conventional nucleic acid purification. High heme background is confined to the solid phase, while amplicons are enriched in the clear supernatant (liquid phase), giving fluorescence change comparable to purified DNA reactions. We demonstrate single-molecule sensitivity using a loop-mediated isothermal amplification reaction in our platform and detect a broad spectrum of pathogens, including gram-positive methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteria, gram-negative Escherichia coli bacteria, and Candida albicans (fungus) from whole blood with a limit of detection (LOD) of 1.2 colony-forming units (CFU)/mL from 0.8 to 1 mL of starting blood volume. We validated our assay using 63 clinical samples (100% sensitivity and specificity) and significantly reduced sample-to-result time from over 20 h to <2.5 h. The reduction in instrumentation complexity and costs compared to blood culture and alternate molecular diagnostic platforms can have broad applications in healthcare systems in developed world and resource-limited settings.

Plasma Cytokines Level and Spinal Cord MRI Predict Clinical Outcome in a Rat Glial Scar Cryoinjury Model.

Traumatic injury of the spinal cord is still one of the most challenging problems in the neurosurgical practice. Despite a long history of implementation of translational medicine in the field of spinal cord injury (SCI), it remains one of the most frequent causes of human disability and a critical situation for world healthcare systems. Here, we used our rat model of the of unilateral controlled SCI induced by a cryoinjury, which consistently reproduces glial scarring and posttraumatic cyst formation, and specifically evaluated histological, bioimaging and cytokine data. We propose a 10-grade scoring scale, which can objectively estimate the extent of damage of the experimental SCI according to the magnetic resonance imaging (MRI) results. It provides a homogeneous and reliable visual control of the dynamics of the posttraumatic processes, which makes it possible to clearly distinguish the extent of early damage, the formation of glial scars and the development of posttraumatic syringomyelic cysts. The concentration of cytokines and chemokines in the plasma following the experimental SCI increased up to two orders of magnitude in comparison with intact animals, suggesting that a traumatic injury of the spinal cord was accompanied by a remarkable cytokine storm. Our data suggested that the levels of IL-1α, IL-1β, TNFα, GRO/KC, G-CSF, IFNγ and IL-13 may be considered as a reliable prognostic index for SCI. Finally, we demonstrated that MRI together with plasma cytokines level directly correlated and reliably predicted the clinical outcome following SCI. The present study brings novel noninvasive and intravital methods for the evaluation of the therapeutic efficacy of SCI treatment protocols, which may be easily translated into the clinical practice.

Підприємницький пілар системи охорони здоров’я: передумови рефомування та розвиток

Introduction. In the conditions of COVID-dependent socio-economic space, martial law, energy and economic crisis threats, it is important to support and ensure resistance to challenges, search for new opportunities for business initiatives, in particular for the health care system of Ukraine. Problem Statement. In the process of reforming the health care system of Ukraine, in addition to changes in the eco-environment of medical organizations, the medical sector has faced a number of crises which are the trigger for the implementation of a radically new approach to conducting business. The use of an entrepreneurial pillar will lay the foundation for the implementation and management of strategic changes in the medical sector. The purpose of the article is to determine the prerequisites for reforming the health care system of Ukraine and the inertial direction of its development in modern economic conditions. Materials and methods. In the process of writing the article, methods of general scientific, empirical and theoretical knowledge were used. They found their place in considering the historical stage of the health care system, providing comparative characteristics, the analysis of current eco-environment situation and so on. Results. The article considers the goals of the health care system and its relevance at the macro, meso- and micro levels, archaic formation and historical formation of the preconditions for the formation of the modern health care system of Ukraine, as well as prospects for its development. An analysis of the world health care systems, comparative characteristics, opportunities and threats of their use. Emphasis is placed on current trends and expertly predicted changes in the living environment of society, its service sector and producers, which require special attention in the areas of nature protection, life and health of the population of each country. The existing pillars and the mechanism of leverage control to influence the blocks of internal goals of the organization, within the implementation of the external goal of the world approved by the WHO, and the block of consumer needs are emphasized. The importance of further direction of reform and ways of flexible response in the conditions of existential challenges of the ecological environment is substantiated. Conclusions. Healthcare facilities in Ukraine need to introduce an established pattern - organizational and management mechanism - for the efficient use of financial resources and strategic development.

How the COVID-19 pandemic has affected the colorectal cancer screening in Italy: A minireview.

The coronavirus disease 2019 (COVID-19) pandemic has caused detrimental effects on many aspects of healthcare practice. Screening programs for the commonest malignancies, namely colorectal cancer (CRC), breast cancer and cervical cancer have been discontinued or interrupted since the beginning of restriction measures aimed to limit transmission of the new coronavirus infection. Robust evidence exists in favour of the role of screening campaigns in reducing mortality from CRC. In fact, the majority of pre-malignant lesions of the colon and rectum can be diagnosed with colonoscopy and treated by endoscopic or surgical resection. Besides, colonoscopy screening allows the diagnosis of CRCs in their pre-clinical stage. Italy was one of the first European countries where a high level of COVID-19 infections and deaths was observed, and one of the first where lockdowns and strict measures were adopted to reduce the risk of COVID-19 diffusion among the population. A systematic review of the literature was performed, including the PubMed, Scopus, Web of Sciences, and Reference Citation Analysis databases, with the aim of critically evaluating the impact of the COVID-19 pandemic on CRC screening in Italy. We found that reduction of CRC screening activity surpassed 50% in most endoscopic units, with almost 600000 fewer CRC screening exams conducted in the first 5 mo of 2020 vs the same period of 2019. While the consequences of the discontinuation of endoscopy screening for the prognosis and mortality of CRC will be evident in the next few years, recent data confirm that CRC is currently treated at a more advanced stage than in the pre-COVID-19 era. Since delays in CRC prevention and early diagnosis may translate to increased CRC-specific mortality, world healthcare systems should adopt strategies to maintain the regularity of CRC screening during subsequent peaks of the COVID-19 pandemic, or future events that might hamper screening programs.

PIKfyve-specific inhibitors restrict replication of multiple coronaviruses in vitro but not in a murine model of COVID-19

The ongoing COVID-19 pandemic has claimed more than 6 million lives and continues to test the world economy and healthcare systems. To combat this pandemic, the biological research community has shifted efforts to the development of medical countermeasures, including vaccines and therapeutics. However, to date, the only small molecules approved for the treatment of COVID-19 in the United States are the nucleoside analogue Remdesivir and the protease inhibitor Paxlovid, though multiple compounds have received Emergency Use Authorization and many more are currently being tested in human efficacy trials. One such compound, Apilimod, is being considered as a COVID-19 therapeutic in a Phase II efficacy trial. However, at the time of writing, there are no published efficacy data in human trials or animal COVID-19 models. Here we show that, while Apilimod and other PIKfyve inhibitors have potent antiviral activity in various cell lines against multiple human coronaviruses, these compounds worsen disease in a COVID-19 murine model when given prophylactically or therapeutically.

The prospects for the use of artificial intelligence in obstetrics in Ukraine

In Ukraine, there is a steady trend towards an increase in the incidence of various forms of hypertensive disorders, both mild and severe, among pregnant women. Preeclampsia and eclampsia are severe forms of hypertensive disorders during pregnancy, the rates of which tend to increase, and lead to severe morbidity, disability and mortality in both women and newborns.The article reveals the question of the artificial intelligence (AI) use in modern midwifery in the national health care system of Ukraine in order to prevent pregnancy complications, in particular eclampsia. Implementation of AI systems in medicine is one of the most important modern trends in global healthcare. AI technologies are fundamentally changing the world health care system, allowing to radically rework the system of medical diagnostics, the development of new medicines, and also to generally improve the quality of health care services while simultaneously reducing costs for medical clinics. AI allows analyzing placenta samples and calculating the risk for a woman’s health in future pregnancies. The system is designed to help obstetrician-gynecologists, who will find it useful to forecast possible complications of future births. The use of AI in clinical practice makes it possible to identify the most serious prognostic signs of damage to blood vessels of the placenta, which is called decidual vasculopathy in preeclampsia and eclampsia. If preeclampsia is detected at an early stage, the patient can be treated before symptoms appear.AI use must be in compliance with six principles of the WHO in order to limit risks and increase the possibilities of using AI technologies: protection of human autonomy; promotion of human well-being and safety and public interests; ensuring transparency and comprehensibility; promoting responsibility and accountability; ensuring inclusiveness and fairness; promotion of AI in accordance with the principles of adaptability and sustainable development. Adherence to the ethical principles of AI gives the green light for the development of modern health care systems. It can become an effective method of preventing eclampsia in pregnant women, women in labor, and newborns and preserving their health and life.

Prognostic Value of Transaminases and Bilirubin Levels at Admission to Hospital on Disease Progression and Mortality in Patients with COVID-19-An Observational Retrospective Study.

Introduction: Given the impact of COVID-19 on the world healthcare system, and the efforts of the healthcare community to find prognostic factors for hospitalization, disease progression, and mortality, the aim of the present study was to investigate the prognostic impact of transaminases and bilirubin levels at admission to hospital on disease progression and mortality in COVID-19 patients. Methods: Using the CoviCamp database, we performed a multicenter, observational, retrospective study involving 17 COVID-19 Units in southern Italy. We included all adult patients hospitalized for SARS-CoV-2 infection with at least one determination at hospital admission of aminotransaminases and/or total bilirubin. Results: Of the 2054 patients included in the CoviCamp database, 1641 were included in our study; 789 patients (48%) were considered to have mild COVID-19, 347 (21%) moderate COVID-19, 354 (22%) severe COVID-19, and 151 patients (9%) died during hospitalization. Older age (odds ratio (OR): 1.02; 95% confidence interval (CI) 1.01–1.03), higher Charlson comorbidity index (CCI) (OR 1.088; 95%CI 1.005–1.18), presence of dementia (OR: 2.20; 95% CI: 1.30–3.73), higher serum AST (OR: 1.002; 95% CI: 1.0001–1.004), and total bilirubin (OR: 1.09; 95% CI: 1.002–1.19) values were associated with a more severe clinical outcome. Instead, the 151 patients who died during hospitalization showed a higher serum bilirubin value at admission (OR 1.1165; 95% CI: 1.017–1.335); the same did not apply for AST. Discussion: Patients with COVID-19 with higher levels of AST and bilirubin had an increased risk of disease progression.

Covid Fungal Detection Using Deep Learning

Abstract: COVID-19, also known as 2019-nCoV, is no longer a pandemic, but a pandemic that has killed more than 651,247 people worldwide. COVID-19 currently has no specific treatment or cure, so living with this condition and its symptoms is unavoidable. This fact has made it increasingly difficult for the world's health care system, especially in developing lands. Despite the fact that there is no effective or proven clinical trial of an antibacterial agent, Although there is no mandatory goal of ending the COVID19 epidemic, there are options that can help reduce the burden. Data development is not only limited health care programs but also economics. Famous learning, data mining, in-depth learning, and other practical strategies are examples. Other alternatives such as the 2019-nCoV epidemiological diagnosis and prognosis will be simplified. shrink, decision tree, vector support machine, Naive Bayes, and neutral network. Prior to the creation of the models, an analysis of the coefficient of correlation between different dependent and independent variables was performed to determine the interaction potential between each dependent element and independent data. Training data collection was used to train models 80 percent of the time, and the remaining 20% was used to test models.