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Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.
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The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.
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Objectives: To analyze the budget impact (BI) of Covid-19 vaccines from a mixed U.S commercial and Medicare payer perspective after depletion of the Federally-Purchased Supply (FPS). Method(s): BI analyses were conducted in a hypothetical one-million member health plan with a mixed commercial (55%) and Medicare (45%) population over a one-year time horizon based on the current (January 2023) Covid-19 vaccine recommendations from the Centers for Disease Control and Prevention (CDC). The two scenarios in the model include 1) the health plan does not pay for Covid-19 vaccines, and 2) after the depletion of FPS, the health plan must cover all costs for Covid-19 vaccines. Model inputs include the market shares of available Covid-19 vaccines in the US as of December 2022, Covid-19 vaccine utilization trends stratified into age groups (<12, 12-17, 18-24, 25-49, 50-64, >=65 years old) between commercial and Medicare populations, and predicted Covid-19 vaccine costs. Model inputs were based on the CDC publicly available data, real world evidence, published literature, and expert opinions. Sensitivity analyses (SA) were conducted to test uncertainties arising from the input values in the model. Result(s): The number of members receiving one primary dose, completed Covid-19 vaccine series, one booster dose and two booster doses was estimated at 9,253, 49,720, 594,933 and 29,387, respectively. The incremental Covid-19 vaccine cost per member per month over one year after depletion of the FPS was $5.92 for the commercial population, $8.93 for the Medicare population, and $7.27 for the total population in the health plan. In the SA, the largest effect was observed for the scenario which varied the percentage of population >=65 years old receiving one booster dose. Conclusion(s): The model results indicate that there will be a high budget impact from a mixed U.S commercial and Medicare perspective after depletion of the FPS of Covid-19 vaccines.Copyright © 2023
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Background: Studies suggest perinatal infection with SARSCoV- 2 can induce adverse birth outcomes, but studies published to date have substantial limitations. Most have identified cases based upon their presentation for clinical care, and very few have examined pandemic-related stress which may also impact adverse birth outcomes. Objective(s): To evaluate the relationships between SARSCoV- 2 infection in pregnancy and pandemic-related stress with birth outcomes. Study Design: We conducted an observational study of 211 mother-newborn dyads in three urban cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) Program. Serology for SARS-CoV-2 was assessed in a convenience sample of prenatal maternal, cord serum or dried blood spots from births occurring between January 2020-September 2021. Specimens were assessed for IgG, IgM, and IgA antibodies to nucleocapsid, S1 spike, S2 spike, and receptor-binding domain. A Pandemic-related Traumatic Stress (PTS) scale was based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition Acute Stress Disorder criteria. Result(s): 36% were positive for at least one antibody type, chiefly IgG. Self-report of infection was not significantly correlated with combined serology. There were no differences in gestational age (GA), birth weight, preterm birth (PTB), or low birth weight (LBW) among seropositive mothers. However, IgM seropositive mothers had children with lower BW (434g, 95% CI: 116- 752), BW Z score-for-GA (0.73 SD, 95% CI 0.10-1.36) and were more likely to deliver preterm (OR 8.75, 95% CI 1.22-62.4). Associations with LBW sustained in sensitivity analyses limited to pre-vaccine samples, and PTS symptoms were not associated with birth outcomes. The addition of PTS did not substantially change associations with BW, although associations with PTB attenuated to near-significance. Conclusion(s): We identified decreased birth weight and increased prematurity in mothers IgM seropositive to SARS-CoV-2, independent of PTS. Though there are limits to interpretation, the data support efforts to prevent SARS-CoV-2 infections in pregnancy.
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Summary Introduction: The SARS-CoV-2 coronavirus, that causes the COVID-19 disease, has become a global public health problem that requires the implementation of rapid and sensitive diagnostic tests. Aim(s): To evaluate and compare the sensitivity of LAMP assay to a standard method and use RT-LAMP for the diagnosis of SARS-CoV-2 in clinical samples from Colombian patients. Method(s): A descriptive and cross-sectional study was conducted. A total of 25 nasopharyngeal swab samples including negative and positive samples for SARS-CoV-2 were analyzed, through the RT-LAMP method compared to the RT-qPCR assay. Result(s): LAMP method detected ~18 copies of the N gene, in 30 min, evidenced a detection limit similar to the standard method, in a shorter time and a concordance in RT-LAMP of 100% with the results. Conclusion(s): RT-LAMP is a sensitive, specific, and rapid method that can be used as a diagnostic aid of COVID-19 disease.Copyright © 2021. All Rights Reserved.
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Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.
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Background: Since patients admitted to the intensive care unit have a compromised im-mune system and are more prone to infection than other patients, timely diagnosis and treatment of corneal ulcers among this group of patients can prevent vision loss. Therefore, it is necessary to treat eye infections and corneal ulcers promptly and economize prohibitive costs. Objective(s): Appropriate treatment with the most effective antibiotic before the answer is available to prevent corneal ulcer complications and blindness. Method(s): This study was conducted from November 2019 to November 2020 and after approval by the ethics committee of Hamedan University of Medical Sciences with the code of ethics: IR.UMSHA.REC.1398.716. First, the corneal secretions of 121 patients admitted to the intensive care unit of Sina Hospital are prepared by an ophthalmologist (after anesthetizing the cornea with tetra-caine drops and sterile swabs) and culture in four growth mediums (blood agar, chocolate agar, thio-glycolate, and EMB). Microbial cultures are examined after 48 hours and a fungal culture is examined one week later. Disc diffusions are placed in positive microbial cultures. Antibiotic susceptibility or resistance of the antibiogram was recorded. Other demographic data, including patients' age and sex, are extracted from ICU files. Also, test results and patient identifications are recorded in a checklist designed for this purpose. Result(s): Of all the antibiotics used against common bacteria, vancomycin (84%), colistin (80.43%), cefazolin (80%), and levofloxacin (60%) had the highest sensitivity and gentamicin (93.75%), ceftazidime (86.42%) Erythromycin (85%) had the highest resistance against isolated bacteria. Conclusion(s): The data obtained from this study showed that the most common microorganisms in the age group under the age of 30 years were Acinetobacter Baumannii, in the group of 30-60 years old was Klebsiella pneumonia, and age group over 61 years old was Staphylococcus aureus, and the most sensitive antibiotics in the age group under 30 years were vancomycin and levofloxacin and the age group30-60 were colistin and vancomycin and in the age group over 61 years were vancomycin and cefazolin.Copyright © 2023 Bentham Science Publishers.
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BACKGROUND: Hepatopancreatobiliary (HPB) cancer incidence and mortality are increasing worldwide. An initial diagnostic predictor is needed for recommending further diagnostic modalities, referral, and curative or palliative decisions. There were no studies conducted in area with limited accessibility setting of the COVID-19 pandemic, coupled with limited human resources and facilities. AIM: We aimed to investigate the advantages of total bilirubin for predicting malignant obstructive jaundice, a combination of the pandemic era and limited resources settings. METHOD(S): Data from all cholestasis jaundice patients at M. Djamil Hospital in Pandemic COVID-19 period from July 2020 to May 2022 were retrospectively collected. The data included demographics, bilirubin fraction results, and final diagnosis. Bivariate analysis for obtain demographic risk factor, and Receiver Operating Characteristics (ROC) analysis for getting bilirubin value. RESULT(S): Of a total 132 patients included, 35.6% were malignant obstructive jaundice, and Pancreatic adeno ca was the most malignant etiology (34.4%). Bivariate analysis showed a significant correlation between age and malignant etiology (p = 0,024). Direct and total Bilirubin reach the same level of Area Under Curve (AUC). Total bilirubin at the cutoff point level of 10.7 mg/dl had the most optimal results on all elements of ROC output, AUC 0.88, sensitivity 76.6%, specificity 90.1%, +LR 8.14, and-LR 0.26. CONCLUSION(S): The bilirubin fraction is a good initial indicator for differentiating benign and malignant etiology (AUC 0.8-0.9) in pandemic era and resource-limited areas to improve diagnostic effectiveness and reduce referral duration.Copyright © 2023 Avit Suchitra, M. Iqbal Rivai, Juni Mitra, Irwan Abdul Rachman, Rini Suswita, Rizqy Tansa.
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In this work, we modified a dynamical system that addresses COVID-19 infection under a fractal-fractional-order derivative. The model investigates the psychological effects of the disease on humans. We establish global and local stability results for the model under the aforementioned derivative. Additionally, we compute the fundamental reproduction number, which helps predict the transmission of the disease in the community. Using the Carlos Castillo-Chavez method, we derive some adequate results about the bifurcation analysis of the proposed model. We also investigate sensitivity analysis to the given model using the criteria of Chitnis and his co-authors. Furthermore, we formulate the characterization of optimal control strategies by utilizing Pontryagin's maximum principle. We simulate the model for different fractal-fractional orders subject to various parameter values using Adam Bashforth's numerical method. All numerical findings are presented graphically. © 2023 by the authors.
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Globally, the COVID-19 pandemic's development has presented significant societal and economic challenges. The carriers of COVID-19 transmission have also been identified as asymptomatic infected people. Yet, most epidemic models do not consider their impact when accounting for the disease's indirect transmission. This study suggested and investigated a mathematical model replicating the spread of coronavirus disease among asymptomatic infected people. A study was conducted on every aspect of the system's solution. The equilibrium points and the basic reproduction number were computed. The endemic equilibrium point and the disease-free equilibrium point had both undergone local stability analyses. A geometric technique was used to look into the global dynamics of the endemic point, whereas the Castillo-Chavez theorem was used to look into the global stability of the disease-free point. The system's transcritical bifurcation at the disease-free point was discovered to exist. The system parameters were changed using the basic reproduction number's sensitivity technique. Ultimately, a numerical simulation was used to apply the model to the population of Iraq in order to validate the findings and define the factors that regulate illness breakout.
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Introduction: Acute appendicitis is the most common cause of acute abdominal pain as well as one of the most frequently performed procedures in general surgery. Different prognostic laboratory markers have been studied to identify patients with complicated appendicitis and it is unknown whether the level of procalcitonin in adults could be used as a predictive marker. From a cut-off point, Does procalcitonin have predictive value for complicated appendicitis? Methods: Prospective, observational study. Patients from the Civil Hospital of Guadalajara with a diagnosis of Appendicitis, presurgical laboratory studies and Procalcitonin, and undergo appendectomy in this institution. A calculated sample was obtained based on the surgeries performed annually. Result(s): 80 appendicectomies were performed in the 12-month period (2021;COVID pandemic) obtaining: 37 patients with uncomplicated appendicitis (Phase I and II) 43 patients with complicated appendicitis (Phase III and IV) The procalcitonin levels of both groups were analyzed to demonstrate differences between them, Mann-Whitney U test gives us as a result a p value <0.05. For the cut-off point at the most suitable procalcitonin level for this sample we decided to use the Yauden index method in the analysis of the ROC curve: it is observed that the cut-off point with a sensitivity of 72.1% and a specificity of 81.1% for the sample is 0.305. Conclusion(s): Procalcitonin has been shown to be a useful marker for discriminating the severity of appendicitis and that the best cutoff point for this sample is 0.3 ng/dl.
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Objective: Intensive care units (ICUs) collapsed under the global wave of coronavirus disease 2019 (COVID-19). Thus, we designed a clinical decision-making model that can help predict at hospital admission what patients with COVID-19 are at higher risk of requiring critical care. Method(s): This was a cross-sectional study in 119 patients that met hospitalization criteria for COVID-19 including less than 30 breaths per minute, peripheral oxygen saturation < 93%, and/or >= 50% lung involvement on imaging. Depending on the need for critical care, patients were retrospectively assigned to ICU and non-ICU groups. Demographic, clinical, and laboratory parameters were collected at admission and analyzed by classification and regression tree (CRT). Result(s): Forty-five patients were admitted to ICU and 80% of them were men older than 57.13 +/- 12.80 years on average. The leading comorbidity in ICU patients was hypertension. The CRT revealed that direct bilirubin (DB) > 0.315 mg/dl together with the neutrophil-to-monocyte ratio (NMR) > 15.90 predicted up to correctly in 92% of the patients the requirement of intensive care management, with sensitivity of 93.2%. Preexisting comorbidities did not influence on the tree growing. Conclusion(s): At hospital admission, DB and NMR can help identify nine in 10 patients with COVID-19 at higher risk of ICU admission.Copyright © 2022 Sociedad Medica del Hospital General de Mexico.
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The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Mortality attributable to COVID-19 remains considerably high, with case fatality rates as high as 8-11%. Early medical intervention in patients who are seriously and critically ill with COVID-19 reduces fatal outcomes. Thus, there is an urgent need to identify biomarkers that could help clinicians determine which patients with SARS-CoV-2 infection are at a higher risk of developing the most adverse outcomes, which include intensive care unit (ICU) admission, invasive ventilation, and death. In COVID-19 patients experiencing the most severe form of the disease, tests of liver function are frequently abnormal and liver enzymes are found to be elevated. For this reason, we examine the most promising liver biomarkers for COVID-19 prognosis in an effort to help clinicians predict the risk of ARDS, ICU admission, and death at hospital admission. In patients meeting hospitalization criteria for COVID-19, serum albumin < 36 g/L is an independent risk factor for ICU admission, with an AUC of 0.989, whereas lactate dehydrogenase (LDH) values > 365 U/L accurately predict death with an AUC of 0.943.The clinical scores COVID-GRAM and SOFA that include measures of liver function such as albumin, LDH, and total bilirubin are also good predictors of pneumonia development, ICU admission, and death, with AUC values ranging from 0.88 to 0.978.Thus, serum albumin and LDH, together with clinical risk scores such as COVID-GRAM and SOFA, are the most accurate biomarkers in the prognosis of COVID-19.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.
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Burkholderia pseudomallei is soil saprophytic Gram-negative bacilli that cause a fatal disease called melioidosis. Melioidosis is capable of causing cutaneous infection and systemic infections in the respiratory tract, cardiovascular, gastrointestinal, urinary, skin and soft tissue, and musculoskeletal and central nervous systems. Here, we report rare forms of pulmonary, cerebral, and splenic abscess case series of melioidosis caused by B. pseudomallei. Imported cases have been reported among tourists, immigrants, and soldiers who returned from endemic areas. The acquisition of infection is through percutaneous, inhalation, and ingestion of contaminated water;person-to-person transmission is very rare. Melioidosis cases are primarily found in the rainfall season and are usually associated with risk factors such as diabetes, alcoholism, and chronic renal diseases. However, 20-26% of cases were not associated with predisposing conditions. The identification is based on colony morphology, Gram stain, antibiotic susceptibility testing, and other supportive automated and molecular assays when we suspect B. pseudomallei. There are two phases, the intensive and eradication phases, in managing melioidosis. In the intensive phase, ceftazidime for 2 weeks showed efficacy in almost 50% of cases, and the eradication phase treatment with co-trimoxazole and doxycycline or amoxicillin/clavulanic acid for 3-6 months showed an excellent response. The improper clinical diagnosis and management of B. pseudomallei can lead to complications. Hence, early diagnosis with microbiological approaches such as culture, biochemical reactions, or automated systems available and antimicrobial sensitivity testing will cure the patient quickly without mortality.Copyright © 2023 The Authors.
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Early identification of PNH, a rare life-threatening disease is essential to ensure appropriate management via the UK PNH service. Since testing for PNH is expensive (75.97 per test), we set out to assess the suitability of PNH requests against guidelines with the aim to feedback to colleagues and reduce unnecessary testing. To determine whether PNH requests at UHNM were in line with criteria in British Society for Haematology (BSH) guidelines. All patients over 18 years of age who had PNH testing for the first time and those who had repeat testing for monitoring between 01/04/2019 and 31/03/2020 were included. Patients were selected from electronic records of PNH sample receipt to laboratories. Hospital records were reviewed for clinical details and investigation Results. 82 requests including 79 individual patients were audited. 57% were male and 43% were female. Median age was 56 years. 97.6% of PNH tests were requested by a haematologist whilst only 2.5% requests were done by non-haematology clinicians. 52.4% requests were in keeping with BSH recommendations, whilst 47.6% tests did not meet criteria for testing. All patients tested outside of guideline recommendations were negative. Of the reasonable requests, only 23.3% (10) were positive. Of the PNH positive patients, 8 patients were known to have a PNH clone with aplastic anaemia;one patient had a hypoplastic bone marrow and a known PNH clone whilst only one patient with cytopenia had a new positivity for PNH. The frequency monitoring for aplastic anaemia and a PNH clone was 100% concordant with BSH recommendations. With appropriate testing, only one new patient was identified. Our audit has limitations. We have not been able to assess whether any patients outside of those monitored for PNH in aplastic anaemia have been overlooked for testing. Also, the time period includes the COVID-19 pandemic so our findings may not reflect usual practice. New BSH guidelines for thrombophilia testing were published in 2022 and recommend testing for PNH in patients with thrombosis at unusual sites and abnormal haematological parameters and for patients with arterial thrombosis and abnormal blood parameters. This will likely limit excess tests although the sensitivity and specificity of such an approach has not been formally evaluated. In a finite health system, it is our responsibility to rationalise investigations. The cost of a year's testing was 6229.54 and that of inappropriate testing was 2962.83. As a department, we could, therefore, save 2962.83.
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Introduction: COVID-19 related encephalitis has been reported in pediatric patients;however, there are no reports in patients with inborn errors of immunity (IEI). Activated PI3K Delta Syndrome (APDS) is a disease of immune dysregulation with immunodeficiency, autoimmunity, and abnormal lymphoproliferation resulting from autosomal dominant gain-offunction variants in PIK3CD or PIK3R1 genes. We investigate a family with APDS, one mother and three children, one of whom developed COVID-19 related encephalitis. Method(s): Patients were consented to an IRB-approved protocol at our institution. Medical records and detailed immunophenotyping were reviewed. Family members were sequenced for IEI with a targeted gene panel. Result(s): The index case is a 10-year-old female with a known pathogenic variant in PIK3CD (c.3061 G > A, p.Glu1021Lys), who contracted SARS-COV-2 despite one COVID-19 vaccination in the series. Her disease course included COVID-related encephalitis with cerebellitis and compression of the pons, resulting in lasting truncal ataxia and cerebellar mutism. At that time, the patient was not on immunoglobulin replacement therapy (IgRT), but was receiving Sirolimus. Besides the index case, 3 family members (2 brothers, 1 mother) also share the same PIK3CD variant with variable clinical and immunological phenotypes. All children exhibited high transitional B-cells, consistent with developmental block to follicular B cell stage. Increased non-class switched IgM+ memory B cells and skewing towards CD21lo B cell subset, which is considered autoreactive-like, was observed in all patients. Of note, the patient had low plasmablasts, but normal immunoglobulins. Of her family members, only one was receiving both sirolimus and IgRT. Conclusion(s): We describe a rare case of COVID-19-related encephalitis in a patient with inborn error of immunity while not on IgRT. This may indicate infection susceptibility because of a lack of sufficient immunity to SARS-CoV-2, unlike the rest of her family with the same PIK3CD variant.Copyright © 2023 Elsevier Inc.
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A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.
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High-sensitivity cardiac troponins expedite the evaluation of patients with chest pain in the emergency department. The utility of troponins extends beyond the acute coronary syndromes to accurate the diagnosis of myocardial injury. Troponins are best friends for physicians;however, they are a double-edged sword if not interpreted appropriately. Misdiagnosis is harmful with regard to patient outcomes. The present review focuses on the recent updates in the understanding and interpretation of high-sensitivity troponins in various acute clinical settings. Common mistakes and gray zones in the interpretation of troponins, the concept of myocardial injury versus infarction, newer entities like myocardial infarction (MI) with Nonobstructive Coronary Arteries, recent controversies over the definition of periprocedural MI, complementary role of imaging in the diagnosis of myocardial injury and the role of troponins in the current COVID-19 pandemic are discussed.Copyright © 2022 Saudi Center for Organ Transplantation.
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Since the end of 2021, Omicron, the new variant of SARS-CoV-2, has continued to spread as the predominant strain of COVID-19. Compared to previous variants, Omicron causes milder symptoms, which are similar to symptoms of other common respiratory infections, such as flu. In this work, we develop a silicon photonic chip-based biosensor for COVID-19 and flu detection using subwavelength grating micro-ring resonator. The biosensor realizes the detection of two pathogens with high sensitivity (1.31 fg/mL) and specificity. Besides, the microfluidic channel offers a promising solution for point-of-care detection. © 2023 SPIE.