COVID-19 and the ageing immune system in an elderly patient: a case report

Background: The severity of COVID-19 infection has an increasing trend in the elderly, which contributes to the high morbidity and mortality rates in this population. Aging itself is a prominent risk factor for severe disease and death from COVID-19. Case Description: This case report a 71-year-old woman who complained of shortness of breath for 3 days before being admitted to the hospital. Bilateral consolidation and increased bronchovascular pattern were found on chest radiograph, and a positive SARS-COV2 nasopharyngeal swab PCR test result was noted. This patient was diagnosed with confirmed severe manifestation of COVID-19, community-acquired pneumonia and type 1 respiratory failure, as well as type II diabetes mellitus and suspicion of acute gastritis. The results of the geriatric status assessment were moderate functional status, risk of malnutrition, and moderate risk of deep vein thrombosis (DVT). This patient underwent treatment in accordance with the COVID-19 protocol along with management for geriatric status improvement. The patient was given permission to return home after 14 days of treatment, during which time her health had improved and her functional status had changed to moderate dependency. During follow-up, the patient continued to receive therapy. She is still being observed and future evaluations will be conducted. Conclusion: The increased susceptibility of the elderly to COVID-19 infection is caused by various factors. A burden of death and long-term disability brought on by this pandemic may be lessened by new or modified therapies that target aging-associated mechanisms. Therefore, COVID-19 case management in this population should be done with a comprehensive approach.

Evaluation of Lung Ultrasound in Assessment of Critically Ill patients in Respiratory Intensive Care Unit

Reaching a proper diagnosis for critically ill patients is like collecting pieces of puzzle and bed side lung ultrasound (LUS) becomes a crucial piece complementary to clinical and laboratory pieces. It is a bed side, real time tool for diagnosis of patients in ICU who are critical to be transferred to radiology unit especially in Covid-19 pandemic with risk of infection transmission. The aim was to evaluate the accuracy of lung ultrasound in assessment of critically ill patients admitted to Respiratory Intensive Care Unit (RICU), moreover to assess its diagnostic performance in different pulmonary diseases as compared to the gold standard approach accordingly. This observational prospective (cross sectional) study with a total 183 patients who met the inclusion criteria,were selected from patients admitted at the RICU;Chest Department, Zagazig University Hospitals, during the period from September 2019 to September 2021. LUS examination was performed to diagnose the different pulmonary diseases causing RF. All cases were examined by LUS on admission. From a total 183 patients, 111 patients 60.7% were males and 72 patients 39.3% were females, with a mean age of 56+/-12.77 years, 130 patients were breathing spontaneously received conservative management with O2 therapy, 32 patients needed NIV while 21 patients needed IMV with ETT. Exacerbated COPD was the most common disease finally diagnosed followed by bacterial pneumonia, exacerbated ILD, post Covid-19 fibrosis and pulmonary embolism in32, 29,27, 19 and 11 patients respectively with corresponding diagnostic accuracy of LUS 97.3%, AUC=0.943, 93.9% (AUC=0.922), 96.7%(AUC=0.920), 97.8%, AUC=0.895, and 97.8% respectively, while Covid-19 pneumonia was the final diagnosis in 8 patients with LUS diagnostic accuracy of 97.8% (AUC=0.869) with no statistical significant difference p-value=0.818 with bacterial pneumonia in distribution of US profiles. A profile was the commonest detected US profile among the studied patients followed by B profile, C profile, A/B profile and A' profile in 37.2%, 24.6%, 15.8% 4.9%, and 3.8% of cases respectively. Bed side LUS has a reliable, valuable diagnostic performance when integrated with clinical and laboratory data for the diagnosis of most pulmonary diseases in RICU.Copyright © 2023, Colegio de Farmaceuticos de la Provincia de Buenos Aires. All rights reserved.

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.

The use of venovenous extracorporeal membrane oxygenation in COVID-19: An updated systematic review and meta-analysis of clinical outcomes

Objectives: The objective of this study is to assess the clinical benefits and potential risks of using venovenous extracorporeal membrane oxygenation (VV ECMO) as a treatment for COVID-19 patients with severe respiratory failure. Method(s): Relevant studies were identified through searches of electronic databases, including PubMed, EMBASE, and the Cochrane Library, from January 2020 to December 2022. We included observational studies on adult patients who received venovenous (VV) ECMO support for COVID-19-induced ARDS. The primary outcome was in-hospital mortality, 3-month mortality, and complications associated with VV ECMO. Statistical analysis was performed using R version 4.0.3 and the metafor and meta packages. Result(s): The final analysis included 39 studies comprising 10,702 patients. In-hospital mortality for adults receiving ECMO was 34.2% (95% CI: 28.5% - 40.3%;I2 = 93%), while the 3-month mortality rate was 50.2% (95% CI: 44.4% - 56.0%;I2 = 51%). Bleeding requiring transfusion occurred in 33.7% of patients (95% CI, 23.9 - 45.1;I2 = 96%). The pooled estimates for other complications were as follows: overall thromboembolic events 40.9% (95% CI, 24.8 - 59.3;I2 = 97%), stroke 8.7% (95% CI, 5.7 - 13.2;I2 = 72%), deep vein thrombosis 15.4% (95% CI, 9.7 - 23.6;I2 = 80%), pulmonary embolism 15.6% (95% CI, 9.3 - 25.1;I2 = 92%), gastrointestinal haemorrhage 8.1% (95% CI, 5.5 - 11.8;I2 = 56%), and the need for any renal replacement therapy in 38.0% of patients (95% CI, 31.6 - 44.8;I2 = 84%). Bacterial pneumonia occurred in 46.4% of patients (95% CI, 32.5 - 61.0;I2 = 96%). Conclusion(s): Venovenous extracorporeal membrane oxygenation (VV ECMO) may be an effective treatment option for COVID-19 patients with severe respiratory failure. The use of VV ECMO was associated with reduced in-hospital and 3-month mortality. However, bleeding is a common complication that should be closely monitored. Further research is needed to determine the optimal use of VV ECMO in this patient population and to identify factors that may predict a favourable response to treatment.

Sars cov-2 infection in a neonate in ghana: A case report

Backgrounds: The clinical and socioeconomic effects of COVID-19 are still being felt through-out the world. The disease affects people of all age groups, but it is known to have a milder clinical course in children including neonates. There is paucity of data from Sub-Saharan Africa on neonatal COVID-19 infection, and no such case has been reported in the literature in Ghana. Case presentation: This study presented a case report of a neonate who was found to be positive for COVID-19 infection after presenting symptoms such as respiratory distress, rhinorrhoea, and cough. This neonate was managed with in-hospital standard protocol for sepsis with a focus on pneumonia. Conclusion(s): The national guidelines on COVID-19 management were used for the neonate who was recovered and discharged.Copyright © 2021, TMU Press.

Pulmonary infections in cancer patients

Lymphangitis carcinomatosa refers to pulmonary interstitial involvement by cancer and is a dreaded clinical finding in oncology because it is a late manifestation indicative of metastatic malignancy, from either a lung or a nonlung primary cancer, and is associated with poor prognosis. Its presentation is nonspecific, often with subacute dyspnoea and a nonproductive cough in a person with a known history of malignancy, but in some cases is the first manifestation of cancer. CT imaging can be suggestive, typically demonstrating thickening of the peribronchovascular interstitium, interlobular septa and fissures. However, a biopsy may be required to confirm the pathological diagnosis as these changes can also be due to concurrent disease such as heart failure, ILD, infection, radiation pneumonitis and drug reactions. Diagnosis allows symptomatic treatment, with personalised treatment directed towards the primary cancer most likely to provide a meaningful benefit. Future research should focus on prospective clinical trials to identify new interventions to improve both diagnosis and treatment of lymphangitis carcinomatosa.Copyright © ERS 2021.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Evaluation of the value of exhaled carbon monoxide in the differentiation of viral and bacterial pneumonia.

Aim: This study compared exhaled carbon monoxide (CO) levels in patients hospitalized for bacterial and COVID-19-related viral community-acquired pneumonia. Materials & methods: The study included a total of 150 patients: 50 patients hospitalized for COVID-19 between February 2021 and March 2022, 50 patients with bacterial community-acquired pneumonia and 50 healthy controls. Results: In comparisons of exhaled CO levels among the groups, there was no significant difference between patients with bacterial pneumonia and controls, whereas patients with COVID-19 pneumonia had significantly higher exhaled CO levels compared with both the bacterial pneumonia and control groups (p < 0.001). Conclusion: Viral agents can directly affect the heme oxygenase system of the lower respiratory tract, leading to greater increases in ferritin and exhaled CO levels compared with bacterial pneumonia.

Infections in the lung tissue cause stress in the body. Several mechanisms are activated in the body to balance this stress. The heme oxygenase system plays a role in suppressing inflammation, and its overactivation can cause an increase in the amount of carbon monoxide (CO) we exhale. This study examined exhaled CO levels in patients with bacterial lung infection and COVID-19 viral lung infection in comparison with the healthy population. We found that patients with COVID-19 lung infection had higher levels of CO in their breath than patients with bacterial lung infection and healthy control subjects. These findings suggest that measurements of exhaled CO levels in people with signs and symptoms of lung infection might be used to differentiate patients with viral and bacterial lung infections.

Global Access to Antimicrobials and Enhanced Emergence of Amr in Covid-19 Era

Of the major global public health issues of the 21st century, antimicrobial resistance (AMR) is still emerging as one of the leading threats, given its significant health, economic and security ramifications. Optimizing the use of antimicrobials through antimicrobial stewardship programs/efforts is a fundamental aspect in increasing clinical outcomes, via cost-effective treatments, as well as in reducing AMR. On the other hand, studies have shown that limited access to antimicrobials was not the answer in several settings. Accordingly, a combined approach of ensuring adequate global access to and appropriate use of antimicrobials was found to be a better response/action plan to the AMR problem. In addition to its serious health, economic and social implications, Covid-19 pandemic was a catalyst for AMR. Several AMR national action plans were affected by the prioritization of COVID-19 emergency, whereby activities and resources were diverted and channeled towards responding to the pandemic and AMR stewardship programs were not being reinforced. Additionally, the increased access to and use of antimicrobials to treat Covid-19 patients further fuelled AMR. Studies assessing the impact of the pandemic on AMR reported that antibiotic treatment was received by up to 70% of the hospitalized Covid-19 patients and among the latter high prevalence of AMR was reported during the first 18 months of the pandemic. Reasons underlying the increased prescribing of antimicrobials by the physicians treating Covid-19 patients included, suspected bacterial/fungal coinfection or superinfection, insufficient knowledge of the natural course of the respiratory illness and misdiagnosing cases due to the resemblance between the symptoms of SARS-Cov2 infection and that of bacterial pneumonia or other respiratory infection.Copyright © 2023

Antibiotic Stewardship Strategies to Limit Inappropriate Antibiotic Use and Reduce Multi-Drug Resistant Pathogens during Covid-19 Pandemic

Antibiotics have been extensively used in COVID-19 patients without a clear indication. COVID-19 pneumonia is associated with a mortality up to 20% varying by country with the number of global deaths over 5 million. Antibiotics have been extensively used in COVID-19 patients in intensive care units (ICUs) without a clear indication. According to a previous study, the frequency of bacterial pneumonia in COVID-19 patients was 6.9%, while >70% of patients received antibiotics. This is likely due to the clinical findings of COVID-19 pneumonia overlapping with those of bacterial pneumonia and the lack of reliable indicators of bacterial infection. Strategies that distinguish bacterial from viral pneumonia are desirable. In this session, I will discuss the impact of inappropriate antibiotic use during pandemic as well as the strategy to limit inappropriate antibiotic use as well as multi-drug resistant pathogen during COVID-19 pandemic among COVID-19 and non-COVID-19 populations.Copyright © 2023

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning.

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

Real-life antimicrobial therapy in hospitalized patients with COVID-19 (preliminary results and recommendations).

Objective. Development of local clinical protocols for antibacterial therapy of COVID-19-associated bacterial pneumonia in the therapeutic department of the city clinical hospital based on an analysis of the treatment process in patients with COVID-19-associated pneumonia. Materials and methods. A retrospective analysis of 1382 cases of hospitalization in the therapeutic department of patients with COVID-19-associated pneumonia for the period from 2020 to 2021 was carried out. The structure of etiotropic therapy, the frequency and timing of microbiological studies of the biomaterial, the manifestations of the main markers of bacterial infection during dynamic monitoring of clinical and laboratory parameters in patients prescribed antibiotic therapy, as well as statistics of the stay of patients in the therapeutic department of the hospital were assessed. Based on the results obtained in the course of microbiological studies, an assessment was made of the microbial landscape of the lower respiratory tract of patients with an analysis of the sensitivity of strains of the leading microflora to a wide range of antibiotics. Results. The study found that the dominant flora in COVID-19-associated pneumonia in hospitalized patients was gram-negative bacteria - K. pneumoniae, P. aeruginosa and A. baumannii - their proportion was more than 50%. Among K. pneumoniae strains, 89.4% were ESBL producers, 63.5% of the strains were resistant to carbapenems, which with a high probability allows them to be considered carbapenemase-producing strains. Among the strains of P. aeruginosa, the proportion of strains resistant to carbapenems and with a high degree of probability being strains - producers of carbapenemase was 41.1%. Among strains of Acinetobacter spp. these were 76.4%, and associated resistance to fluoroquinolones and aminoglycosides was also demonstrated. Gram-positive microorganisms were found in 34.3% of cases and were mainly represented by strains of S. aureus (74.9%), only 26.4% of strains of this pathogen were methicillin-resistant. Conclusions. Microbiological monitoring conducted in 2020-2021 revealed the presence, among the pathogens of viral-bacterial pneumonia, at an early stage of hospitalization, a significant proportion of gram-negative bacteria with resistance of the MDR and XDR types. Based on the obtained microbiological data, starting empirical schemes for antibacterial therapy of secondary viral and bacterial pneumonia, which complicated the course of a new coronavirus infection COVID-19 caused by the SARS-CoV-2 virus, were developed and proposed.Copyright © 2022, Interregional Association for Clinical Microbiology and Antimicrobial Chemotherapy. All rights reserved.

Smoking as one of the predictors of the severity of the condition of patients suffering from a new Coronavirus infection

The relationship between smoking and the lung damage volume in patients with a confirmed new coronavirus infection diagnosis, hospitalized in a temporary infectious hospital for the treatment of patients suffering from a new coronavirus infection and community-acquired pneumonia was evaluated. This was in the Odintsovo District's Patriot Park of the Moscow region. Smoking cigarettes, both active and passive, as well as exposure to tobacco smoke on the body, are important upper and lower respiratory tract infection risk factors due to local immune response suppression. Nevertheless, data from a number of international studies indicate a significantly lower number of hospitalized smoking patients compared to non-smokers. These indicators were investigated as the percentage and degree of lung damage, smoking history, the number of cigarettes smoked per day, and the smoker's index. In the course of the study, the data on a smaller percentage of smokers admitted to inpatient treatment were confirmed in comparison with non-smokers and smokers in the general population. There was no statistically significant difference in the volume of lung damage between smoking and non-smoking patients according to the chest organs computed tomography. At the same time, there was an increase in the volume of lung tissue damage, depending on the smoking experience. This is apparently due to the irreversible changes formation in lung tissue against a long-term smoking background. The median age of smoking patients was 56 years with a variation from 46 to 68 years. The minimum and maximum ages were 29 and 82. The median lung lesion was 32% with a variation from 23% to 39%. The minimum and maximum lung damage is 10% and 40%, respectively. A moderate correlation was found between the smoking experience and the volume of lung damage. An increase in lung damage by 0.309% should be expected with an increase in smoking experience by one full year. There was also no statistically significant difference in the number of cigarettes smoked per day and the smoker's index.

Rituximab in the Treatment of Idiopathic Inflammatory Myopathies with Interstitial Lung Disease, Long-Term Follow-Up

Background. Interstitial lung disease (ILD) is the common internal organ manifestation of idiopathic inflammatory myopathies (IIM) that can severely affect the course and prognosis of the disease. Rituximab (RTX) has been used to treat IIM, including variants with ILD. Objectives. To describe the course of disease in IIM patients with ILD, treated with RTX in long-term follow-up. Methods. Our prospective study included 35 pts with IIM fulfilling Bohan and Peter criteria and having ILD. The mean age was 51.8+/-11.9 years, female-26 pts (74%);24 (68.5%) with antisynthetase syndrome, 5 (14.3%) dermatomyositis (DM), 5 (14.3%) with a-Pm/Scl overlap myositis and 1 (2,9%) with a-SRP necrotizing myopathy were included. 25 (71,4% ) patients had nonspecific interstitial pneumonia, 9 (25,7%) organizing pneumonia (OP) and 1 (2,9%) OP, transformed to diffuse alveolar damage. All pts had the standard examination including manual muscle testing (MMT), creatinkinase (CK) anti-Jo-1 antibodies (anti-Jo-1) assay;forced vital capacity (FVC) and carbon monoxide diffusion capacity (DLCO) evaluation as well as high-resolution computed tomography (HRCT) scanning of the chest were performed at baseline, and 36 and more months. The median disease duration was 3.2 [0.16-18] years, 21 (60%) of pts were positive for a-Jo-1 antibody. All pts received prednisolone at a mean dose of 24.3+/-13 mg/day, immunosupressants at inclusion received 25 (71%) pts: cyclophosphamide 18 , mycophenolate mofetil 6 and comdination 1;Rituximab (RTX) was administered in case of severe course of disease and intolerance or inadequate response to GC and other immunosuppressive drugs. Results. The mean follow-up period after the first infusion of RTX was 47.2+/-11.9 months. Pts received 1-11 courses of RTX . The cumulative mean dose of RTX was 4.6 +/-2.5g. MMT 8 increased from 135.8+/-13.5 to 148.75+/-3.5 (p=0.000001). CK level decreased DELTACK - 762 u/l(median 340;25th% 9;75th% 821). anti-Jo-1 decreased from 173.4+/-37 to 96.5+/-79 u/ml (p=0.00002), FVC increased from 82+/-22.6 to 96,9+/-22% (p=0.00011). DLCO increased from 51.4+/-15.2 to 60+/-77.8% (p=0.0001). The mean prednisone dose was reduced from 24.3+/-13 to 5.7+/-2.4 mg/day. 3 pts died: ILD progression was the cause of death in 1 case, 1 bacterial pneumonia and COVID19 pneumonia. Conclusions. The results of this study confirm the positive effect of RTX in IIM patients with ILD (increase of muscle strength and improve lung function, decrease in anti-Jo-1 levels) and also its good steroid-sparing effect. RTX could be considered as an effective drug for the complex therapy of IIM patients with ILD when standard therapy is ineffective or impossible.

COVID-19 infection in children through lung radiographic imaging changes

Background: Radiological changes in children with lung damage caused by SARS-CoV-2 can be diagnosed by radiology exams with the identification of interstitial inflammation characterized of viral pneumonia. Aim(s): Evaluation of pulmonary radiological manifestations in children with COVID-19 infection. Method(s): Expected research evaluated pulmonary radiological changes among 315 children with SARS-CoV-2 infection, with moderate and severe form, from the age of 2 days till 18 years. Age distribution of hospitalized children with clinical signs of COVID-19 infection:<28 days-36 children(11,4%:95%CI8,2-15,6),28 days-1year-72 children(22,9%:95%CI18,4-28),1-3years-65 children(20,6%:95%CI16,4-25,6),4-7 years-66 children(21%:95%CI16,7-26),7-18years-76 children(24,1%:95%CI19,6-29,3). Result(s): Chest X-ray in children with SARS-CoV-2 infection found interstitial changes of the ground glass"type among 161 children(52.8%:95%CI 47-58.5) Condensation opacities in 51 children(16.7%:95%CI12.8-21.5)confirmed pneumonia of bacterial etiology associated with COVID-19 infection. Bronchitis was confirmed in 62 cases among hospitalized children(20.3%:95% CI16-25.4),and obstructive bronchitis characterized by imaging of hyperinflation- among 25 children(12.6%:95%CI8,3-18). Young children and infants had a thymus hyperplasia in 21.6%:95%CI17.2- 26.8 cases. At the acute stage of COVID-19 infection signs of fibrosis, atelectasis, traction bronchiectasis were detected in unique cases(0.3%:95%CI0-2.1). Conclusion(s): Lung damage caused by COVID-19 infection in children is generally characterized by changes with interstitial inflammation that are confirmed by Chest X-ray.

An Early Detection Of NIPAH Infectious Disease Based On Integrated Medical Features For Human Using Ensemble RBM Techniques

Background: The importance of early diagnosis of a hazardous illness cannot be overstated. The transmission rate is extremely high, especially in the current pandemic condition. The ability to predict epidemics will aid public health in reducing mortality and morbidity. Machine Learning (ML) approaches are used in the construction of an effective disease prognosis model. Furthermore, only if the model learns good associated features from the data is it possible to generate a speedy outcome. As a result, selecting features is also necessary before beginning the forecasting process. Objective(s): However, because of the virus's dynamic structure, it's difficult to predict Nipah disease and/or zoonotic infection. Furthermore, there is no clinical treatment for Nipah. The major goal of this research is to develop a prognostic model for early diagnosis of Nipah disease using a combination of several clinical factors such as symptoms, disease incubation information, and routine blood test results confirmed by a lab technician.Proposed System: The healthcare application and data are more complex to handle than other ML applications since various clinical features are assessed throughout disease manifestation. As a result, selecting the most relevant variables is critical when designing a prognosis model for any viral disease. To deal with clinical features from a vast number of features, we proposed a Restricted Boltzmann Machine (RBM) method in this research. Additionally, we employed a hybrid ensemble learning method to predict if the patient was infected with NiV after choosing features using the RBM. Data Collection: The proposed system is being implemented using the NiV infection dataset that erupted in Kozhikode, Kerala in 2018 and 2019. Result(s): The developed stacking-based ensemble Meta classifier was successfully implemented using the python programming language, and its performance was evaluated using a variety of metrics includingaccuracy, precision, recall, f1-score, log loss, AUROC and MCC. Our proposed Stacking Ensemble Meta Classifier (SEMC) model achieved an accuracy rate of 88.3% with a log loss of 0.36. Model precision, recall, f1-score, AUROC, and MCC value were 92.5%, 89.2%, 90.9%, 92.1%, and 0.74 respectively. In addition, we calculated the gravitational pull of each feature using the SHAP approach and discovered that altered sensorium, fever, headache, and cough were the most critical clinical indicators that distinguished NiVD infection from our dataset. Therefore, this classification may assist the pathologist in diagnosing NiVD with symptoms before performing the RT-PCR medical test. Conclusion(s): Using our proposed SEMC technique, we developed a prognostic model for the diagnosis of Nipah in humans. The proposed technique's discriminatory efficiency exhibited good NiVD diagnosis efficacy. We anticipate that this model will aid medics in determining a prognosis more quickly during future epidemics. However, to achieve maximum accuracy, the model requires more unique samples.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

The features of thrombogenesis in COVID-19-associated pneumonia compared to bacterial pneumonia: the key role of fibrinolysis

The aim was to estimate the autopsy features of COVID-19 comparing with bacterial pneumonia. Material(s) and Method(s): 15 patients died from COVID-19 pneumonia and 13 patients with CABP. On autopsy of COVID-19 patients macroscopically - enlarged, plethoric lungs, exudate with hemorrhagic components, areas of thrombosis, developing fibrosis (Figure 1A). Figure 1B - microscopical changes in COVID-19: artery of medium caliber, branching of the pulmonary artery, vascular endothelial integrity violation, the arrow indicates a pale pink non-nuclear mass in the form of threads - fibrin clot. On autopsy of CABP patients macroscopically - compressed, dense, infiltrated lungs, filled with purulent exudate (Figure 1C). Figure 1D demonstrates an example of microscopical changes in CABP: mixed thrombi in the lumen of the arteries, more often in average caliber were found. This thrombus had a head (the structure of a white thrombus), a body (actually a mixed thrombus) and a tail (the structure of a red thrombus). The head was attached to the endothelial lining of the vessel, which distinguishes a thrombus from a posthumous blood clot or from an embolus. Conclusion(s): 1) problems in fibrinolysis system, which is the main difference between CABP;in died patients with COVID-19 pneumonia the level of PAI-1 is associated with the disease severity and could be the crucial marker for patients' distribution. (Figure Presented).

The diagnostic and prognostic role of plasminogen activator inhibitor-1 (PAI-1) in hospitalized patients with pneumonias of different etiologies

Aim: to estimate the diagnostic and prognostic role of PAI-1 on admission in hospitalized patients with confirmed COVID-19 pneumonia, comparing with bacterial pneumonia. Material(s) and Method(s): We observed 3 groups: Main (1) - 85 patients (59 (52;65) years, men - 45 (52.9%)), hospitalized with COVID-19 pneumonia (40 patients with moderate, 25 patients with severe, 20 patients with critical);Comparative (2) - 55 patients (48.9 (34;62) years, men - 30 (54.5%)), hospitalized with communityacquired pneumonia of bacterial etiology (CABP) without COVID-19;Control (3) - 25 healthy volunteers (50.0 (35;65) years, men - 13 (52.0%)). General tests, plasma level of PAI-1 (ELISA Kit, Elabscience) before starting of anticoagulants, statistic. Result(s): The highest level of PAI-1 (6.1 [0.15;18] ng/ml) was in Main group and exceeds the Control group (0.1 [0.09;0.11] ng/ml), p1-3=0,000) in more than 60 times. PAI-1 in CABP didn't differ from Control group (Fig.1) The highest levels of PAI-1 at admission had patients with severe and critical course of COVID-19. Conclusion(s): 1) significantly increase of PAI-1 in COVID-19 pneumonia demonstrates the cornerstone in thrombogenesis of this disease - problems in fibrinolysis system, which is the main difference between CABP;2) PAI-1 is associated with COVID-19 severity and could be the crucial marker for patients' distribution. (Figure Presented).

Characterization and antibiogram study of pneumonia causing bacteria isolated from blood culture of children in northern Bangladesh

Children are usually affected by pneumonia, which is a common ailment caused by Pathogenic Streptococcus pneumoniae. This study's objective was to isolate and identify S. pneumoniae, which was recovered from blood samples of suspected paediatric pneumonia patients using conventional techniques, such as antibiotic sensitivity profiles and molecular approaches. In this study, forty (40) samples from three major hospitals in the Dinajpur region of Bangladesh were collected and assessed using various bacteriological, biochemical, antibiotic susceptibility test, and molecular techniques. 37.5% of the 40 samples tested positive for pneumonia, and 15 isolates were discovered. In terms of age, pneumonia was more common in children aged 3-5 years (50%) than in those aged 6 to 8 (33.33%), 9 to 11 (25%) and 12 to 15 (20%). According to the results of the current study, the study area had no statistically significant impact (P > 0.05), while age and socioeconomic status had a significant impact on the prevalence of pneumonia in patients with pneumonia (P 0.05). The age group for which pneumonia was most prevalent (at 50%) was that for children between the ages of 3-5. Poor socioeconomic status was associated with the highest prevalence of pneumonia (54.54%). By sequencing the 16S rRNA gene, S. pneumoniae was identified as S. pneumoniae NBRC102642. In the antibiotic investigation, S. pneumoniae was found to be extremely resistant to ciprofloxacin, amikacin, vancomycin, and cefexime, but responsive to erythromycin and azithromycin, as well as neomycin, kanamycin, streptomycin, and bacitracin. S. pneumoniae causes serious complications in paediatric patients, and this scenario requires prevention through vaccination and the development of new, efficient antibiotic therapies for pneumonia. If specific laboratory features of paediatric patients with pneumonia are understood, sepsis will be easier to detect early, treat, and reduce mortality.

Automatic detection of COVID-19 pneumonia through artificial intelligence applied to chest X-rays

Artificial intelligence techniques, such as Deep Learning, aimed at the analysis of radiological images are having a continuous advance, which will allow an optimization in radiological diagnosis. The pandemic caused by COVID-19 has been a major diagnostic challenge, where chest radiography is a crucial technique due to its availability and accessibility. However, it is sometimes difficult to differentiate pneumonia caused by COVID-19 from that caused by other germs. To evaluate different architectures based on convolutional neural networks and Deep Learning techniques for the diagnosis of coronavirus pneumonia and its differentiation from pneumonia of other origins. We have retrospectively analyzed 1.341 normal chest X-rays, 1.200 X-rays of pneumonia caused by COVID-19, and 1.345 X-rays of pneumonia of bacterial or non-coronavirus viral origin. The Deep Learning architectures applied for image analysis were RestNet50, ResNet101, VGG, and inception. Explainability techniques were applied to choose the most suitable model according to the clinical interpretation of the image. The best Deep Learning-based model was built with the architecture ResNet50 with a diagnostic efficiency of 0.91. This model correctly diagnosed 83.1% of normal chest X-rays and 100% of pneumonias caused by COVID-19. Both accuracy and understandability were considered to choose the best-performing model. Because of that, ResNet101 based model was discarded even being the diagnostic efficiency of 0.94. Deep Learning using the architecture RestNet50 based on a convolutional neural network allows a diagnosis of COVID-19 pneumonia with high diagnostic efficiency and could be used in routine clinical practice.