Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

IRF2BP2 mutation with autoimmune enteropathy, immune deficiency, and respiratory failure

Case history: We present the case of a 31-year-old Hispanic male with history of recurrent bronchiectasis, invasive aspergillosis, and severe persistent asthma, who is now status post lung transplant for end-stage lung disease. He initially presented at 7 years of age with diarrhea, failure to thrive, and nearly absent immunoglobulin levels (IgG < 33 mg/dL, IgA < 7 mg/dL, IgM = 11 mg/dL, IgE = 4 IU/dL) necessitating IVIG treatment. Small intestinal biopsy showed villous atrophy consistent with autoimmune enteropathy. Sweat chloride was reported as indeterminate (44 me/dL). Initial WBC, platelet, and T- and NK-cell counts were within normal range, and B-cell count and percentage were borderline low. Most recently, he was found to have increased immature B-cell count (CD21low), decreased memory B-cells, and poor pneumococcal vaccine antibody response. Patient has been hospitalized numerous times with increasingly severe bronchiectasis, pneumonitis, and COVID-19 infections twice despite vaccination, leading to respiratory failure and lung transplantation. Family history is negative for immune deficiency and lung diseases. Discussion(s): Of these 3 VUSs (see the table), the one in IRF2BP2 has the most pathogenic potential due to its autosomal dominant inheritance, its location in a conserved domain (Ring), and previous case reports of pathogenic variants at the same or adjacent alleles 1-3. Baxter et al reported a de novo truncating mutation in IRF2BP2 at codon 536 (c.1606CinsTTT), which is similar to our patient's mutation. This patient was noted to have an IPEX-like presentation, with chronic diarrhea, hypogammaglobulinemia, and recurrent infections. Variant Functional Prediction Score for our variant predicts a potentially high damage effect. There are 2 other case reports of heterozygous mutations in loci adjacent to this allele;one (c.1652G>A)2 with a similar clinical phenotype to our patient and the other (C.625-665 del)3 with primarily inflammatory features and few infections. Impact: This case highlights a variant in IRF2BP2 associated with severe hypogammaglobulinemia, recurrent pulmonary infections, and autoimmune enteropathy. [Table presented]Copyright © 2023 Elsevier Inc.

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

X-ray study as an alternative method for visualizing changes in lungs, with infection caused by COVID-19.

Introduction. Despite the existence of generally accepted diagnostic protocols, when a new coronavirus infection is suspected, in some cases, it is increasingly difficult to detect changes in the lung tissue in a timely manner due to the heavy workload of the main method of radiation diagnostics - computed tomography. Purpose of the study. To determine the effectiveness of the appointment of an X-ray examination as first-line metgod, as well as to carry out a comparative analysis of the use of radiation diagnostics methods - computed tomography and radiography in relation to the diagnostic sensitivity to changes in lung tissue when a person is infected with the SARS-COV-2 virus. Materials and methods. 150 patients (63.0 +/- 8.4 years) with confirmed coronavirus infection were examined. Each of the participants underwent X-ray examination and computed tomography of the chest organs. The percentage of subjects studied for each of the degrees of severity of lung damage was determined to identify the proportion of involvement of lung tissue in the pathological process in the bulk of the examined individuals. Results. Of the 150 patients, changes in the lung tissue during chest X-ray were detected in 97 (65%), respectively, in 53 (35%), pathological changes in the lungs were not visualized. When examining patients by computed tomography, changes in the lungs were detected in 143 patients (95%), X-ray morphological changes were not detected in 7 subjects (5%). When detecting the volume of lung damage, it turned out that the majority of the subjects - 86 people (57%) - had the degree of damage CT-2. The degree of CT-1 and CT-3 was determined in 26 (17%) and 25 (17%) patients, respectively. CT-4 was observed in 6 patients (4%), and in 5% of cases, CT was not able to detect pathological changes in the lung tissue, the degree of CT-0 was established. Conclusion. In the assessment of viral lung damage, radiography takes a significant place, but in 35% of cases, radiographic examination failed to identify the existing pathological changes. CT of the chest organs confirms its value as the "gold standard" in the study of pulmonary pathology in coronavirus infection, but if it is impossible to perform it, radiography is recommended.Copyright © 2021 Vigne et Vin Publications Internationales. All rights reserved.

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.

The Relationship between COVID-19 and Pulmonary Fibrosis After of Infection in AL-Najaf Province, Iraq

Background and Purpose: A significant part of the "post-acute COVID-19 syndrome" that may significantly aggravate patients' clinical history is pulmonary fibrosis (PF), a pathological result of chronic and acute interstitial lung illnesses linked to impaired wound repair. Despite being inconclusive, the information that is currently available suggests that more than a third of COVID-19 hospital patients experience aberrant lung fibrosis after leaving the hospital. The current study's goal is to ascertain if pulmonary fibrosis and COVID-19 susceptibility are related. Material(s) and Method(s): The Al-Amal Hospital provided data on coronavirus infections. Regarding Pulmonary Fibrosis, Age, and Gender in the Najaf Province in 2022. The results were evaluated using the Statistical Analysis System application's Chi-squared test (SPSS). Result(s): In the study results of our study were as follows, where it was found that (11.21%) of the total patients in the age group 18-25 are prone to suffering from pulmonary fibrosis, while (20%) of the age group were 25-36, and also found that (29.08%, 45.74% and 31.19%) for the following age groups, respectively: 36-47y, 47-57y and 57-67y. Finally, it was found that 117 (26.77%) patients out of 320 suffer from pulmonary fibrosis symptoms of the age group 67-77 years, where it formed a significant difference compared with the rest of the age groups. Conclusion(s): There is a link between infection with COVID 19 and pulmonary fibrosis, among other conditions. However, our study shows that severe COVID-19 is linked with considerable respiratory symptoms and morbidity, in-cluding dyspnea, which was reported by many survivors. There is an urgent need for more research to understand the connection more generally and to identify therapies that might help prevent similar lung infections in the future.Copyright © 2023, Colegio de Farmaceuticos de la Provincia de Buenos Aires. All rights reserved.

Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-The-Art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-Trained models;second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease. © 2023 IEEE.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

Pharmacological effects of baicalin in lung diseases.

The flavonoids baicalin and baicalein were discovered in the root of Scutellaria baicalensis Georgi and are primarily used in traditional Chinese medicine, herbal supplements and healthcare. Recently, accumulated investigations have demonstrated the therapeutic benefits of baicalin in treating various lung diseases due to its antioxidant, anti-inflammatory, immunomodulatory, antiapoptotic, anticancer, and antiviral effects. In this review, the PubMed database and ClinicalTrials website were searched with the search string "baicalin" and "lung" for articles published between September 1970 and March 2023. We summarized the therapeutic role that baicalin plays in a variety of lung diseases, such as chronic obstructive pulmonary disease, asthma, pulmonary fibrosis, pulmonary hypertension, pulmonary infections, acute lung injury/acute respiratory distress syndrome, and lung cancer. We also discussed the underlying mechanisms of baicalin targeting in these lung diseases.

IDWeek 2022

IDWeek is the joint annual meeting of the Infectious Diseases Society of America (IDSA), Society for Healthcare Epidemiology of America (SHEA), the HIV Medicine Association (HIVMA), the Pediatric Infectious Diseases Society (PIDS) and the Society of Infectious Diseases Pharmacists (SIDP). For the first time since the COVID-19 public health emergency began, IDWeek 2022 returned to in-person attendance. It was held in Washington, D.C., and the meeting comprised 5 days of live sessions and on-demand content that included posters and oral presentations.Copyright © 2023 Clarivate.

High Mortality in Hospitalised Virally Suppressed Adults with Hiv in South Africa

Background: Mortality in PWH has been markedly improved by antiretroviral therapy (ART) but there are few reports describing this in the ~5 million virally suppressed (VS) PWH in South Africa(SA). We describe cause of death(CoD) in adults admitted to hospital with suspected pneumonia in SA. Method(s): We enrolled patients from June 2019-October 2021 at four hospitals and then followed them up for >=1 year. Eligibility included: Age >18 years, >=2 signs/symptoms of pneumonia, < 48 hrs since admission. Medical records were reviewed. All had HIV status ascertained and sputum sent for Xpert Ultra and mycobacterial culture. In PWH CD4 count, viral load (VL) and urine lipoarabinomannan were assessed. For those who died, CoD were ed from medical charts and interview of family. We categorised deaths as early: while admitted or to < 30 days after discharge;or late: >=30 days after discharge. We report mortality and CoD in VSPWH (VL<=50 copies/ml), unsuppressed and HIV uninfected(HUI) adults. Result(s): Of 1999 adults, 54% were PWH;61.2% reported receiving ART of whom 43.1% were VS;55.5% were women. Overall median age of VS was 48 years (IQR: 40-55) at entry;34.3% had comorbidities: hypertension (70.1%, obesity 41.3%, diabetes 28.9%) . Only 11.3% were diagnosed with HIV in the past year, 35.0%, had prior TB. Median CD4 count of VS patients was 289 cells/ mm3 (IQR:133-490) and Hb, 12.5g/dL (IQR:10.5-14.0);53.0% had CRP >100mg/ dL and 69.6% had oxygen saturation < 93% on room air;14.8% had >=1 assay positive for TB;and 42.9% were SARS-CoV-2 positive. Overall 25.4% VSPWH died compared to 31.2% and 22.9% of unsuppressed and HUI, respectively;median ages at death were 49 (IQR:43-59), 38 (IQR: 32-47) and 62 (IQR: 53-69) years respectively. Overall median times to early and late death was 8 (IQR: 4-16) and 104 (IQR: 75-254) days, respectively. The leading CoD in VSPWH were: COVID-19 (22.9%), chronic lung disease(CLD) (17.1%),malignancy (12.9%),sepsis, (12.9%) and TB (8.7%);in HIV unsuppressed, CoD were: advanced HIV and opportunistic infections-(TB,PJP)(55.5%), sepsis(9.6%), COVID-19(8.6%);and in HUI: COVID- 19(43.0%), cardiovascular disease (9.0%), TB(9.0%), malignancy (8.5%). Conclusion(s): Mortality in VSPWH admitted with suspected pneumonia was higher than in HUI and occurred 12 years earlier. The challenge for clinicians is to screen for diseases that disproportionately affect VSPWH and to try to prevent recurrent lung infections thereby increasing their comorbidity-free years and reduce mortality gaps.

Longitudinal Follow-up of Pulmonary Function in Patients Hospitalized with Pulmonary Covid-19

Purpose of Study: The post-acute sequelae of COVID-19, as a multisystemic disease have been described in adults. Although some studies have described the pulmonary complications up to 3 months post-COVID infection, longitudinal data on pulmonary sequalae are sparse. The objective of this review was to summarize the findings of studies that included a longitudinal follow-up of patients with moderate to severe pulmonary COVID-19 infection. Methods Used: We performed a literature search using Pubmed, Google Scholar and Medline using key words: "pulmonary function test", PFT?, "long-COVID", longitudinal? and sequalae?. We included studies of adult patients (>18 years of age) who had been hospitalized with acute COVID-19 infection and had at least two follow-ups with PFT measurements, including one follow-up at least 6 months post-infection. Studies that did not account for co-morbidities and other lung diseases or those which only included one-time follow-up were excluded. Summary of Results: Five studies satisfied our inclusion criteria (See Table). The studies showed persistent lung injury for at least 3 months after discharge, with decreased forced expiratory volume (FEV1), total lung capacity (TLC), forced vital capacity (FVC), diffusion vital capacity of the lungs for carbon monoxide (DCLO) and carbon monoxide transfer coefficient (KCO). Although these values improved at 6 and 12 months of follow-up, those with more severe disease continued to have decreased DLCO suggestive of restrictive lung damage. Studies that included symptomatic assessment revealed that a minority of patients continued with fatigue and dyspnea uf to 12 months after the infection. The limitations of the studies include availability of data from a single center, small sample size and the variability in controlling for different co-morbidities. In addition, baseline PFT measurement before COVID-19 infection was not available for most patients. Most of the studies were done at the time that the Delta variant was dominant, therefore the data may not be applicable to other variants. Conclusion(s): Our literature review shows that some adult patients hospitalized with acute covid pulmonary infection continue to have abnormal PFTs for up to 12 months after infection. Although PFTs improve overtime, a minority of patients with more severe disease on admission continue with abnormal functional abnormalities, specifically restrictive ventilatory pattern with impaired DLCO at 12 months of follow-up. It is important that patients hospitalized with moderate to severe pulmonary COVID-19 infection be followed up and managed for at least 12 months after the initial infection. Larger prospective studies including different variants of COVID-19 that take into account various co-morbidities and different management strategies are warranted.

Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image

COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.

The Extent of Lung Involvement Based on CT-Scores, Compared to Clinical Symptoms and Laboratory Findings in COVID-19 Patients

Novel coronavirus disease 2019 (COVID-19) has become a public health concern with over 6.5 million cases and 390,000 deaths around the world. This research aimed to find an association between computed tomography (CT) scores and clinical and laboratory findings to estimate the extent of lung infection in patients with COVID-19. The study sample enrolled 129 patients diagnosed with COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, images were segmented and scored by the degree of involvement from 0 to 4, and the relationship between them and clinical and laboratory findings were analyzed statistically. This study included 74 men and 59 women with a mean age of 55.08 years. Different abnormalities were observed;the mean CT score was 8.52 (7.83 to 9.21) and the most frequent lesions were GGO and consolidation. Our results revealed significant differences between groups categorized by dyspnea, sore throat, and low oxygen saturation concerning CT scores. There was also a significant correlation between CT scores and WBC counts and CRP levels (P<0.05). The evidence from this study implies that clinical and laboratory data, such as CRP, dyspnea, lymphopenia, and symptom onset closely correspond to chest CT scores and may be employed as initial tools to estimate the extent of lung involvement in COVID-19 patients.Copyright © 2023 Tehran University of Medical Sciences.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Repeated COVID-19 infection in recipients of post-living donor liver transplantation: Two real-life cases

COVID-19 is an emerging pandemic. The course and management of the disease in the liver transplant setting may be difficult due to a long-standing immunosuppressive state. In Egypt, the only available option is living donor liver transplantation (LDLT). In our centre, we have transplanted 440 livers since 2008. In this study, we report a single-centre experience with COVID-19 infection in long-term liver transplant recipients. A total of 25 recipients (5.7 %) had COVID-19 infections since March 2020. Among these recipients, two developed COVID-19 infections twice, approximately three and two months apart, respectively.Copyright © 2021 The Author(s)

The role of Trans-thoracic ultrasonography for rapid detection of Covid-19 pandemic-pneumonia(262cases)?

Background: Corona virus pandemic pneumonia has caused unmatchable damage to humanity. Early detection and prompt treatment is the only answer for effective control. RT-PCR is the gold standard diagnostic test but displays high false-negative rate. A large number of undetected patients remain a constant source of inadvertent disease dissemination in community, potentiating the ongoing pandemic. Objective(s): To determine the usefulness of transthoracic ultrasonography for early detection of covid-19 pulmonary infection during a pandemic. Methodology: This cross-sectional study was conducted in Pulmonology-OPD of Gulab Devi Teaching Hospital, Lahore-Pakistan, from April 21, 2020 to September 30, 2020. Total 262 patients with dry cough, fever and shortness of breath of sudden onset were included. Patients were investigated with chest x-ray/HRCT, transthoracic ultrasonography, covid-19-PCR and hematological tests. Sensitivity, Specificity, positive predictive value(PPV), negative predictive value(NPV) and diagnostic accuracy was calculated with clinical diagnosis as reference. Data was analyzed by SPSS-24-software. Result(s): Of 262-patients, 248 were detected as covid-19 pneumonia by ultrasound. Bilateral, multifocal, posteriolateral involvement and B-lines were common features. Ultrasound displayed sensitivity 99.60%, specificity, 69.23%, PPV 98.41%, NPV 90.0% and diagnostic accuracy 98.09%. PCR diagnosed 155/228(59.16%) cases. The P-value was 0.00001-significant at P<.05. Conclusion(s): Transthoracic ultrasonography is a tremendous tool furnishing instant detection of covid-19 pneumonia with high sensitivity and provides foundations for evidence based management during pandemic.

Microbiological profile of post covid patients admitted in tertiary care center with pulmonary infections

The second wave of COVID 19 started in India in the month of February 2021 lasted till June 2021. The second wave of COVID 19 was devastating in India. There were many fatalities. But the patient who recovered of COVID pneumonia also had many pulmonary complications, pulmonary infections being the most common. The use of steroids and immunosuppressive agents were considered to be one the causative factors. At our center, we did retrospective analysis of patients who were admitted with pulmonary infections in month of June and July 2021 and had history of COVID during second wave. There were in total 98 patients. The sputum and bronchial wash analysis were done for these patients. Out of 98 patients, 38 diagnosed to have tuberculosis, 18 mucormycosis, 10 aspergillosis, 22 bacterial infection (Pseudomonas and klebsiella), 2 non tubercular mycobacteria, 2 nocardiosis, 6 had mixed infections (2 NTM and Klebsiella, 3 TB with Aspergillosis and 1 Aspergillosis with mucor). Thus, it was concluded, that post covid status predisposed to pulmonary infections with not only common organism like Tuberculosis (being endemic in India) but also rare organism like mucor, nocardia, NTM. Though most of the patients received steroids and immunosuppressive therapy, 22 patients had mild COVID and didn't receive any steroids or immunosuppressive therapy. Thus, implying that steroids and immunosuppressive therapy are not the only cause of increased incidence of pulmonary infections. More such observations from different centers are required for confirmation of the observations.

Pulmonary infection characteristics leading to respiratory failure, a modelling approach

How an infection propagates inside the lung is not well understood. Capturing its dynamics might help to understand how pathologies such as COVID19 can lead to rapid airways inflammation and respiratory failure. We hypothesized that respiratory failure might result from the interaction between the propagation of the infection from airway to airway (inner contagiousness) and the pathogen virulence. We develop a mathematical model of the infection and inflammation of the proximal lung (511 susceptible airways) by a generic pathogen that propagates from neighbor to neighbor between the airways. The degree of respiratory failure is evaluated by computing the mean number of infected airways (NI) and the mean drop in oxygen transfer to blood (DOx), assuming no compensation from patient ventilation. We simulated 840 idealized patients, covering 3 different degrees of virulence (Cured (C), Aseptic (A) and Septic (S) outcomes) and 14 degrees of contagiousness (1<=c<=14, arbitrary units). When virulence increases, the pathogens remain longer in the airways, increasing the propagation probability: NI(C)=51, DOx(C)=8.9%;NI(A)=410, DOx(A)=47.2%;NI(S)=511, DOx(S)=55.5%. For low contagiousness, c=1, NI(C)=1.6, DOx(C)=2.2%;NI(A)=132, DOx(A)=25.8%. However, NI(S)=511 and DOx(S)=52.2%. High contagiousness, c=14, leads to a large propagation whatever the virulence (NI(S/A)=511 and DOx(S/A)=57.5%;NI(C)=428 and DOx(C)=38.6%). Medium virulence and contagiousness also lead to a large propagation: for c=7, NI(A)=508, DOx=52.5%. Residence time of pathogens and inner contagiousness are interacting factors that might bring high NI and DOx. This interaction might be a core determinant of potential respiratory failure.