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1.
Chest ; 162(2): e85-e88, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1972016

ABSTRACT

CASE PRESENTATION: A 54-year-old man with chronic hepatitis B was admitted to the hospital with progressive dyspnea on exertion. He reported experiencing intermittent fever, dyspnea on exertion, and relapsing pleuritic chest pain starting 6 months prior, after his first dose of the ChAdOx1 nCoV-19 vaccine. In the past 2 months, he had been admitted to the hospital twice and diagnosed with recurrent pneumonia. Under antibiotic treatment, his dyspnea and low-grade fever demonstrated waxing and waning behaviors. Migratory pulmonary consolidation, which moved from the left lower lobe to the right middle lobe, was identified and diagnosed as relapsing pneumonia. Chest CT scan was performed in a previous admission 2 months earlier that revealed multifocal peripheral consolidation in the left lower lobe and right middle lobe. His occupation required the maintenance of overall fitness, and he denied immunosuppressant use, illicit drug abuse, cigarette smoking, suspicious travel, suspicious contact, or family history. No recent history of trauma, surgery, or air travel was reported.


Subject(s)
ChAdOx1 nCoV-19 , Lung Diseases , Chest Pain/diagnosis , Diagnosis, Differential , Dyspnea/diagnosis , Dyspnea/etiology , Fever/diagnosis , Humans , Lung Diseases/diagnosis , Male , Middle Aged , Tomography, X-Ray Computed
2.
Rev Soc Bras Med Trop ; 55: e06152021, 2022.
Article in English | MEDLINE | ID: covidwho-1963139

ABSTRACT

BACKGROUND: Coronavirus disease-2019 (COVID-19) results in acute lung injury. This study examined the usefulness of serum transforming growth factor-beta 1 (TGF-ß1) and connective tissue growth factor (CTGF) levels in predicting disease severity in COVID-19 patients with pulmonary involvement. METHODS: Fifty patients with confirmed COVID-19 and pulmonary involvement between September 2020, and February 2021 (Group 1) and 45 healthy controls (Group 2) were classified into three subgroups based on clinical severity: moderate, severe, and critical pneumonia. Serum TGF-ß1 and CTGF concentrations were measured on days 1 and 7 of admission in Group 1 using an enzyme-linked immunosorbent assay. These concentrations were also measured in control cases. The mean serum TGF-ß1 and CTGF levels were then compared among COVID-19 patients, based on clinical severity. RESULTS: Significantly higher mean serum TGF-ß1 and CTGF levels were observed on both days in Group 1 than in the control group. The mean serum TGF-ß1 and CTGF levels on day 7 were also significantly higher than those on day 1 in Group 1. The critical patient group had the highest serum TGF-ß1 and CTGF levels on both days, and the difference between this group and the moderate and severe pneumonia groups was significant. Cutoff values of 5.36 ng/mL for TGF-ß1 and 626.2 pg/mL for CTGF emerged as predictors of COVID-19 with pulmonary involvement in receiver-operating characteristic curve analysis. CONCLUSIONS: TGF-ß1 and CTGF are potential markers that can distinguish COVID-19 patients with pulmonary involvement and indicate disease severity. These findings may be useful for initiating treatment for early-stage COVID-19.


Subject(s)
COVID-19 , Connective Tissue Growth Factor , Lung Diseases , Transforming Growth Factor beta1 , COVID-19/complications , Cohort Studies , Connective Tissue Growth Factor/blood , Humans , Lung Diseases/virology , Prospective Studies , Transforming Growth Factor beta1/blood
3.
J Mater Chem B ; 10(30): 5666-5695, 2022 Aug 04.
Article in English | MEDLINE | ID: covidwho-1947652

ABSTRACT

Lung diseases remain a global burden today. Lower respiratory tract infections alone cause more than 3 million deaths worldwide each year and are on the rise every year. In particular, with coronavirus disease raging worldwide since 2019, we urgently require a treatment for lung disease. Metal organic frameworks (MOFs) have a broad application prospect in the biomedical field due to their remarkable properties. The unique properties of MOFs allow them to be applied as delivery materials for different drugs; diversified structural design endows MOFs with diverse functions; and they can be designed as various MOF-drug synergistic systems. This review concentrates on the synthesis design and applications of MOF based drugs against lung diseases, and discusses the possibility of preparing MOF-based inhalable formulations. Finally, we discuss the chances and challenges of using MOFs for targeting lung diseases in clinical practice.


Subject(s)
Lung Diseases , Metal-Organic Frameworks , Drug Delivery Systems , Humans , Lung Diseases/drug therapy , Metal-Organic Frameworks/pharmacology , Metal-Organic Frameworks/therapeutic use
4.
Med Biol Eng Comput ; 60(9): 2681-2691, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1930529

ABSTRACT

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.


Subject(s)
COVID-19 , Deep Learning , Lung Diseases , Pneumonia, Viral , Tuberculosis , Humans , Pneumonia, Viral/diagnostic imaging
5.
BMJ Open Respir Res ; 9(1)2022 07.
Article in English | MEDLINE | ID: covidwho-1923269

ABSTRACT

INTRODUCTION: Responses to COVID-19 vaccination in patients with chronic pulmonary diseases are poorly characterised. We aimed to describe humoral responses following two doses of BNT162b2 mRNA COVID-19 vaccine and identify risk factors for impaired responses. METHODS: Prospective cohort study including adults with chronic pulmonary diseases and healthcare personnel as controls (1:1). Blood was sampled at inclusion, 3 weeks, 2 and 6 months after first vaccination. We reported antibody concentrations as geometric means with 95% CI of receptor binding domain (RBD)-IgG and neutralising antibody index of inhibition of ACE-2/RBD interaction (%). A low responder was defined as neutralising index in the lowest quartile (primary outcome) or RBD-IgG <225 AU/mL plus neutralising index <25% (secondary outcome), measured at 2 months. We tested associations using Poisson regression. RESULTS: We included 593 patients and 593 controls, 75% of all had neutralising index ≥97% at 2 months. For the primary outcome, 34.7% of patients (n=157/453) and 12.9% of controls (n=46/359) were low responders (p<0.0001). For the secondary outcome, 8.6% of patients (n=39/453) and 1.4% of controls (n=5/359) were low responders (p<0.001). Risk factors associated with low responder included increasing age (per decade, adjusted risk ratio (aRR) 1.17, 95% CI 1.03 to 1.32), Charlson Comorbidity Index (per point) (aRR 1.15, 95% CI 1.05 to 1.26), use of prednisolone (aRR 2.08, 95% CI 1.55 to 2.77) and other immunosuppressives (aRR 2.21, 95% CI 1.65 to 2.97). DISCUSSION: Patients with chronic pulmonary diseases established functional humoral responses to vaccination, however lower than controls. Age, comorbidities and immunosuppression were associated with poor immunological responses.


Subject(s)
COVID-19 , Lung Diseases , Adult , Antibody Formation , BNT162 Vaccine , COVID-19/prevention & control , COVID-19 Vaccines , Humans , Immunoglobulin G , Prospective Studies , Risk Factors , Vaccination
6.
Med Intensiva (Engl Ed) ; 46(6): 353, 2022 06.
Article in English | MEDLINE | ID: covidwho-1921255
7.
Int J Comput Assist Radiol Surg ; 17(5): 857-865, 2022 May.
Article in English | MEDLINE | ID: covidwho-1914006

ABSTRACT

PURPOSE: Bronchoscopic intervention is a widely used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be extracted from chest computed tomography (CT) scans automatically by airway segmentation methods. Due to the complex tree-like structure of the airway, preserving its topology completeness while maintaining the segmentation accuracy is a challenging task. METHODS: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans. We also design a two-stage end-to-end segmentation framework utilizing the LTSP method in the decoding process. Stage 1 is used to generate a coarse feature map by an encoder-decoder architecture. Stage 2 is to adopt the proposed LTSP method for exploiting the continuity information and enhancing the weak airway features in the coarse feature map. The final segmentation result is predicted from the refined feature map. RESULTS: Extensive experiments were conducted to evaluate the performance of the proposed method on 70 clinical CT scans. The results demonstrate the considerable improvements of the proposed method compared to some state-of-the-art methods as most breakages are eliminated and more tiny bronchi are detected. The ablation studies further confirm the effectiveness of the constituents of the proposed method and the efficacy of the framework design. CONCLUSION: Slice continuity information is beneficial to accurate airway segmentation. Furthermore, by propagating the long-term slice feature, the airway topology connectivity is preserved with overall segmentation accuracy maintained.


Subject(s)
Lung Diseases , Thorax , Bronchi , Humans , Image Processing, Computer-Assisted/methods , Tomography, X-Ray Computed/methods
8.
Int J Tuberc Lung Dis ; 26(7): 629-635, 2022 07 01.
Article in English | MEDLINE | ID: covidwho-1912011

ABSTRACT

BACKGROUND: The prevalence of persistent respiratory symptoms tends to be low in patients with a longer recovery time after COVID-19. However, some patients may present persistent pulmonary abnormalities.OBJECTIVE: To evaluate the prevalence of tomographic abnormalities 90 days after symptom onset in patients with COVID-19 and compare two chest high-resolution computed tomography (HRCT) analysis techniques.METHODS: A multicentre study of patients hospitalised with COVID-19 having oxygen saturation <93% on room air at hospital admission were evaluated using pulmonary function and HRCT scans 90 days after symptom onset. The images were evaluated by two thoracic radiologists, and were assessed using software that automatically quantified the extent of pulmonary abnormalities.RESULTS: Of the 91 patients included, 81% had at least one pulmonary lobe with abnormalities 90 days after discharge (84% were identified using the automated algorithm). Ground-glass opacities (76%) and parenchymal bands (65%) were the predominant abnormalities. Both chest HRCT technical assessments presented high sensitivity (95.9%) and positive predictive value (92%), with a statistically significant correlation at baseline (R = 0.80) and after 90 days (R = 0.36).CONCLUSION: The prevalence of pulmonary abnormalities on chest HRCT 90 days after symptom onset due to COVID-19 was high; both technical assessments can be used to analyse the images.


Subject(s)
COVID-19 , Lung Diseases , Humans , Lung/diagnostic imaging , Prevalence , Tomography, X-Ray Computed/methods
9.
PLoS One ; 17(3): e0265691, 2022.
Article in English | MEDLINE | ID: covidwho-1910563

ABSTRACT

Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated disease detection. We evaluate this hypothesis using a custom ensemble of convolutional neural network models, which we call DeBoNet, that suppresses bones in frontal CXRs. First, we train and evaluate variants of U-Nets, Feature Pyramid Networks, and other proposed custom models using a private collection of CXR images and their bone-suppressed counterparts. The DeBoNet, constructed using the top-3 performing models, outperformed the individual models in terms of peak signal-to-noise ratio (PSNR) (36.7977±1.6207), multi-scale structural similarity index measure (MS-SSIM) (0.9848±0.0073), and other metrics. Next, the best-performing bone-suppression model is applied to CXR images that are pooled from several sources, showing no abnormality and other findings consistent with COVID-19. The impact of bone suppression is demonstrated by evaluating the gain in performance in detecting pulmonary abnormality consistent with COVID-19 disease. We observe that the model trained on bone-suppressed CXRs (MCC: 0.9645, 95% confidence interval (0.9510, 0.9780)) significantly outperformed (p < 0.05) the model trained on non-bone-suppressed images (MCC: 0.7961, 95% confidence interval (0.7667, 0.8255)) in detecting findings consistent with COVID-19 indicating benefits derived from automatic bone suppression on disease classification. The code is available at https://github.com/sivaramakrishnan-rajaraman/Bone-Suppresion-Ensemble.


Subject(s)
COVID-19/diagnostic imaging , Lung Diseases/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Clavicle/diagnostic imaging , Humans , Ribs/diagnostic imaging , Signal-To-Noise Ratio
10.
Bull World Health Organ ; 100(6): 375-384, 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1902860

ABSTRACT

Objective: To estimate the prevalence and explore the predictors of vaccine uptake among older adults in India. Methods: We used data from the national Longitudinal Ageing Study in India, a national household survey conducted during 2017-2018. Based on interviewees' self-reports, we calculated population-weighted estimates of the uptake of influenza, pneumococcal, typhoid and hepatitis B vaccines among 64 714 Indian adults aged 45 years or older. We performed multivariable binary logistic regression analysis to examine the sociodemographic and health-related predictors of uptake of the vaccinations. Findings: The coverage of each of the studied vaccinations was less than 2%. The estimated percentages of respondents reporting ever being vaccinated were 1.5% (95% confidence interval, CI: 1.4-1.6) for influenza, 0.6% (95% CI: 0.6-0.7) for pneumococcal disease, 1.9% (95% CI: 1.8-2.0) for typhoid and 1.9% (95% CI: 1.8-2.0) for hepatitis B. Vaccine uptake was higher among respondents with cardiovascular disease, diabetes or lung disease than those without any of these conditions. Uptake of influenza vaccine was higher among those with lung disease, while hepatitis B vaccine uptake was higher among those with cardiovascular disease or diabetes. Male sex, urban residence, wealthier household, more years of schooling, existing medical conditions and sedentary behaviours were significant predictors of vaccine uptake. Conclusion: Targeted policies and programmes are needed for improving the low vaccination coverage among older adults in India, especially among those with chronic diseases. Further research could examine vaccine access, vaccine hesitancy, and vaccine-related information and communication channels to older adults and their health-care providers.


Subject(s)
Cardiovascular Diseases , Influenza Vaccines , Influenza, Human , Lung Diseases , Typhoid Fever , Aged , Hepatitis B Vaccines , Humans , Influenza Vaccines/therapeutic use , Influenza, Human/epidemiology , Influenza, Human/prevention & control , Male , Vaccination , Vaccination Coverage
11.
N Z Med J ; 135(1550): 13-25, 2022 02 25.
Article in English | MEDLINE | ID: covidwho-1897652

ABSTRACT

AIM: The primary aim of this survey was to develop an understanding of current pulmonary rehabilitation practices in New Zealand. The onset of a COVID-19 lockdown in New Zealand in March 2020, shortly after completion of the initial survey, enabled a follow-up survey to determine how services had adapted in response to the global pandemic. METHODS: A cross-sectional observational design using two sequential purpose designed online surveys administered before (Survey 1) and after COVID-19 lockdowns (Survey 2) in New Zealand. RESULTS: Survey 1 was completed by 36 PR services across New Zealand and showed homogeneity in the content and structure of services provided. PR was primarily funded by district health boards, run by a multi-disciplinary team of health professionals and included participants with a range of chronic respiratory conditions. All programmes completed pre- and post-PR assessments, were a minimum of eight weeks in duration and included exercise and education. Survey 2 showed that, during level 4 and level 3 COVID-19 restrictions, 11 (40.7%) of services paused PR programmes, with 16 (59%) adapting the service to provide home-based rehabilitation via telephone or teleconference facilities. CONCLUSION: PR programmes in New Zealand report following Australian and New Zealand PR best practice guidelines and are homogenous in content and structure, but COVID-19 restrictions highlighted the need for services to provide more diverse options for service delivery. Future service development should focus on providing a range of delivery options allowing increased access to PR, tailoring therapy to meet individual needs and ensuring services are engaging for all participants to optimise participation.


Subject(s)
COVID-19 , Lung Diseases , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Cross-Sectional Studies , Health Care Surveys , Humans , Lung Diseases/rehabilitation , New Zealand/epidemiology
12.
Comput Methods Programs Biomed ; 222: 106947, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1881800

ABSTRACT

BACKGROUND AND OBJECTIVES: Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. METHODS: The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. RESULTS: The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (JI) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. CONCLUSIONS: The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results.


Subject(s)
COVID-19 , Lung Diseases , COVID-19/diagnostic imaging , Diagnosis, Computer-Assisted/methods , Humans , Thorax , X-Rays
13.
J Pediatr Surg ; 57(5): 829-832, 2022 May.
Article in English | MEDLINE | ID: covidwho-1873173

ABSTRACT

PURPOSE: The benefit of elective resection of congenital lung malformations continues to be debated. Proponents of resection endorse a decreased risk of respiratory complications as one indication for surgery. Our study aimed to compare the prevalence of respiratory infections in cases, before and after resection of congenital lung malformations, to controls without a history of congenital lung malformation. METHODS: We performed a retrospective cohort study of children born from 1991 to 2007 who underwent congenital lung malformation resection. Patients were identified from Winnipeg´s Surgical Database of Outcomes and Management (WiSDOM), and a 10:1 date-of-birth matched control group was generated from a population-based administrative data repository. International Classification of Disease codes were used to assess pulmonary infection outcomes. Relative rates (RR) were calculated to compare the frequency of pneumonia, respiratory infections and influenza between cases and controls. RESULTS: We included 31 congenital lung malformation cases and 310 controls. Cases consisted of 14 (45.16%) congenital pulmonary airway malformations, 9 (29.03%) bronchopulmonary sequestrations and 8 (25.81%) hybrid lesions. Before resection, pneumonia was more common in cases than controls (RR 6.85; 95%CI 3.89, 11.9), while the risk of acute respiratory infections (RR 1.21; 95%CI 0.79, 1.79) and influenza (RR 0.46; 95%CI 0.01, 3.22) were similar to controls. Post-resection, the risk of pneumonia (RR 9.75; 5.06, 18.50) was still higher in cases than controls, and respiratory infections (RR 1.77; 95%CI 1.20, 2.53) and influenza (RR 3.98; 95%CI 1.48, 9.36) were more common in cases than controls. CONCLUSION: Our study demonstrated that after resection of congenital lung malformations, children experience more frequent respiratory infections compared to the general population. Resection does not eliminate the increased risk of pneumonia.


Subject(s)
Bronchopulmonary Sequestration , Cystic Adenomatoid Malformation of Lung, Congenital , Influenza, Human , Lung Diseases , Pneumonia , Respiratory System Abnormalities , Respiratory Tract Infections , Bronchopulmonary Sequestration/surgery , Child , Cohort Studies , Cystic Adenomatoid Malformation of Lung, Congenital/epidemiology , Cystic Adenomatoid Malformation of Lung, Congenital/surgery , Humans , Lung/abnormalities , Lung/surgery , Lung Diseases/congenital , Respiratory System Abnormalities/epidemiology , Respiratory System Abnormalities/surgery , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/etiology , Retrospective Studies
14.
Int J Mol Sci ; 23(10)2022 May 19.
Article in English | MEDLINE | ID: covidwho-1862814

ABSTRACT

The identification of markers of inflammatory activity at the early stages of pulmonary diseases which share common characteristics that prevent their clear differentiation is of great significance to avoid misdiagnosis, and to understand the intrinsic molecular mechanism of the disorder. The combination of electrophoretic/chromatographic methods with mass spectrometry is currently a promising approach for the identification of candidate biomarkers of a disease. Since the fluid phase of sputum is a rich source of proteins which could provide an early diagnosis of specific lung disorders, it is frequently used in these studies. This report focuses on the state-of-the-art of the application, over the last ten years (2011-2021), of sputum proteomics in the investigation of severe lung disorders such as COPD; asthma; cystic fibrosis; lung cancer and those caused by COVID-19 infection. Analysis of the complete set of proteins found in sputum of patients affected by these disorders has allowed the identification of proteins whose levels change in response to the organism's condition. Understanding proteome dynamism may help in associating these proteins with alterations in the physiology or progression of diseases investigated.


Subject(s)
Lung Diseases , Proteome , Sputum , Biomarkers/metabolism , Humans , Lung/metabolism , Lung Diseases/diagnosis , Proteome/metabolism , Proteomics/methods , Sputum/chemistry
16.
BMJ Case Rep ; 15(4)2022 Apr 29.
Article in English | MEDLINE | ID: covidwho-1854260

ABSTRACT

A transgender man in his late teens presented with signs of multisystem disease, including hepatitis, mucositis and bone marrow suppression. He later developed dyspnoea, leucocytosis and bilateral pulmonary infiltrates on chest radiograph. He was treated for community-acquired pneumonia. After several days of treatment, he developed hypoxaemic respiratory failure due to bronchoscopy-confirmed diffuse alveolar haemorrhage (DAH). The differential diagnosis and workup were extensive, and he was ultimately treated with intravenous steroids and five sessions of plasmapheresis for a presumed autoimmune aetiology. Investigations were remarkable only for elevated IgM and IgG to Mycoplasma pneumoniae (MP). This case represents a rare presentation of multisystem disease secondary to MP in adults. Clinicians should consider Mycoplasma infection in cases of multisystem disease and observe for DAH even after initiation of appropriate therapy.


Subject(s)
Community-Acquired Infections , Lung Diseases , Adolescent , Adult , Bronchoscopy , Hemorrhage/etiology , Hemorrhage/therapy , Humans , Lung Diseases/diagnosis , Lung Diseases/etiology , Male , Mycoplasma pneumoniae
17.
Respir Care ; 67(7): 801-806, 2022 07.
Article in English | MEDLINE | ID: covidwho-1835300

ABSTRACT

BACKGROUND: Pulse oximeters are often used at home by patients with chronic respiratory diseases and more recently for remote monitoring of patients with COVID-19. There are no published data outside a supervised telemedicine setting regarding patients' experiences with these devices. Our objective was to explore patients' usage patterns and perceptions of using pulse oximetry at home. METHODS: Patients with chronic respiratory disease who had a pulse oximeter at home were recruited to complete a structured survey. RESULTS: Thirty participants with a range of chronic respiratory diseases (mean age 71 y, 16 females) were recruited. Most participants (83%) used home oxygen therapy. Pulse oximeters were bought online (46.7%), at a pharmacy (40%), at a medical equipment store (6.7%), through a clinic (3.3%), or from an oxygen supplier (3.3%). Use was self-initiated in 56.7% of cases and was based on a health care-related recommendation in 26.7% of cases. Sixty percent of participants used the oximeter daily, with 90% expressing confidence in interpreting their oximeter readings primarily due to education from health care professionals and in-patient experiences. Almost all participants adjusted their activity levels or management based upon oximeter readings. Most participants reported that using a pulse oximeter at home was helpful in judging their physical limitations and provided reassurance and confidence in their disease management. CONCLUSIONS: Subjects appeared confident in their use of home pulse oximetry. Health professionals should identify patients who use pulse oximeters for monitoring and ensure that they are able to interpret readings correctly and, if appropriate, adjust management safely.


Subject(s)
COVID-19 , Lung Diseases , Respiration Disorders , Aged , Female , Humans , Oximetry , Oxygen
18.
Biomed Pharmacother ; 150: 113041, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1821148

ABSTRACT

BACKGROUND: Lung diseases including chronic obstructive pulmonary disease (COPD), infections like influenza, acute respiratory distress syndrome (ARDS), asthma and pneumonia lung cancer (LC) are common causes of sickness and death worldwide due to their remoteness, cold and harsh climatic conditions, and inaccessible health care facilities. PURPOSE: Many drugs have already been proposed for the treatment of lung diseases. Few of them are in clinical trials and have the potential to cure infectious diseases. Plant extracts or herbal products have been extensively used as Traditional Chinese Medicine (TCM) and Indian Ayurveda. Moreover, it has been involved in the inhibition of certain genes/protiens effects to promote regulation of signaling pathways. Natural remedies have been scientifically proven with remarkable bioactivities and are considered a cheap and safe source for lung disease. METHODS: This comprehensive review highlighted the literature about traditional plants and their metabolites with their applications for the treatment of lung diseases through experimental models in humans. Natural drugs information and mode of mechanism have been studied through the literature retrieved by Google Scholar, ScienceDirect, SciFinder, Scopus and Medline PubMed resources against lung diseases. RESULTS: In vitro, in vivo and computational studies have been explained for natural metabolites derived from plants (like flavonoids, alkaloids, and terpenoids) against different types of lung diseases. Probiotics have also been biologically active therapeutics against cancer, anti-inflammation, antiplatelet, antiviral, and antioxidants associated with lung diseases. CONCLUSION: The results of the mentioned natural metabolites repurposed for different lung diseases especially for SARS-CoV-2 should be evaluated more by advance computational applications, experimental models in the biological system, also need to be validated by clinical trials so that we may be able to retrieve potential drugs for most challenging lung diseases especially SARS-CoV-2.


Subject(s)
COVID-19 , Lung Diseases , COVID-19/drug therapy , Dietary Supplements , Humans , Lung Diseases/drug therapy , Medicine, Chinese Traditional , Phytochemicals/pharmacology , Phytochemicals/therapeutic use , Phytotherapy , Plant Extracts/pharmacology , SARS-CoV-2
20.
Med Intensiva (Engl Ed) ; 46(6): 354, 2022 06.
Article in English | MEDLINE | ID: covidwho-1804832
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