Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study

Trends, seasonal variations and forecasting of chronic respiratory disease morbidity in charcoal producing areas, northwest Ethiopia: time series analysis

Objective

This study analyzed the trend, seasonal variations and forecasting of chronic respiratory disease morbidity in charcoal producing areas, northwest Ethiopia, aiming to provide evidences in planning, designing strategies, and decision-makings for preparedness and resource allocation to prevent CRD and reduce public health burden in the future.

Materials and methods

The trend, seasonal variation, and forecasting for CRD were estimated using data collected from the three zones of Amhara region annual reports of DHIS2 records. Smoothing decomposition analysis was employed to demonstrate the trend and seasonal component of CRD. The ARIMA (2, 1, 2) (0, 0, 0) model was used to forecast CRD morbidity. The model's fitness was checked based on Bayesian information criteria. The stationarity of the data was assessed with a line chart and statistically with the Ljung-Box Q-test. SPSS version 27 was utilized for statistical analysis.

Results

The annual morbidity rate of CRD has shown an increasing trend in both sexes over a seven-year period among people aged 15 years and older. Seasonal variation in CRD morbidity was observed. The smoothing decomposition analysis depicted that the seasonal component was attributed to 44.47% and 19.16% of excess CRD cases in the period between September to November, and June to August, respectively. A substantial difference among the three zones of the Amhara region in CRD morbidity rate was noted, with the highest observed in the Awi zone. Forecasting with the ARIMA model revealed that CRD-related morbidity will continue to increase from 2020 to 2030.

Conclusion

The study revealed that the CRD morbidity rate has shown an increasing trend from 2013 to 2019. Seasonal variation in the CRD morbidity rate was observed, with the highest peak from September to November. The morbidity attributed to CRD will continue to increase for the next ten years (2020-2030). Therefore, this study could potentially play a groundbreaking role. Further study is warranted to understand the risk factors and facility readiness through a further understanding of seasonality and future trends.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

The effectiveness of probiotics as an adjunct therapy in patients under mechanical ventilation: an umbrella systematic review and meta-analysis

The literature regarding the role of probiotics in critically ill patients who have undergone mechanical ventilation (MV) is unclear; therefore, this umbrella systematic review and meta-analysis was carried out to clarify the effects of probiotics on the clinical outcomes of mechanically ventilated patients. The Scopus, PubMed/Medline, ISI Web of Science, and Google Scholar online databases were searched up to February 2023. All meta-analyses evaluating the impact of probiotics in patients under MV were considered eligible. The assessment of multiple systematic reviews (AMSTAR) questionnaire was used to evaluate the quality of the studies. Data were pooled using the random-effects approach. Thirty meta-analyses and nine clinical outcomes were re-analyzed. Probiotics significantly decreased ventilator-associated pneumonia (VAP) incidence, nosocomial infections, intensive care unit (ICU) length of stay, hospital length of stay, ICU mortality, hospital mortality, MV duration, duration of antibiotic use, and diarrhea. The obtained results of the current umbrella meta-analysis indicate that probiotic administration could be considered an adjunct therapy for critically ill patients; however, no specific probiotic treatment regimen can be recommended due to the diverse probiotics used in the included meta-analyses. The following microorganisms were used at various doses and combinations throughout the studies: Lacticaseibacillus casei, Lactiplantibacillus plantarum, L. acidophilus, L. delbrueckii, L. bulgaricus, Bifidobacterium longum, B. breve, B. salivarius, Pediococcus pentosaceus, Lactococcus raffinolactis, B. infantis, B. bifidum, Streptococcus thermophilus, Ligilactobacillus salivarius, L. lactis, B. lactis, Saccharomyces boulardii, L. rhamnosus GG, L. johnsonii, L. casei, S. faecalis, Clostridium butyricum, Bacillus mesentericus, L. sporogenes, S. boulardii, L. paracasei, B. subtilis, and Enterococcus faecium.

Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model

Background

Artificial intelligence (AI) techniques have been ascertained useful in the analysis and description of infectious areas in radiological images promptly. Our aim in this study was to design a web-based application for detecting and labeling infected tissues on CT (computed tomography) lung images of patients based on the deep learning (DL) method as a type of AI.

Materials and Methods

The U-Net architecture, one of the DL networks, is used as a hybrid model with pre-trained densely connected convolutional network 121 (DenseNet121) architecture for the segmentation process. The proposed model was constructed on 1031 persons' CT-scan images from Ibn Sina Hospital of Iran in 2021 and some publicly available datasets. The network was trained using 6000 slices, validated on 1000 slices images, and tested against the 150 slices. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) were calculated to evaluate model performance.

Results

The results indicate the acceptable ability of the U-Net-DenseNet121 model in detecting COVID-19 abnormality (accuracy = 0.88 and AUC = 0.96 for thresholds of 0.13 and accuracy = 0.88 and AUC = 0.90 for thresholds of 0.2). Based on this model, we developed the "Imaging-Tech" web-based application for use at hospitals and clinics to make our project's output more practical and attractive in the market.

Conclusion

We designed a DL-based model for the segmentation of COVID-19 CT scan images and, based on this model, constructed a web-based application that, according to the results, is a reliable detector for infected tissue in lung CT-scans. The availability of such tools would aid in automating, prioritizing, fastening, and broadening the treatment of COVID-19 patients globally.

Knowledge and awareness of warning signs about Lung cancer among Pharmacy and Nursing undergraduates in Riyadh, Saudi Arabia - an observational study

Background: Cancer is becoming more common, regardless of gender or type. Cancer was determined to be the leading cause of death, with lung cancer (LC) patients having the highest rate of cancer-related deaths. The purpose of this study was to analyze undergraduates' knowledge and awareness of LC early warning signs in Riyadh, Saudi Arabia. Methods: Between May and September 2022, a cross-sectional, prospective paper-based survey-type study was conducted among undergraduates (n=202) from the faculty of pharmacy and nursing at King Saud University (KSU) in Riyadh, Saudi Arabia. The data was gathered from third and fourth-year undergraduates. The statistical package for social science (SPSS Inc., Chicago, IL, U.S.) was used to perform the analysis. Results: The mean age of the undergraduates was 22.47 ± 2.35(SD) years. Most of them were from nursing 54% (n=109), while 46% (n=93) belonged to a pharmacy. In terms of awareness of warning signs of lung cancer, 48.6% of the students believed that unexplained weight loss, followed by persistent chest infection (36.6%) and cough that does not go away easily (37.6%). Over 45.1 % of students opted that coughing up blood, pain during the cough (46.5%), and worsening or change in an existing cough (42.1%) were reported as a sign of LC. In this study, the overall good awareness score was 60(29.7%). The awareness was significantly associated with gender (p = 0.0001), the course of study (p=0.018), the educational level (p = 0.003), smoking cigarettes (p = 0.003), and chronic disease status (p = 0.0001). Conclusion: Undergraduates attending university in this study indicated various levels of awareness of LC symptoms. The undergraduate's educational background, study program, and gender all greatly influence their level of awareness. It is necessary to inform future medical professionals about this growing condition.

Quantitative Assessment of COVID-19 Lung Disease Severity: A Segmentation-based Approach

We present the use of mean Hounsfield units within lungs as a metric of disease severity for the comparison of image analysis models in patients with COPD and COVID. We used this metric to assess the performance of a novel 3D global context attention network for image segmentation that produces lung masks from thoracic HRCT scans. Results showed that the mean Hounsfield units enable a detailed comparison of our 3D implementation of the GC-Net model to the V-Net segmentation algorithm. We implemented a biomimetic data augmentation strategy and used a quantitative severity metric to assess its performance. Framing our investigation around lung segmentation for patients with respiratory diseases allows analysis of the strengths and weaknesses of the implemented models in this context.Clinical Relevance - Mean Hounsfield units within the lung volume can be used as an objective measure of respiratory disease severity for the comparison of CT scan analysis algorithms.

Circulating eosinophils associated with responsiveness to COVID-19 vaccine and the disease severity in patients with SARS-CoV-2 omicron variant infection

OBJECTIVE

This study aimed to investigate the longitudinal circulating eosinophil (EOS) data impacted by the COVID-19 vaccine, the predictive role of circulating EOS in the disease severity, and its association with T cell immunity in patients with SARS-CoV-2 Omicron BA.2 variant infection in Shanghai, China.

METHODS

We collected a cohort of 1,157 patients infected with SARS-CoV-2 Omicron/BA.2 variant in Shanghai, China. These patients were diagnosed or admitted between Feb 20, 2022, and May 10, 2022, and were classified as asymptomatic (n = 705), mild (n = 286) and severe (n = 166) groups. We compiled and analyzed data of patients' clinical demographic characteristics, laboratory findings, and clinical outcomes.

RESULTS

COVID-19 vaccine reduced the incidence of severe cases. Severe patients were shown to have declined peripheral blood EOS. Both the 2 doses and 3 doses of inactivated COVID-19 vaccines promoted the circulating EOS levels. In particular, the 3rd booster shot of inactivated COVID-19 vaccine was shown to have a sustained promoting effect on circulating EOS. Univariate analysis showed that there was a significant difference in age, underlying comorbidities, EOS, lymphocytes, CRP, CD4, and CD8 T cell counts between the mild and the severe patients. Multivariate logistic regression analysis and ROC curve analysis indicate that circulating EOS (AUC = 0.828, p = 0.025), the combination of EOS and CD4 T cell (AUC = 0.920, p = 0.017) can predict the risk of disease severity in patients with SARS-CoV-2 Omicron BA.2 variant infection.

CONCLUSIONS

COVID-19 vaccine promotes circulating EOS and reduces the risk of severe illness, and particularly the 3rd booster dose of COVID-19 vaccine sustainedly promotes EOS. Circulating EOS, along with T cell immunity, may have a predictive value for the disease severity in SARS-CoV-2 Omicron infected patients.

[New therapeutic advances in patients with lung cancer immunosuppressed with chronic lung diseases in the period 2014-2022 from the review of the literature.]

Lung cancer is a malignant neoplasm with a high prevalence and mortality, more so in patients with respiratory comorbidities, whose cells have a massive proliferation capacity in the lung tissue, managing to invade other organs, which deteriorates the patient's physical and emotional state, decreasing their quality of life and defense system; therefore, treatment today is not sufficient for patient survival and there has been evidence of a certain evolution in the treatment of the disease or early detection to prevent it. This article aimed to analyze the new therapeutic advances in patients with lung cancer associated with chronic lung diseases in the period 2014-2022 based on a review of the literature. Several parameters were used to limit the search, extrapolating the articles of interest, validating fifty three articles, six doctoral theses and two books, which were in Spanish and English.The various search strategies used were keywords, subject and author follow-up. The sections developed in this review are the concept of Lung Cancer (LC), clinical manifestations, risk factors, relationship between LC and chronic lung diseases, diagnosis, treatment, prevention and new therapeutic advances. All the filtered information of the selected articles shows us the importance that the use of various biomarkers is taking for its early detection; however, the transfer of antitumor T cells in patients with underlying lung disease had an efficiency of 48.

Comparison of Incremental Shuttle Walking Test, 6-Minute Walking Test, and Cardiopulmonary Exercise Stress Test in Patients with Myocardial Infarction

BACKGROUND This study aimed to analyze the correlations among peak oxygen uptake (VO2) in cardiopulmonary exercise test (CPX), incremental shuttle walking test (ISWT), and 6-minute walking test (6MWT) distances in patients with myocardial infarction (MI). Additionally, we aimed to determine the relationship between the maximum heart rate (HRmax) and the rate of perceived exertion (RPE) among the tests and compare the changes in heart rate to verify the clinical benefit of the submaximal stress test. MATERIAL AND METHODS We analyzed the correlation by using the ISWT and 6MWT at 30-min intervals after 24 h of CPX in patients with MI. The differences in HRmax and RPE between the tests were also compared. Additionally, changes in heart rate were analyzed using descriptive statistics. RESULTS The ISWT distance was more strongly correlated with peak VO₂ (r=.823: 95% CI, 0.681-0.910) than was 6MWT (r=0.776: 95% CI, 0.683-0.870). HRmax in the CPX demonstrated a significant correlation with that in the ISWT and 6MWT (P<0.05), with the ISWT (r=0.815: 95% CI, 0.451-0.996) having a stronger correlation than the 6MWT (r=0.664: 95% CI, 0.146-0.911). The value of RPE was significantly different (P<0.05); however, there was no significant correlation. Changes in heart rate in the 6MWT plateaued after the initial increase, while the heart rate in the ISWT and CPX increased gradually. CONCLUSIONS We recommend the ISWT as a submaximal exercise test to evaluate exercise capacity in patients with MI.

Interobserver Variability in CT Severity Scoring System in COVID-19 Positive Patients

Background: Chest CT scans are done in cases of coronavirus disease 2019 (COVID-19)-positive patients to understand the severity of the disease and plan treatment accordingly. Severity is determined according to a 25-point scoring system, however, there could be interobserver variability in using this scoring system thus leading to the different categorization of patients. We tried to look for this interobserver variability and thus find out its reliability.

Methods: The study was retrospective and was done in a designated COVID center. Some 100 patients were involved in the study who tested positive for COVID-19 disease. The research was conducted over six months (January 2021 to June 2021). Images were given to three radiologists with a minimum of 10 years of experience in thoracic imaging working in different setups at different places for interpretation and scoring further and their scores were compared. Before the study, the local ethics committee granted its approval.

Results: There was no significant variability in the interobserver scoring system thus proving its reliability. The standard deviation between different observers was less than three. There was almost perfect agreement amongst all the observers (Fleiss’ K=0.99 [95% confidence interval, CI: 0.995-0.998]). Maximum variations were observed in the moderate class. 

Conclusion: There was minimum inter-observer variability in the 25-point scoring system thus proving its reliability in categorizing patients according to severity. There was no change in the class of the patient according to its severity. A 25-point scoring system hence can be used by clinicians to plan treatment and thus improve a patient's prognosis.