Lungs Of Patients Research Articles

Fuzzy Logic-based Current Control Logic for Proportional Flow Control Valve System of Medical Ventilators

The critical control parameters in ventilators that provide life support to patients are the flow of air and oxygen. Ventilators typically use Proportional Flow Control Valves (PFCV) and flow sensors to achieve precise flow accuracy. Maintaining accurate flow from the PFCV is essential to minimize the thrust exerted on the patient's lungs. Fuzzy logic is particularly well-suited for this task, as it effectively captures the subjective human judgments often required in clinical settings. This study aimed to develop a fuzzy logic algorithm to control pressure support ventilation. The research describes the fuzzy logic algorithm and presents experimental studies that evaluated the flow accuracy of a commercially used PFCV in ventilators. Additionally, the reduction in output flow due to thermal effects was investigated. A correlation between the solenoid coil current (measured as voltage across the coil) and the actual output flow through the PFCV was experimentally established using a current sensing circuit. Based on the characteristic curves obtained from the test setup, a Fuzzy Logic system was developed to minimize the error in the PFCV's flow. The implementation of PFCV using this Fuzzy Logic-based current control approach demonstrated a significant reduction in error, from 10% to 3%, in airflow delivery across its operating range, with additional sample results discussed. This reduction in flow error is crucial for assessing the suitability of PFCVs in infant ventilators. The described methodology can be experimentally applied to any PFCV variant before its use in medical ventilator applications to enhance delivered flow accuracy.

A comparative study of the efficacy of alginate lyases in the presence of metal ions elevated in the cystic fibrosis lung milieu

Pseudomonas aeruginosa, a common cause of morbidity in cystic fibrosis, chronically infects the patient's lungs by forming an alginate-containing biofilm. Alginate lyases are polysaccharide lyases that lyse alginate and are, therefore, potential biofilm-disruptive agents. However, cystic fibrosis sputum contains high levels of metals such as iron and zinc. The efficacy of alginate lyases under these conditions of elevated metal concentrations has not been categorically determined. Here, we have assessed the enzyme activity of two exolytic and five endolytic alginate lyases in the presence of metal ions (Fe2+, Zn2+, Mn2+, Mg2+, Ca2+, Ni2+, Cu2+) elevated in the cystic fibrosis lung milieu. Several of these alginate lyases exhibited increased activity in the presence of Ca2+, and the polysaccharide lyase family 7 members studied here exhibited decreased activity in the presence of Zn2+. The enzyme activity of the PL7 alginate lyases from Cellulophaga algicola (CaAly/CaFLDAly) and Vibrio sp. (VspAlyVI) was not affected in the presence of a mix of all the above-mentioned metal ions at the elevated concentrations found in the cystic fibrosis lung milieu. Specific alginate lyases might, therefore, retain the ability to degrade the alginate-containing Pseudomonas biofilm in the presence of metal ions elevated in the cystic fibrosis lung milieu.

Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we retrospectively reviewed 99 oncological patients (median age in years: 64 (range: 28-92 years); percentage of female: 39.4%) who received unenhanced and contrast-enhanced chest CT examinations in one session between January 2020 and October 2022 and who were diagnosed incidentally with PE. Findings in the unenhanced images were correlated with the contrast-enhanced images, which were considered the gold standard for central, segmental and subsegmental PE. The new algorithm was trained and tested based on the 99 unenhanced chest-CT image data sets. Based on them, candidate boxes, which were output by the model, were post-processed by evaluating whether the predicted box intersects with the patient's lung segmentation at any position. The AI-based algorithm proved to have an overall sensitivity of 54.5% for central, of 81.9% for segmental and 80.0% for subsegmental PE if taking n = 20 candidate boxes into account. Depending on the localization of the pulmonary embolism, the detection rate for only one box was: 18.1% central, 34.7% segmental and 0.0% subsegmental. The median volume of the clots differed significantly between the three subgroups and was 846.5 mm3 (IQR:591.1-964.8) in central, 201.3 mm3 (IQR:98.3-390.9) in segmental and 110.6 mm3 (IQR:94.3-128.0) in subsegmental PA (p < 0.05). The new algorithm proved to have high sensitivity in detecting PE in particular in segmental/subsegmental localization and may guide to decide whether a second contrast enhanced CT is necessary.

Safety and efficacy of CT-guided percutaneous radiofrequency ablation for non-small cell lung cancer: a single-center, single-arm analysis.

Non-small cell lung cancer (NSCLC) is a prevalent malignant tumor, and the commonly treatment modalities include surgery, radiotherapy, chemotherapy, etc. Currently, CT-guided percutaneous radiofrequency ablation (RFA) for the treatment of cancers has been widely performed. This study aimed to evaluate the safety and efficacy of this therapy in NSCLC patients. Thirty-five NSCLC patients were enrolled in this study and all received CT-guided percutaneous RFA therapy. The outcome measures included the changes in forced respiratory volume in the first second (FEV1), total lung volume (TLC), lesion size and computed tomography (CT) values of the region of interest (ROI) before and after treatment. The main efficacy measures comprised complete tumor ablation and local recurrence after initial treatment, as well as the objective response rate (ORR) and disease control rate (DCR) after 6 months of treatment. After receiving CT-guided percutaneous RFA therapy, the target lesion was effectively controlled and CT values gradually decreased. Besides, no significant changes were observed in the patient's lung function, postoperative complications were experienced by a total of 10 patients, primarily including pneumothorax, infection, lung hollowing. Fortunately, all these complications were successfully managed with appropriate treatment. Following the initial RFA treatment, 31 patients (88.57%) achieved complete ablation, while 6 patients experienced local recurrence. After 6 months of treatment, the ORR and DCR were found to be 68.57% and 82.86% respectively. CT-guided percutaneous RFA has demonstrated favorable safety and efficacy in the treatment of patients with NSCLC at different stages, which represented a promising therapeutic modality.

Implementation of CNN of Mobile-based COVID-19 Chest X-Ray Images

The COVID-19 pandemic outbreak is the most significant event from 2019 until 2021. A medical examination of radiological images is carried out to check the condition of the patient's lungs. The limitations of this examination need alternative computer-assisted applications for patient CXR. This research aims to implement a back-end and front-end-based Convolutional Neural Network (CNN) model. Its advantage is that it can detect CXR images in real-time and non-real-time using multi-classification, namely normal, pneumonia, and COVID-19. The CNN model carries out the process of convolutional feature extraction and multi-layer perceptron classification at the back-end stage. In contrast, it uses an Android mobile-based application at the front-end stage. The research results show that the non-real-time condition has an accuracy of 98%, while the real-time is 95% lower. This research produces model and application performance that is flexible for user needs. The results can be recommended for developing applications for more comprehensive users.

Establishment of a predictive model of CT value in the diagnosis and differential diagnosis of pulmonary cryptococcosis

ObjectiveTo investigate the application value of computed tomography (CT) value (HU) in the diagnosis and differential diagnosis of pulmonary cryptococcosis (PC) and to construct a prediction model. MethodsRetrospective analysis of the clinical data of 73 patients who presented with nodular/mass-type occupations on lung CT and confirmed by histopathology in our hospital from January 2019 to May 2022 were divided into PC group (23 patients) and non-PC group (50 patients) according to the pathological findings, and the CT values of each patient's lung lesions were measured. The differences in age, gender, symptoms, lesion involvement in one/both lungs, lung lobe distribution, number of lesions, maximum lesion diameter (cm), lesion margin condition, and CT value results were compared between the two groups. Independent risk factors for PC were analyzed for indicators with statistically significant differences, clinical prediction models were constructed and column line plots were drawn, C (correction) indices were calculated, subject characteristics (ROC) curves were drawn, calibration curves and clinical decision curve analysis (DCA) were performed to further evaluate the predictive efficacy of the models. ResultsComparative analysis of patient data between the two groups showed statistically significant differences in central, peripheral and global CT values (P < 0.05), and multiple regression analysis indicated that central CT value, peripheral CT value and global CT value could be used as independent risk factors for the diagnosis and differential diagnosis of PC. The area under the ROC curve of the model predicting PC was 0.814 (95 % CI: 0.7011–0.9267), and the corrected C-index (Bootstrap = 1000) was 0.781; the actual curve overlapped well with the calibration curve; the DCA results indicated that the column line graph model has high clinical application value. ConclusionsCT value measurements of lesions can be used as an independent risk factor for PC, and clinical prediction models based on the above factors are predictive for the diagnosis and differential diagnosis of PC.

Estimating lung function from computed tomography at the patient and lobe level using machine learning.

Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction. To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function. We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe-level and a patient-level model. The lobe-level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient-level model directly estimates patient level lung function from a CT scan and is used to re-scale the output of the lobe-level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8433 CT volumes for training, 1775 CT volumes for validation, and 1873 CT volumes fortesting. First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe-level quantities, such as COVID-19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient-level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono-oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377L, 0.297L, and 2.800mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand-challenge.org/algorithms/lobe-wise-lung-function-estimation/ CONCLUSIONS: I3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volumereduction.

Bacterial load of the surroundings during rigid diagnostic bronchoscopy under high frequency jet-ventilation

High-frequency jet ventilation (HFJV) is used in pneumological endoscopy for rigid, diagnostic, and therapeutic bronchoscopies. It is unclear to what extent the unobstructed flow of respiratory gas from the patient's lungs causes microbial contamination of the surrounding air. After the start of the HFJV (15 min) in 16 rigid bronchoscopies, airborne pathogen measurements were taken directly at the distal endoscope outlet, at examiner height (40 cm above the endoscope outlet), at a 2 m distance from the endoscope in the room and at the supply air outlet of the examination room using an RCS air sampler. The number and type of pathogens isolated in the air samples were then determined, as well as germs in the bronchoalveolar lavage fluid (BALF) from the patient's lungs. An increased bacterial density (136 and 114 CFU/m3) was detected directly at the distal end of the endoscope and at examiner height at a distance of 40 cm, which decreased significantly with increasing distance from the bronchoscope (98 CFU/m3 at a distance of 2 m and 82 CFU/m3 at the supply air outlet). The most frequently detected bacteria were Staphylococcus spp., Micrococcus spp. and Bacillus spp. In the BALF, pathogens could only be cultivated in four of 16 samples, but the same pathogens were detected in the BALF and the ambient air. When performing a rigid bronchoscopy, in which patients are mechanically ventilated in a controlled manner using an open HFJV system, there is an increased pathogen load in the ambient air and therefore a potential risk for the examiner.

An automatic cough counting method and system construction for portable devices.

Cough is a common symptom of respiratory diseases, and prolonged monitoring of cough can help assist doctors in making judgments about patients' conditions, among which cough frequency is an indicator that characterizes the state of the patient's lungs. Therefore, the aim of this paper is to design an automatic cough counting system to monitor the number of coughs per minute for a long period of time. In this paper, a complete cough counting process is proposed, including denoising, segment extraction, eigenvalue calculation, recognition, and counting process; and a wearable automatic cough counting device containing acquisition and reception software. The design and construction of the algorithm is based on realistically captured cough-containing audio from 50 patients, combined with short-time features, and Meier cepstrum coefficients as features characterizing the cough. The accuracy, sensitivity, specificity, and F1 score of the method were 93.24%, 97.58%, 86.97%, and 94.47%, respectively, with a Kappa value of 0.9209, an average counting error of 0.46 counts for a 60-s speech segment, and an average runtime of 2.80 ± 2.27s. This method improves the double threshold method in terms of the threshold and eigenvalues of the cough segments' sensitivity and has better performance in terms of accuracy, real-time performance, and computing speed, which can be applied to real-time cough counting and monitoring in small portable devices with limited computing power. The developed wearable portable automatic cough counting device and the accompanying host computer software application can realize the long-term monitoring of patients' coughing condition.

Patient-specific Effective Dose Estimation from Dose-Length Product in Lung Computed Tomography Using Monte Carlo Simulation.

Computed tomography (CT) imaging has a large portion in the dose of patients from radiological procedures; therefore, accurate calculation of radiation risk estimation in this modality is inevitable. In this study, a method for determining the patient-specific effective dose using the dose-length product (DLP) index in lung CT scan using Monte Carlo (MC) simulation is introduced. EGSnrc/BEAMnrc MC code was used to simulate a CT scanner. The DOSxyznrc simulation code was used to simulate a specific voxelized phantom from the patient's lungs and irradiate it according to X-ray parameter of routing lung CT scan, and dose delivered to thorax organs was calculated. Three types of phantoms were simulated according to three different body habits (slim, standard, and fat patients) in two groups of men and women. A factor was used to convert the relative dose per particle in MC code to the absolute dose. The dose was calculated in all lung organs, and the effective dose was calculated for all three groups of patient body habits. DLP index and volume CT dose index (CTDIvol) were extracted from the patient's dose report in the CT scanner. The DLP to effective dose conversion factor (k-factor) for patients with different body habitus was calculated. Lung radiation dose in slim, standard, and fat patients in men was 0.164, 0.103, and 0.078 mGy/mAs and in women was 0.164, 0.105, and 0.079 mGy/mAs, respectively. The k-factor in the group of slim patients, especially in women, was higher than in other groups. CT scan dose indexes for slim patients are reported to be underestimated in studies. The dose report in CT scan systems should be modified in proportion to the patient's body habitus, to accurately estimate the radiation risk.

Pathological and immunohistochemical findings of lungs, heart, liver, and kidneys, and unexpected findings of fungi and parasites in lungs of deceased COVID-19 patients: A case series

Objective: To define histopathologic and immunohistochemical features of the lungs, heart, liver, and kidneys in patients who died from coronavirus disease 2019 (COVID-19), and to determine the presence of SARS-CoV-2 in all tissues, as well as the presence of fungi and parasites in lung tissues. Methods: This retrospective case study was conducted in the intensive care units of Dokuz Eylül University Hospital, and patients (≥18 years) who died due to COVID-19 between October 2020 and April 2021 were included. The biopsy samples of the patient's lung, heart, liver, and kidney tissues were studied. Results: In the study, we enrolled 12 patients (mean age: 70 years; 50% male). Alveolar epithelial cell damage and diffuse alveolar damage were predominant in lung tissues. Lobular lymphocyte infiltration, centrilobular sinusoidal dilatation, and microvesicular steatosis in the liver, together with pigmented cast, non-isometric vacuolar degeneration, and capillary plugging in the kidneys, were commonly found among the patients. SARS-CoV-2 nucleocapsid protein antibodies were detected in three lung and two kidney tissues, and so did angiotensin-converting enzyme 2 receptor positivity in one lung and more than half of the kidney tissues. The RT-PCR tests were positive in three lungs and one kidney tissue. After DNA isolation from lung tissues, Pneumocystis jirovecii was detected in nine patients, Aspergillus fumigatus in two, Microsporidia in three, and Cryptosporidium in two. Conclusions: SARS-CoV-2 is a multisystemic disease. Fungi and parasites should be investigated in critically ill COVID-19 patients prescribed corticosteroids.

Photodynamic therapy for treatment of recurrent hemoptysis secondary to pulmonary endometriosis: a case report.

Pulmonary endometriosis (PEM) is rare, and drug therapy remains the primary treatment. However, patients with PEM frequently experience recurrent hemoptysis that is refractory to pharmacological intervention. We herein describe a patient with PEM who developed recurrent hemoptysis and was successfully treated with photodynamic therapy (PDT) after drug withdrawal. The patient was admitted to our hospital because of recurrent hemoptysis despite repeated drug treatments for more than 1 year. Given that PDT targets specific tissues and destroys vascular endothelial cells through the cytotoxic effect produced by the photodynamic reaction of the photosensitizer, we considered that it may effectively control hemoptysis secondary to vascular morphological changes in PEM. Therefore, we performed PDT in this case, and the patient's recurrent hemoptysis regressed. Approximately 2 years following PDT, the patient had recovered well and reported no discomfort. We recommend consideration of PDT as a treatment option for patients with PEM who develop recurrent hemoptysis after drug withdrawal. Notably, the patient's lung lesions should be superficial and limited, and no contraindications should be present.

Öksürük belirtisi sonrası Schwannoma tanısı konulan bir olgu

The complaints of sore throat and cough are the symptoms that we encounter frequently in our daily routine, and it is important to remember that if not thoroughly examined, different underlying diseases can be revealed. Our case was applied to the family medicine outpatient clinic with complaints of sore throat and cough, and a mass appearance was observed during the chest radiography requested due to abnormal findings from the sounds coming from the patient's lung. Thus, the mass was detected early, and we presented the patient with the diagnosis of Schwannoma, whose mass was removed.

A Japanese case of ovarian mucinous adenocarcinoma with germline double variants of MSH2 and BRCA2.

Germline double heterozygosity (GDH) is rarely reported in cases of inherited cancer syndromes, and GDH of a mismatch repair gene and BRCA has never been reported in Japan. Nonetheless, the current report demonstrates a case of ovarian mucinous adenocarcinoma with initiated Lynch syndrome (LS)-related surveillance because of a known germline MSH2 variant. Six and a half years after oophorectomy, multiple tumors developed in the patient's lungs, bones, and lymph nodes, and histology results confirmed mucinous adenocarcinoma. Systemic chemotherapy including an anti-PD-L1 antibody was effective for >1 year, but brain metastases developed. Pathology of the brain tumors showed mucinous adenocarcinoma without expression of MSH2 and MSH6, while multi-gene panel testing demonstrated not only high microsatellite instability and a high tumor mutation burden, but also germline BRCA2 variants. Further, germline testing in relatives confirmed both variants were from the paternal line, from which many LS-related cancers develop, but not BRCA-related cancer.

Deep Learning-Based Pneumonia Classification Based on Respiratory Sounds

A crucial aspect of pneumonic pathology is attributed and analyzed by respiratory sound, which also provides information on the patient's lung symptoms. Almost all health institutions worldwide acquire pneumonia as a common and therapeutically challenging diagnosis that can lead to a severe body response to an infection, critical illness, and respiratory failure.

Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.

Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.

Combined Tui na and Western medicine treatment improves pulmonary function and quality of life in patients with amyotrophic lateral sclerosis: A case report.

Amyotrophic lateral sclerosis is a rare disease that cannot be cured. We report a case of a patient with amyotrophic lateral sclerosis whose pulmonary function and quality of life were improved by a combined tui na treatment and Western medicine. A 48-year-old male was diagnosed with ALS 1 year ago and was treated with western medicine and herbal medicine with no significant effect. This time, he was admitted to our department because of slurred speech, coughing and choking, and weakness of the left upper limb for more than 1 year. After 1 month of treatment with tui na and traditional western medicine, the patient's lung function and quality of life improved and he was discharged from the hospital. Motor neuron disease. Amyotrophic lateral sclerosis. The physiological function of ALS patients can be improved through the intervention of tui na.

Clinical efficacy observation of omalizumab on patients with moderate to severe allergic asthma for one year

To observe the symptom control, pulmonary function changes and safety of use of omalizumab in patients with moderate to severe allergic asthma for 1 year. A small sample self-controlled study before and after treatment was conducted to retrospective analysis involved 17 patients with moderate to severe asthma who received omalizumab therapy for 12 months in Peking University People's Hospital and Beijing Jishuitan Hospital from January 2020 to December 2021. The clinical symptoms and pulmonary function changes were compared before treatment, after 6 months and 12 months of treatment, and the clinical data such as the use of other drugs and adverse reactions were observed. Statistical data are collected using the median method, and non-parametric paired Wilcoxon analysis was used for pairwise comparison. Before treatment with omalizumab, the patients' FeNO value was 79(58, 121) ppb, and the total serum IgE was 228(150.5, 345.5) IU/ml. After 6 months of omalizumab therapy, the percent predicted value of the forced expiratory volume in 1 second (FEV1%) before inhaled bronchodilator increased from 86.70(82.65, 91.35)% to 90.90(87.70, 95.85)% (Z=-3.626, P<0.001). The FEV1%pred after inhaled bronchodilator increased from 92.60(85.75, 96.90)% to 94.30(89.95, 98.15)% (Z=-2.178, P=0.029). The absolute value of improvement in FEV1 decreased from 150(95, 210)ml to 50(20, 125) ml (Z=-2.796, P=0.005), and the improvement rate decreased from 6.60(3.80, 7.85)% to 1.90(0.75, 4.85)% (Z=-2.922, P=0.003). After 12 months of treatment, the FEV1%pred before inhaled bronchodilator further increased to 92.90 (91.60, 98.15)% (Z=-3.575, -2.818, and P<0.001, 0.005 compared with before treatment and 6 months after treatment, respectively). The FEV1%pred after inhaled bronchodilator increased to 96.80 (91.90, 101.25)% (Z=-3.622, -1.638, and P<0.001, 0.008 compared with before treatment and after 6 months of treatment, respectively). The absolute value of improvement in FEV1 was 70 (35, 120) ml (P=0.004, 0.842 before treatment and 6 months after treatment, respectively), and the improvement rate was 3.0(1.0, 5.0)% (Z=-2.960, -0.166, and P=0.003, 0.868, compared with before treatment and after 6 months of treatment, respectively). After 12 months of treatment, ACT increased from 13 (10.5, 18) before treatment to 24 (23, 25) (Z=-3.626,P<0.001). Only 1 patient experienced an injection site skin reaction during treatment. Therefore, after 6 months and 12 months of treatment with omalizumab, the patient's lung function improved and symptoms were relieved, which could effectively prevent the acute exacerbation of asthma. Omalizumab treatment is safe and well tolerated, and no effect on blood pressure and blood glucose was observed.

Expression and prognostic impact of DNA-PK in human lung cancer.

Among all cancer patient's lung cancer is the leading cause of death. Prognostic biomarkers continue to be investigated for the detection and stratification of lung cancer for clinical use. The DNA-dependent protein kinase is involved in mechanisms of DNA damage repair. Deregulation and overexpression of DNA-dependent protein kinase is associated with poor prognosis in various tumor entities. In this study, we investigated the expression of DNA-dependent protein kinase in relation to clinicopathological features and overall survival in patients with lung cancer. By immunohistochemistry, expression of DNA-dependent protein kinase was analyzed in 205 cases of lung cancer; 95 cases of adenocarcinoma, 83 cases of squamous cell lung carcinoma and 27 cases of small cell lung cancer and correlated with clinicopathological characteristics as well as patient's overall survival. In patients with adenocarcinoma, a significant correlation between strong expression of DNA-dependent protein kinase and worse overall survival was found. No significant association was observed in patients with squamous cell lung carcinoma and small cell lung cancer. Strong detection of DNA-dependent protein kinase expression was most evident in small cell lung cancer (81.48 %), followed by squamous cell lung carcinoma (62.65 %) and adenocarcinoma (61.05 %). In our study, expression of DNA-dependent protein kinase was associated with poor overall survival in patients with adenocarcinoma. DNA-dependent protein kinase could serve as a new prognostic biomarker.

Prevention of Scarring of Lung Parenchyma in COVID-19 Patients through Glucocorticoid Treatment

Objective: The objective of this research was to determine the impact of glucocorticoid treatment on preventing scarring of lung parenchyma in COVID-19 patients by considering their health behavior. Methods: A sample of 65 Turkish patients who had pneumonia and were hospitalized between March/December 2020 were included in this research. The data for this research was collected after the consent of the hospital. The structural equation model approach was used in data analysis and empirical findings. Results: The research identified that the patients with appropriate health behavior were satisfied with their clinical treatment of scarring of lung parenchyma by the method of glucocorticoid treatment. The research also identified that the patient's health behavior was a significant indicator to improve their perception of the clinical treatment. Conclusion: This study has reliable theoretical implications that are significantly important in the literature because of the nature and uniqueness of the findings. However, this research also has some practical implications related to the patient's lungs mostly infected by COVID-19. Furthermore, the findings of this research can be generalized in a significant way because the respondents of this research belonged to a diverse population.