Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives.

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Differing worldviews: The politics of happiness, meaning, and psychological richness.

OBJECTIVE/BACKGROUND: Conservative ideology, broadly speaking, has been widely linked to greater happiness and meaning in life. Is that true of all forms of a good life? We examined whether a psychologically rich life is associated with political orientation, system justification, and Protestant work ethic, independent of two other traditional forms of a good life: a happy life and a meaningful life. METHOD: Participants completed a questionnaire that assessed conservative worldviews and three aspects of well-being (N = 583 in Study 1; N = 348 in Study 2; N = 436 in Study 3; N = 1,217 in Study 4; N = 2,176 in Study 5; N = 516 in Study 6). RESULTS: Happiness was associated with political conservatism and system justification, and meaning in life was associated with Protestant work ethic. In contrast, zero-order correlations showed that psychological richness was not associated with conservative worldviews. However, when happiness and meaning in life were included in multiple regression models, the nature of the association shifted: Psychological richness was consistently inversely associated with system justification and on average less political conservatism, suggesting that happiness and meaning in life were suppressor variables. CONCLUSIONS: These findings suggest that happiness and meaning in life are associated with conservative ideology, whereas psychological richness is not.

Performance of 1-mm non-gated low-dose chest computed tomography using deep learning-based noise reduction for coronary artery calcium scoring.

OBJECTIVE: To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique. METHODS: This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1-10, 11-100, 101-400, and > 400. We compared CACS from LDCTs with that from calcium CT. RESULTS: Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945-0.972) for DL, 0.969 (95% CI, 0.956-0.978) for IR, and 0.952 (95% CI, 0.932-0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893-0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866-0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780-0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). CONCLUSION: DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel. KEY POINTS: • Deep learning (DL)-based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10).

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

BACKGROUND. Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. OBJECTIVE. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. METHODS. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs. The DL model was evaluated in a cohort of patients diagnosed with CAP during emergency department visits at the same institution from January 2020 to March 2020 (temporal test cohort [n = 947]) and in two additional cohorts from different institutions (external test cohort A [n = 467], January 2020 to December 2020; external test cohort B [n = 381], March 2019 to October 2021). AUCs were compared between the DL model and an established risk prediction tool based on the presence of confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age 65 years or older (CURB-65 score). The combination of CURB-65 score and DL model was evaluated with a logistic regression model. RESULTS. The AUC for predicting 30-day mortality was significantly larger (p < .001) for the DL model than for CURB-65 score in the temporal test set (0.77 vs 0.67). The larger AUC for the DL model than for CURB-65 score was not significant (p > .05) in external test cohort A (0.80 vs 0.73) or external test cohort B (0.80 vs 0.72). In the three cohorts, the DL model, in comparison with CURB-65 score, had higher (p < .001) specificity (range, 61-69% vs 44-58%) at the sensitivity of CURB-65 score. The combination of DL model and CURB-65 score, in comparison with CURB-65 score, yielded a significant increase in AUC in the temporal test cohort (0.77, p < .001) and external test cohort B (0.80, p = .04) and a nonsignificant increase in AUC in external test cohort A (0.80, p = .16). CONCLUSION. A DL-based model consisting of initial chest radiographs was predictive of 30-day mortality among patients with CAP with improved performance over CURB-65 score. CLINICAL IMPACT. The DL-based model may guide clinical decision-making in the care of patients with CAP.

Icariin Supplementation Suppresses the Markers of Ferroptosis and Attenuates the Progression of Nonalcoholic Steatohepatitis in Mice Fed a Methionine Choline-Deficient Diet.

Icariin, a flavonoid abundant in the herb Epimedium, exhibits anti-ferroptotic activity. However, its impact on nonalcoholic steatohepatitis (NASH) development remains unclear. This study aimed to investigate the potential role of icariin in mitigating methionine choline-deficient (MCD) diet-induced NASH in C57BL/6J mice. The results showed that icariin treatment significantly reduced serum alanine aminotrasferase and aspartate aminotransferase activities while improving steatosis, inflammation, ballooning, and fibrosis in the liver tissues of mice fed the MCD diet. These improvements were accompanied by a substantial reduction in the hepatic iron contents and levels of malondialdehyde and 4-hydroxynonenal, as well as an increase in the activities of catalase and superoxide dismutase. Notably, icariin treatment suppressed the hepatic protein levels of ferroptosis markers such as acyl-CoA synthetase long-chain family member 4 and arachidonate 12-lipoxygenase, which were induced by the MCD diet. Furthermore, transmission electron microscopy confirmed the restoration of morphological changes in the mitochondria, a hallmark characteristic of ferroptosis, by icariin. Additionally, icariin treatment significantly increased the protein levels of Nrf2, a cystine/glutamate transporter (xCT), and glutathione peroxidase 4 (GPX4). In conclusion, our study suggests that icariin has the potential to attenuate NASH, possibly by suppressing ferroptosis via the Nrf2-xCT/GPX4 pathway.

CT-defined visual emphysema in smokers with normal spirometry: association with prolonged air leak and other respiratory complications after lobectomy for lung cancer.

OBJECTIVES: To evaluate the association of visual emphysema on preoperative CT with respiratory complications and prolonged air leak (PAL) in smokers with normal spirometry who underwent lobectomy for lung cancer. METHODS: Among patients who underwent lobectomy for lung cancer between 2018 and 2019 at a single center, ever-smokers with normal spirometry were identified retrospectively. Visual emphysema was graded for centrilobular emphysema (CLE) and paraseptal emphysema (PSE), respectively, by two thoracic radiologists. The associations of visual emphysema with PAL and respiratory complications (except PAL) were investigated. RESULTS: In total, 282 patients were evaluated (257 men; mean age, 64.6 ± 9.8 years). Visual emphysema was present in 126 patients (44.7%) (CLE, 26; PSE, 40; combined CLE and PSE, 60). PAL and respiratory complications occurred in 34 (12.1%) and 26 patients (26.9%), respectively. Greater frequency of PAL and respiratory complications were observed in patients with higher grades of CLE (p = 0.002 for PAL; p = 0.039 for respiratory complications) and PSE (p < 0.001 for PAL; p < 0.001 for respiratory complications). For reader 1 evaluation, the presence of both CLE and PSE was associated with PAL (adjusted odds ratio [OR], 4.94; 95% confidence interval [CI], 1.75-13.95; p = 0.003). For reader 2 evaluation, PSE (adjusted OR, 4.26; 95% CI, 1.22-14.97; p = 0.024) and combined CLE and PSE (adjusted OR, 3.49; 95% CI, 12.1-10.06; p = 0.020) were associated with PAL. The presence of solely CLE was not associated with any adverse outcome (all p > 0.05) for both readers. CONCLUSIONS: Visual assessment of PSE in smokers with normal spirometry may help identify those who develop PAL after lobectomy. KEY POINTS: • Visual emphysema was highly prevalent (44.7%) in smokers with normal lung function who underwent lobectomy for lung cancer. • Increasing tendency of postoperative complications was observed as the grade of visual emphysema increased. • The presence of paraseptal emphysema was associated with prolonged air leak.

Artificial intelligence system for identification of false-negative interpretations in chest radiographs.

OBJECTIVES: To investigate the efficacy of an artificial intelligence (AI) system for the identification of false negatives in chest radiographs that were interpreted as normal by radiologists. METHODS: We consecutively collected chest radiographs that were read as normal during 1 month (March 2020) in a single institution. A commercialized AI system was retrospectively applied to these radiographs. Radiographs with abnormal AI results were then re-interpreted by the radiologist who initially read the radiograph ("AI as the advisor" scenario). The reference standards for the true presence of relevant abnormalities in radiographs were defined by majority voting of three thoracic radiologists. The efficacy of the AI system was evaluated by detection yield (proportion of true-positive identification among the entire examination) and false-referral rate (FRR, proportion of false-positive identification among all examinations). Decision curve analyses were performed to evaluate the net benefits of applying the AI system. RESULTS: A total of 4208 radiographs from 3778 patients (M:F = 1542:2236; median age, 56 years) were included. The AI system identified initially overlooked relevant abnormalities with a detection yield and an FRR of 2.4% and 14.0%, respectively. In the "AI as the advisor" scenario, radiologists detected initially overlooked relevant abnormalities with a detection yield and FRR of 1.2% and 0.97%, respectively. In a decision curve analysis, AI as an advisor scenario exhibited a positive net benefit when the cost-to-benefit ratio was below 1:0.8. CONCLUSION: An AI system could identify relevant abnormalities overlooked by radiologists and could enable radiologists to correct their false-negative interpretations by providing feedback to radiologists. KEY POINTS: • In consecutive chest radiographs with normal interpretations, an artificial intelligence system could identify relevant abnormalities that were initially overlooked by radiologists. • The artificial intelligence system could enable radiologists to correct their initial false-negative interpretations by providing feedback to radiologists when overlooked abnormalities were present.

Central Tumor Location at Chest CT Is an Adverse Prognostic Factor for Disease-Free Survival of Node-Negative Early-Stage Lung Adenocarcinomas.

Background The prognostic value of primary tumor location in the central lung is unclear because of heterogeneity in definitions of central lung cancer (CLC). Purpose To (a) validate the prognostic value of two recently proposed definitions of CLC by using a method designed to offset the shortcomings of existing evidence and (b) investigate the prognostic implications of a quantitative definition of CLC at chest CT. Materials and Methods Patients with pathologic stage T1a-bN0M0 lung adenocarcinomas resected between 2009 and 2015 at a single tertiary care center were retrospectively identified. The primary end point was disease-free survival. The associations of multiple definitions of central tumor location with survival were evaluated by using multivariable Cox regression. Time-dependent discrimination measures and interreader agreement were assessed for each definition. Results A total of 436 patients (median age, 62 years [interquartile range, 55-69 years]; 245 women) were evaluated. Tumor location at CT in the inner one-third of the lung defined by concentric lines arising from the hilum was adversely associated with survival (five events among 34 patients with CLC and 27 events among 402 patients with peripheral lung cancer; adjusted hazard ratio, 2.90 [95% CI: 1.06, 7.96; P = .04]) and showed moderate interreader agreement (Cohen κ = 0.52 [95% CI: 0.37, 0.68]). Quantitatively determined location in the inner two-thirds of the lung was also an independent prognostic factor (16 events among 130 patients with CLC and 16 events among 306 patients with peripheral lung cancer; adjusted hazard ratio, 2.77 [95% CI: 1.36, 5.65]; P = .005), with higher interreader agreement (Cohen κ = 0.86 [95% CI: 0.80, 0.91]; P < .001). The quantification-based definition exhibited higher time-dependent sensitivity (48.2% [14.27/29.61; 95% CI: 28.8, 67.6] vs 15.1% [4.47/29.61; 95% CI: 1.3, 28.9]; P < .001). Conclusion Central lung cancer at chest CT, defined qualitatively or quantitatively, is an independent adverse prognostic factor in patients with node-negative, early-stage lung adenocarcinomas. The quantification-based approach has advantages in terms of time-dependent sensitivity and reproducibility. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wandtke and Hobbs in this issue.

Association of Adipopenia at Preoperative PET/CT with Mortality in Stage I Non-Small Cell Lung Cancer.

Background Body mass index (BMI) and sarcopenia status are well-established prognostic factors in patients with lung cancer. However, the relationship between the amount of adipose tissue and survival remains unclear. Purpose To investigate the association between baseline adipopenia and outcomes in patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods Consecutive patients who underwent surgical resection for stage I NSCLC between 2011 and 2015 at a single tertiary care center were retrospectively identified. The primary outcome was the 5-year overall survival (OS) rate, and secondary outcomes were the 5-year disease-free survival (DFS) rate and the major postoperative complication rate. The abdominal total fat volume at the waist and the skeletal muscle area at the L3 level were obtained from preoperative PET/CT data and were normalized by the height squared to calculate the fat volume index (FVI) and skeletal muscle index. Adipopenia was defined as the sex-specific lowest quartile of the FVI for the study sample, and sarcopenia was determined using the skeletal muscle index reference value (men, <55 cm2/m2; women, <39 cm2/m2). The association between body composition and outcomes was evaluated using Cox regression analysis. Results A total of 440 patients (median age, 65 years [interquartile range, 58-72 years]; 243 men) were evaluated. Most underweight patients (<20 kg/m2) had adipopenia (97%, 36 of 37 patients), but overweight patients (25-30 kg/m2, n = 138) and obese patients (>30 kg/m2, n = 14) did not have adipopenia (3%, four of 152 patients). In the group with a normal BMI (20-25 kg/m2), 28% (70 of 251 patients) had adipopenia and 67% (168 of 251 patients) had sarcopenia. After adjusting for age, sex, smoking history, surgical procedure, stage, histologic type, BMI, and sarcopenia, adipopenia was associated with reduced 5-year OS (hazard ratio [HR] = 2.2; 95% CI: 1.1, 3.8; P = .02) and 5-year non-cancer-specific OS rates (HR = 3.2; 95% CI: 1.2, 8.7; P = .02). There was no association between adipopenia and postoperative complications (P = .45) or between adipopenia and the 5-year DFS rate (P = .18). Conclusion Baseline adipopenia was associated with a reduced 5-year overall survival rate in patients with early-stage non-small cell lung cancer and may indicate risk for non-cancer-related death. © RSNA, 2021 Online supplemental material is available for this article.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

OBJECTIVE: To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. METHODS: Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. RESULTS: DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. CONCLUSION: Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. KEY POINTS: • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.

OBJECTIVES: To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer. METHODS: In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed. RESULTS: The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67-0.84), which was comparable to those of board-certified radiologists (AUC, 0.73-0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03-1.11; p < 0.001). CONCLUSIONS: The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs. KEY POINTS: • The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network.

OBJECTIVE: To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,045 chest radiographs (CRs; 2,463 normal and 2,393 LAE) and 1,012 CRs (456 normal and 456 LVE) matched with the same-day echocardiography were collected, respectively. External validation was performed using 107 temporally independent CRs. Reader performance test was conducted using the external validation dataset by five cardiothoracic radiologists without and with the results of DLCE. Classification performance of DLCE was evaluated and compared with those of the readers and conventional radiographic features, including cardiothoracic ratio, carinal angle, and double contour. In addition, DLCE-LAE was tested on 5,277 CRs from a healthcare screening program cohort. RESULTS: DLCE-LAE showed areas under the receiver operating characteristics curve (AUROCs) of 0.858 on external validation. On reader performance test, DLCE-LAE showed better results than pooled radiologists (AUROC 0.858 vs. 0.651; p < .001) and significantly increased their performance when used as a second reader (AUROC 0.651 vs. 0.722; p < .001). DLCE-LAE also showed a significantly higher AUROC than conventional radiographic findings (AUROC 0.858 vs. 0.535-0.706; all ps < .01). In the healthcare screening cohort, DLCE-LAE successfully detected 71.0% (142/200) CRs with moderate-to-severe LAE (93.5% [29/31] of severe cases), while yielding 11.8% (492/4,184) false-positive rate. DLCE-LVE showed AUROCs of 0.966 and 0.594 on internal and external validation, respectively. CONCLUSION: DLCE-LAE outperformed and improved cardiothoracic radiologists' performance in detecting LAE and showed promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort. KEY POINTS: • Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs. • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm. • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.

Efficient brazzein production in yeast (Kluyveromyces lactis) using a chemically defined medium.

The sweet-tasting protein brazzein offers considerable potential as a functional sweetener with antioxidant, anti-inflammatory, and anti-allergic properties. Here, we optimized a chemically defined medium to produce secretory recombinant brazzein in Kluyveromyces lactis, with applications in mass production. Compositions of defined media were investigated for two phases of fermentation: the first phase for cell growth, and the second for maximum brazzein secretory production. Secretory brazzein expressed in the optimized defined medium exhibited higher purity than in the complex medium; purification was by ultrafiltration using a molecular weight cutoff, yielding approximately 107 mg L-1. Moreover, the total media cost in this defined medium system was approximately 11% of that in the optimized complex medium to generate equal amounts of brazzein. Therefore, the K. lactis expression system is useful for mass-producing recombinant brazzein with high purity and yield at low production cost and indicates a promising potential for applications in the food industry.

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS: We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists. RESULTS: Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8-97.7%) and higher specificity (97.6% vs. 81.7-96.0%) than the radiologists. CONCLUSION: The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice. KEY POINTS: • A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation. • The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.

MiR-24 is required for hematopoietic differentiation of mouse embryonic stem cells.

Overexpression of miRNA, miR-24, in mouse hematopoietic progenitors increases monocytic/ granulocytic differentiation and inhibits B cell development. To determine if endogenous miR-24 is required for hematopoiesis, we antagonized miR-24 in mouse embryonic stem cells (ESCs) and performed in vitro differentiations. Suppression of miR-24 resulted in an inability to produce blood and hematopoietic progenitors (HPCs) from ESCs. The phenotype is not a general defect in mesoderm production since we observe production of nascent mesoderm as well as mesoderm derived cardiac muscle and endothelial cells. Results from blast colony forming cell (BL-CFC) assays demonstrate that miR-24 is not required for generation of the hemangioblast, the mesoderm progenitor that gives rise to blood and endothelial cells. However, expression of the transcription factors Runx1 and Scl is greatly reduced, suggesting an impaired ability of the hemangioblast to differentiate. Lastly, we observed that known miR-24 target, Trib3, is upregulated in the miR-24 antagonized embryoid bodies (EBs). Overexpression of Trib3 alone in ESCs was able to decrease HPC production, though not as great as seen with miR-24 knockdown. These results demonstrate an essential role for miR-24 in the hematopoietic differentiation of ESCs. Although many miRNAs have been implicated in regulation of hematopoiesis, this is the first miRNA observed to be required for the specification of mammalian blood progenitors from early mesoderm.

Patient-reported assessment of self-management strategies of health in cancer patients: development and validation of the Smart Management Strategy for Health Assessment Tool (SAT).

OBJECTIVE: The objective of this study was to evaluate the psychometric properties of the Smart Management Strategy for Health Assessment Tool (SAT), which we developed to enable cancer patients to assess their self-management (SM) strategies of health by themselves. PATIENTS AND METHODS: The development of the questionnaire included four phases: item generation, construction, pilot testing, and field testing. To assess the instrument's sensitivity and validity, we recruited 300 cancer patients from three Korean hospitals who were 18 or more years old and accustomed to using the Internet or email. Using the appropriate and priority criteria for pilot and field testing, we tightened the content and constructed the first version of the SAT. RESULTS: We developed the core strategies with 28 items, preparation strategies with 30 items, and implementation strategies with 33 items. Factor analysis of data from 300 patients resulted in core strategies with four factors, preparation strategies with five factors, and implementation strategies with six factors. All the SAT subscales demonstrated a high reliability with good internal consistency. The total scores of the three SAT sets differentiated participant groups well according to their stage of goal implementation and proportions of action of the 10 Rules for Highly Effective Health Behavior. Each factor of the three SAT sets correlated positively with the scores for additional assessment tool. CONCLUSION: The SAT is a three-set, 16-factor, 91-item tool that assesses the SM strategies of health that patients use to overcome a crisis. Patients can use the SAT to assess their SM strategies of health and obtain feedback from clinicians in the practice setting.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). METHODS: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. RESULTS: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). CONCLUSION: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators' competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its performance and efficacy on inter-reader variability. We retrospectively collected LUS and labeled as normal, B-line, consolidation, and effusion from patients undergoing thoracentesis at a tertiary institution between January 2018 and January 2022. The development and internal testing involved 7580 images from January 2018 and December 2020, and the model's performance was validated on a temporally separated test set (n = 985 images collected after January 2021) and two external test sets (n = 319 and 54 images). Two radiologists interpreted LUS with and without DL assistance and compared diagnostic performance and agreement. The model demonstrated robust performance with AUCs: 0.93 (95% CI 0.92-0.94) for normal, 0.87 (95% CI 0.84-0.89) for B-line, 0.82 (95% CI 0.78-0.86) for consolidation, and 0.94 (95% CI 0.93-0.95) for effusion. The model improved reader accuracy for binary discrimination (normal vs. abnormal; reader 1: 87.5-95.6%, p = 0.004; reader 2: 95.0-97.5%, p = 0.19), and agreement (k = 0.73-0.83, p = 0.01). In conclusion, the DL-based model may assist interpretation, improving accuracy and overcoming operator competence limitations in LUS.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.