APP96-110 Elicits Neuroprotective Effects Following Ischemic Insult in Animal Models.

Competitive amyloidogenic pathways play an important role in many neurological diseases such as the onset of various degenerative diseases and ischemic stroke. Overexpression of amyloid precursor protein (APP) and amyloid-beta is modulated via the amyloidogenic pathway, which plays a crucial role in neuroinflammation. During ischemic conditions, the activity of the anti-inflammatory non-amyloidogenic pathway decreases, thus increasing the activity of amyloidogenic pathway. The soluble alpha form of APP (sAPPα), formed via the non-amyloidogenic pathway, exhibits neuroprotective effects against neurological diseases. sAPPα is thought to have a modulatory effect on several cell survival pathways, including its ability to inhibit the phosphoinositide 3-kinases (PI3K) pathway, thereby inhibiting the inflammatory response. The APP derivative, APP96-110, could act as a functional substitute for native sAPPα. Herein, we investigated whether APP96-110 has neuroprotective effects against neuroinflammation and damage following cerebral ischemic stroke. Treatment with diluted APP96-110 (0.005 mg/kg) in mice after 30 min of transient middle cerebral artery occlusion (tMCAO) showed improved motor function and reduced expression of the inflammatory marker CD86. APP96-110 decreased the infarct size and induced an anti-inflammatory response by inhibiting the PI3K pathway. These results suggest that the treatment of APP96-110 is efficacious in reducing neuroinflammation and infarct size in ischemic stroke.

Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs.

Background Determining the activity of pulmonary tuberculosis on chest radiographs is difficult. Purpose To develop a deep learning model to identify active pulmonary tuberculosis on chest radiographs. Materials and Methods Chest radiographs were retrospectively gathered from a multicenter consecutive cohort with pulmonary tuberculosis who were successfully treated between 2011 and 2017, along with normal radiographs to enrich a negative class. The pretreatment and posttreatment radiographs were labeled as positive and negative classes, respectively. A neural network was trained with those radiographs to calculate the probability of active versus healed tuberculosis. A single-center consecutive cohort (test set 1; 89 patients, 148 radiographs) and data from one multicenter randomized controlled trial (test set 2; 366 patients, 3774 radiographs) were used to test the model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model and of the four expert readers. Results In total, 6654 pre- and posttreatment radiographs from 3327 patients (mean age ± standard deviation, 55 years ± 19; 1884 men) with pulmonary tuberculosis and 3182 normal radiographs from as many patients (mean age, 53 years ± 14; 1629 men) were gathered. For test set 1, the model showed a higher AUC (0.83; 95% CI: 0.73, 0.89) than one pulmonologist (0.69; 95% CI: 0.61, 0.76; P < .001) and performed similarly to the other readers (AUC, 0.79-0.80; P = .14-.23). For 200 randomly selected radiographs from test set 2, the model had a higher AUC (0.84) than the pulmonologists (0.71 and 0.74; P < .001 and .01, respectively) and performed similarly to the radiologists (0.79 and 0.80; P = .08 and .06, respectively). The model output increased by 0.30 on average with a higher degree of smear positivity (95% CI: 0.20, 0.39; P < .001) and decreased during treatment (baseline, 3 months, and 6 months: 0.85, 0.51, and 0.26, respectively). Conclusion A deep learning model performed similarly to radiologists for accurately determining the activity of pulmonary tuberculosis on chest radiographs; it also was able to follow posttreatment changes. © RSNA, 2021 Online supplemental material is available for this article.

Ischemic stroke outcome after promoting CD4+CD25+ Treg cell migration through CCR4 overexpression in a tMCAO animal model.

The importance of neuroinflammation during the ischemic stroke has been extensively studied. The role of CD4+CD25+ regulatory T (Treg) cells during the recovery phase have shown infarct size reduction and functional improvement, possibly through the mitigation of inflammatory immune responses. We aimed to investigate the molecular factors involved in microglia-Treg cell communication that result in Treg trafficking. First, we observed the migration patterns of CD8+ (cytotoxic) T cells and Treg cells and then searched for chemokines released by activated microglia in an oxygen-glucose deprivation (OGD) model. The transwell migration assay showed increased migration into OGD media for both cell types, in agreement with the increase in chemokines involved in immune cell trafficking from the mouse chemokine profiling array. MSCV retrovirus was transduced to overexpress CCR4 in Treg cells. CCR4-overexpressed Treg cells were injected into the mouse transient middle cerebral artery occlusion (tMCAO) model to evaluate the therapeutic potential via the tetrazolium chloride (TTC) assay and behavioral tests. A general improvement in the prognosis of animals after tMCAO was observed. Our results suggest the increased mobility of CCR4-overexpressed Treg cells in response to microglia-derived chemokines in vitro and the therapeutic potential of Treg cells with increased mobility in cellular therapy.

Post-treatment Radiographic Severity and Mortality in Mycobacterium avium Complex Pulmonary Disease.

Rationale: Imaging studies are widely performed when treating Mycobacterium avium complex pulmonary disease (MAC-PD); however, the clinical significance of post-treatment radiographic change is unknown. Objectives: To determine whether a deep neural network trained with pulmonary tuberculosis could adequately score the radiographic severity of MAC-PD and then to examine relationships between post-treatment radiographic severity and its change from baseline and long-term prognosis. Methods: We retrospectively collected chest radiographs of adult patients with MAC-PD treated for ⩾6 months at baseline and at 3, 6, 9, and 12 months of treatment. We correlated the radiographic severity score generated by a deep neural network with visual and clinical severity as determined by radiologists and mycobacterial culture status, respectively. The associations between the score, improvement from baseline, and mortality were analyzed using Cox proportional hazards regression. Results: In total, 342 and 120 patients were included in the derivation and validation cohorts, respectively. The network's severity score correlated with radiologists' grading (Spearman coefficient, 0.40) and mycobacterial culture results (odds ratio, 1.02; 95% confidence interval [CI], 1.0-1.05). A significant decreasing trend in the severity score was observed over time (P < 0.001). A higher score at 12 months of treatment was independently associated with higher mortality (adjusted hazard ratio, 1.07; 95% CI, 1.03-1.10). Improvements in radiographic scores from baseline were associated with reduced mortality, regardless of culture conversion (adjusted hazard ratio, 0.42; 95% CI, 0.22-0.80). These findings were replicated in the validation cohort. Conclusions: Post-treatment radiographic severity and improvement from baseline in patients with MAC-PD were associated with long-term survival.

Material flow analysis-based assessment of polypropylene-fiber-containing microplastics released from disposable masks: Characterizing distribution in the environmental media.

With the upsurge in the use of disposable masks during the coronavirus disease pandemic, improper disposal of discarded masks and their negative impact on the environment have emerged as major issues. Improperly disposed of masks release various pollutants, particularly microplastic (MP) fibers, which can harm both terrestrial and aquatic ecosystems by interfering with the nutrient cycling, plant growth, and the health and reproductive success of organisms. This study assesses the environmental distribution of polypropylene (PP)-containing MPs, generated from disposable masks, using material flow analysis (MFA). The system flowchart is designed based on the processing efficiency of various compartments in the MFA model. The highest amount of MPs (99.7 %) is found in the landfill and soil compartments. A scenario analysis reveals that waste incineration significantly reduces the amount of MP transferred to landfills. Therefore, considering cogeneration and gradually increasing the incineration treatment rate are crucial to manage the processing load of waste incineration plants and minimize the negative impact of MPs on the environment. The findings provide insights into the potential environmental exposure associated with the improper disposal of waste masks and indicate strategies for sustainable mask disposal and management.

Deep Learning-Based Prediction Model Using Radiography in Nontuberculous Mycobacterial Pulmonary Disease.

BACKGROUND: Prognostic prediction of nontuberculous mycobacteria pulmonary disease using a deep learning technique has not been attempted. RESEARCH QUESTION: Can a deep learning (DL) model using chest radiography predict the prognosis of nontuberculous mycobacteria pulmonary disease? STUDY DESIGN AND METHODS: Patients who received a diagnosis of nontuberculous mycobacteria pulmonary disease at Seoul National University Hospital (training and validation dataset) between January 2000 and December 2015 and at Seoul Metropolitan Government-Boramae Medical Center (test dataset) between January 2006 and December 2015 were included. We trained DL models to predict the 3-, 5-, and 10-year overall mortality using baseline chest radiographs at diagnosis. We tested the predictability for the corresponding mortality using only DL-driven radiographic scores and using both radiographic scores and clinical information (age, sex, BMI, and mycobacterial species). RESULTS: The datasets comprised 1,638 (training and validation set) and 566 (test set) chest radiographs from 1,034 and 200 patients, respectively. The Dl-driven radiographic score provided areas under the receiver operating characteristic curve (AUC) of 0.844, 0.781, and 0.792 for 10-, 5-, and 3-year mortality, respectively. The logistic regression model using both the radiographic score and clinical information provided AUCs of 0.922, 0.942, and 0.865 for the 10-, 5, and 3-year mortality, respectively. INTERPRETATION: The DL model we developed could predict the mid-term to-long-term mortality of patients with nontuberculous mycobacteria pulmonary disease using a baseline radiograph at diagnosis, and the predictability increased with clinical information.

Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance. MATERIALS AND METHODS: To develop the deep-learning model, 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution were split into a training set (1012 pairs, 79.9%) and a validation set (254 pairs, 20.1%). We performed external tests using 2 types of distinct datasets: one temporally and the other geographically separated from the model development. We used 258 pairs of radiographs examined in 2018 at the same institution as a temporal test set and 95 examined between January 2016 and December 2018 at another hospital as a geographic test set. Images underwent preprocessing, including cropping and histogram equalization, and were input into a dual-input neural network constructed by merging 2 ResNet models. An observer study was performed by radiologists on the geographic test set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model and human readers were calculated and compared. RESULTS: Our trained model showed an AUC of 0.976 in the validation set, 0.985 in the temporal test set, and 0.992 in the geographic test set. In AUC comparison, the model showed comparable results to the human readers in the geographic test set; the AUCs of human readers were in the range of 0.977 to 0.997 (P's > 0.05). The model had a sensitivity of 93.9%, a specificity of 92.2%, a PPV of 80.5%, and an NPV of 97.8% in the temporal test set, and a sensitivity of 100%, a specificity of 86.1%, a PPV of 69.7%, and an NPV of 100% in the geographic test set. Compared with the developed deep-learning model, all 3 human readers showed a significant difference (P's < 0.05) using the McNemar test, with lower specificity and PPV in the model. On the other hand, there was no significant difference (P's > 0.05) in sensitivity and NPV between all 3 human readers and the proposed model. CONCLUSIONS: The proposed dual-input deep-learning model that interprets both AP and lateral elbow radiographs provided an accurate diagnosis of pediatric supracondylar fracture comparable to radiologists.

Percutaneous Bronchial Embolization to Treat Intractable Bronchobiliary Fistula.

Sixty-four-year-old female who underwent hemi-hepatectomy for intrahepatic cholangiocarcinoma a year ago presented with biliary sputum, cough and fever. Cross-sectional imaging showed a recurred tumor involving right diaphragmatic area and an abscess formation in liver dome with adjacent right lower lobe of lung. Percutaneous transhepatic biliary drainage and percutaneous drainage of lung abscess were performed. Tubogram showed connections between the lung abscess cavity and multiple distal bronchi, suggesting bronchobiliary fistulas. Two weeks of drainage treatment did not relieve symptoms. We successfully treated intractable bronchobiliary fistula via image-guided percutaneous access to closest distal bronchi near abscess with subsequent tandem placement of vascular plugs.