Machine learning approach for predicting inhalation injury in patients with burns.

BACKGROUND: The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury and whether patients with burns experienced inhalation injury. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. METHODS: A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected inhalation injury was established. The medical data on day one of admission and bronchoscopy-diagnosed inhalation injury grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe inhalation injury; and model 2, no inhalation injury vs. inhalation injury. RESULTS: The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P < 0·001) and mortality rate (P < 0·001), but not duration of hospitalisation (P = 0·1052), were significantly higher in patients with severe inhalation injury. In model 2, the incidence of pneumonia (P < 0·001), mortality (P < 0·001), and duration of hospitalisation (P = 0·021) were significantly higher in patients with inhalation injury. CONCLUSIONS: We developed the first machine-learning tool for differentiating between mild and severe inhalation injury, and the absence/presence of inhalation injury in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

BACKGROUND: To verify efficacy of automatic screening and classification of glaucoma with deep learning system. METHODS: A cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with the pre-trained EfficientNet-b0 model was selected for machine learning. Glaucoma screening (Binary) and ternary classification with or without additional demographics (age, gender, high myopia) were evaluated, followed by creating confusion matrix and heatmaps. Area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score were viewed as main outcome measures. RESULTS: Two hundred and twenty-two cases (421 eyes) were enrolled, with 1851 images in total (1207 normal and 644 glaucomatous disc). Train set and test set were comprised of 1539 and 312 images, respectively. If demographics were not provided, AUC, accuracy, precision, sensitivity, F1 score, and specificity of our deep learning system in eye-based glaucoma screening were 0.98, 0.91, 0.86, 0.86, 0.86, and 0.94 in test set. Same outcome measures in eye-based ternary classification without demographic data were 0.94, 0.87, 0.87, 0.87, 0.87, and 0.94 in our test set, respectively. Adding demographics has no significant impact on efficacy, but establishing a linkage between eyes and images is helpful for a better performance. Confusion matrix and heatmaps suggested that retinal lesions and quality of photographs could affect classification. Colour fundus images play a major role in glaucoma classification, compared to red-free fundus images. CONCLUSIONS: Promising results with high AUC and specificity were shown in distinguishing normal optic nerve from glaucomatous fundus images and doing further classification.

Sumoylation participates in the regulation of YB-1-mediated mismatch repair deficiency and alkylator tolerance.

Numerous reports indicate that enhanced expression of Y-box binding protein-1 (YB-1) in tumor cells is strongly associated with tumorigenesis, aggressiveness, drug resistance, as well as poor prognosis in several types of cancers, and YB-1 is considered to be an oncogene. The molecular mechanism contributing to the regulation of the biological activities of YB-1 remains obscure. Sumoylation, a post-translational modification involving the covalent conjugation of small ubiquitin-like modifier (SUMO) proteins to a target protein, plays key roles in the modulation of protein functions. In this study, our results revealed that YB-1 is sumoylated and that Lys26 is a critical residue for YB-1 sumoylation. Moreover, YB-1 was found to directly interact with SUMO proteins, and disruption of the SUMO-interacting motif (SIM) of YB-1 not only interfered with this interaction but also diminished YB-1 sumoylation. The subcellular localization, protein stability, and transcriptional regulatory activity of YB-1 were not significantly affected by sumoylation. However, decreased sumoylation disrupted the interaction between YB-1 and PCNA as well as YB-1-mediated inhibition of the MutSα/PCNA interaction and MutSα mismatch binding activity, indicating a functional role of YB-1 sumoylation in inducing DNA mismatch repair (MMR) deficiency and spontaneous mutations. The MMR machinery also recognizes alkylator-modified DNA adducts to signal for cell death. We further demonstrated that YB-1 sumoylation is crucial for the inhibition of SN1-type alkylator MNNG-induced cytotoxicity, G2/M-phase arrest, apoptosis, and the MMR-dependent DNA damage response. Collectively, these results provide molecular explanations for the impact of YB-1 sumoylation on MMR deficiency and alkylator tolerance, which may provide insight for designing therapeutic strategies for malignancies and alkylator-resistant cancers associated with YB-1 overexpression.