Progression prediction in idiopathic inflammatory myopathy-associated interstitial lung disease: a combination of initial high attenuation areas and anti-melanoma differentiation-associated gene 5-positive.

OBJECTIVES: To analyse quantitative lung densitometry and clinical baseline data of individuals with idiopathic inflammatory myopathy (IIM) and identify risk factors capable of predicting the progression of interstitial lung disease (ILD). METHODS: We utilised quantitative lung densitometry and clinical baseline data as explanatory variables. Univariate and multivariate Cox regression analyses were employed to pinpoint effective risk factors for predicting ILD progression in IIM patients. RESULTS: The findings from the Cox univariate regression analysis indicate that elevated carcinoembryonic antigen levels (HR=1.036, 95% CI 1.004-1.069) are connected to an elevated risk of ILD progression in patients with IIM (P=0.027), while PO2 (HR=0.980, 95% CI 0.962-0.997) , forced vital capacity (HR=0.551, 95% CI 0.320-0.946) are protective factors for ILD progression in patients with IIM (p=0.025, p=0.031, respectively), anti-EJ positivity (HR=0.399, 95% CI 0.175-0.912) and anti-Ro52 positivity (HR=0.437, 95% CI 0.199-0.960) are risk factors for ILD progression in patients with IIM (p=0.029, p=0.039, respectively). Furthermore, the results of Cox multivariate regression analysis reveal that high attenuation areas (HAA) (>465.745 cm3) (HR=5.007, 95% CI 1.773-14.144) and anti-melanoma differentiation-associated gene 5 (Anti-MDA5) positivity (HR=0.127, 95% CI 0.041-0.396) are autonomous prognostic risk factors for ILD progression in individuals with IIM (p=0.002, p<0.001, respectively). CONCLUSIONS: Among IIM patients, those who are anti-MDA5-positive, and exhibit HAA (>465.745cm3) are more likely to experience ILD progression.

Generating adversarial examples without specifying a target model.

Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work. In a more practical situation, the attacker will be easily detected because of too many queries, and this problem is especially obvious under the black-box setting. To solve the problem, we propose the Attack Without a Target Model (AWTM). Our algorithm does not specify any target model in generating adversarial examples, so it does not need to query the target. Experimental results show that it achieved a maximum attack success rate of 81.78% in the MNIST data set and 87.99% in the CIFAR-10 data set. In addition, it has a low time cost because it is a GAN-based method.

Abnormal topological organization in white matter structural networks in survivors of acute lymphoblastic leukaemia with chemotherapy treatment.

Previous diffusion tensor imaging (DTI) studies have detected white matter (WM) integrity abnormalities in some specific fibre bundles in acute lymphoblastic leukaemia (ALL) patients with chemotherapy. However, little is known about the changes in the topological organization of the WM structural network in ALL patients with chemotherapy. In the present study, we acquired DTI datasets from 28 ALL patients (mean age: 40.71 ± 8.58 years, years since diagnosis: 7-38) with chemotherapy and 20 matched healthy controls (mean age: 42.95 ± 6.39 years) and performed WM network analysis using a deterministic fibre-tracking approach. Graph theoretical analysis was used to compare the topological parameters of the WM networks between the two groups. Both ALL patients with chemotherapy and healthy controls had small-worldness in their WM networks. ALL patients showed significantly reduced global network efficiency, as indicated by the abnormally decreased clustering coefficient Cp and the normalized clustering coefficient γ and increased shortest path length Lp compared with healthy controls. Moreover, hubs were located more in parietal regions of healthy controls and in temporal regions in the ALL patients. We revealed the abnormal topological organization of the WM networks of ALL patients with chemotherapy, which may improve our understanding of the neural mechanism of chemotherapy in ALL from a WM topological organization level.