This retrospective study aimed to develop and validate deep-learning-based models for grading clear cell renal cell carcinoma (ccRCC) patients. A cohort enrolling 706 patients (n = 706) with pathologically verified ccRCC was used in this study. A temporal split was applied to verify our models: the first 83.9% of the cases (years 2010–2017) for development and the last 16.1% (year 2018–2019) for validation (development cohort: n = 592; validation cohort: n = 114). Here, we demonstrated a deep learning(DL) framework initialized by a self-supervised pre-training method, developed with the addition of mixed loss strategy and sample reweighting to identify patients with high grade for ccRCC. Four types of DL networks were developed separately and further combined with different weights for better prediction. The single DL model achieved up to an area under curve (AUC) of 0.864 in the validation cohort, while the ensembled model yielded the best predictive performance with an AUC of 0.882. These findings confirms that our DL approach performs either favorably or comparably in terms of grade assessment of ccRCC with biopsies whilst enjoying the non-invasive and labor-saving property.

Chronic obstructive pulmonary disease (COPD) is a leading aging related cause of global mortality. Small airway narrowing is recognized as an early and significant factor for COPD development. Senescent fibroblasts were observed to accumulate in lung of COPD patients and promote COPD progression through aberrant extracellular matrix (ECM) deposition and senescence-associated secretory phenotype (SASP). On the basis of our previous study, we further investigated the the causes for the increased levels of miR-377-3p in the blood of COPD patients, as well as its regulatory function in the pathological progression of COPD. We found that the majority of up-regulated miR-377-3p was localized in lung fibroblasts. Inhibition of miR-377-3p improved chronic smoking-induced COPD in mice. Mechanistically, miR-377-3p promoted senescence of lung fibroblasts, while knockdown of miR-377-3p attenuated bleomycin-induced senescence in lung fibroblasts. We also identified ZFP36L1 as a direct target for miR-377-3p that likely mediated its pro senescence activity in lung fibroblasts. Our data reveal that miR-377-3p is crucial for COPD pathogenesis, and may serve as a potential target for COPD therapy.

The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-based network architecture, ResTransNet, that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to automatically acquire a persuasive feature to obtain a prediction score using a nonlinear classifier. The area under the curve, receiver operating characteristic curve, and decision curves were applied to evaluate the prediction model’s performance. Then, Kaplan–Meier survival analyses were applied to evaluate the overall survival (OS) and recurrence-free survival (RFS) in PD-1-positive and PD-1-negative patients. The proposed transformer-based model obtained an accuracy of 88.2% with a sensitivity of 88.5%, a specificity of 88.9%, and an area under the curve of 91.1% in the testing cohort.

Background: Deep learning has been successfully applied to low‐dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per‐pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image.Purpose: In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high‐quality diagnostic value using LDCT images, providing strong support for condition diagnosis.Methods: We propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high‐frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high‐frequency information of the image, we use HFLoss to calculate the difference between CT images in the high‐frequency information part.Results: Our hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal‐to‐noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.Conclusions: We propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high‐quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

Chronic obstructive pulmonary disease (COPD) is an irreversible and progressive chronic inflammatory lung disease which affects millions of people worldwide. Activated fibroblasts are observed to accumulate in lung of COPD patients and promote COPD progression through aberrant extracellular matrix (ECM) deposition. In this study, we identified that miR-1307-5p expression was significantly increased in lung fibroblasts derived from COPD patients. Mechanistically, we found that upregulation of miR-1307-5p promoted TGF-β induced lung fibroblast activation and transdifferentiation. We also identified FBXL16 as a direct target for miR-1307-5p mediated myofibroblast activation in COPD. Knockdown of FBXL16 by siRNA prominently increased the expression of myofibroblast markers in MRC-5 fibroblasts after TGF-β administration. Ectopic expression of FBXL16 in MRC-5 counteracted miR-1307-5p agomir-induced fibroblast transdifferentiation. Furthermore, We found that miR-1307-5p promoted pulmonary fibroblast transdifferentiation through FBXL16 regulated HIF1α degradation. In general, our findings indicate that miR-1307-5p is important for COPD pathogenesis, and may serve as a potential target for COPD treatment.