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As a kind of renewable and high oxygen content fuel, polyoxymethylene dimethyl ether (PODE) can be added in diesel to realize energy saving and emissions reduction. To evaluate the combustion and emission characteristics of a diesel engine fueled with diesel and diesel/PODE mixtures, exhaust gas recirculation (EGR) and main-pilot injection strategies with various injection timings were applied. PODE was blended with diesel by volume to form mixtures which were marked as D100 (pure diesel), D90P10 (90% diesel + 10% PODE), and D80P20 (80% diesel + 20% PODE). The results showed that the ignition delay (ID) and combustion duration (CD) of D80P20 were the shortest because of the highest cetane number (CN) and high oxygen content of PODE, indicating more concentrated heat release. At low and medium loads, D80P20 achieved the highest peak heat release ratio (PHRR) and peak combustion temperature (PCT) among the three fuels, and it was 14.3% and 3.6% higher than those of D100. PODE blending with diesel can significantly reduce particulate matter (PM) and D80P20 has the lowest PM emissions at all loads. Compared with D100, both PM and nitrogen oxide (NOx) emissions of PODE blends decreased simultaneously with 20% EGR at all loads. With the increase of pilot-main interval, the ID and CD of all test fuels increased, while the NOx and PM emissions decreased. The conclusions of the present research provide a state of the application in light-duty engines fueled with diesel/PODE blends in future work.

ALKBH5 is one of the demethylases involved in the regulation of RNA m6A modification. In addition to its role in the dynamic regulation of RNA m6A modification, ALKBH5 has been found to play important roles in various tissues fibrosis processes in recent years. However, the mechanisms and effects of ALKBH5 in fibrosis have been reported inconsistently. Multiple cell types, including parenchymal cells, immune cells (neutrophils and T cells), macrophages, endothelial cells, and fibroblasts, play roles in various stages of fibrosis. Therefore, this review analyzes the mechanisms by which ALKBH5 regulates these cells, its impact on their functions, and the outcomes of fibrosis. Furthermore, this review summarizes the role of ALKBH5 in fibrotic diseases such as pulmonary fibrosis, liver fibrosis, cardiac fibrosis, and renal fibrosis, and discusses various ALKBH5 inhibitors that have been discovered to date, exploring the potential of ALKBH5 as a clinical target for fibrosis.

Abstract Objectives To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. Methods A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. Results SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. Conclusion The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Critical relevance statement Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. Key points • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer. Graphical Abstract

BackgroundImmunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis.MethodIn this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients’ survival outcomes.ResultExperiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients.ConclusionTo the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.