Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.

Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.

Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.

From registration, protocol to report: are COVID-19-related RCTs in mainland China consistent? A systematic review of clinical trial registry and literature.

OBJECTIVE: To provide a comprehensive review of registered COVID-19-related randomised controlled trials (RCTs) in mainland China and evaluate the transparency of reporting through comparison of registrations, protocols and full reports. DESIGN: Systematic review of trial registrations and publications. DATA SOURCES: International Clinical Trials Registry Platform, Chinese Clinical Trial Registry, ClinicalTrials.gov, the ISRCTN registry and EU Clinical Trial Register were accessed on 1 February 2022. Publications were searched in PubMed, Embase, Cochrane Library, Google Scholar, CNKI.net and Wanfangdata from 10 February 2022 to 12 February 2022. ELIGIBILITY CRITERIA: Eligible trials were COVID-19 related RCTs carried out in mainland China. Observational studies, non-randomised trials and single-arm trials were excluded. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data from registrations, publications and performed risk of bias assessment for trial reports. Information provided by registrations and publications was compared. The findings were summarised with descriptive statistics. RESULTS: The number of eligible studies was 415. From these studies 20 protocols and 77 RCT reports were published. Seven trials published both protocol and RCT full report. Between registrations and publications, discrepancy or omission was found in sample size (7, 35.0% for protocols and 47, 61.0% for reports, same below), trial setting (13, 65.0% and 43, 55.8%), inclusion criteria (12, 60.0% and 57, 74.0%), exclusion criteria (10, 50.0% and 54, 70.1%), masking method (9, 45.0% and 35, 45.5%) and primary outcome or time frame of primary outcome measurement (14, 70.0% and 51, 66.2%). Between protocols and full reports, 5 (71.4%) reports had discrepancy in primary outcome or time frame of primary outcome measurement. CONCLUSIONS: Discrepancy among registrations, protocols and reports revealed compromised transparency in reporting of COVID-19-related RCTs in mainland China. The importance of trial registration should be further emphasised to enhance transparent RCT reporting.

LRRK2 enhances Nod1/2-mediated inflammatory cytokine production by promoting Rip2 phosphorylation.

The innate immune system is critical for clearing infection, and is tightly regulated to avert excessive tissue damage. Nod1/2-Rip2 signaling, which is essential for initiating the innate immune response to bacterial infection and ER stress, is subject to many regulatory mechanisms. In this study, we found that LRRK2, encoded by a gene implicated in Crohn's disease, leprosy and familial Parkinson's disease, modulates the strength of Nod1/2-Rip2 signaling by enhancing Rip2 phosphorylation. LRRK2 deficiency markedly reduces cytokine production in macrophages upon Nod2 activation by muramyl dipeptide (MDP), Nod1 activation by D-gamma-Glu-meso-diaminopimelic acid (iE-DAP) or ER stress. Our biochemical study shows that the presence of LRRK2 is necessary for optimal phosphorylation of Rip2 upon Nod2 activation. Therefore, this study reveals that LRRK2 is a new positive regulator of Rip2 and promotes inflammatory cytokine induction through the Nod1/2-Rip2 pathway.

Commensal bacteria direct selective cargo sorting to promote symbiosis.

Mucosal immunity protects a host from intestinal inflammation and infection and is profoundly influenced by symbiotic bacteria. Here we report that in mice symbiotic bacteria directed selective cargo sorting in Paneth cells to promote symbiosis through Nod2, a cytosolic bacterial sensor, and the multifunctional protein kinase LRRK2, both encoded by inflammatory bowel disease (IBD)-associated genes. Commensals recruited Nod2 onto lysozyme-containing dense core vesicles (DCVs), which was required for DCV localization of LRRK2 and a small GTPase, Rab2a. Deficiency of Nod2, LRRK2 or Rab2a or depletion of commensals resulted in lysosomal degradation of lysozyme. Thus, commensal bacteria and host factors orchestrate the lysozyme-sorting process to protect the host from enteric infection, implicating Paneth cell dysfunction in IBD pathogenesis.