Purpose: Long noncoding RNAs (lncRNA) have essential roles in diverse cellular processes, both in normal and diseased cell types, and thus have emerged as potential therapeutic targets. A specific member of this family, the SWI/SNF complex antagonist associated with prostate cancer 1 (SChLAP1), has been shown to promote aggressive prostate cancer growth by antagonizing the SWI/SNF complex and therefore serves as a biomarker for poor prognosis. Here, we investigated whether SChLAP1 plays a potential role in the development of human glioblastoma (GBM). Experimental Design: RNA-ISH and IHC were performed on a tissue microarray to assess expression of SChLAP1 and associated proteins in human gliomas. Proteins complexed with SChLAP1 were identified using RNA pull-down and mass spectrometry. Lentiviral constructs were used for functional analysis in vitro and in vivo. Results: SChLAP1 was increased in primary GBM samples and cell lines, and knockdown of the lncRNA suppressed growth. SChLAP1 was found to bind heterogeneous nuclear ribonucleoprotein L (HNRNPL), which stabilized the lncRNA and led to an enhanced interaction with the protein actinin alpha 4 (ACTN4). ACTN4 was also highly expressed in primary GBM samples and was associated with poorer overall survival in glioma patients. The SChLAP1–HNRNPL complex led to stabilization of ACTN4 through suppression of proteasomal degradation, which resulted in increased nuclear localization of the p65 subunit of NF-κB and activation of NF-κB signaling, a pathway associated with cancer development. Conclusions: Our results implicated SChLAP1 as a driver of GBM growth as well as a potential therapeutic target in treatment of the disease.

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We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available.

BackgroundAlzheimer’s disease (AD) is the most common type of dementia. It causes progressive brain disorder involving loss of normal memory and thinking skills. The transplantation of neural stem cells (NSCs) has been reported to improve learning and memory function of AD rats, and protects basal forebrain cholinergic neurons. Nerve growth factor – poly (ethylene glycol) – poly (lactic-co-glycolic acid)-nanoparticles (NGF-PEG-PLGA-NPs) can facilitate the differentiation of NSCs in vitro. This study thus investigated the treatment efficacy of NGF-PEG-PLGA-NPs combining NSC transplantation in AD model rats.Material/MethodsAD rats were prepared by injection of 192IgG-saporin into their lateral ventricles. Embryonic rat NSCs were separated, induced by NGF-PEG-PLGA-NPs in vitro, and were transplanted. The Morris water-maze test was used to evaluate learning and memory function, followed by immunohistochemical staining for basal forebrain cholinergic neurons, hippocampal synaptophysin, and acetylcholine esterase (AchE) fibers.ResultsRats in the combined treatment group had significantly improved spatial learning ability compared to AD model animals (p<0.05). The number of basal forebrain cholinergic neurons, hippocampal synaptophysin, and AchE-positive fibers were all significantly larger than in the NSC-transplantation group, with no difference from control animals.ConclusionsNGF-PEG-PLGA-NPs plus NSC transplantation can significantly improve learning and memory functions of AD rats, replenish basal forebrain cholinergic neurons, and help form hippocampal synapses and AchE-positive fibers. These findings may offer practical support for and insight into treatment of Alzheimer’s disease.

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.