Corosolic acid enhances oxidative stress-induced apoptosis and senescence in pancreatic cancer cells by inhibiting the JAK2/STAT3 pathway.

BACKGROUND: Pancreatic cancer (PC) is a fatal human malignancy with a poor prognosis. Corosolic acid (CRA) is a triterpenoid, has been reported to have inhibitory effects on tumor growth. However, the role of CRA on PC has not been explored. Here, we aimed to uncover the molecular mechanisms of CRA in PC progression. METHODS: Cell viability, lactate dehydrogenase (LDH) release, cell apoptosis and senescence were detected by cell counting kit-8 (CCK-8), LDH, flow cytometry and senescence associated-ß-galactosidase (SA-ß-gal) assay. Levels of relevant proteins and oxidative stress (OS) markers were evaluated by Western blot and enzyme-linked immunosorbent assay (ELISA). A xenograft tumor model was established to explore the in vivo effects of CRA on PC. RESULTS: We found that CRA inhibited PC cell viability and promoted LDH release in a dose-dependent manner, but had no significant effect on human normal pancreatic ductal epithelial cells HPDE6C7. CRA increased OS-induced cell apoptosis and senescence in HAPC and SW1990 cells. And CRA decreased the levels of anti-apoptotic protein Bcl-2, and elevated the expression of pro-apoptotic protein Bax and senescence-associated proteins P21 and P53. Besides, CRA decreased tumor growth in xenograft models. Furthermore, CRA inactivated the Janus kinase-2 (JAK2)/Signal Transducer and Activator of Transcription 3 (STAT3) signaling pathway in HAPC and SW1990 cells. Functional experiments demonstrated that activation of the JAK2/STAT3 pathway by the JAK2 activator coumermycin A1 (C-A1) or the STAT3 activator colivelin (col) reduced the contribution effect of OS, apoptosis and senescence by CRA. CONCLUSION: Taken together, our findings indicated that CRA exerted anti-cancer effects in PC by inhibiting the JAK2/STAT3 pathway.

Multi-task deep learning for medical image computing and analysis: A review.

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Artificial intelligence techniques for neuropathological diagnostics and research.

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.

The Berberis vulgaris L. extract berberine exerts its anti-oxidant effects to ameliorate cholesterol overloading-induced cell apoptosis in the primary mice hepatocytes: an in vitro study.

Cholesterol overloading stress damages normal cellular functions in hepatocytes and induces metabolic disorders to facilitate the development of multiple diseases, including cardiovascular diseases, which seriously degrades the life quality of human beings. Recent data suggest that the Berberis vulgaris L. extract berberine is capable of regulating cholesterol homeostasis, which is deemed as potential therapeutic drug for the treatment of cholesterol overloading-associated diseases, but its detailed functions and molecular mechanisms are still largely unknown. In the present study, we evidenced that berberine suppressed cell apoptosis in high-cholesterol-diet mice liver and cholesterol-overloaded mice hepatocytes. Also, cholesterol overloading promoted reactive oxygen species (ROS) generation to trigger oxidative damages in hepatocytes, which were reversed by co-treating cells with both berberine and the ROS scavenger N-acetylcysteine (NAC). Moreover, the underlying mechanisms were uncovered, and we validated that berberine downregulated Keap1, and upregulated Nrf2 to activate the anti-oxidant Nrf2/HO-1 signaling pathway in cholesterol overloading-treated hepatocytes, and both Keap1 upregulation and Nrf2 downregulation abrogated the suppressing effects of berberine on cell apoptosis in the hepatocytes with cholesterol exposure. Taken together, we concluded that berberine activated the anti-oxidant Keap1/Nrf2/HO-1 pathway to eliminate cholesterol overloading-induced oxidative stress and apoptotic cell death in mice hepatocytes, and those evidences hinted that berberine might be used as putative therapeutic drug for the treatment of cholesterol overloading-associated cardiovascular diseases.

An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma.

Routine examination of entire histological slides at cellular resolution poses a significant if not insurmountable challenge to human observers. However, high-resolution data such as the cellular distribution of proteins in tissues, e.g., those obtained following immunochemical staining, are highly desirable. Our present study extends the applicability of the PathoFusion framework to the cellular level. We illustrate our approach using the detection of CD276 immunoreactive cells in glioblastoma as an example. Following automatic identification by means of PathoFusion's bifocal convolutional neural network (BCNN) model, individual cells are automatically profiled and counted. Only discriminable cells selected through data filtering and thresholding were segmented for cell-level analysis. Subsequently, we converted the detection signals into the corresponding heatmaps visualizing the distribution of the detected cells in entire whole-slide images of adjacent H&E-stained sections using the Discrete Wavelet Transform (DWT). Our results demonstrate that PathoFusion is capable of autonomously detecting and counting individual immunochemically labelled cells with a high prediction performance of 0.992 AUC and 97.7% accuracy. The data can be used for whole-slide cross-modality analyses, e.g., relationships between immunochemical signals and anaplastic histological features. PathoFusion has the potential to be applied to additional problems that seek to correlate heterogeneous data streams and to serve as a clinically applicable, weakly supervised system for histological image analyses in (neuro)pathology.

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment.

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.

Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects.

BACKGROUND: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. METHODS: 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. FINDINGS: Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). INTERPRETATION: This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. FUNDING: The complete list of funders can be found in the Acknowledgement section.

PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data.

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.

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis.

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

Long Noncoding RNA SChLAP1 Forms a Growth-Promoting Complex with HNRNPL in Human Glioblastoma through Stabilization of ACTN4 and Activation of NF-κB Signaling.

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.

Treatment Efficacy of NGF Nanoparticles Combining Neural Stem Cell Transplantation on Alzheimer's Disease Model Rats.

BACKGROUND: Alzheimer'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 AND METHODS: AD 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. RESULTS: Rats 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. CONCLUSIONS: NGF-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.

Andrographolide causes apoptosis via inactivation of STAT3 and Akt and potentiates antitumor activity of gemcitabine in pancreatic cancer.

Gemcitabine is a first-line drug utilised in the chemotherapy of pancreatic cancer; however, this drug induces chemo-resistance and toxicity to normal tissue during treatment. Here, we firstly report that andrographolide (ANDRO) alone not only has anti-pancreatic cancer activity, but it also potentiates the anti-tumour activity of gemcitabine. Treatment with ANDRO alone inhibits proliferation of the pancreatic cancer cell lines in a dose- and time-dependent manner in vitro. Interestingly, ANDRO induces cell cycle arrest and apoptosis of pancreatic cancer cells by inhibiting STAT3 and Akt activation, upregulating the expression of p21(WAF1) and Bax, and downregulating the expression of cyclinD1, cyclinE, survivin, X-IAP and Bcl-2. Additionally, ANDRO combined with gemcitabine significantly induce stronger cell cycle arrest and more obvious apoptosis than each single treatment. The mechanistic study demonstrates that this synergistic effect is also dependent on the inhibition of STAT3 and Akt activations which subsequently regulates the pathways involved in the apoptosis and cell cycle arrest. Furthermore, both ANDRO alone and the combination treatments exhibit efficacious anti-tumour activity in vivo. Overall, our results provide solid evidence supporting that ANDRO alone or its combination with gemcitabine is a potential chemotherapeutic approach for treating human pancreatic cancer in clinical practice.

Effects of legumain as a potential prognostic factor on gastric cancers.

Although legumain has been found to be a prognostic factor in both breast cancer and colorectal cancer, its effects on gastric cancer are unknown. In this study, we investigated effects of legumain on gastric cancer and the correlation between legumain expression and prognosis of gastric cancer patients. SGC7901 cells were transduced with legumain cDNA (SGC7901-hLeg) for overexpression of legumain or with legumain shRNA to knock down legumain. In vitro tumor migration was examined by wound healing assay. Furthermore, a tumorigenicity and metastasis mouse model was used to examine legumain function in vivo; asparaginyl endopeptidase inhibitor (AEPI, an inhibitor of legumain) was injected to the mice (i.p.) to evaluate its therapeutic effect. Tissue microarray analysis from 112 gastric cancer patients was performed to evaluate the association between legumain expression and the cumulative survival time. Legumain was highly expressed in gastric cancer patients and some gastric cancer cell lines. Legumain promoted gastric cell migration in vitro and promoted gastric tumor growth and metastasis in vivo, and these effects were reversed by knockdown of legumain with shRNA or treated with AEPI. In gastric cancer clinical samples, legumain expression in tumor was significantly higher than in non-tumor and was negatively associated with the cumulative survival rate. In conclusion, legumain was highly expressed in gastric adenocarcinoma; legumain promoted gastric cancer tumorigenesis and metastasis in vitro and in vivo. Legumain expression in tumor was a poor prognostic factor for gastric cancer patients, and legumain could be a potential target molecule for gastric cancer therapy in clinic.