The efficacy of ferroptosis-inducing compounds IKE and RSL3 correlates with the expression of ferroptotic pathway regulators CD71 and SLC7A11 in biliary tract cancer cells.

INTRODUCTION: Biliary tract cancer (BTC) is a lethal disease with a bad overall survivability, partly arising from inadequate therapeutic alternatives, detection at a belated stage, and a resistance to common therapeutic approaches. Ferroptosis is a form of programmed cell death that depends on reactive oxygen species (ROS) and iron, causing excessive peroxidation of polyunsaturated fatty acids (PUFAs). Therefore, the objective of this investigation is, whether ferroptosis can be induced in BTC in vitro and whether this induction is dependent on specific molecular markers. METHODS: The study conducted resazurin assay and IC25/50 calculation to explore the possible cytotoxic outcomes of different classes of ferroptosis-inducing substances (FINs) on a comprehensive in vitro model of 11 BTC cell lines. Combinatory treatments with different cell death inhibitors were performed to evaluate the magnitude of ferroptosis induction. To ascertain whether ferroptotic cell death occurred, liperfluo and iron assay kits were employed to evaluate lipid ROS and intracellular iron abundance. Potential biomarkers of ferroptosis sensitivity were then assessed via western blot analysis, a rtPCR panel and functional assay kits. RESULTS: The study found that different FINs reduced cell viability in a cell line-dependent manner. In addition, we measured increased lipid ROS and intracellular Fe2+ levels upon exposure to FINs in BTC cells. Combining FINs with inhibitors of ferroptosis, necroptosis or apoptosis suggests the occurrence of ferroptotic events in BTC cell lines CCC-5, HuH-28 and KKU-055. Furthermore, we found that BTC cells display a heterogeneous profile regarding different molecular genes/markers of ferroptosis. Subsequent analysis revealed that sensitivity of BTC cells towards IKE and RSL3 positively correlated with CD71 and SLC7A11 protein expression. CONCLUSION: Our results demonstrate that induction of ferroptosis is a promising approach to inhibit BTC cell growth and that the sensitivity of BTC cells towards ferroptosis induction might be dependent on molecular markers such as CD71 and SLC7A11.

Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2.

The potential low-energy feature of the spiking neural network (SNN) engages the attention of the AI community. Only CPU-involved SNN processing inevitably results in an inherently long temporal span in the cases of large models and massive datasets. This study introduces the MAC array, a parallel architecture on each processing element (PE) of SpiNNaker 2, into the computational process of SNN inference. Based on the work of single-core optimization algorithms, we investigate the parallel acceleration algorithms for collaborating with multi-core MAC arrays. The proposed Echelon Reorder model information densification algorithm, along with the adapted multi-core two-stage splitting and authorization deployment strategies, achieves efficient spatio-temporal load balancing and optimization performance. We evaluate the performance by benchmarking a wide range of constructed SNN models to research on the influence degree of different factors. We also benchmark with two actual SNN models (the gesture recognition model of the real-world application and balanced random cortex-like network from neuroscience) on the neuromorphic multi-core hardware SpiNNaker 2. The echelon optimization algorithm with mixed processors realizes 74.28% and 85.78% memory footprint of the original MAC calculation on these two models, respectively. The execution time of echelon algorithms using only MAC or mixed processors accounts for ≤ 24.56% of the serial ARM baseline. Accelerating SNN inference with algorithms in this study is essentially the general sparse matrix-matrix multiplication (SpGEMM) problem. This article explicitly expands the application field of the SpGEMM issue to SNN, developing novel SpGEMM optimization algorithms fitting the SNN feature and MAC array.

Ouabain at nanomolar concentrations is cytotoxic for biliary tract cancer cells.

Biliary tract cancer is a deadly disease with limited therapeutic options. Ouabain is a well-known inhibitor of the pumping function of Na+/K+-ATPase, though there is evidence that low concentrations of ouabain lead to a reduction of cell viability of cancer cells independent of its inhibition of the pumping function of the Na+/K+-ATPase. Regarding the impact of ouabain on biliary tract cancer, no data is currently available. Therefore, we aimed for a first-time investigation of ouabain as a potential anti-neoplastic biliary tract cancer agent using comprehensive human biliary tract cancer in vitro models. We found that ouabain has a strong cell line-dependent cytotoxic effect with IC50 levels in the (low) nanomolar-range and that this effect was not associated with the mRNA expression levels of the Na+/K+-ATPase α, ß and fxyd-subunits. Regarding the mode of cytotoxicity, we observed induction of apoptosis in biliary tract cancer cells upon treatment with ouabain. Interestingly, cytotoxic effects of ouabain at sub-saturating (< µM) levels were independent of cellular membrane depolarization and changes in intracellular sodium levels. Furthermore, using a 3D cell culture model, we found that ouabain disturbs spheroid growth and reduces the viability of biliary tract cancer cells within the tumor spheroids. In summary, our data suggest that ouabain possesses anti-biliary tract cancer potential at low µM-concentration in 2D and 3D in vitro biliary tract cancer models and encourage further detailed investigation.

Evaluation of Tazemetostat as a Therapeutically Relevant Substance in Biliary Tract Cancer.

Biliary tract cancer (BTC) is a gastrointestinal malignancy associated with a poor survival rate. Current therapies encompass palliative and chemotherapeutic treatment as well as radiation therapy, which results in a median survival of only one year due to standard therapeutic ineffectiveness or resistance. Tazemetostat is an FDA-approved inhibitor of enhancer of Zeste homolog 2 (EZH2), a methyltransferase involved in BTC tumorigenesis via trimethylation of histone 3 at lysine 27 (H3K27me3), an epigenetic mark associated with silencing of tumor suppressor genes. Up to now, there are no data available regarding tazemetostat as a possible treatment option against BTC. Therefore, the aim of our study is a first-time investigation of tazemetostat as a potential anti-BTC substance in vitro. In this study, we demonstrate that tazemetostat affects cell viability and the clonogenic growth of BTC cells in a cell line-dependent manner. Furthermore, we found a strong epigenetic effect at low concentrations of tazemetostat, which was independent of the cytotoxic effect. We also observed in one BTC cell line that tazemetostat increases the mRNA levels and protein expression of the tumor suppressor gene Fructose-1,6-bisphosphatase 1 (FBP1). Interestingly, the observed cytotoxic and epigenetic effects were independent of the mutation status of EZH2. To conclude, our study shows that tazemetostat is a potential anti-tumorigenic substance in BTC with a strong epigenetic effect.

HDACs as an emerging target in endocrine tumors: a comprehensive review.

INTRODUCTION: The pathogenic role of deregulated histone (de-)acetylation by histone deacetyles (HDACs) has been demonstrated in several human cancers. While some HDAC inhibitors (HDACi) have been approved for individual entities, for endocrine tumors such translation into clinical practice has not yet been achieved. AREAS COVERED: Relevant results identified by structured searches in PubMed as well as in reference lists are summarized in a narrative review to discuss the current knowledge of HDAC involvement and their therapeutic relevance in endocrine tumors. For thyroid, neuroendocrine, and adrenal tumors, various oncogenic mechanisms of HDAC deregulation and effects of HDAC inhibitors (HDACi) have been identified in preclinical studies including direct cancer cell toxicity and modification of differentiation status. EXPERT OPINION: Based on positive pre-clinical results, the research on HDAC (inhibition) in the various endocrine tumors should be intensified - yet, it needs to be considered that i) HDACs' oncogenic actions might constitute only a part of epigenetic mechanisms driving cancer, ii) individual HDAC has different roles in different endocrine tumor entities, iii) inhibition of HDACs might be especially attractive in combination with conventional or other targeted therapies, and iv) new HDAC-inhibiting drugs with improved specificity or functionally modified HDACi might further improve their efficacy.

E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware.

Introduction: In recent years, the application of deep learning models at the edge has gained attention. Typically, artificial neural networks (ANNs) are trained on graphics processing units (GPUs) and optimized for efficient execution on edge devices. Training ANNs directly at the edge is the next step with many applications such as the adaptation of models to specific situations like changes in environmental settings or optimization for individuals, e.g., optimization for speakers for speech processing. Also, local training can preserve privacy. Over the last few years, many algorithms have been developed to reduce memory footprint and computation. Methods: A specific challenge to train recurrent neural networks (RNNs) for processing sequential data is the need for the Back Propagation Through Time (BPTT) algorithm to store the network state of all time steps. This limitation is resolved by the biologically-inspired E-prop approach for training Spiking Recurrent Neural Networks (SRNNs). We implement the E-prop algorithm on a prototype of the SpiNNaker 2 neuromorphic system. A parallelization strategy is developed to split and train networks on the ARM cores of SpiNNaker 2 to make efficient use of both memory and compute resources. We trained an SRNN from scratch on SpiNNaker 2 in real-time on the Google Speech Command dataset for keyword spotting. Result: We achieved an accuracy of 91.12% while requiring only 680 KB of memory for training the network with 25 K weights. Compared to other spiking neural networks with equal or better accuracy, our work is significantly more memory-efficient. Discussion: In addition, we performed a memory and time profiling of the E-prop algorithm. This is used on the one hand to discuss whether E-prop or BPTT is better suited for training a model at the edge and on the other hand to explore architecture modifications to SpiNNaker 2 to speed up online learning. Finally, energy estimations predict that the SRNN can be trained on SpiNNaker2 with 12 times less energy than using a NVIDIA V100 GPU.

MicroRNA mimics can distort physiological microRNA effects on immune checkpoints by triggering an antiviral interferon response.

The microRNA-200 family has wide-ranging regulatory functions in cancer development and progression. Above all, it is strongly associated with the epithelial-to-mesenchymal transition (EMT), a process during which cells change their epithelial to a mesenchymal phenotype and acquire invasive characteristics. More recently, miR-200 family members have also been reported to impact the immune evasion of cancer cells by regulating the expression of immunoinhibitory immune checkpoints (ICs) like PD-L1. Therefore, we aimed to comprehensively characterize this miR-200 family as a regulatory interface between EMT and immune evasion mechanisms in biliary tract cancer. Initial correlation analyses and transient overexpression experiments using miRNA mimics suggested miR-200c-3p as a putative regulator of ICs including PD-L1, LGALS9, and IDO1. However, these effects could not be confirmed in stable miR-200c-3p overexpression cell lines, nor in cells transiently transfected with miR-200c-3p mimic from an independent manufacturer. By shifting our efforts towards dissecting the mechanisms leading to these disparate effects, we observed that the initially used miR-200c-3p mimic triggered a double-stranded (ds)RNA-dependent antiviral response. Besides upregulating the ICs, this had substantial cellular consequences including an induction of interferon type I and type III expression, increased levels of intracellular dsRNA sensors, and a significantly altered cellular growth and apoptotic activity.Our study highlights the capability of miRNA mimics to non-specifically induce a dsRNA-mediated antiviral interferon response. Consequently, phenotypic alterations crucially distort physiological miRNA functions and might result in a major misinterpretation of previous and future miRNA studies, especially in the context of IC regulation.

New method for real-time visualization and quantitative characterization of early colorectal cancer in endoscopy: a pilot study.

Background and study aims Endoscopic optical diagnosis is crucial to the therapeutic strategy for early gastrointestinal cancer. It accurately (> 85 %) predicts pT category based on microsurface (SP) and vascular patterns (VP). However, interobserver variability is a major problem. We have visualized and digitalized the graded irregularities based on bioinformatically enhanced quantitative endoscopic image analysis (BEE) of high-definition white-light images. Methods In a pilot study of 26 large colorectal lesions (LCLs, mean diameter 39 mm), we retrospectively compared BEE variables with corresponding histopathology of the resected LCLs. Results We included 10 adenomas with low-grade intraepithelial neoplasia (LGIN), nine with high-grade intraepithelial neoplasia (HGIN) and early adenocarcinoma (EAC), and seven deeply submucosal invasive carcinomas. Quantified density (d) and nonuniformity (C U ) of vascular and surface structures correlated with histology (r s d VP: -0.77, r s C U VP: 0.13, r s d SP: -0.76, and r s C U SP: 0.45, respectively). A computed BEE score showed a sensitivity and specificity of 90 % and 100 % in the group with LGINs, 89 % and 41 % in the group with HGINs and EACs, and 100 % and 95 % in the group with deeply invasive carcinoma, respectively. Conclusions In this pilot study, BEE showed promise as a tool for endoscopic characterization of LCLs during routine endoscopy. Prospective clinical studies are needed.

Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold.

It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low-precision during inference, thereby saving memory footprint and power consumption. However, neural network quantization is always accompanied by accuracy degradation. Here, we propose a quantization method called double-stage Squeeze-and-Threshold (double-stage ST) to close the accuracy gap with full-precision models. While accurate colors in pictures can be pleasing to the viewer, they are not necessary for distinguishing objects. The era of black and white television proves this idea. As long as the limited colors are filled reasonably for different objects, the objects can be well identified and distinguished. Our method utilizes the attention mechanism to adjust the activations and learn the thresholds to distinguish objects (features). We then divide the numerically rich activations into intervals (a limited variety of numerical values) by the learned thresholds. The proposed method supports both binarization and multi-bit quantization. Our method achieves state-of-the-art results. In binarization, ReActNet [Z. Liu, Z. Shen, S. Li, K. Helwegen, D. Huang and K. Cheng, arXiv:abs/2106.11309] trained with our method outperforms the previous state-of-the-art result by 0.2 percentage points. Whereas in multi-bit quantization, the top-1 accuracy of the 3-bit ResNet-18 [K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conf. Computer Vision and Pattern Recognition, CVPR 2016, 27-30 June 2016, Las Vegas, NV, USA (IEEE Computer Society, 2016), pp. 770-778] model exceeds the top-1 accuracy of its full-precision baseline model by 0.4 percentage points. The double-stage ST activation quantization method is easy to apply by inserting it before the convolution. Besides, the double-stage ST is detachable after training and introducing no computational cost in inference.

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

Ferroptosis in Hepatocellular Carcinoma: Mechanisms, Drug Targets and Approaches to Clinical Translation.

Ferroptosis, an iron and reactive oxygen species (ROS)-dependent non-apoptotic type of regulated cell death, is characterized by a massive iron overload and peroxidation of polyunsaturated fatty acids (PUFAs), which finally results in cell death. Recent studies suggest that ferroptosis can influence carcinogenesis negatively and therefore may be used as a novel anti-cancer strategy. Hepatocellular carcinoma (HCC) is a deadly malignancy with poor chances of survival and is the second leading cause of cancer deaths worldwide. Diagnosis at an already late stage and general resistance to current therapies may be responsible for the dismal outcome. As the liver acts as a key factor in iron metabolism, ferroptosis is shown to play an important role in HCC carcinogenesis and, more importantly, may hold the potential to eradicate HCC. In this review, we summarize the current knowledge we have of the role of ferroptosis in HCC and the application of ferroptosis as a therapy option and provide an overview of the potential translation of ferroptosis in the clinical practice of HCC.

Chemoresistance and resistance to targeted therapies in biliary tract cancer: what have we learned?

INTRODUCTION: Biliary tract cancer (BTC), including intra- and extrahepatic cholangiocarcinoma and gallbladder cancer, is a rare and highly difficult to manage human malignancy. Besides late diagnosis and associated unresectability, frequently observed unresponsiveness toward and recurrence following chemotherapy or targeted therapy essentially contribute to the dismal prognosis of BTC patients. AREAS COVERED: The review provides an update on individual mechanisms involved resistance of BTC toward conventional chemotherapy as well as targeted therapies. We review the distinct mechanisms of pharmacoresistance (MPRs) which have been defined in BTC cells on a molecular basis and examine the specific consequences for the various approaches of chemo-, targeted or immunomodulatory therapies. EXPERT OPINION: Based on currently available experimental and clinical data, the present knowledge about these MPRs in BTCs are summarized. While some possible tactics for overcoming these mechanisms of resistance have been investigated, a BTC-specific and efficient approach based on comprehensive in vitro and in vivo experimental systems is not yet available. Additionally, a reliable monitoring of therapy-relevant cellular changes needs to be established which allows for choosing the optimal drug (combination) before and/or during pharmacological therapy.

A 16-Channel Fully Configurable Neural SoC With 1.52 µW/Ch Signal Acquisition, 2.79 µW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI.

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 µW/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 µW/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.

HDAC Screening Identifies the HDAC Class I Inhibitor Romidepsin as a Promising Epigenetic Drug for Biliary Tract Cancer.

Inhibition of histone deacetylases (HDACs) is a promising anti-cancer approach. For biliary tract cancer (BTC), only limited therapeutic options are currently available. Therefore, we performed a comprehensive investigation of HDAC expression and pharmacological HDAC inhibition into a panel of eight established BTC cell lines. The screening results indicate a heterogeneous expression of HDACs across the studied cell lines. We next tested the effect of six established HDAC inhibitors (HDACi) covering pan- and class-specific HDACis on cell viability of BTC cells and found that the effect (i) is dose- and cell-line-dependent, (ii) does not correlate with HDAC isoform expression, and (iii) is most pronounced for romidepsin (a class I HDACi), showing the highest reduction in cell viability with IC50 values in the low-nM range. Further analyses demonstrated that romidepsin induces apoptosis in BTC cells, reduces HDAC activity, and increases acetylation of histone 3 lysine 9 (H3K9Ac). Similar to BTC cell lines, HDAC 1/2 proteins were heterogeneously expressed in a cohort of resected BTC specimens (n = 78), and their expression increased with tumor grading. The survival of BTC patients with high HDAC-2-expressing tumors was significantly shorter. In conclusion, HDAC class I inhibition in BTC cells by romidepsin is highly effective in vitro and encourages further in vivo evaluation in BTC. In situ assessment of HDAC 2 expression in BTC specimens indicates its importance for oncogenesis and/or progression of BTC as well as for the prognosis of BTC patients.

MiR-200c-3p Modulates Cisplatin Resistance in Biliary Tract Cancer by ZEB1-Independent Mechanisms.

Biliary tract cancer is a major global health issue in cancer-related mortality. Therapeutic options are limited, and cisplatin-based treatment schedules represent the mainstay of first-line therapeutic strategies. Although the gain of survival by the addition of cisplatin to gemcitabine is moderate, acquired cisplatin resistance frequently leads to treatment failures with mechanisms that are still poorly understood. Epithelial-mesenchymal transition (EMT) is a dynamic process that changes the shape, function, and gene expression pattern of biliary tract cancer cells. In this study, we explored the influence of the EMT-regulating miR-200c-3p on cisplatin sensitivity in biliary tract cancer cells. Using gain of function experiments, we demonstrated that miR-200c-3p regulates epithelial cell markers through the downregulation of the transcription factor ZEB1. MiR-200c-3p upregulation led to a decreased sensitivity against cisplatin, as observed in transient overexpression models as well as in cell lines stably overexpressing miR-200c-3p. The underlying mechanism seems to be independent of miR-200c-3p's influence on ZEB1 expression, as ZEB1 knockdown resulted in the opposite effect on cisplatin resistance, which was abolished when ZEB1 knockdown and miR-200c-3p overexpression occurred in parallel. Using a gene panel of 40 genes that were previously associated with cisplatin resistance, two (Dual Specificity Phosphatase 16 (DUSP16) and Stratifin (SFN)) were identified as significantly (>2 fold, p-value < 0.05) up-regulated in miR-200c-3p overexpressing cells. In conclusion, miR-200c-3p might be an important contributor to cisplatin resistance in biliary tract cancer, independently of its interaction with ZEB1.

Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey.

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-threshold region, and pre-processing libraries for universal EEG-based datasets.

Immunmodulatory Treatment Strategies of Hepatocellular Carcinoma: From Checkpoint Inhibitors Now to an Integrated Approach in the Future.

BACKGROUND: Hepatocellular carcinoma (HCC) still represents a human tumor entity with very limited therapeutic options, especially for advanced stages. Here, immune checkpoint modulating drugs alone or in combination with local ablative techniques could open a new and attractive therapeutic "door" to improve outcome and response rate for patients with HCC. METHODS: Published data on HCC experimental to pre-(clinical) treatment strategies from standard of care to novel immunomodulatory concepts were summarized and discussed in detail. RESULTS: Overall, our knowledge of the role of immune checkpoints in HCC is dramatically increased in the last years. Experimental and pre-clinical findings could be translated to phase 1 and 2 clinical trials and became standard of care. Local ablative techniques of HCC could improve the effectivity of immune checkpoint inhibitors in situ. CONCLUSIONS: This review demonstrates the importance of immunomodulatory treatment strategies of HCC, whereby the "best treatment code" of immune checkpoint drugs, combination with ablative techniques and of timing must be evaluated in coming clinical trials.