PREX2 contributes to radiation resistance by inhibiting radiotherapy-induced tumor immunogenicity via cGAS/STING/IFNs pathway in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) lacks established biomarkers or molecular targets for predicting or enhancing radiation response. Phosphatidylinositol-3,4,5-triphosphate-dependent Rac exchange factor 2 (PREX2) exhibits intricate implications in tumorigenesis and progression. Nevertheless, the precise role and underlying mechanisms of PREX2 in CRC radioresistance remain unclear. METHODS: RNA-seq was employed to identify differentially expressed genes between radioresistant CRC cell lines and their parental counterparts. PREX2 expression was scrutinized using Western blotting, real-time PCR, and immunohistochemistry. The radioresistant role of PREX2 was assessed through in vitro colony formation assay, apoptosis assay, comet assay, and in vivo xenograft tumor models. The mechanism of PREX2 was elucidated using RNA-seq and Western blotting. Finally, a PREX2 small-molecule inhibitor, designated PREX-in1, was utilized to enhance the efficacy of ionizing radiation (IR) therapy in CRC mouse models. RESULTS: PREX2 emerged as the most significantly upregulated gene in radioresistant CRC cells. It augmented the radioresistant capacity of CRC cells and demonstrated potential as a marker for predicting radioresistance efficacy. Mechanistically, PREX2 facilitated DNA repair by upregulating DNA-PKcs, suppressing radiation-induced immunogenic cell death, and impeding CD8+ T cell infiltration through the cGAS/STING/IFNs pathway. In vivo, the blockade of PREX2 heightened the efficacy of IR therapy. CONCLUSIONS: PREX2 assumes a pivotal role in CRC radiation resistance by inhibiting the cGAS/STING/IFNs pathway, presenting itself as a potential radioresistant biomarker and therapeutic target for effectively overcoming radioresistance in CRC.

BSN-ESC: A Big-Small Network-Based Environmental Sound Classification Method for AIoT Applications.

In recent years, environmental sound classification (ESC) has prevailed in many artificial intelligence Internet of Things (AIoT) applications, as environmental sound contains a wealth of information that can be used to detect particular events. However, existing ESC methods have high computational complexity and are not suitable for deployment on AIoT devices with constrained computing resources. Therefore, it is of great importance to propose a model with both high classification accuracy and low computational complexity. In this work, a new ESC method named BSN-ESC is proposed, including a big-small network-based ESC model that can assess the classification difficulty level and adaptively activate a big or small network for classification as well as a pre-classification processing technique with logmel spectrogram refining, which prevents distortion in the frequency-domain characteristics of the sound clip at the joint part of two adjacent sound clips. With the proposed methods, the computational complexity is significantly reduced, while the classification accuracy is still high. The proposed BSN-ESC model is implemented on both CPU and FPGA to evaluate its performance on both PC and embedded systems with the dataset ESC-50, which is the most commonly used dataset. The proposed BSN-ESC model achieves the lowest computational complexity with the number of floating-point operations (FLOPs) of only 0.123G, which represents a reduction of up to 2309 times in computational complexity compared with state-of-the-art methods while delivering a high classification accuracy of 89.25%. This work can achieve the realization of ESC being applied to AIoT devices with constrained computational resources.

An Ultra-Low Power Reconfigurable Biomedical AI Processor With Adaptive Learning for Versatile Wearable Intelligent Health Monitoring.

Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection). This requires ultra-low power and reconfigurable biomedical AI processor to support battery-supplied wearable devices and versatile intelligent health monitoring applications while achieving high classification accuracy. However, existing designs have issues in meeting one or more of the above requirements. In this work, a reconfigurable biomedical AI processor (named BioAIP) is proposed, mainly featuring: 1) a reconfigurable biomedical AI processing architecture to support versatile biomedical AI processing. 2) an event-driven biomedical AI processing architecture with approximate data compression to reduce the power consumption. 3) an AI-based adaptive-learning architecture to address patient-to-patient variation and improve the classification accuracy. The design has been implemented and fabricated using a 65nm CMOS process technology. It has been demonstrated with three typical biomedical AI applications, including ECG arrythmia classification, EEG-based seizure detection and EMG-based hand gesture recognition. Compared with the state-of-the-art designs optimized for single biomedical AI tasks, the BioAIP achieves the lowest energy per classification among the designs with similar accuracy, while supporting various biomedical AI tasks.

A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor.

The respiratory rate is widely used for evaluating a person's health condition. Compared to other invasive and expensive methods, the ECG-derived respiration estimation is a more comfortable and affordable method to obtain the respiration rate. However, the existing ECG-derived respiration estimation methods suffer from low accuracy or high computational complexity. In this work, a high accuracy and ultra-low power ECG-derived respiration estimation processor has been proposed. Several techniques have been proposed to improve the accuracy and reduce the computational complexity (and thus power consumption), including QRS detection using refractory period refreshing and adaptive threshold EDR estimation. Implemented and fabricated using a 55 nm processing technology, the proposed processor achieves a low EDR estimation error of 0.73 on CEBS database and 1.2 on MIT-BIH Polysomnographic Database while demonstrating a record-low power consumption (354 nW) for the respiration monitoring, outperforming the existing designs. The proposed processor can be integrated in a wearable sensor for ultra-low power and high accuracy respiration monitoring.

An Ultra-Energy-Efficient and High Accuracy ECG Classification Processor With SNN Inference Assisted by On-Chip ANN Learning.

The ECG classification processor is a key component in wearable intelligent ECG monitoring devices which monitor the ECG signals in real time and detect the abnormality automatically. The state-of-the-art ECG classification processors for wearable intelligent ECG monitoring devices are faced with two challenges, including ultra-low energy consumption demand and high classification accuracy demand against patient-to-patient variability. To address the above two challenges, in this work, an ultra-energy-efficient ECG classification processor with high classification accuracy is proposed. Several design techniques have been proposed, including a reconfigurable SNN/ANN inference architecture for reducing energy consumption while maintaining classification accuracy, a reconfigurable on-chip learning architecture for improving the classification accuracy against patent-to-patient variability, and a dual-purpose binary encoding scheme of ECG heartbeats for further reducing the energy consumption. Fabricated with a 28nm CMOS technology, the proposed design consumes extremely low classification energy (0.3µJ) while achieving high classification accuracy (97.36%) against patient-to-patient variability, outperforming several state-of-the-art designs.

ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network.

ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In the past, traditional machine learning methods such as support vector machine (SVM) and K-nearest neighbor (KNN) have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for ECG classification and shows high classification accuracy. However, the end-to-end neural network has large computational complexity including a large number of parameters and operations. Although dedicated hardware such as field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) can be developed to accelerate the neural network, they result in large power consumption, large design cost, or limited flexibility. In this work, we have proposed an ultra-lightweight end-to-end ECG classification neural network that has extremely low computational complexity (∼8.2k parameters & ∼227k multiplication/addition operations) and can be squeezed into a low-cost microcontroller (MCU) such as MSP432 while achieving 99.1% overall classification accuracy. This outperforms the state-of-the-art ECG classification neural network. Implemented on MSP432, the proposed design consumes only 0.4 mJ and 3.1 mJ per heartbeat classification for normal and abnormal heartbeats respectively for real-time ECG classification.

Does post-operative radiotherapy improve the treatment outcomes of intracranial hemangiopericytoma? A retrospective study.

BACKGROUND: Intracranial hemangiopericytoma is a rare disease and surgery is the mainstay treatment. Although postoperative adjuvant radiotherapy is often used, there are no reports comparing different radiotherapy techniques. The purpose of this study is to analyze the impact of post-operative radiotherapy and different radiotherapy technique on the results in patients with intracranial hemangiopericytoma (HPC). METHODS: We retrospectively reviewed 66 intracranial HPC patients treated between 1999 and 2019 including 29 with surgery followed by radiotherapy (11 with intensity-modulated radiotherapy (IMRT) and 18 with stereotactic radiosurgery (SRS)) and 37 with surgery alone. Chi-square test was used to compare the clinical characteristic between the groups. The Kaplan-Meier method was used to analyze overall survival (OS) and recurrence-free survival (RFS). Multivariate Cox proportional hazards models were used to examine prognostic factors of survival. We also underwent a matched-pair analysis by using the propensity score method. RESULTS: The crude local control rates were 58.6% in the surgery plus post-operative radiotherapy group (PORT) and 67.6% in the surgery alone group (p = 0.453). In the subgroup analysis of the PORT patients, local controls were 72.7% in the IMRT group and 50% in the SRS group (p = 0.228). The median OS in the PORT and surgery groups were 122 months and 98 months, respectively (p = 0.169). The median RFS was 96 months in the PORT group and 72 months in the surgery alone group (p = 0.714). Regarding radiotherapy technique, the median OS and RFS of the SRS group were not significantly different from those in the IMRT group (p = 0.256, 0.960). The median RFS were 112 and 72 months for pathology grade II and III patients, respectively (p = 0.001). Propensity score matching did not change the observed results. CONCLUSION: In this retrospective analysis, PORT did not improve the local control rates nor the survivals. The local control rates after IMRT and SRS were similar even though the IMRT technique had a much higher biological dose compared with the SRS technique.

Inhibition of CCL7 derived from Mo-MDSCs prevents metastatic progression from latency in colorectal cancer.

In colorectal cancer (CRC), overt metastases often appear after years of latency. But the signals that cause micro-metastatic cells to remain indolent, thereby enabling them to survive for extended periods of time, are unclear. Immunofluorescence and co-immunoprecipitation assays were used to explore the co-localization of CCL7 and CCR2. Immunohistochemical (IHC) assays were employed to detect the characters of metastatic HT29 cells in mice liver. Flow cytometry assays were performed to detect the immune cells. Bruberin vivo MS FX Pro Imager was used to observe the liver metastasis of CRC in mice. Quantitative real-time PCR (qRT-PCR) and western blot were employed to detect the expressions of related proteins. Trace RNA sequencing was employed to identify differentially expressed genes in MDSCs from liver micro-M and macro-M of CRC in mice. Here, we firstly constructed the vitro dormant cell models and metastatic dormant animal models of colorectal cancer. Then we found that myeloid-derived suppressor cells (MDSCs) were increased significantly from liver micro-metastases to macro-metastases of CRC in mice. Moreover, monocytic MDSCs (Mo-MDSC) significantly promoted the dormant activation of micro-metastatic cells compared to polymorphonuclear MDSCs (PMN-MDSC). Mechanistically, CCL7 secreted by Mo-MDSCs bound with membrane protein CCR2 of micro-metastatic cells and then stimulated the JAK/STAT3 pathway to activate the dormant cells. Low-dose administration of CCL7 and MDSCs inhibitors in vivo could significantly maintain the CRC metastatic cells dormant status for a long time to reduce metastasis or recurrence after radical operation. Clinically, the level of CCL7 in blood was positively related to the number of Mo-MDSCs in CCR patients, and highly linked with the short-time recurrence and distant metastasis. CCL7 secreted by Mo-MDSCs plays an important role in initiating the outgrowth of metastatic latent CRC cells. Inhibition of CCL7 might provide a potential therapeutic strategy for the prevention of metastasis recurrence.

CMTM6 expression in M2 macrophages is a potential predictor of PD-1/PD-L1 inhibitor response in colorectal cancer.

BACKGROUND: CMTM6 is a novel key regulator of PD-L1. High expression of both CMTM6 and PD-L1 may predict the benefit of PD-1 axis blockade in lung cancer. We aimed to investigate the expression pattern of CMTM6 between mismatch repair-defective (dMMR) and mismatch repair-proficient (pMMR) colorectal cancer (CRC) tissues and assess its correlation with the response to PD-1/PD-L1 pathway blockade. METHODS: Immunohistochemistry (IHC) was used to analyze CMTM6 and PD-L1 expression and immune cell density in dMMR/pMMR CRC. Quantitative multiplex immunofluorescence (IF) was performed to detect CMTM6, PD-L1, CD4, CD8, CD68 and CD163 expression in CRC patients treated with PD-1/PD-L1 inhibitors. RESULT: IHC analysis showed that CMTM6 and PD-L1 were both expressed in tumor cells (TCs) and invasion front immune cells (ICs). CMTM6 and PD-L1 expression and CD4+, CD8+, CD68+ or CD163+ cell density were significantly higher in dMMR CRC patients than in pMMR CRC patients. CMTM6 expression was positively correlated with PD-L1 expression and CD163+ M2 macrophage density in dMMR CRC. IF analysis showed that the coexpression rate of CMTM6/PD-L1 and the expression rate of CMTM6 in CD8+ T cells and CD163+ M2 macrophages were significantly increased in the group that exhibited clinical benefit. CMTM6 expression in M2 macrophages was identified as the best biomarker for predicting the responsiveness to PD-1/PD-L1 inhibitors. CONCLUSIONS: CMTM6 expression in M2 macrophages may predict the PD-1/PD-L1 inhibitor response rate in CRC patients more accurately than dMMR/microsatellite instability-high (MSI-H) status. It can also identify pMMR CRC patients who could benefit from PD-1/PD-L1 inhibitors.

The association of HMGB1 gene with the prognosis of HCC.

High-mobility group box 1 protein (HMGB1) is an evolutionarily ancient and critical regulator of cell death and survival. HMGB1 is a chromatin-associated nuclear protein molecule that triggers extracellular damage. The expression of HMGB1 has been reported in many types of cancers, but the role of HMGB1 in hepato cellular carcinoma (HCC) is unknown.The aim of this study was to analyze the roles of HMGB1 in HCC progression using HCC clinical samples. We also investigated the clinical outcomes of HCC samples with a special focus on HMBG1 expression. In an immunohistochemical study conducted on 208 cases of HCC, HMGB1 had high expression in 134 cases(64.4%).The HMGB1 expression level did not correlate with any clinicopathological parameters, except alpha fetoprotein (AFP) (p = 0.041) and CLIP stage (p = 0.007). However, survival analysis showed that the group with HMBG1 overexpression had a significantly shorter overall survival time than the group with a down-regulated expression of HMBG1 (HR = 0.568, CI (0.398, 0.811), p = 0.002). Multivariate analysis showed that HMGB1 expression was a significant and independent prognostic parameter (HR = 0.562, CI (0.388, 0.815), p = 0.002) for HCC patients. The ability of proliferation, migration and invasion of HCC cells was suppressed with the disruption of endogenous HMGB1 using small interfering RNAs. On the other hand, the ability of proliferation, migration and invasion of HCC cells was strengthened when the expression endogenous HMGB1 was enhanced using HMGB1 DNA. HMGB1 expression may be a novel and independent predictor for the prognosis of HCC patients. The overexpression of HMGB1 in HCC could be a novel, effective, and supplementary biomarker for HCC, since it plays a vital role in the progression of HCC.