Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System

The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or content that reinforces their existing dominant preferences and beliefs. This results in a lack of exposure to diverse and varied content. Many existing works have predominantly examined filter bubbles in static or relatively-static recommendation settings. However, filter bubbles will be continuously intensified over time due to the feedback loop between the user and the system in the real-world online recommendation. To address these issues, we propose a novel paradigm, Multi-Facet Preference Learning for Pricking Filter Bubbles in Conversational Recommender System (FacetCRS), which aims to burst filter bubbles in the conversational recommender system (CRS) through timely user-item interactions via natural language conversations. By considering diverse user preferences and intentions, FacetCRS automatically model user preference into multi-facets, including entity-, word-, context-, and review-facet, to capture diverse and dynamic user preferences to prick filter bubbles in the CRS. It is an end-to-end CRS framework to adaptively learn representations of various levels of preference facet and diverse types of external knowledge. Extensive experiments on two publicly available benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance in mitigating filter bubbles and enhancing recommendation quality in CRS

Salmonella Infection Causes Hyperglycemia for Decreased GLP-1 Content by Enteroendocrine L Cells Pyroptosis in Pigs

Inflammatory responses have been shown to induce hyperglycemia, yet the underlying mechanism is still largely unclear. GLP-1 is an important intestinal hormone for regulating glucose homeostasis; however, few studies have investigated the influence of digestive tract Salmonella infection on enteroendocrine L cell secretions. In this study, we established a model of Salmonella-infected piglets by oral gavage in order to analyze the effects of Salmonella infection on enteroendocrine L cell function. Furthermore, in vitro lipopolysaccharide (LPS) was administered to STC-1 cells to clarify its direct effect on GLP-1 secretion. The results showed that significantly increased blood glucose in the group of Salmonella-infected piglets was observed, and Salmonella infection decreased blood GLP-1 content. Then, ileal epithelium damage was observed by histological detection, and this was further verified by TUNEL staining. We identified activation of TLR signaling demonstrating up-regulated expressions of TLR4 and nuclear factor-kappa B (NF-ΚB). Furthermore, it was shown that Salmonella induced pyroptosis of enteroendocrine L cells and enhanced the secretion of IL-1β through augmenting gene and protein expressions of NOD-like receptor protein 3 (NLRP3), apoptosis-associated speck-like protein containing a carboxyl-terminal CARD (ASC), Caspase 1, and gasdermin D (GSDMD). Meanwhile, in vitro LPS treatment induced the pyroptosis of STC-1 cells and reduced the secretion of GLP-1. Altogether, the results demonstrated that Salmonella infection can reduce secretion of GLP-1 by inducing pyroptosis of intestinal L cells, which may eventually result in hyperglycemia. The results provided evidence for the cause of hyperglycemia induced by inflammation and shed new light on glucose homeostasis regulation

Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels deconfounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical result verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The fake invariance severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal remedies are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks

A 672-nW, 670-n<italic>Vrms</italic> ECG Acquisition AFE With Noise-Tolerant Heartbeat Detector

This paper presents an electrocardiogram acquisition analog front-end (AFE) with a noise tolerant heartbeat (HB) detector. Source degradation and transconductance bootstrap techniques are incorporated into the AFE to reduce the 1/f noise of the amplifier. Furthermore, the chopper modulation, DC-servo loop (DSL) and pre-charge technology are combined to reduce interference from the environment. A mixed-signal implementation of HB detector with the symmetric-comparison loop is proposed to reduce the power consumption and area, which also suppresses motion artifact interference by adaptive thresholds. Implemented in $0.18 ~\mu \text{m}$ CMOS process, the circuit only occupies an area of $0.122 mm^{2}$ and consumes $0.62 ~\mu \text{W}$ at a 1.2-V supply, of which AFE and HB detector consume 507 nW and 110 nW, respectively. Simulation results show that the gain and the CMRR of AFE range from 30&#x2013;45 dB and 65&#x2013;105 dB, respectively. The input-referred noise is 670 nVrms with a mid-band gain of 42 dB and a bandwidth ranging from 0.5 Hz to 1 kHz

Magnolia officinalis Extract Contains Potent Inhibitors against PTP1B and Attenuates Hyperglycemia in db/db Mice

Protein tyrosine phosphatase 1B (PTP1B) is an established therapeutic target for type 2 diabetes mellitus (T2DM) and obesity. The aim of this study was to investigate the inhibitory activity of Magnolia officinalis extract (ME) on PTP1B and its anti-T2DM effects. Inhibition assays and inhibition kinetics of ME were performed in vitro. 3T3-L1 adipocytes and C2C12 myotubes were stimulated with ME to explore its bioavailability in cell level. The in vivo studies were performed on db/db mice to probe its anti-T2DM effects. In the present study, ME inhibited PTP1B in a reversible competitive manner and displayed good selectivity against PTPs in vitro. Furthermore, ME enhanced tyrosine phosphorylation levels of cellular proteins, especially the insulin-induced tyrosine phosphorylations of insulin receptor β-subunit (IRβ) and ERK1/2 in a dose-dependent manner in stimulated 3T3-L1 adipocytes and C2C12 myotubes. Meanwhile, ME enhanced insulin-stimulated GLUT4 translocation. More importantly, there was a significant decrease in fasting plasma glucose level of db/db diabetic mice treated orally with 0.5 g/kg ME for 4 weeks. These findings indicated that improvement of insulin sensitivity and hypoglycemic effects of ME may be attributed to the inhibition of PTP1B. Thereby, we pioneered the inhibitory potential of ME targeted on PTP1B as anti-T2DM drug discovery

Sapphire-Derived Fiber Bragg Gratings for High Temperature Sensing

In this paper, a sapphire-derived fiber (SDF) with a core diameter of 10 μm and a cladding diameter of 125 μm is fabricated by the melt-in-tube method, and fiber Bragg gratings (FBGs) with reflectivity over 80% are prepared by the femtosecond laser point-by-point direct writing method. By analyzing the refractive index distribution and reflection spectral characteristics of the SDF, it can be seen that the SDF is a graded refractive index few-mode fiber. In order to study the element composition of the SDF core, the end-face element distribution of the SDF is analyzed, which indicates that element diffusion occurred between the core and the cladding materials. The temperature and stress of the SDF gratings are measured and the highest temperature is tested to 1000 °C. The temperature and strain sensitivities are 15.64 pm/°C and 1.33 pm/με, respectively, which are higher than the temperature sensitivity of the quartz single-mode fiber. As a kind of special fiber, the SDF expands the application range of sapphire fiber, and has important applications in the fields of high-temperature sensing and high-power lasers

Identification of TEFM as a potential therapeutic target for LUAD treatment

Abstract Background Molecularly targeted therapies have recently become a hotspot in the treatment of LUAD, with ongoing efforts to identify new effective targets due to individual variability. Among these potential targets, the mitochondrial transcription elongation factor (TEFM) stands out as a crucial molecule involved in mitochondrial synthetic transcriptional processing. Dysregulation of TEFM has been implicated in the development of various diseases; however, its specific role in LUAD remains unclear. Methods We conducted a comprehensive analysis of TEFM expression in LUAD, leveraging data from the TCGA database. Subsequently, we validated these findings using clinical specimens obtained from the First Affiliated Hospital of Soochow University, employing western blotting and qRT-PCR techniques. Further experimental validation was performed through the transfection of cells with TEFM overexpression, knockdown, and knockout lentiviruses. The effects of TEFM on LUAD were evaluated both in vitro and in vivo using a range of assays, including CCK-8, colony formation, EdU incorporation, Transwell migration, Tunel assay, flow cytometry, JC-1 staining, and xenograft tumour models. Results Our investigation uncovered that TEFM exhibited elevated expression levels in LUAD and exhibited co-localization with mitochondria. Overexpression of TEFM facilitated malignant processes in LUAD cells, whereas its silencing notably curbed these behaviors and induced mitochondrial depolarization, along with ROS production, culminating in apoptosis. Moreover, the absence of TEFM substantially influenced the expression of mitochondrial transcripts and respiratory chain complexes. Results from nude mouse xenograft tumors further validated that inhibiting TEFM expression markedly hindered tumor growth. Conclusion TEFM promotes LUAD malignant progression through the EMT pathway and determines apoptosis by affecting the expression of mitochondrial transcripts and respiratory chain complexes, providing a new therapeutic direction for LUAD-targeted therapy. Graphical Abstrac