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Resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is a major challenge for clinicians and patients with non-small cell lung cancer (NSCLC). Serine-arginine protein kinase 1 (SRPK1) is a key oncoprotein in the EGFR/AKT pathway that participates in tumorigenesis. We found that high SRPK1 expression was significantly associated with poor progression-free survival (PFS) in patients with advanced NSCLC undergoing gefitinib treatment. Both in vitro and in vivo assays suggested that SRPK1 reduced the ability of gefitinib to induce apoptosis in sensitive NSCLC cells independently of its kinase activity. Moreover, SRPK1 facilitated binding between LEF1, β-catenin and the EGFR promoter region to increase EGFR expression and promote the accumulation and phosphorylation of membrane EGFR. Furthermore, we verified that the SRPK1 spacer domain bound to GSK3β and enhanced its autophosphorylation at Ser9 to activate the Wnt pathway, thereby promoting the expression of Wnt target genes such as Bcl-X. The correlation between SRPK1 and EGFR expression was confirmed in patients. In brief, our research suggested that the SRPK1/GSK3β axis promotes gefitinib resistance by activating the Wnt pathway and may serve as a potential therapeutic target for overcoming gefitinib resistance in NSCLC.

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning (DL)-based convolution neural networks (CNNs) has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multiscale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder–decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original $3\,\, \times 3$ convolution, the multiscale convolution kernels are used in depthwise separable convolution (DSConv) layers of the network. Furthermore, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and interchannel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly available benchmark datasets, i.e., the Crack500 (500 crack images with $2000\,\, \times 1500$ pixels) and CFD (118 crack images with $480\,\, \times 320$ pixels) datasets. The mean intersection over union (MIoU) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multiscale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with $1024\,\, \times 768$ pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at https://github.com/xtu502/pavement-defects.

With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion and RUL prediction methods are either not flexible enough to capture the highly nonlinear relationship between the health condition and the multisensor data or have not fully utilized the past observations to capture the degradation trajectory. In this article, we propose a joint prognostic model (JPM), where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensor data, and the observation epoch. A Bayesian updating scheme is developed to calculate the posterior distributions of the model parameters of each sensor data, which are further used to estimate the posterior predictive distributions of the residual life. The effectiveness and advantages of the proposed JPM are demonstrated using the commercial modular aero-propulsion system simulation data set. [ABSTRACT FROM AUTHOR]

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability. [ABSTRACT FROM AUTHOR]

The operating status, control resources, and accidental events within the power system exhibit significant uncertainty, and the integration of cluster electric vehicles (EVs), characterized by high permeability. These introduce both opportunities and challenges to power system regulation. This paper proposes a multi-layer intelligent control strategy for multi-regional power generation that incorporates the participation of EVs. Initially, a multi-regional interconnected control structure is designed, predicated on the frequency modulation ancillary service market. The upper-level model predictive controller rapidly issues total power generation commands based on regional control deviations, while a lower-level deep reinforcement learning controller executes comprehensive control. This control considers system frequency modulation mileage, regional control deviation, and the demand loss of EV users as objectives. Subsequently, an autonomous decision-making model for EV owners' charging modes is established. Based on this, charging and discharging participation coefficients for EVs are formulated, and the corresponding classification of EVs is conducted, delineating the regulatory margin boundary for individual EVs. Utilizing Minkowski sum theory, the stochastic boundary of the regulation margin for an EV charging station (CS) cluster is calculated. Consequently, the adjustable capacity range for future CSs can be determined in the current stage. Finally, the multi-experience replay pool theory is applied to enhance the MATD3 algorithm. Simulation results demonstrate that the proposed strategy can effectively reduce regional control deviations and control costs, and achieve more intelligent CSs management.

To develop computed tomography (CT)-based models to increase the prediction accuracy of spread through air spaces (STAS) in clinical-stage T1N0 lung adenocarcinoma.

Nitric oxide (NO) is a crucial signal molecule closely linked to the biological immune response, especially in macrophage polarization. When activated, macrophages enter a pro-inflammatory state and produce NO, a marker for the M1 phenotype. In contrast, the anti-inflammatory M2 phenotype does not produce NO. We developed a mitochondria-targeted two-photon iridium-based complex (Ir-ImNO) probe that can detect endogenous NO and monitor macrophages’ different immune response states using various imaging techniques, such as one- and two-photon phosphorescence imaging and phosphorescence lifetime imaging. Ir-ImNOwas used to monitor the immune activation of macrophages in mice. This technology aims to provide a clear and comprehensive visualization of macrophage immune responses.

ABSTRACTPatients with non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping can benefit from crizotinib treatment. Currently, the main resistance mechanisms to crizotinib are MET D1228N and Y1230C mutations. We reported a case of a Chinese NSCLC patient with MET exon 14 skipping detected by targeted next-generation sequencing (NGS) achieved clinical and imaging remission after crizotinib treatment. Then, amplification of multiple genes such as erb-b2 receptor tyrosine kinase 2 (HER2) was detected when disease progressed, indicating novel resistance mechanisms to crizotinib. Ultimately the patient died from cancer-related factors. This was the first NSCLC case with MET exon 14 skipping which reported the HER2 gene amplification at the time of progression during crizotinib treatment, indicating that bypass mechanisms contribute to the development of acquired resistance to MET inhibitors.

ABSTRACTAfatinib exhibits therapeutic efficacy for lung adenocarcinoma patients harboring HER2exon 20 insertions. HER2S310Y single site substitution was discovered in recent years and afatinib efficacy for adenocarcinoma patients harboring S310Y mutation has not been reported. We presented a case of a 41-year-old male patient with lung adenocarcinoma harboring the HER2S310Y mutation obtained clinical response to the treatment of afatinib, an oral HERfamily blocker. After the treatment of afatinib, the patient achieved partial response (PR) in chest lesions and almost complete response (CR) in intracranial lesions. He experienced progressive disease (PD) with liver metastasis and achieved a progression-free survival (PFS) of 5 months. He continually treated with afatinib after CT guided percutaneous radiofrequency ablation to eradicate the hepatic tumor cells and achieved stable disease (SD). In this study, we reported the first clinical evidence of efficacy generated by afatinib, the irreversible HERfamily inhibitor, targeting HER2S310Y single site mutation in lung adenocarcinoma.

Nitric oxide (NO) serves as a ubiquitous and fundamental signaling molecule involved in intricate effects on both physiological and pathological processes. NO, biosynthesized by nitric oxide synthase (NOS) or generated from nitrite, can form nitrosation reagent N2O3(4NO + O2= 2N2O3) through its oxidation or quickly produce peroxynitrite anion ONOO–(NO + •O2–= ONOO–) by reacting with superoxide anion (•O2–). However, most of the existing luminescent probes for NO just focus on specificity and utilize only a single signal to distinguish products N2O3or ONOO–. In most of the present work, they differentiate one product from another simply by fluorescence signal or fluorescence intensity, which is not enough to distinguish accurately the behavior of NO in living cells. Herein, a new mitochondria-targeted and two-photon near-infrared (NIR) phosphorescent iridium(III) complex, known as Ir-NBD, has been designed for accurate detection and simultaneous imaging of two downstream products of endogenous NO, i.e., N2O3and ONOO–. Ir-NBDexhibits a rapid response to N2O3and ONOO–in enhanced phosphorescence intensity, increased phosphorescence lifetime, and an exceptionally high two-photon cross-section, reaching values of 78 and 85 GM, respectively, after the reaction. Furthermore, we employed multiple imaging methods, phosphorescence intensity imaging, and phosphorescence lifetime imaging together to image even distinguish N2O3and ONOO–by probe Ir-NBD. Thus, coupled with its excellent photometrics, Ir-NBDenabled the detection of the basal level of intracellular NO accurately by responding to N2O3and ONOO–in the lipopolysaccharide-stimulated macrophage model in virtue of fluorescence signal and phosphorescence lifetime imaging, revealing precisely the endogenous mitochondrial NO distribution during inflammation in a cell environment.

Huidoubais a kind of fungus plant that grows by spiders parasitizing tea trees at an altitude of 1500 m above Mount Emei. The bioactive polysaccharides extracted from Huidoubahave been traditionally used in the treatment of diabetes in China. In this study, the optimal hot-water extraction condition of crude Huidoubapolysaccharides was obtained using Box–Behnken design: the temperature of 93.7 °C, the time of 3.57 h, the liquid-to-solid ratio of 28.47 mL/g, and the yield was 0.46%. Three purified Huidoubapolysaccharides (HDBPs, including HDBP-d, HDBP-1, and HDBP-2) were separated through deproteinization, ultrafiltration, and column chromatography. Then the inhibition mechanism and kinetics of HDBPs against α-glucosidase were investigated. The inhibitory activity of HDBPs on α-glucosidase was positively correlated with their concentration, and their mechanism of quenching fluorescence was ascertained to be the static type induced by the formation of HDBPs-α-glucosidase complex. HDBP-d and HDBP-2 inhibited the activity of α-glucosidase in a reversible competitive-type manner while HDBP-1 was a noncompetitive-type inhibitor. The inhibitory activities of HDBPs on α-glucosidase correlated inversely with temperature in most cases, and showed weaker inhibitory activity at extreme acidic or alkaline conditions. These findings highlight the efficacy, kinetics and mechanism of HDBPs inhibiting α-glucosidase in vitro. The study provided a replaceable approach for chemical inhibitors of α-glucosidase with natural fungus plant extracts, and suggested the theoretical basis that the plant-derived polysaccharides with glucosidase inhibition effects were applied in medicines.

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) in distinguishing between tampered and untampered images. For deep learning models, the five models are used for feature extraction, then each pre-trained model is fine-tuned. The findings of this work show near perfect accuracy in detecting instances of tumor injections and removals.

Involvement of adult Ixodes persulcatus ticks in the transmission of Lyme disease in Hailin County, Heilongjiang Province, China, is reported. In 1986 from April through August adult I. persulcatus was the dominant tick in this endemic area with an infection rate of 43% for the Lyme disease spirochaete, Borrelia burgdorferi. The incidence of Lyme disease cases presenting the symptom of erythema chronicum migrans (ECM) within this area was correlated with the seasonal abundance of adult I. persulcatus and the number of people bitten by ticks. The frequency of ECM formation in all age groups varied and was associated with the frequency of tick bites. In addition, a strain of B. burgdorferi was isolated from a pool of six female I. persulcatus collected from this area. We demonstrate that the seasonal abundance of adult I. persulcatus and its frequent attachment to humans result in the spring and summer transmission of Lyme disease in this endemic area. The role of immature I. persulcatus in Lyme disease transmission is apparently minimal.