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Miniature radar has demonstrated its great potential in smart homes to understand the wellness of the residents and provide ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3:4kbps data rate while it preserves the sensing resolution of the radar.

Neurotransmitter-mediated acupuncture analgesia has been widely studied in nervous systems. It remains largely unclear if peripheral substances are involved the acupuncture analgesia. Adiponectin (APN), a circulating adipokine, shows analgesic effects. The study aimed to examine whether APN regulates analgesic effects of electroacupuncture (EA) in the complete Freund’s adjuvant (CFA)-induced mouse model. APN wild type (WT) and knockout (KO) mouse were employed in the study. We found that EA attenuates the CFA-induced pain as demonstrated by the Hargreaves thermal test and the von Frey filament test. The deletion of APN significantly reduced the acupuncture analgesia in the CFA-treated APN KO mice while the intrathecal administration of APN mimicked the analgesic effects of EA. We further revealed that EA produced analgesic effects mainly via APN/AdipoR2-mediated AMPK pathway by the siRNA inhibitions of APN receptors (adipoR1/2) in the spinal cord. The immunofluorescence staining analysis showed that EA increased the APN accumulation in spinal cord through the blood circulation. In conclusion, the study indicates a novel mechanism that acupuncture produces analgesic effects at least partially via APN/AdipoR2-AMPK pathway in the spinal cord.

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks' location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies e-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

Motion information is one important cue in unsupervised video salient object detection. In order to estimate motion in videos, most of the methods adopt time-consuming algorithms such as large displacement optical flow estimation (needs more than 8-40s with 640X480 size per frame), which leads to saliency detection with only 0.01-0.1 FPS speed and limits its application. In human visual system, the motion of one object is usually considered as a whole. Therefore, we need not compute the motion of each pixel. Instead, it is desirable to estimate the probability of each pixel belonging to a well identifiable object, which is proposed as maximally stable region (MSR) in recent work, and compute object-level motion. Motivated by this intuition, we firstly propose one fast object-level video motion model based on MSR, which only needs 49 ms for 640X480 size frame. Next, we present spatial-temporal boundary connectivity (BndCon) and spatial-temporal Minimum Barrier Distance (MBD) to estimate background probability and saliency. Then, we propose the repeatability saliency which means the frequency of the object recurs in all video sequences. Besides, we propose one simple yet effective method to combine our unsupervised method and deep learning model to further boost performance. Compared with the state-of-the-art unsupervised methods, our method shows significantly better performance with 12 FPS speed on normal CPU hardware.

Medulloblastoma is the most common primary pediatric malignancy of the central nervous system. Recurrent and refractory patients account for approximately 30% of them. Immune cells are an important component of the brain tumor microenvironment, including tumor‐associated macrophages, T lymphocytes, natural killer cells, dendritic cells, neutrophils and B lymphocytes. Understanding how they behave and interact is important in the investigation of the onset and progression of medulloblastoma. Here, we overview the features and recent advances of each component of immune cells in medulloblastoma. Meanwhile, immunotherapy is a promising but also challenging treatment strategy for medulloblastoma. At present, there are a growing number of immunotherapeutic approaches under investigation including immune checkpoint inhibitors, oncolytic viruses, cancer vaccines, chimeric antigen receptor T cell therapies, and natural killer cells in recurrent and refractory medulloblastoma patients.

The random finite difference method (RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels. However, the high computational cost is an ongoing challenge for its application in complex scenarios. To address this limitation, a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed. The proposed method uses one-dimensional convolutional neural network (CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output. The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96, respectively. It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples (about 40 samples for each case in this study). It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given. This calls for an approach to gauge the model's confidence interval. It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference. The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

QRS distortion (G3I) and frontal QRS-T angle (fQRS-T angle) are both electrocardiographic (ECG) signs of ongoing ischemia and depolarization-repolarization heterogeneity, which always occur in patients with ST-segment elevation acute myocardial infarction (STEMI).We retrospectively collected 592 STEMI patients who underwent coronary angiography and follow-up for 42 months. 1. We divided the patients into two groups according to whether they had G3I on admission, compared the differences in examination data and endpoint events between these two groups. 2. Group patients according to whether the endpoint events happened in hospital, at 12 and 42 months, compare whether there is a difference in fQRS-T angle at the same time point, and find out the predictive cutoff value of all-cause death. 3. Combined G3I and fQRS-T angle together to enhance the predictive value.G3I and fQRS-T angle are both independent risk factors for all-cause death in STEMI patients within 12 months (G3I P = 0.014, fQRS-T angle P 0.001) and within 42 months (P 0.001). The cutoff values of fQRS-T angle for predicting all-cause death are 66.5° at 12 months and 90.5° at 42 months. When G3I and fQRS-T angle are combined used to predict the mortality, the specificity is significantly improved, but the sensitivity decreased.G3I and fQRS-T angles are valuable in the prognostic assessment of STEMI patients, especially when combined. These findings help clinicians to identify high-risk patients early for more aggressive treatment.

OBJECTIVES Metastatic renal cell carcinoma can occur synchronously or metachronously. We characterized the time from diagnosis to systematic therapy as a categorical variable to analyze its effect on the overall survival and first-line treatment efficacy of metastatic renal cell carcinoma patients. METHODS We initially enrolled 949 consecutive metastatic renal cell carcinoma patients treated with targeted therapies retrospectively from December 2005 to December 2019. X-tile analysis was used to determine cut-off values of time from diagnosis to systematic therapy referring to overall survival. Patients were divided into different groups based on the time from diagnosis to systematic therapy and then analyzed for survival. RESULTS Of 358 eligible patients with metastatic renal cell carcinoma, 125 (34.9%) had synchronous metastases followed by cytoreductive nephrectomy, and 233 (65.1%) had metachronous metastases. A total of 28 patients received complete metastasectomy. Three optimal cut-off values for the time from diagnosis to systematic therapy (months) - 1.1, 7.0 and 35.9 - were applied to divide the population into four groups: the synchro group (time from diagnosis to systematic therapy ≤1.0), early group (1.0

Acute myocardial infarction (AMI) has been a preclinical and clinical concern due to high hospitalization rate and mortality. This study was aimed at evaluating the effectiveness and safety of Shexiang Baoxin Pill (SBP) for AMI and exploring the possible mechanism of oxidative stress. Six databases were searched on March 26, 2021. Twenty-four studies were included and accessed by the RoB 2.0 or SYRCLE tool. Compared with routine treatment (RT), SBP showed the effectiveness in the clinical efficacy ( RR = 1.15 , 95% CI [1.06, 1.25]), left ventricular ejection fraction (LVEF) ( SMD = 0.73 , 95% CI [0.62, 0.95]), glutathione (GSH) ( SMD = 2.07 , 95% CI [1.51, 2.64]), superoxide dismutase (SOD) ( SMD = 0.92 , 95% CI [0.58, 1.26]), malondialdehyde (MDA) ( SMD = − 4.23 , 95% CI [-5.80, -2.66]), creatine kinase-myocardial band (CK-MB) ( SMD = − 4.98 , 95% CI [-5.64, -4.33]), cardiac troponin I (cTnI) ( SMD = − 2.17 , 95% CI [-2.57, -1.76]), high-sensitivity C-reactive protein (Hs-CRP) ( SMD = − 1.34 , 95% CI [-1.56, -1.12]), interleukin-6 (IL-6) ( SMD = − 0.99 , 95% CI [-1.26, -0.71]), triglycerides (TG) ( SMD = − 0.52 , 95% CI [-0.83, -0.22]), flow-mediated dilation (FMD) ( SMD = 1.39 , 95% CI [1.06, 1.72]), von Willebrand Factor (vWF) ( SMD = − 1.77 , 95% CI [-2.39, -1.15]), nitric oxide (NO) ( SMD = 0.89 , 95% CI [0.65, 1.13]), and recurrent rate ( RR = 0.30 , 95% CI [0.15, 0.59]). But SBP adjunctive to RT plus PCI had no improvements in almost pooled outcomes except for the Hs-CRP ( SMD = − 1.19 , 95% CI [-1.44, -0.94]) and TG ( SMD = − 0.25 , 95% CI [-0.48, -0.02]). Laboratory findings showed that SBP enhanced the endothelial nitric oxide synthase (eNOS) activity and regulated laboratory indexes especially for homocysteine. In conclusion, SBP has adjunctive effects on AMI via the mechanism of antioxidative stress. The current evidence supports the use of SBP for mild and moderate AMI patients.

Lung cancer is the most frequently life-threatening disease and the prominent cause of cancer-related mortality among human beings worldwide, where poor early diagnosis and expensive detection costs are considered as significant reasons. Here, we try to tackle this issue by proposing a novel label-free and low-cost strategy for rapid detection and distinction of lung cancer cells relying on plasmonic toroidal metasurfaces at terahertz frequencies. Three disjoint regions are displayed in identifiable intensity-frequency diagram, which could directly help doctors determine the type of lung cancer cells for clinical treatment. The metasurface is generated by two mirrored gold split ring resonators with subwavelength sizes. When placing analytes on the metasurface, apparent shifts of both the resonance frequency and the resonance depth can be observed in the terahertz transmission spectra. The theoretical sensitivity of the biosensor over the reflective index reaches as high as 485.3 GHz/RIU. Moreover, the proposed metasurface shows high angular stability for oblique incident angle from 0 to 30°, where the maximum resonance frequency shift is less than 0.66% and the maximum transmittance variation keeps below 1.33%. To experimentally verify the sensing strategy, three types of non-small cell lung cancer cells (Calu-1, A427, and 95D) are cultured with different concentrations and their terahertz transmission spectra are measured with the proposed metasurface biosensor. The two-dimensional fingerprint diagram considering both the frequency and transmittance variations of the toroidal resonance dip is obtained, where the curves for different cells are completely separated with each other. This implies that we can directly distinguish the type of the analyte cells and its concentration by only single spectral measurement. We envisage that the proposed strategy has potential for clinical diagnosis and significantly expands the capabilities of plasmonic metamaterials in biological detection.

Computer vision-based control is a nonintrusive, cost-effective, and reliable technique for flotation process control. It is known that deep learning features can depict the complex behavior of the froth surface more comprehensively and accurately than handcrafted features. However, few studies have tried to use additional information to improve flotation performance through optimal control. To this end, we have attempted to develop a novel deep learning feature-based two-layer optimal control scheme. The first layer is proposed for setpoint generation of high-dimensional features using improved fuzzy association rule reasoning. Then, an offline conservative double Q-learning control layer that can learn from historical industrial records by mitigating bootstrapping error in action value functions is developed. The proposed method can adapt the setpoint to the change in process feeds. Meanwhile, in contrast to traditional approximate dynamic programming methods that need to interact with real/simulated process systems, this controller can work without any further interactions, which makes it possible to transfer the success of reinforcement learning algorithms to complex industrial process control where opportunities to explore are missing. Experiments demonstrate that the proposed method is effective and promising for practical flotation process control.

Background Esophageal cancer (EC) is a common and lethal carcinoma; however, the effectiveness and feasibility of the chemo- and radio-therapy (CRT) for the elderly patients (≥ 70 years) with surgery have not been fully discussed. The purpose of this study was to investigate the potential effect of CRT on the prognosis. Methods A total of 1085 patients (534 CRT patients vs. 551 non-CRT patients) from 1998 to 2016 were collected from the Surveillance, Epidemiology, and End Results database according to the inclusion and exclusion criteria. Using the competing risk regression and survival analysis, an overall estimation of the effectiveness of CRT was performed on a well-balanced cohort via performing propensity score matching. Then, the specific impact of CRT on high- (n = 557) and low-risk (n = 528) cohorts derived from the nomogram’s risk quantification for every patient were further evaluated respectively. Additionally, the advantages of the nomogram model and the conventional tumor, node, metastasis (TNM, 6th revision) staging system were compared. Results A better survival outcome was observed among patients receiving both surgery and CRT than those who underwent surgery alone (HR: 0.55, 95% CI 0.45–0.68, P P Conclusion Aggressive treatments, such as surgery, chemotherapy, and radiotherapy, were considered effective for selected elderly patients with EC according to the newly established nomogram model.

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

High-frequency responsive electrochemical capacitor (EC), as an ideal lightweight filtering capacitor, can directly convert alternating current (AC) to direct current (DC) . However, current electrodes are stuck in limited electrode area and tortuous ion transport. Herein, strictly vertical graphene arrays (SVGAs) prepared by electric-field-assisted plasma enhanced chemical vapour deposition have been successfully designed as the main electrode to ensure ions rapidly adsorb/desorb in richly available graphene surface. SVGAs exhibit an outstanding specific areal capacitance of 1.72 mF‧cm -2 at Φ 120 = 80.6° even after 500,000 cycles, which is far better than that of most carbon-related materials. Impressively, the output voltage could also be improved to 2.5 V when using organic electrolyte. An ultra-high energy density of 0.33 μWh‧cm -2 can also be handily achieved. Moreover, ECs-SVGAs can well smooth arbitrary AC waveforms into DC signals, exhibiting excellent filtering performance.

BACKGROUND Endothelium injury and coagulation dysfunction play an important role in the pathogenesis of sepsis. Soluble thrombomodulin (sTM), tissue plasminogen activator-inhibitor complex (t-PAIC), thrombin-antithrombin complex (TAT) and α2-plasmin inhibitor-plasmin complex (PIC) are biomarkers of endothelium injury and coagulation dysfunction. This study aimed to explore the prognostic values and diagnostic performance for septic shock and sepsis-induced disseminated intravascular coagulation (DIC) of endothelial biomarkers. METHODS We conducted an observational study on patients with sepsis admitted to intensive care unit (ICU) at a teaching hospital from January 2016 to December 2018. Levels of sTM, t-PAIC, TAT and PIC were measured at admission day and day 5-7 after admission and detected by qualitative chemiluminescence enzyme immunoassay performed on HISCL automated analyzers. RESULTS A total of 179 septic patients and 125 non-septic ICU controls were enrolled. The level of sTM was higher in septic patients compared to ICU controls (OR =1.093, 95% CI: 1.045-1.151, P

This paper is concerned with the security control problem for a class of networked systems subject to deception attacks and packet dropouts. First, by taking into account the deception attacks and packet dropouts in an unified framework, a discrete-time stochastic system is presented. In virtue of matrix exponential computation, an equivalent discrete-time stochastic model is established. Based on this, the security analysis is given by the law of total expectation and some sufficient conditions are provided. Subsequently, a controller is designed. Finally, the effectiveness of the proposed method is illustrated by two examples including a practical power grid system.

Objective To retrospectively assess the mid‐term clinical and radiological results of hip arthroscopic treatment of labral tears in patients with borderline developmental dysplasia of the hip (BDDH). Methods From January 2010 and December 2019, data were retrospectively reviewed for all patients who underwent arthroscopic surgery of the hip for the treatment of intra‐articular abnormalities. Only the Patients who had borderline developmental dysplasia (BDDH) were included. All operations were performed by two senior surgeons, the arthroscopic treatment including labral repair, labral debridement, minimal acetabuloplasty, femoroplasty and capsular closure. The evaluation consisted of pain evaluation (visual analog scale [VAS]), the modified Harris hip score (MHHS), range of motion, the radiological evaluation of plain film and MRI analysis of the hip joint. The plain film evaluation included anteroposterior views of the pelvis to assess lateral center‐edge angle (LCEA) and acetabular inclination (AI), frog‐leg lateral views of the hip to assess α angle. Results There were 34 patients (36 hips) ultimately enrolled in this study. The follow‐up duration of the patients were minimal 2 years (average, 69.2 months) postoperatively. The patient group included seven men and 27 women, the mean age at the time of surgery was 30.9 years. The mean BMI was 22.3 kg/m2. From the pre‐operative status to the final follow‐up visit, mean mHHS score increased from 64.5 to 92.7, mean VAS score decreased from 6.8 to 1.3. All scores exhibited statistically significant differences (P 0.05). However, the mean α angle was significantly decreased from 48.3° to 40.1° (P, Arthroscopic treatment of labral tears in patients with BDDH may provide safe and durable favorable results – 34 patients mean 5.8 years' evidence.

With epidemics and pandemics like COVID-19, many offline healthcare services have been suspended and shifted to online, where patients and doctors typically communicate by typing texts. The limited communication poses a threat to the service quality of E-health, and also raises higher demand on the language skills of doctors, in which medical terms are a common concern. Traditional studies of offline healthcare mostly hold a negative attitude towards the use of medical terms by doctors. However, should we still advise doctors to avoid using medical terms in E-health? To answer this question, this paper conducts a study combining technical and empirical analyses based on real data. In this paper, a novel unsupervised text-mining method is proposed to automatically identify medical terms with crowd wisdom from large-scale doctor-patient communication texts. Then, a TREC-type experiment is carried out to validate the proposed method in terms of Precision, Recall, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1-measure, demonstrating that it can identify accurate and comprehensive medical terms. Based on the identified medical terms, an empirical analysis is conducted to verify the influence of medical terms used by doctors on the service quality of E-health. The analysis results show that for patients with low health literacy, the use of medical terms by doctors would decrease their service quality. However, for patients with high health literacy, the use of medical terms by doctors can significantly increase their service quality, revealing that doctors could improve their service quality in E-health by adjusting their medical term usage according to the health literacy of patients.

Seismic reliability analysis is crucially important for the seismic performance evaluation and probabilistic seismic risk assessment of bridges. However, the exact and efficient computation of seismic reliability for a practical bridge with uncertainty is still challenging. This study presents a composite method of maximum entropy principle with fractional moments, partially stratified sampling, and Nataf transformation for the seismic reliability analysis of bridge with correlated random parameters, and then evaluates the impact of random variable correlation on the probabilistic seismic performance of high-pier continues rigid frame bridge (CRFB). Different from the existing maximum entropy method, a unbiased likelihood function-based maximum entropy method is proposed to compute the extreme value distribution (EVD) of structural nonlinear seismic response. To illustrate the efficiency and accuracy of the proposed method, a hysteretic nonlinear single-degree-of-freedom (SDOF) system and a simplified two-span bridge are first taken as examples. The obtained EVD is compared with the results of Monte Carlo simulation, current maximum entropy method, and widely used probability distribution model fitting. The proposed method is then applied to evaluate the seismic reliability of a practical high-pier CRFB. The influence of uncertainties and variable correlation of structural parameters on the seismic reliability of high-pier CRFB is discussed in detail. The analysis results indicate the high efficiency and accuracy of the proposed method and demonstrate the significant importance of structural parameter correlation on the probabilistic seismic performance assessment of high-pier CRFB.

Septoplasty is widely used in the treatment of structural nasal obstructions, and it also has a good effect and a high degree of postoperative satisfaction. However, there a large number of structures demonstrate abnormalities related to structural nasal obstruction, including the external nose, maxilla, nasal cavity and paranasal sinus. Nasal septum deviation is only one signs of structural nasal obstruction and does not represent all possible structural abnormalities of the nasal cavity and its surrounding structure. Septoplasty is only performed to correct deviations of the nasal septum, which in many cases is obviously insufficient in restoring the symmetry of the nasal structure. Therefore, septoplasty alone is not suitable for the treatment of most structural nasal obstructions. Nasal ventilation expansion surgery, which typically covers more abnormal structural correction procedures than septoplasty, should be used when describing the treatment of structural nasal obstruction.

Background: Cells of the immune system can inhibit tumor growth and progression; however, immune cells can also promote tumor cell growth, survival, and angiogenesis as a result of the immunosuppressive microenvironments. In the last decade, a growing number of new therapeutic strategies focused on reversing the immunosuppressive status of tumor microenvironments (TMEs), to reprogram the TME to be normal, and to further activate the antitumor functions of immune cells. Most of the “hot tumors” are encompassed with M2 macrophages promoting tumor growth, and the accumulation of M2 macrophages into tumor islets leads to poor prognosis in a wide variety of tumors. Summary: Therefore, how to uncover more immunosuppressive signals and to reverse the M2 tumor-associated macrophages (TAMs) to M1-type macrophages is essential for reversing the immunosuppressive state. Except for reeducation of TAMs in the cancer immunotherapy, macrophages as central effectors and regulators of the innate immune system have the capacity of phagocytosis and immune modulation in macrophage-based cell therapies. Key Messages: We review the current macrophage-based cell therapies that use genetic engineering to augment macrophage functionalities with antitumor activity for the application of novel genetically engineered immune cell therapeutics. A combination of TAM reeducation and macrophage-based cell strategy may bring us closer to achieving the original goals of curing cancer. In this review, we describe the characteristics, immune status, and tumor immunotherapy strategies of macrophages to provide clues and evidences for future macrophage-based immune cell therapies.

With the proliferation of human-carried mobile devices, spatial crowdsourcing has emerged as a transformative system, where requesters outsource their spatiotemporal tasks to a set of workers who are willing to perform the tasks at the specified locations. However, in order to make efficient assignments, the existing spatial crowdsourcing system usually requires workers and/or tasks to expose their locations, which raises a significant concern of compromising location privacy. In addition, traditional spatial crowdsourcing systems employ a centralized server to manage the information of workers and tasks. Such a centralized design does not scale to a large number of workers/tasks, making the server easily a bottleneck. In this article, we present an online framework for assigning tasks to workers without compromising the location privacy in a fully distributed manner . Our system protects the location privacy of both workers and tasks through homomorphic encryption . We further propose a novel wait-and-decide mechanism and a proportional-backoff mechanism to increase the number of assigned tasks. Extensive experiments on real-world data sets illustrate that our proposed system achieves a large number of task assignments in an efficient and privacy-preserving manner.

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

There are increasing number of abandoned mines, which has gradually become a striking challenge for sustainable development and resource recycling. Safety risks and environmental pressures have obl...

Multimodal medical image fusion technology can assist doctors diagnose diseases accurately and efficiently. However the multi-scale decomposition based image fusion methods exhibit low contrast and energy loss. And the sparse representation based fusion methods exist weak expression ability caused by the single dictionary and the spatial inconsistency. To solve these problems, this paper proposes a novel multimodal medical image fusion method based on nonsubsampled shearlet transform (NSST) and convolutional sparse representation (CSR). First, the registered source images are decomposed into multi-scale and multi-direction sub-images, and then these sub-images are trained respectively to obtain different sub-dictionaries by the alternating direction product method. Second, different scale sub-images are encoded by the convolutional sparse representation to get the sparse coefficients of the low frequency and the high frequency, respectively. Third, the coefficients of the low frequency are fused by the regional energy and the average $${L}_{1}$$ norm. Meanwhile the coefficients of the high frequency are fused by the improved spatial frequency and the average $${l}_{1}$$ norm. Finally, the final fused image is reconstructed by inverse NSST. Experimental results on serials of multimodal brain images including CT,MR-T2,PET and SPECT demonstrate that the proposed method has the state-of-the-art performance compared with other current popular medical fusion methods whatever in objective and subjective assessment.

Breast cancer has become the most common malignant tumor worldwide. However, the survival rate of breast cancer patients in China is 8% lower compared to that in European and American countries. Insufficient screening coverage and low rate of early detection are key problems. Therefore, it is imperative to develop a set of guidelines for breast cancer screening, early diagnosis, and treatment strategies suitable for Chinese women. The National Health Commission, the National Cancer Center, and the National Cancer Quality Control Center collectively organized a committee of specialists with multidisciplinary backgrounds in breast surgery, oncology, radiotherapy, diagnostic imaging, ultrasound, pathology, epidemiology, and health economics. The committee collectively published "China Breast Cancer Screening, Early Diagnosis and Treatment Specification", in which the committee outlined a set of strategies of breast cancer screening, early diagnosis, and treatment suitable for China's national conditions. The strategies were formulated based on clinical experience in breast cancer prevention and treatment, characteristics of breast cancer patients in China, and socioeconomic conditions. The purpose of this article is to provide an interpretation of the above guidelines as a reference for breast cancer screening.

The differential phase computed tomography (DP-CT) imaging is a kind of X-ray-phase-based CT methods, which is widely used in various X-ray imaging laboratories. At present, a collection of projections with the horizontal direction (perpendicular to the sample rotation axis) DP information is often acquired in DP-CT. In an ideal experiment, if the background is zero, then we can accurately reconstruct the information of each direction of the sample with horizontal data only. Actually, background information will produce streaking artifacts to affect the resolution in directions other than the horizontal direction. To mitigate the streaking artifacts in the conventionally reconstructed DP-CT images. This study develops a novel analytical DP-CT reconstruction framework by jointly using the bilateral DP information obtained along two perpendicular directions. In addition, a new data acquisition scheme is also proposed to quickly acquire the bilateral DP signal without the need of changing the direction of refraction signal acquisition. Experimental results demonstrate that this new algorithm is able to greatly reduce the streaking artifacts on DP-CT images.

A photonic broadband signal frequency conversion and bandwidth multiplication system is demonstrated based on a Fourier domain mode-locked optoelectronic oscillator (FDML-OEO). In this method, freq...

Reasonable layout of logistics centers in the ASEAN countries has important practical significance for improving logistics network efficiency and reducing logistics transportation costs. This paper based on the current status of the ASEAN logistics network and the characteristics of the hub-and-spoke network, taking the minimum total cost as the optimization goal, establishing the central location model, and the design self-adaptive genetic algorithms to solve it. Therefore, the number of central nodes in the ASEAN logistics network has been determined, the radiation range between nodes, and route allocation. The results indicate that the ASEAN network layout model is a hub-and-spoke logistics network. With Singapore as the logistic center hub node. Jakarta logistics development belt will form the main radiating logistics demand cities of Kuching and Ho Chi Minh city. Kuala Lumpur logistics development belt’s main radiating logistics demand cities will be East Kalimantan and Da Nang city. Chon Buri logistics development belt mainly radiates the cities of Phnom Penh and Riau city. Bangkok logistics development belt mainly radiates the cities of Hanoi, Vientiane, and Yangon city.

A pseudospark (PS) discharge can generate an electron beam with a high current density. The electron beam can be self-focused by an ion channel and transported over a long distance without the need for an external magnetic field. Such features make it attractive to drive millimeter-wave/THz interaction circuits for the generation of high-power radiation from a compact device. This paper presents the experimental results on the generation and transportation of the PS-sourced beam with different cross-sections, as well as the differences of the PS-sourced beam with and without post acceleration. Its application in millimeter-wave/THz sources was demonstrated by the operation of extended interaction oscillators (EIOs) at different frequencies is presented.

In this paper, an endfire dual-polarized phased array is introduced for 5G mobile applications, which operates from 26 GHz to 30 GHz with low profile of only 1.1 mm ( ${\bf 0.1\lambda _0}$ at 26 GHz) and small clearance of 3.5 mm. The array consists of interleaved substrate integrated waveguide (SIW) based vertical-polarized elements and notch type horizontal-polarized elements. The bandwidth of each SIW element is greatly improved by introducing a wide-band impedance transition, which is composed by parallel plate resonators and a metallic via in between. The notch elements have very compact size, which helps to keep the half-wavelength inter-element distance. The array has a planar structure and is easy to fabricate. The measured and simulated results align well with each other.

Depression is a common psychiatric illness affecting over 300 million people globally. Acupuncture has been reported to be a safe complementary treatment for depression. This study is aimed to investigate the efficacy and mechanism of combining acupuncture with antidepressants in treating depression compared to the sole use of antidepressants. Seventy depression patients were randomly assigned to the treatment group (n = 50) and control group (n = 20). The treatment group received acupuncture combined antidepressants treatment for 3 weeks, while the control group took antidepressants monotherapy for 3 weeks. Among the 70 patients, 40 participants (20 control; 20 treatment) were randomized for studying functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) measured by the functional near-infrared spectroscopy. The primary outcome was HAMD-17 and secondary outcomes were PHQ-9, and the relationships of resting-state FC (rsFC) with the depression severity. PHQ-9 and HAMD-17 scores in the treatment group were significantly lower than those in the control group at Week 3 (p = 0.01) with effect sizes of −0.4 and −0.61 respectively. The rsFC in F1, F3, AF3, AF7, FC3, FC5 (left DLPFC, 10–20 system), AF8, and F6 (right DLPFC) in the treatment group had significant temporal correlation (p

In order to accurately analyze the seismic response of the site and their effects on a railway cable-stayed bridge, the site condition of the railway cable-stayed bridge was surveyed, and geotechnical exploration holes ZK1 and ZK2 were set near the left and right tower of the bridge to gain the detailed geological data, then 32 representative ground motions records were selected corresponding to the site characteristics, and the equivalent linear and nonlinear model were established to analyze the site seismic response of 32 representative ground motions based on the Deepsoil Software. Next, to investigate the influence of site seismic response on the long-span railway cable-stayed bridge, the seismic response of the cable-stayed bridge considering site effect are calculated and evaluated via time history analysis under one-dimensional and multi-point excitation based on the ANSYS platform. At last, several critical and meaningful conclusions are drawn.

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios. Fil: Zhang, Jin. Nankai University; China Fil: Feng, Fan. Nankai University; China Fil: Han, TianYi. Nankai University; China Fil: Duan, Feng. Nankai University; China Fil: Sun, Zhe. Riken. Brain Science Institute; Japón Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina Fil: Solé Casals, Jordi. Central University of Catalonia; España

Pathological states in the early life environment of mammalian offspring, including maternal obesity and intrauterine overnutrition, can induce obesity and metabolic disorder later in life. Leptin resistance caused by upregulation of Socs3 in the hypothalamus of offspring was believed to be the main mechanism of this effect. In this study, obese mother (OM) and lean mother (LM) models were generated by feeding C57BL/6N female mice a high-fat diet or standard lean diet, respectively. Additionally, an obese mother with intervention (OMI) model was generated by injecting the high-fat diet group with Socs3-shRNA lentivirus during early pregnancy. The offspring of the groups was correspondingly named OM-F1 , LM-F1 , and OMI-F1 , representing progeny mouse models of different early life environments. The offspring were fed a high-fat diet to test their propensity for obesity. The body weight, food intake and fat accumulation were higher, while glucose intolerance and insulin resistance were worse in the OM-F1 group than LM-F1 group. By contrast, the obesity phenotype, hyperphagia and metabolic disorder were alleviated in the OMI-F1 group compared with the OM-F1 group. The mechanism was identified that downregulation of hypothalamic SOCS3 resulted in an increased level of p-STAT3 and p-JAK2, which ameliorated the leptin resistance and restored the lean expression of appetite regulatory genes (Pomc and Agrp) in hypothalamus of OMI-F1 group. Taken together, these results indicate that reducing maternal Socs3 expression during pregnancy can attenuate obesity caused by the early life environment in mice, which may inspire therapies that enable obese mothers to bear metabolically healthy children.

Genetic matching in personalized medicine is becoming more popular in cloud computing, whereby a cloud server performs genetic matching from the genetic data outsourced by a gene provider (e.g., a genetic lab) and an authorized party (e.g., a doctor) for diagnosing the patients’ diseases. Due to sensitive privacy, we should protect genetic data before outsourcing it to the untrusted cloud. However, traditional differential privacy schemes do not support genetic matching and ciphertext methods hinder data availability. In this article, we propose a differential privacy-based genetic matching (DPGM) scheme to achieve effective genetic matching and protect genetic privacy. Specifically, DPGM first uses a DP-based EIGENSTRAT (DPE) algorithm to construct a published sequence that contains significantly noisy single-nucleotide polymorphisms (SNPs) associated with diseases, thereby ensuring outsourced genetic data privacy. Second, DPGM adopts a DP-based N-order Markov (DPNM) algorithm to generate a noisy query sequence, which considers query privacy and the similarity between the noisy query and the actual query. Finally, DPGM calculates the longest common subsequence (LCS) based on a dynamic programming algorithm, which achieves effective matching results. Detailed theoretical analysis proves that our DPGM scheme achieves $\epsilon$ e -differential privacy. Extensive experiments over actual genetic datasets demonstrate that our scheme achieves high efficiency and data utility.

Wing-Fai Yeung,1 Branda Yee-Man Yu,2 John Wai-Man Yuen,1 Janice Yuen Shan Ho,1 Ka-Fai Chung,3 Zhang-Jin Zhang,4 Deejay Suen Yui Mak,5 Lorna Kwai-Ping Suen,6 Lai-Ming Ho7 1School of Nursing, The Hong Kong Polytechnic University, The Hong Kong SAR, People’s Republic of China; 2Department of Psychology, The University of Hong Kong, The Hong Kong SAR, People’s Republic of China; 3Department of Psychiatry, The University of Hong Kong, The Hong Kong SAR, People’s Republic of China; 4School of Chinese Medicine, The University of Hong Kong, The Hong Kong SAR, People’s Republic of China; 5School of Medical and Health Sciences, Tung Wah College, The Hong Kong SAR, People’s Republic of China; 6School of Nursing, Tung Wah College, The Hong Kong SAR, People’s Republic of China; 7School of Public Health, The University of Hong Kong, The Hong Kong SAR, People’s Republic of ChinaCorrespondence: Wing-Fai YeungSchool of Nursing, The Hong Kong Polytechnic University, Room FG429, Hung Hom, Hong Kong, People’s Republic of ChinaEmail jerry-wf.yeung@polyu.edu.hkBackground: Acupuncture is an alternative treatment for improving sleep, and it may attenuate oxidative stress, which is a possible pathophysiological factor in insomnia. The aim of this study was to examine the efficacy and safety of a semi-individualized acupuncture in improving sleep and explore its effect on oxidative stress parameters in adults with insomnia disorder.Methods: In this randomized sham-controlled trial, 140 participants were randomly assigned to either a 4-week semi-individualized traditional acupuncture (TA) or noninvasive sham acupuncture (SA). The primary outcome measure was the sleep-diary-derived sleep efficiency. Other outcomes included sleep diary and actigraphy, Insomnia Severity Index, anxiety and depressive symptoms, and quality of life. Blood samples were taken to measure oxidative stress parameters (malondialdehyde, glutathione peroxidase, paraoxonase, and arylesterase).Results: Although no significant difference was found in the primary outcome measure, both sleep-diary-derived and actigraphy-derived total sleep time (TST) were significantly increased in the TA group at 1-week posttreatment (mean difference in sleep diary = 22.0 min, p = 0.01, actigraphy = 18.8 min, p = 0.02). At 5-week posttreatment follow-up, a significantly higher proportion of participants in the TA group showed sleep-diary-derived sleep efficiency (SE) ≥ 85% than in the SA group (55.6% versus 36.4%, p = 0.03).Conclusion: TA and SA did not significantly differ in improving subjective sleep efficiency in individuals with insomnia disorder. However, the TA group showed a short-term effect on improving TST as measured by both sleep diary and actigraphy at 1-week posttreatment, but there were no differences in the oxidative stress parameters.Clinical Trial Registration: Registry: ClinicalTrials.gov; Title: Acupuncture in the Modulation of Peripheral Oxidative Stress Insomnia; Identifier NCT03447587; URL: https://clinicaltrials.gov/ct2/show/NCT03447587.Keywords: sleep, electroacupuncture, oxidative stress, placebo, Chinese medicine, acupoints

Background To explore the role of family with sequence similarity 13 member A (FAM13A) in TGF-β1-induced EMT in the small airway epithelium of patients with chronic obstructive pulmonary disease (COPD). Methods Small airway wall thickness and protein levels of airway remodeling markers, EMT markers, TGF-β1, and FAM13A were measured in lung tissue samples from COPD and non-COPD patients. The correlations of FAM13A expression with COPD severity and EMT marker expression were evaluated. Gain- and loss-of-function assays were performed to explore the functions of FAM13A in cell proliferation, motility, and TGF-β1-induced EMT marker alterations in human bronchial epithelial cell line BEAS-2B. Results Independent of smoking status, lung tissue samples from COPD patients exhibited significantly increased small airway thickness and collagen fiber deposition, along with enhanced protein levels of remodeling markers (collagen I, fibronectin, and MMP-9), mesenchymal markers (α-SMA, vimentin, and N-cadherin), TGF-β1, and FAM13A, compared with those from non-COPD patients. FAM13A expression negatively correlated with FEV1% and PO2 in COPD patients. In small airway epithelium, FAM13A expression negatively correlated with E-cadherin protein levels and positively correlated with vimentin protein levels. In BEAS-2B cells, TGF-β1 dose-dependently upregulated FAM13A protein levels. FAM13A overexpression significantly promoted cell proliferation and motility in BEAS-2B cells, whereas FAM13A silencing showed contrasting results. Furthermore, FAM13A knockdown partially reversed TGF-β1-induced EMT marker protein alterations in BEAS-2B cells. Conclusions FAM13A upregulation is associated with TGF-β1-induced EMT in the small airway epithelium of COPD patients independent of smoking status, serving as a potential therapeutic target for anti-EMT therapy in COPD.

In recent years, the repurposing of conventional and chemotherapeutic drugs is recognized as an alternative strategy for health care. The main purpose of this study is to strengthen the application of non‐oncological drug metformin on breast cancer treatment in the perspective of epigenetics. In the present study, metformin was found to inhibit cell proliferation, promote apoptosis and induce cell cycle arrest in breast cancer cells at a dose‐dependent manner. In addition, metformin treatment elevated acH3K9 abundance and decreased acH3K18 level. The expression of lncRNA MALAT1, HOTAIR, DICER1‐AS1, LINC01121 and TUG1 was up‐regulated by metformin treatment. In metformin‐treated cells, MALAT1 knock‐down increased the Bax/Bcl2 ratio and enhanced p21 but decreased cyclin B1 expression. The expression of Beclin1, VDAC1, LC3‐II, CHOP and Bip was promoted in the cells received combinatorial treatment of metformin and MALAT1 knock‐down. The reduced phosphorylation of c‐Myc was further decreased in the metformin‐treated cells in combination with MALAT1 knock‐down than metformin treatment alone. Taken together, these results provide a promising repurposed strategy for metformin on cancer treatment by modulating epigenetic modifiers.

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

The accuracy of the prediction and reliability of the deflection basin area index method for rigid pavement heavily depend on the sensor layout scheme and the calculation theory. A total of 154 groups of deflection data were generated by the finite element software in different conditions, and the simulated results were in good agreement with the analytical solutions of thin plates on elastic foundation. The accuracy of several types of deflection basin area index methods was assessed with the database of pavement deflection results. It is found that, apart from densifying the distal sensor layouts locally, replacing the deflection value of a specific point with the average of all measured points in the deflection basin area index was also an effective measure to improve the back-calculation accuracy of pavement structural parameters up to 40%. Finally, a new deflection basin area index method was proposed based on the deflection database with the regression analysis. Comparisons between the theoretical calculations of several models and the practical deflection data of an airport in northern China revealed that the newly proposed method performs better on the back-calculation accuracy and efficiency, which can provide a valuable guideline for the practical engineering in the design.

The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

This study aimed to explore the efficacy of trypsin inhibitors in the treatment of severe pancreatitis (SP) by meta-analysis.The Boolean logic retrieval method was adopted to recruit the relevant literature. Chinese and English databases were respectively searched using a combination of the following search terms: "trypsin inhibitor", "acute pancreatitis", and "severe pancreatitis". The trypsin-treated SP group was set as the experimental group, and the blank control was set as the control group. Meta-analysis was performed using RevMan 5.3 software provided by the Cochrane system.A total of 12 references were included. The meta-analysis suggested that the heterogeneity test results of pancreatic infection incidence were Chi2 =0.51, degrees of freedom (df) =7, I2=0%50%, and P=1.000.01, with risk ratio (RR): 0.80, 95% confidence interval (CI): (0.64, 1.01), Z=1.88, and P=0.06. The heterogeneity test results of extra-pancreatic infection incidence were Chi2 =0.20, df =5, I2=0%50%, and P=1.000.01, with RR: 0.81, 95% CI: (0.62, 1.05), Z=1.60, and P=0.11. The heterogeneity test results of the length of hospital stay were Tau2 =65.69, Chi2 =75.05, df =6, I2=92%50%, and P0.0001. The length of hospital stay of the experimental group was shorter than that of the control group, with mean difference (MD): -23.31 and the 95% CI: (-29.60, -17.02), and the difference was statistically significant (Z=7.26, P0.0001). The heterogeneity test results of the inflammatory factor tumor necrosis factor-α (TNF-α) level were Chi2 =67.28, df =3, I2=96%50%, and P0.0001. The level of TNF-α in the experimental group was lower than that in the control group, with MD: -11.69, 95% CI: (-12.51, -10.87), and the difference was statistically significant (Z=27.88, P0.0001). The heterogeneity test results of the mortality rate were Chi2 =2.52, df =5, I2=0%50%, and P=0.770.01. The mortality rate of the experimental group was lower than that of the control group, with RR: 0.27 and 95% CI: (0.19, 0.40), and the difference was notable (Z=6.75, P0.0001).The meta-analysis performed in this study confirmed that trypsin inhibitors can inhibit the release of inflammatory factors and reduce mortality rate of SP patients.