HOXB5 promotes the progression and metastasis of osteosarcoma cells by activating the JAK2/STAT3 signalling pathway.

Objective: To investigate the involvement of the homeobox gene B5 (HOXB5) in the progression and metastasis of osteosarcoma. Methods: The expression of HOXB5 in human osteosarcoma tissues and its correlation with clinical indicators were investigated using bioinformatics analysis and immunohistochemical labelling. Human osteosarcoma cells (HOS, MG63, U2OS, and Saos-2) and normal human osteoblasts (hFOB1.19) were cultivated. The expression of HOXB5 in these cells was detected using western blotting (WB) and RT‒PCR. Two cell lines exhibiting elevated HOXB5 expression were chosen and divided into three groups: the blank group (mock), control group (control) and transfection group (shHOXB5). The transfection group was infected with lentivirus expressing shRNAs targeting HOXB5. The transfection efficiency was detected by WB. Cell proliferation suppression was measured by CCK-8 and 5-ethynyl-2'-deoxyuridine (EdU) assays; the percentage of apoptotic cells was determined by flow cytometry; and cell migration and invasion were detected via the Transwell chamber test. WB was utilized to determine the protein expression of genes linked to metastasis (MMP2, MMP9), apoptosis (Bax, Bcl-2), and the JAK2/STAT3 pathway (JAK2, p-JAK2, STAT3, p-STAT3). Results: In osteosarcoma tissues, HOXB5 expression was elevated and strongly correlated with distant metastasis. Silencing HOXB5 reduced the proliferation, migration and invasion of osteosarcoma cells; prevented the progression and metastasis of tumours in tumour-bearing nude mice; and reduced the activation of key proteins in the JAK2/STAT3 signalling pathway. Conclusion: Through the JAK2/STAT3 signalling pathway, HOXB5 plays a crucial role in the malignant progression of osteosarcoma and is a promising target for osteosarcoma treatment.

DyCARS: A dynamic context-aware recommendation system.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

Mannose enhances intestinal immune barrier function and dextran sulfate sodium salt-induced colitis in mice by regulating intestinal microbiota.

Background: Inflammatory bowel disease (IBD) greatly affects human quality of life. Mannose has been reported to be used to treat IBD, but the mechanism is currently unknown. Methods: C57/BL mice were used as research subjects, and the mouse acute colitis model was induced using dextran sulfate sodium salt (DSS). After oral administration of mannose, the body weights and disease activity index (DAI) scores of the mice were observed. The colon lengths, histopathological sections, fecal content microbial sequencing, colon epithelial inflammatory genes, and tight junction protein Occludin-1 expression levels were measured. We further used the feces of mice that had been orally administered mannose to perform fecal bacterial transplantation on the mice with DSS-induced colitis and detected the colitis-related indicators. Results: Oral administration of mannose increased body weights and colon lengths and reduced DAI scores in mice with DSS-induced colitis. In addition, it reduced the expression of colon inflammatory genes and the levels of serum inflammatory factors (TNF-α, IL-6, and IL-1ß), further enhancing the expression level of the colonic Occludin-1 protein and alleviating the toxic response of DSS to the intestinal epithelium of the mice. In addition, gut microbial sequencing revealed that mannose increased the abundance and diversity of intestinal flora. Additionally, after using the feces of the mannose-treated mice to perform fecal bacterial transplantation on the mice with DSS-induced colitis, they showed the same phenotype as the mannose-treated mice, and both of them alleviated the intestinal toxic reaction induced by the DSS. It also reduced the expression of intestinal inflammatory genes (TNF-α, IL-6, and IL-1ß) and enhanced the expression level of the colonic Occludin-1 protein. Conclusion: Mannose can treat DSS-induced colitis in mice, possibly by regulating intestinal microorganisms to enhance the intestinal immune barrier function and reduce the intestinal inflammatory response.

Inulin ameliorates metabolic syndrome in high-fat diet-fed mice by regulating gut microbiota and bile acid excretion.

Background: Inulin is a natural plant extract that improves metabolic syndrome by modulating the gut microbiota. Changes in the gut microbiota may affect intestinal bile acids. We suggest that inulin may improve metabolism by inducing bile acid excretion by gut microbes. Methods: Male C57/BL mice were fed either a high-fat diet (60% calories) or a regular diet for 16 weeks, with oral inulin (10% w/w). At the end of the experiment, the gene expression levels (FGF15, CD36, Srebp-1c, FASN, and ACC) in the liver and intestines, as well as the serum levels of triglycerides (TGs), low-density lipoprotein (LDL) cholesterol, total cholesterol, and free fatty acids, were collected. The expression of FGF15 was examined using Western blot analysis. The fat distribution in the liver and groin was detected by oil red and hematoxylin and eosin staining. Simultaneously, the levels of serum inflammatory factors (alanine aminotransferase and aspartate aminotransferase) were detected to explore the side effects of inulin. Results: Inulin significantly improved glucose tolerance and insulin sensitivity, and decreased body weight and serum TG and LDL levels, in mice fed normal diet. Furthermore, inulin increased the α-diversity of the gut microbiota and increased the fecal bile acid and TG excretion in inulin-treated mice. In addition, inulin significantly reduced lipid accumulation in liver and inguinal fat, white fat weight, and hepatic steatosis. Western blot analysis showed that inulin reduced the expression of FGF15, a bile acid reabsorption protein. Conclusion: Inulin ameliorates the glucose and lipid metabolic phenotypes of mice fed a normal diet, including decreased intestinal lipid absorption, increased glucose tolerance, increased insulin sensitivity, and decreased body weight. These changes may be caused by an increase in bile acid excretion resulting from changes in the gut microbiota that affect intestinal lipid absorption.

Sulfosuccinimidyl oleate ameliorates the high-fat diet-induced obesity syndrome by reducing intestinal and hepatic absorption.

Background: A high-fat Western diet is a risk factor for obesity and steatosis. Reducing intestinal absorption of a high-fat diet (HFD) is a feasible strategy to control obesity. Sulfosuccinimidyl oleate (SSO) inhibits intestinal fatty acid transport. Therefore, the aim of this study was to investigate the effects of SSO on HFD-induced glucose and lipid metabolism in mice and its possible underlying mechanisms. Methods: Male C57/BL were fed a HFD (60% calories) for 12 weeks and were administered an oral dose of SSO (50 mg/kg/day). The expression of lipid absorption genes (CD36, MTTP, and DGAT1) and the serum levels of triglycerides (TGs), total cholesterol (TC), and free fatty acids (FFAs) were detected. Lipid distribution in the liver was detected by oil red and hematoxylin and eosin staining. In addition, serum levels of inflammatory factors, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were measured to detect side effects. Results: SSO was effective in the treatment of obesity and metabolic syndrome induced by HFD in mice. It attenuated the assembly of intestinal epithelial chylomicrons by inhibiting intestinal epithelial transport and absorption of fatty acids, thereby reducing the gene expression levels of MTTP and DGAT1, resulting in decreased plasma TG and FFA levels. At the same time, it inhibited the transport of fatty acids in the liver and improved the steatosis induced by a HFD. The results of oil red staining showed that SSO treatment can reduce lipid accumulation in the liver by 70%, with no drug-induced liver injury detected on the basis of interleukin-6, C-reactive protein, ALT, and AST levels. In addition, SSO treatment significantly improved insulin resistance, decreased fasting blood glucose levels, and improved glucose tolerance in HFD-fed mice. Conclusion: SSO is effective in the treatment of obesity and metabolic syndrome induced by a HFD in mice. SSO reduces intestinal fatty acid absorption by reducing the inhibition of intestinal CD36 expression, followed by decreased TG and FFA levels, which attenuates HFD-induced fatty liver.

Anti-Her2 affibody-decorated arsenene nanosheets induce ferroptosis through depleting intracellular GSH to overcome cisplatin resistance.

Ferroptosis, a form of regulated cell death induced by excessive accumulation of reactive oxygen species and lipid peroxidation, has recently attracted extensive attention due to its ability to effectively suppress tumors and overcome drug resistance. Unlike previously reported metal nanomaterials that induce ferroptosis via the Fenton reaction, arsenene nanosheets can effectively deplete intracellular glutathione and then induce ferroptosis by inhibiting glutathione peroxidase 4. In this study, we designed target-modified arsenene nanosheets loaded with cisplatin (Her2-ANs@CDDP), which are capable of selective uptake by tumor cells. Her2-ANs@CDDP promotes both apoptosis and ferroptosis through a reciprocal cascade reaction between cisplatin and the carrier, respectively, and we demonstrate that it can significantly inhibit the activity of drug-resistant cells. Arsenene nanosheets kill drug-resistant tumor cells by inducing ferroptosis and restoring the sensitivity of drug-resistant cells to cisplatin. Cisplatin-loaded arsenene nanosheets can be prepared simply, and exert synergistic effects that overcome drug resistance. They show great potential for applications in the clinical treatment of chemotherapy-insensitive osteosarcoma, expanding the uses of arsenic in the treatment of solid tumors.

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems.

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines.

An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder.

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination R2 of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission.

Mechanically Induced Vasospasm-Evaluation of Spasmolytic Efficacy of 10 Pharmaceutical Agents Using Laser Speckle Contrast Imaging.

BACKGROUND AND OBJECTIVES: Vasospasm is a thorny problem often encountered in microvascular surgery that seriously threatens the survival of vascularized tissue transfers. This investigation is dedicated to establishing a model of vasospasm and to evaluating the antispasmodic efficacy of 10 pharmacologic agents. STUDY DESIGN/MATERIALS AND METHODS: Eighty Sprague-Dawley rats were used. After anesthesia and depilation, the femoral neurovascular bundle was exposed, and a pair of microsurgical forceps were used to trigger vasospasm of the femoral vessels by blunt dissection. Then, 10 pharmacological agents, namely, prostaglandin E1, sodium nitroprusside, magnesium sulfate, papaverine, normal saline, phentolamine, verapamil, 2% lidocaine hydrochloride, amrinone, and 12% lidocaine hydrochloride, were dripped to the femoral vessels, after which laser speckle contrast imaging was used to collect perfusion images, acquiring the perfusion and the inner caliber of the femoral vessels at multiple timepoints. Furthermore, blood perfusion and the time consumed to escape vasospasm and reach hyperperfusion in each group were calculated. The difference of spasmolytic efficacy among the agents was statistically analyzed by one-way analysis of variance. RESULTS: There was a significant difference in antispasmodic ability among the 10 agents (P < 0.001). 10% magnesium sulfate and 12% lidocaine were distinguished among the 10 agents in resolving the vasospasm. 10% magnesium sulfate demonstrated the best antispasmodic potency, which enabled the shortest time consumed for vessels to escape spasm and reach hyperperfusion. 12% lidocaine ranked second in efficacy, demonstrating a similar effect except that it could not propel the femoral vein to a state of hyperperfusion. For the remaining agents, the time consumed for the artery to escape spasm was all significantly shortened when compared with normal saline (P < 0.001). For the venous spasm, all agents except prostaglandin E1 could significantly shorten the time consumed for the vein to escape spasm (P < 0.001). CONCLUSIONS: In terms of resolving mechanically induced vasospasm, 10% magnesium sulfate is the best antispasmodic, followed by 12% lidocaine. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.

Matched beam-intensity processing for a deep vertical line array.

A vertical line array can be deployed in deep water below the critical depth, the depth where the sound speed equals the sound speed at the surface, to take advantage of the lower ambient noise level (compared with above the critical depth) for target detection. To differentiate a submerged source from a surface source, a Fourier transform based method [McCargar and Zurk, J. Acoust. Soc. Am. 133, EL320-325 (2013)] was proposed for a narrowband signal that exploits the depth-related harmonic (oscillation) feature of the beam power time series associated with the target arrival. In this paper, incoherent matched beam processing is used to estimate the target depth. Where the replica (calculated) beam intensity or amplitude time series best matches that of the data is used to estimate the source depth. This method is shown, based on simulated data, to provide a better depth resolution in general and better ability to estimate the depth of a very shallow source (say at 10 m) and can be used to complement the Fourier transform based method. It can be extended to process (random) broadband signals and to environments where the Lloyd's mirror theory is not valid.

Effectiveness and safety of the use of antifibrinolytic agents in total-knee arthroplasty: A meta-analysis.

BACKGROUND: Antifibrinolytic agents have been successfully used to reduce blood transfusion demand in patients undergoing elective knee arthroplasty. The purpose of this study was to investigate different antifibrinolytic agents for patients undergoing total-knee arthroplasty (TKA). METHODS: We searched the randomized controlled trials assessing the effect of antifibrinolytic agents on TKA in MEDLINE, PubMed, Embase, and the Cochrane Library. Participants are divided into antifibrinolytic agent group and control group under TKA. Double extraction technology is used and the quality of its methodology is evaluated before analysis. Outcomes analyzed included blood loss, number of blood transfusions, rates of blood transfusion, and deep vein thrombosis (DVT). RESULTS: A total of 28 randomized controlled trials involving 1899 patients were included in this study. Compared with the control group, the antifibrinolytic agents group exhibited significantly reduced the amounts of total blood loss (weighted mean difference [WMD] with 95% confidence interval [CI]: -272.19, -338.25 to -206.4), postoperative blood loss (WMD with 95% CI: -102.83, -157.64 to -46.02), average units of blood transfusion (risk ratio with 95% CI: 0.7, 0.12 to 0.24), and average blood transfusion volumes (WMD with 95% CI: -1.34, -1.47 to -1,21). Antifibrinolytic agents significantly reduced the rate of blood transfusions and did not increase the occurrence risk of intraoperative blood loss and DVT. Several limitations should also be acknowledged such as the heterogeneity among the studies. CONCLUSION: The application of antifibrinolytic agents can significantly reduce blood loss and blood transfusion requirements. Additionally, these agents did not increase the risk of DVT in patients undergoing TKAs.

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods.

Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

A novel ECG signal compression method using spindle convolutional auto-encoder.

BACKGROUND AND OBJECTIVES: With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various ECG compression methods. The important objective of compression method is to achieve a high-ratio and quality guaranteed compression. Consequently, to achieve this objective, this work presents a deep-learning-based spindle convolutional auto-encoder. The spindle structure achieves the high-ratio compression by reducing the dimension and guarantees the quality by increasing the dimension and end-to-end framework. METHODS: The spindle convolutional auto-encoder provides a high-ratio and quality-guaranteed ECG compression. It is composed of two parts as convolutional encoder and convolutional decoder with functional layers. By convolutional operation, the local information can be extracted. The spindle structure is increasing dimension in first few layers to obtain sufficient information to guarantee compression quality. And it is reducing dimension in last few layers to merge the information into a code for high-ratio compression. Meanwhile, the end-to-end framework is to obtain the optimum encoding for compression to improve the reconstruction performance. RESULTS: Compression performance is validated with records from MIT-BIH database. The proposed method achieves high compression ratio of 106.45 and low percentage root mean square difference of 8.00%. Compared with basic convolutional auto-encoder, the spindle structure improves the compression quality with lower losses. CONCLUSIONS: The spindle convolutional auto-encoder performs a high-ratio and quality-guaranteed compression. It can be considered as a promising compression technique used in tele-transmission and data storage.