Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC. METHODS: RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8). RESULTS: The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups. CONCLUSION: The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

A GAPDH serotonylation system couples CD8+ T cell glycolytic metabolism to antitumor immunity.

Apart from the canonical serotonin (5-hydroxytryptamine [5-HT])-receptor signaling transduction pattern, 5-HT-involved post-translational serotonylation has recently been noted. Here, we report a glyceraldehyde-3-phosphate dehydrogenase (GAPDH) serotonylation system that promotes the glycolytic metabolism and antitumor immune activity of CD8+ T cells. Tissue transglutaminase 2 (TGM2) transfers 5-HT to GAPDH glutamine 262 and catalyzes the serotonylation reaction. Serotonylation supports the cytoplasmic localization of GAPDH, which induces a glycolytic metabolic shift in CD8+ T cells and contributes to antitumor immunity. CD8+ T cells accumulate intracellular 5-HT for serotonylation through both synthesis by tryptophan hydroxylase 1 (TPH1) and uptake from the extracellular compartment via serotonin transporter (SERT). Monoamine oxidase A (MAOA) degrades 5-HT and acts as an intrinsic negative regulator of CD8+ T cells. The adoptive transfer of 5-HT-producing TPH1-overexpressing chimeric antigen receptor T (CAR-T) cells induced a robust antitumor response. Our findings expand the known range of neuroimmune interaction patterns by providing evidence of receptor-independent serotonylation post-translational modification.

Update on the outcome of M-protein screening program of multiple myeloma in China: A 7-year cohort study.

BACKGROUND: To improve the early detection rate of multiple myeloma (MM), the M-protein screening system has been performed in the hospital population at Zhongshan Hospital Fudan University since 2014, with electrophoretic-based monoclonal immunoglobulin (M-protein) screening integrated into the blood biochemistry panel. This study updated 7-year follow-up findings of MM patients diagnosed by screening-driven and symptom-driven approaches. METHODS: The retrospective study compared the characteristics and outcomes of patients diagnosed through two patterns by reviewing the plasma cell disease database from January 2014 to October 2021. The screening-driven group included patients diagnosed through the screening system during workups of unrelated medical conditions or routine checkups. In contrast, patients who visited or were referred to the hematological department due to myeloma-related end-organ damage were categorized into the symptom-driven group. RESULTS: There were 3,110,218 serum protein electrophoresis (SPEP) tests performed during 7 years, with 1.95% (60,609) patients yielding positive SPEP results. Of 911 confirmed MM cases (excluding concurrent amyloidosis), 366 were assigned to the screening-driven group, while 545 were to the symptom-driven group. Compared to the symptom-driven group, the screening group had more IgG subtypes, earlier International Stage System stages, fewer disease-related symptoms, lower ECOG scores, less extramedullary disease, a lower percentage of bone marrow plasma cells, and a lower level of lactate dehydrogenase. Frontline response results of two groups were similar. Patients detected through screening had a significantly improved median progression-free survival (PFS) than the symptom-driven group (62.2 vs. 24.9 months, p < 0.001, HR: 2.12, 95% CIs: 1.69-2.65), with median follow-ups of 32.6 and 27.4 months. Furthermore, the median overall survival (OS) was significantly longer in patients of the screening group (not reached vs. 62.3 months, p < 0.001, HR: 2.49, 95% CIs: 1.81-3.41). After being adjusted for well-acknowledged myeloma prognostic factors, the screening-driven diagnostic pattern remained an independent prognostic factor indicating improved PFS and OS in MM patients. CONCLUSION: Routine M-protein screening for MM in the hospital population results in an earlier diagnosis and better patient outcomes.

Clinical profiles in multiple myeloma patients with extreme survivals: a study from a National Medical Center in China.

OBJECTIVES: The clinical outcomes of multiple myeloma (MM) patients are highly variable in the real-world setting. Some MM patients may have clinical endings that do not abide by the book. We aim to describe features of MM patients with extreme survivals in real-world practice. METHODS: This retrospective study enrolled 941 patients consecutively visited a national medical center, China, between July 1995 and December 2021. Among patients, we identified two groups of MM patients with extreme survivals, 56 were in the long-term remission (LR) group with progression-free survival (PFS) ≥ 60 months, and 82 were in the rapid progression (RP) group with PFS ≤ 6 months. RESULTS: CRAB features, of which hypercalcemia, renal insufficiency, and anemia were more common in the RP group, except for bone disease, with a comparable incidence at diagnosis in both groups (88.8 vs 85.7%, P = 0.52). High-risk cytogenetics was detected in 45.7% of patients in the RP group. Of note, 14.3% of MM patients in the LR group harbored del (17p). According to the Revised International Staging System (R-ISS), 9% of patients belonged to stage I in the RP group, and 19% of patients in the LR group were found in stage III. There were 8 (15.7%) patients in the LR group only achieved partial response (PR) as the best response. Median time to best response (TBR) for LR and RP group patients was 4.6 and 1.4 months, respectively. CONCLUSIONS: The disparities in the survivals of MM patients indicated that some unexpected factors have influenced the outcomes in the real-world setting.

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification.

Text pre-processing is an important component of a Chinese text classification. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.

MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery.

Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation between the theoretical mechanism calculation results and the experimental data. Machine learning method provides a promising solution. However, the process is lack of interpretability, and the reliability and the generalization depend on the training data. In this paper, a mechanism correction model combined with graph neural network (GNN) model which is based on the fusion of graph embedding and descriptors vector is proposed as backbone network to proceed molecule properties prediction and new material discovery. The molecular structure is input to graph neural network and the abstracted features are fused with numerical features together for training. The experiment data and computing data are designed as label constructor, and then the theoretical computation (mechanism driven model) is fused with the output of GNN (data-driven model) to form a fused model to modulate the output for the molecular property prediction. Experiments for public data set are executed and the results show that Mechanism-Data-Driven Graph Neural Network (MD-GNN) can effectively make the predicted results more accurate. Nineteen molecules by different construction are designed for potential drug discovery, the prediction from the proposed MD-GNN model shows that there are 9 candidates are discovered.

Real world outcomes of lenalidomide or bortezomib maintenance in patients with multiple myeloma not undergoing stem cell transplantation.

Maintenance is one form of long-term therapies in multiple myeloma (MM). Lenalidomide and bortezomib are two commonly used options. The role of maintenance in patients not undergoing transplant remains unclear. A total of 248 newly diagnosed MM patients who received over 180 days of any standard-of-care induction therapy and did not receive autologous stem cell transplantation were included. Patients either receive lenalidomide, bortezomib or no maintenance. Patterns of usage, survival benefit, discontinuation status were analyzed. 93, 99 and 56 patients received no, lenalidomide (Len) and bortezomib (Bor) maintenance respectively. Patients receiving Bor had a higher incidence of traditional high-risk cytogenetics (14.0% (No) vs 14.1% (Len) vs 41.1% (Bor), P < 0.001). Len maintenance conferred a superior progression-free survival (PFS) and overall survival (OS) compared to no maintenance (median PFS, 60.1 vs 26.9 months, P = 0.003; median OS, NR vs 56.7 months, P = 0.046), with a near independent impact on PFS (adjusted HR 0.580, P = 0.058). The PFS and OS benefit of Len maintenance was seen in subgroups of ISS stage I/II, traditional standard-risk cytogenetics, and pre-maintenance < CR. Bor maintenance did not confer PFS or OS benefit for the entire cohort, but improved OS in patients with pre-maintenance < CR. Discontinuation due to toxicity was recorded in 11.1% and 8.9% of patients receiving Len or Bor maintenance respectively. Our study supports lenalidomide maintenance as the standard-of-care in MM patients not undergoing transplant. Further studies are warranted for bortezomib maintenance in the non-transplant setting, and better maintenance strategy is needed for patients with adverse prognostic factors.

The evolving diagnosis and treatment paradigms of multiple myeloma in China: 15 years' experience of 1256 patients in a national medical center.

BACKGROUND: Significant advances in multiple myeloma (MM) over the past 15 years led to exciting changes in the management of MM patients in China, which in turn brought about the early diagnoses, precise risk stratifications, and improved prognoses. METHODS: We summarized the dynamic changes in the management of newly diagnosed (ND) MM in a national medical center, crossing the old and novel drug era. Demographics, clinical characteristics, first-line treatment, response rate, and survival were retrospectively collected among NDMMs diagnosed in Zhongshan Hospital Fudan University from January 2007 to October 2021. RESULTS: Of the 1256 individuals, median age was 64 (range 31-89) with 45.1% patients >65 years. About 63.5% were male, 43.1% were at ISS stage III and 9.9% had light-chain amyloidosis. Patients with abnormal ratio of free light chain (80.4%), extramedullary disease (EMD, 22.0%), and high-risk cytogenetic abnormalities (HRCA, 26.8%) were detected by novel detection techniques. The best confirmed ORR was 86.5%, including 39.4% with CR. Short- and long-term PFS and OS rates persistently increased each year along with increasing novel drug applications. Median PFS and OS were 30.9 and 64.7 months. Advanced ISS stage, HRCA, light-chain amyloidosis and EMD independently predicted an inferior PFS. First-line ASCT indicated a superior PFS. Advanced ISS stage, elevated serum LDH, HRCA, light-chain amyloidosis, and receiving PI/IMiD-based regimen versus PI+IMiD-based regimen independently indicated a poorer OS. CONCLUSIONS: In brief, we illustrated a dynamic landscape of MM patients in a national medical center. Chinese MM patients evidently benefited from newly introduced techniques and drugs in this field.

The prognostic role of 1q21 gain/amplification in newly diagnosed multiple myeloma: The faster, the worse.

BACKGROUND: The prognostic value of additional copies of chromosome 1q (1q gain/amplification [amp]) in multiple myeloma (MM) remains controversial. In the meantime, the kinetics of the response to MM therapy has long been an area of debate. Few studies have pointed out the relationship of response kinetics with cytogenetic abnormalities (CAs) in MM. METHODS: The authors retrospectively analyzed the data of 1068 real-world newly diagnosed MM patients from a Chinese national medical center. RESULTS: Overall, 405 (51.9%) patients had 1q gain/amp, with aggressive clinical characteristics and significant inferior survival. The variation in copy number (CN) of 1q (CN = 3 or CN >3) had no significant impact on the survival of MM patients with 1q abnormalities. No difference was found in the outcome of 1q gain/amp patients treated with doublet or triplet regimens. Upfront autologous stem cell transplantation could eliminate the adverse prognostic effect of 1q gain but not 1q amp. The duration from diagnosis to the first time achieving very good partial response (VGPR) or better was significantly shorter in patients with 1q gain/amp (77 days vs. 100 days, p = .001). Finally, multifactor regression analysis was performed to construct a new risk stratification model in MM patients with 1q gain/amp, which was validated in the Multiple Myeloma Research Foundation CoMMpass study cohort and worked better than the Revised International Staging System and Second Revision of the International Staging System (Harrell's concordance index: 0.631 vs. 0.598 and 0.537). CONCLUSIONS: In the setting of novel therapy, 1q gain/amp still acts as an independent adverse prognostic factor. Patients with 1q gain/amp achieved VGPR rapidly but had inferior survival.

Inhibiting Interleukin-6/Signal Transducers and Activators of Transduction-3/Hypoxia-Inducible Factor-1α Signaling Pathway Suppressed the Growth of Infantile Hemangioma.

OBJECTIVE: This study aims to evaluate the expression of interleukin 6 (IL-6) in patients with infantile hemangioma (IH) and investigate the role of the IL-6/signal transducers and activators of transduction-3 (STAT3)/hypoxia-inducible factor-1α (HIF-1α) pathways in the progression of IH. METHODS: Serum samples were obtained from the patients with IH and normal infants to measure IL-6 expression. Hemangioma-derived stem cells (HemSCs) were transfected with small interfering RNA (siRNA) targeting IL-6, HIF-1α, or STAT3. Then, cell viability and wound healing assays were conducted. After that, the HemSC tumor mouse model was established. The in vivo anticancer effect of the IL-6 inhibitor was investigated. RESULTS: The patients with IH had much higher IL-6 levels compared with the healthy controls (p = 0.005). HemSCs transfected with IL-6 siRNA had significantly lower viability and migration rates than normal HemSCs. HemSCs transfected with STAT3 siRNA or HIF-1α siRNA had similar tendencies. On tumor-bearing mice, the IL-6 inhibitor treatment significantly delayed tumor growth. Compared with the control group, caspase-3 was significantly increased in the IL-6 inhibitor group (p < 0.05), whereas Ki-67 was decreased in the IL-6 inhibitor group (p < 0.05). In the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, the IL-6 inhibitor group had much higher apoptosis rates than the controls (p < 0.05). CONCLUSION: Our findings indicate that inhibiting the IL-6/STAT3/HIF-1α signaling pathways could suppress IH growth.

MiRNA-494 specifically inhibits SIRT3-mediated microglia activation in sepsis-associated encephalopathy.

Background: Sepsis-associated encephalopathy (SAE) is characterized by the activation of inflammatory cascades, in which microglia play a key role. The activation of Recombinant Sirtuin 3 (SIRT3) was shown to significantly reduce the susceptibility of microglia to inflammatory stress. The purpose of this study is to determine whether miRNA-494 can regulate the activation and oxidative stress of SAE microglia through SIRT3. Methods: An SAE rat model was established, and the expression of Ionized calcium bindingadaptor molecule-1 (Iba-1) in rat brain tissue was detected by immunohistochemistry. Enzyme-linked immuno sorbent assay was performed to detect the expression of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) in brain tissue. Real time quantitative PCR was performed to detect the relative expression of SIRT3 and related miRNAs, while Western blot was used to detect the protein expression of SIRT3. Rat microglia cells were cultured in vitro. After miRNA-494 was transfected, the expression of TNF-α and IL-6 was detected. Western blot was used to detect the protein expression of SIRT3 and Iba-1 in microglia. Results: The results showed that the expression of Iba-1 in the brain tissue of the SAE model group increased, and the expression of inflammatory factors TNF-α and IL-6 increased significantly (P<0.01). The expression of SIRT3 protein and mRNA in the brain tissue of the SAE model group also significantly increased (P<0.05). The relative expression of miRNA-494 in the SAE model group was significantly lower than that in the control group (P<0.01). After miRNA-494 was transfected into microglia, cells were treated with lipopolysaccharide. In the miRNA transfection group, the expression levels of TNF-α and IL-6 were significantly lower than those in the negative control (NC) group (P<0.01), and the protein expression levels of Iba-1 and SIRT3 were also significantly lower than those in the NC (P<0.01). Conclusions: MiRNA-494 may further regulate the activation of microglia in SAE by regulating mitochondrial function, providing basic research data for the development of new SAE treatment methods.

Spatiotemporal and kinematic characteristics augmentation using Dual-GAN for ankle instability detection.

Obtaining massive amounts of training data is often crucial for computer-assisted diagnosis using deep learning. Unfortunately, patient data is often small due to varied constraints. We develop a new approach to extract significant features from a small clinical gait analysis dataset to improve computer-assisted diagnosis of Chronic Ankle Instability (CAI) patients. In this paper, we present an approach for augmenting spatiotemporal and kinematic characteristics using the Dual Generative Adversarial Networks (Dual-GAN) to train a series of modified Long Short-Term Memory (LSTM) detection models making the training process more data-efficient. Namely, we use LSTM-, LSTM-Fully Convolutional Networks (FCN)-, and Convolutional LSTM-based detection models to identify the patients with CAI. The Dual-GAN enables the synthesized data to approximate the real data distribution visualized by the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Then we trained the proposed detection models using real data collected from a controlled laboratory study and mixed data from real and synthesized gait features. The detection models were tested in real data to validate the positive role in data augmentation as well as to demonstrate the capability and effectiveness of the modified LSTM algorithm for CAI detection using spatiotemporal and kinematic characteristics in walking. Dual-GAN generated efficient spatiotemporal and kinematic characteristics to augment the training set promoting the performance of CAI detection and the modified LSTM algorithm yielded an enhanced classification outcome to identify those CAI patients from a group of control subjects based on gait analysis data than any previous reports.

Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification.

At present, short text classification is a hot topic in the area of natural language processing. Due to the sparseness and irregularity of short text, the task of short text classification still faces great challenges. In this paper, we propose a new classification model from the aspects of short text representation, global feature extraction and local feature extraction. We use convolutional networks to extract shallow features from short text vectorization, and introduce a multi-level semantic extraction framework. It uses BiLSTM as the encoding layer while the attention mechanism and normalization are used as the interaction layer. Finally, we concatenate the convolution feature vector and semantic results of the semantic framework. After several rounds of feature integration, the framework improves the quality of the feature representation. Combined with the capsule network, we obtain high-level local information by dynamic routing and then squash them. In addition, we explore the optimal depth of semantic feature extraction for short text based on a multi-level semantic framework. We utilized four benchmark datasets to demonstrate that our model provides comparable results. The experimental results show that the accuracy of SUBJ, TREC, MR and ProcCons are 93.8%, 91.94%, 82.81% and 98.43%, respectively, which verifies that our model has greatly improves classification accuracy and model robustness.

An Improved Multitask Learning Model with Matching Network and Its Application in Traditional Chinese Medicine Syndrome Recommendation.

Multitask learning (MTL) is an open and challenging problem in various real-world applications, such as recommendation systems, natural language processing, and computer vision. The typical way of conducting multitask learning is establishing some global parameter sharing mechanism among all tasks or assigning each task an individual set of parameters with cross-connections between tasks. However, for most existing approaches, the raw features are abstracted step by step, semantic information is mined from input space, and matching relation features are not introduced into the model. To solve the above problems, we propose a novel MMOE-match network to model the matches between medical cases and syndrome elements and introduce the recommendation algorithm into traditional Chinese medicine (TCM) study. Accurate medical record recommendation is significant for intelligent medical treatment. Ranking algorithms can be introduced in multi-TCM scenarios, such as syndrome element recommendation, symptom recommendation, and drug prescription recommendation. The recommendation system includes two main stages: recalling and ranking. The core of recalling and ranking is a two-tower matching network and multitask learning. MMOE-match combines the advantages of recalling and ranking model to design a new network. Furtherly, we try to take the matching network output as the input of multitask learning and compare the matching features designed by the manual. The data show that our model can bring significant positive benefits.

Self-Assembly of Au-Ag Alloy Hollow Nanochains for Enhanced Plasmon-Driven Surface-Enhanced Raman Scattering.

In this paper, Au-Ag alloy hollow nanochains (HNCs) were successfully prepared by a template-free self-assembly method achieved by partial substitution of ligands. The obtained Au-Ag alloy HNCs exhibit stronger enhancement as surface-enhanced Raman scattering (SERS) substrates than Au-Ag alloy hollow nanoparticles (HNPs) and Au nanochains substrates with an intensity ratio of about 1.3:1:1. Finite difference time domain (FDTD) simulations show that the SERS enhancement of Au-Ag alloy HNCs substrates is produced by a synergistic effect between the plasmon hybridization effect associated with the unique alloy hollow structure and the strong "hot spot" in the interstitial regions of the nanochains.

Novel carrier-mediated membrane-assisted three-phase liquid-liquid extraction coupled with liquid chromatography-mass spectrometry for the determination of eight biogenic amines in foods.

Analysis of biogenic amines (BAs) is of great importance due to their toxicity and usage as indicators of food freshness. In this study, membrane-assisted three-phase liquid-liquid extraction (MA-3pLLE) was proposed to integrate extraction and back-extraction into a single step using a specially designed U-shaped device. Parameters affecting the performance of the extraction method were optimized. Coupled with liquid chromatography-mass spectrometry, the method was successfully applied for the simultaneous determination of eight BAs without derivatization in apple juice, orange juice, red wine, soy sauce and milk granules, with satisfactory recoveries and RSDs. The calibration curve of the method was linear in the range of 1.00~100.00 ng/mL, with a correlation coefficient (r2) ≥ 0.9908. The limits of detection were in the range of 0.03~1.00 ng/mL. MA-3pLLE is efficient, reproducible, and green and has great potential for application in the one-step extraction of analytes from complex matrices.

miR-125a-5p in astrocytes attenuates peripheral neuropathy in type 2 diabetic mice through targeting TRAF6.

INTRODUCTION: Elimination or blocking of astrocytes could ameliorate neuropathic pain in animal models. MiR-125a-5p, expressed in astrocyte derived extracellular vesicles, could mediate astrocyte function to regulate neuron communication. However, the role of miR-125a-5p in DPN (diabetic peripheral neuropathy) remains elusive. MATERIALS AND METHODS: Type 2 diabetic mouse (db/db) was used as DPN model, which was confirmed by detection of body weight, blood glucose, mechanical allodynia, thermal hyperalgesia, glial fibrillary acidic protein (GFAP) and monocyte chemoattractant protein-1 (MCP-1). Astrocyte was isolated from db/db mouse and then subjected to high glucose treatment. The expression of miR-125a-5p in db/db mice and high glucose-induced astrocytes was examined by qRT-PCR analysis. Downstream target of miR-125a-5p was clarified by luciferase reporter assay. Tail vein injection of miR-125a-5p mimic into db/db mice was then performed to investigate role of miR-125a-5p on DPN. RESULTS: Type 2 diabetic mice showed higher body weight and blood glucose than normal db/m mice. Thermal hyperalgesia and mechanical allodynia were decreased in db/db mouse compared with db/m mouse, while GFAP and MCP-1 were increased in db/db mouse. High glucose treatment enhanced the protein expression of GFAP and MCP-1 in astrocytes. Sciatic nerve tissues in db/db mice and high glucose-induced astrocytes exhibited a decrease in miR-125a-5p. Systemic administration of miR-125a-5p mimic increased mechanical allodynia and thermal hyperalgesia, whereas it decreased GFAP and MCP-1. TRAF6 (tumor necrosis factor receptor associated factor 6) was validated as target of miR-125a-5p. CONCLUSION: MiR-125a-5p in astrocytes attenuated DPN in db/db mice by up-regulation of TRAF6, which indicated the potential therapeutic effect.

Ankle-Injury Patients Perform More Microadjustments during Walking: Evidence from Velocity Profiles in Gait Analysis.

INTRODUCTION: We evaluated the velocity profiles of patients with lateral collateral ligament (LCL) injuries of the ankle with a goal of understanding the control mechanism involved in walking. METHODS: We tracked motions of patients' legs and feet in 30 gait cycles recorded from patients with LCL injuries of the ankle and compared them to 50 gait cycles taken from normal control subjects. Seventeen markers were placed on the foot following the Heidelberg foot measurement model. Velocity profiles and microadjustments of the knee, ankle, and foot were calculated during different gait phases and compared between the patient and control groups. RESULTS: Patients had a smaller first rocker percentage and larger second rocker percentage in the gait cycle compared to controls. Patients also displayed shorter stride length and slower strides and performed more microadjustments in the second rocker phase than in other rocker/swing phases. Patients' mean velocities of the knee, ankle, and foot in the second rocker phase were also significantly higher than that in control subjects. Discussion. Evidence from velocity profiles suggested that patients with ligament injury necessitated more musculoskeletal microadjustments to maintain body balance, but these may also be due to secondary injury. Precise descriptions of the spatiotemporal gait characteristics are therefore crucial for our understanding of movement control during locomotion.

Circular RNA mmu_circ_0001598 Contributes to IL-1ß-Induced Osteoarthritis Progression by Regulating miR-127-3p.

Osteoarthritis (OA), a chronic disease characterized by articular cartilage degeneration, is a leading cause of disability and pain worldwide. Accumulating evidence indicates that circular RNAs (circRNAs) play a critical role in various diseases, but the function of circRNAs in OA remains largely unknown. In this study, we found that circ_0001598 was significantly upregulated in chondrocytes treated with IL-1ß and in cartilage tissue from mice with severed anterior cruciate ligament surgery (ACLT) induced OA models. Interference with circ_0001598 in vitro restored IL-1ß-induced chondrocyte proliferation and apoptosis. Silencing circ_0001598 significantly alleviated ACLT-induced OA in mice. Mechanistically, knockdown of circ_0001598 affected chondrocyte proliferation, apoptosis, and matrix degradation by regulating miR-127-3p. Taken together, our results demonstrate the fundamental role of circ_0001598 and provide new ideas for the prevention and treatment of osteoarthritis.

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation.

Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is often the case that user interests may change from their past routines. However, multi-perspective attention has broad horizon, which covers different characteristics of human reasoning, emotions, perception, attention, and memory. In this paper, we attempt to introduce the multi-perspective attention and sequence behaviour into multitask learning. Our proposed method offers better understanding of user interest and decision. To achieve more flexible parameter sharing and maintaining the special feature advantage of each task, we improve the attention mechanism at the view of expert interactive. To the best of our knowledge, we firstly propose the implicit interaction mode, the explicit hard interaction mode, the explicit soft interaction mode, and the data fusion mode in multitask learning. We do experiments on public data and lab medical data. The results show that our model consistently achieves remarkable improvements to the state-of-the-art method.