Comparative Genomics Reveal Phylogenetic Relationship and Chromosomal Evolutionary Events of Eight Cervidae Species.

Cervidae represents a family that is not only rich in species diversity but also exhibits a wide range of karyotypes. The controversies regarding the phylogeny and classification of Cervidae still persist. The flourishing development of the genomic era has made it possible to address these issues at the genomic level. Here, the genomes of nine species were used to explore the phylogeny and chromosomal evolutionary events of Cervidae. By conducting whole-genome comparisons, we identified single-copy orthologous genes across the nine species and constructed a phylogenetic tree based on the single-copy orthologous genes sequences, providing new insights into the phylogeny of Cervidae, particularly the phylogenetic relationship among sika deer, red deer, wapiti and Tarim red deer. Gene family analysis revealed contractions in the olfactory receptor gene family and expansions in the histone gene family across eight Cervidae species. Furthermore, synteny analysis was used to explore the chromosomal evolutionary events of Cervidae species, revealing six chromosomal fissions during the evolutionary process from Bovidae to Cervidae. Notably, specific chromosomal fusion events were found in four species of Cervus, and a unique chromosomal fusion event was identified in Muntiacus reevesi. Our study further completed the phylogenetic relationship within the Cervidae and demonstrated the feasibility of inferring species phylogeny at the whole-genome level. Additionally, our findings on gene family evolution and the chromosomal evolutionary events in eight Cervidae species lay a foundation for comprehensive research of the evolution of Cervidae.

Multiobjective Evolutionary Learning for Multitask Quality Prediction Problems in Continuous Annealing Process.

In industrial production processes, the mechanical properties of materials will directly determine the stability and consistency of product quality. However, detecting the current mechanical property is time-consuming and labor-intensive, and the material quality cannot be controlled in time. To achieve high-quality steel materials, developing a novel intelligent manufacturing technology that can satisfy multitask predictions for material properties has become a new research trend. This article proposes a multiobjective evolutionary learning method based on a two-stage model with topological sparse autoencoder (TSAE) and ensemble learning. For the structure characteristics of a typical autoencoder (AE), a topology-related constraint is incorporated into the loss function of the AE, thus maintaining the global relationship among multistage input data to improve the data reconstruction quality. Then, a sparse representation of the data is added to the AE to achieve dimensionality reduction. Moreover, the extreme gradient boosting (XGBoost) method is applied to predict the mechanical properties of steel materials through collaboration learning mechanisms. To enhance the model accuracy, a multiobjective evolutionary algorithm (MOEA) with a knee solution strategy is used to optimize the network structure and hyperparameters of the two-stage model. Experiments are conducted using real steel production data from a continuous annealing process (CAP). The results verify that the proposed method obtains a higher prediction accuracy than other state-of-the-art methods and can guide practical production and new material design.

A Novel Dynamic Operation Optimization Method Based on Multiobjective Deep Reinforcement Learning for Steelmaking Process.

This article studies a dynamic operation optimization problem for a steelmaking process. The problem is defined to determine optimal operation parameters that bring smelting process indices close to their desired values. The operation optimization technologies have been applied successfully for endpoint steelmaking, but it is still challenging for the dynamic smelting process because of the high temperature and complex physical and chemical reactions. A framework of deep deterministic policy gradient is applied to solve the dynamic operation optimization problem in the steelmaking process. Then, an energy-informed restricted Boltzmann machine method with physical interpretability is developed to construct the actor and critic networks in reinforcement learning (RL) for dynamic decision-making operations. It can provide a posterior probability for each action to guide training in each state. Furthermore, in terms of the design of neural network (NN) architecture, a multiobjective evolutionary algorithm is used to optimize the model hyperparameters, and a knee solution strategy is designed to balance the model accuracy and complexity of neural networks. Experiments are conducted on real data from a steelmaking production process to verify the practicability of the developed model. The experimental results show the advantages and effectiveness of the proposed method compared with other methods. It can meet the requirements of the specified quality of molten steel.

An Off-Policy Reinforcement Learning-Based Adaptive Optimization Method for Dynamic Resource Allocation Problem.

In this article, an adaptive optimization method is proposed for the dynamic resource allocation problem (RAP) with multiple objectives in the manufacturing industry. In the proposed method, a novel reinforcement learning method () is designed to adaptively set the weights for multiple objectives, and then the optimization method is adopted to generate the noninferior solutions in each time period. To ensure 's performance in dynamic and complex resource allocation environments, we develop a state-encoding network with a proposed information entropy attention mechanism to encode the state. Then, we introduce a new reward function to escape from the local optima of the policy and further present a conditional entropy policy to enhance the policy network. In addition, we demonstrate the feasibility of improving the quality of actions and present a boundary method for high-quality actions. We also introduce an optimization model to automatically adjust the temperature parameter in . Furthermore, we compare and analyze our approach with other state-of-the-art reinforcement learning methods. The experiments illustrate that outperforms state-of-the-art reinforcement learning methods. Moreover, can be generalized to solve optimization problems with two to five objectives, problems with linear, quadratic, cubic, logarithmic, or inverse objectives, and problems with diverse structures.

Improvement of Reinforcement Learning With Supermodularity.

Reinforcement learning (RL) is a promising approach to tackling learning and decision-making problems in a dynamic environment. Most studies on RL focus on the improvement of state evaluation or action evaluation. In this article, we investigate how to reduce action space by using supermodularity. We consider the decision tasks in the multistage decision process as a collection of parameterized optimization problems, where state parameters dynamically vary along with the time or stage. The optimal solutions of these parameterized optimization problems correspond to the optimal actions in RL. For a given Markov decision process (MDP) with supermodularity, the monotonicity of the optimal action set and the optimal selection with respect to state parameters can be obtained by using the monotone comparative statics. Accordingly, we propose a monotonicity cut to remove unpromising actions from the action space. Taking bin packing problem (BPP) as an example, we show how the supermodularity and monotonicity cut work in RL. Finally, we evaluate the monotonicity cut on the benchmark datasets reported in the literature and compare the proposed RL with some popular baseline algorithms. The results show that the monotonicity cut can effectively improve the performance of RL.

Role of Mitophagy in Regulating Intestinal Oxidative Damage.

The mitochondrion is also a major site for maintaining redox homeostasis between reactive oxygen species (ROS) generation and scavenging. The quantity, quality, and functional integrity of mitochondria are crucial for regulating intracellular homeostasis and maintaining the normal physiological function of cells. The role of oxidative stress in human disease is well established, particularly in inflammatory bowel disease and gastrointestinal mucosal diseases. Oxidative stress could result from an imbalance between ROS and the antioxidative system. Mitochondria are both the main sites of production and the main target of ROS. It is a vicious cycle in which initial ROS-induced mitochondrial damage enhanced ROS production that, in turn, leads to further mitochondrial damage and eventually massive intestinal cell death. Oxidative damage can be significantly mitigated by mitophagy, which clears damaged mitochondria. In this review, we aimed to review the molecular mechanisms involved in the regulation of mitophagy and oxidative stress and their relationship in some intestinal diseases. We believe the reviews can provide new ideas and a scientific basis for researching antioxidants and preventing diseases related to oxidative damage.

LncRNA LINC01833 is a Prognostic Biomarker and Correlates with Immune Infiltrates in Patients with Lung Adenocarcinoma by Integrated Bioinformatics Analysis.

Due to the absence of accurate tools for early detection and successful treatment, lung adenocarcinoma (LUAD) is one of the most aggressive tumors with high morbidity and mortality globally. It is absolutely necessary to investigate the process behind its development and search for new biomarkers that could aid in the early detection of LUAD. There is a correlation between the immune microenvironment of the tumor and the prognosis of lung cancer as well as the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been identified as potential prognostic biomarkers linked to immunological activities. In this study, we identified 1 downregulated lncRNA and 76 upregulated lncRNAs in LUAD samples from TCGA datasets. Among the 77 dysregulated lncRNAs, our attention focused on lncRNA LINC01833 (LINC01833). When compared with nontumor specimens, the level of expression of LINC01833 was shown to be significantly elevated in LUAD samples. In addition, the data of the ROC study revealed that LUAD patients with high LINC01833 expression had an AUC value of 0.840 (95% confidence interval: 0.804 to 0.876). There was a correlation between high LINC01833 expression and an advanced clinical stage. Patients who had a high expression of LINC01833 were shown to have a lower overall survival rate (p < 0.001) and a lower disease-specific survival rate (p = 0.004) in comparison to patients who were in the low LINC01833 group, according to the data on survival. In addition, the results of the multivariate analysis revealed that high LINC01833 expression was an independent predictor of poor survival in LUAD. Moreover, the immune analysis revealed that we found that the expression of LINC01833 was positively associated with Th2 cells, aDC, and Tgd, while negatively associated with Mast cells, Tcm, Eosinophils, iDC, DC, Tem, Th17 cells, and pDC. Overall, our data point to the possibility that the unique lncRNA LINC01833 might be employed as a diagnostic and prognostic marker, and as a result, it has a significant impact on clinical practice.

The First High-quality Reference Genome of Sika Deer Provides Insights into High-tannin Adaptation.

Sika deer are known to prefer oak leaves, which are rich in tannins and toxic to most mammals; however, the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear. In identifying the mechanism responsible for the tolerance of a highly toxic diet, we have made a major advancement by explaining the genome of sika deer. We generated the first high-quality, chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments. Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food, especially the expansion of the UGT family 2 subfamily B of UGT genes. The first chromosome-level assembly and genetic characterization of the tolerance to a highly toxic diet suggest that the sika deer genome may serve as an essential resource for understanding evolutionary events and tannin adaptation. Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.

Current Situation and Utilization of Velvet Deer Germplasm Resources in China.

Velvet deer are not only a representative special economic animal but also an important part of livestock. With the increasing awareness of international competition for germplasm resources in China, more and more attention has been paid to the protection and utilization of germplasm resources. However, there is poor understanding about velvet deer resources. Therefore, we are providing a comprehensive introduction of Chinese velvet deer germplasm resources from the aspects of ecological distribution, domestication and breeding.

Dendrobium nobile-derived polysaccharides ameliorate spermatogenic disorders in mice with streptozotocin-induced diabetes through regulation of the glycolytic pathway.

Dysfunction of spermatogenesis is a major complication of diabetes mellitus (DM). This study characterized the protective effects of Dendrobium nobile-derived polysaccharides (DNP) against spermatogenetic dysfunction in mice with streptozotocin (STZ)-induced diabetes. The diabetic mice had lower body and testicular mass, and fewer spermatozoa with a higher incidence of malformation. The testicular histology showed disordered narrow seminiferous tubules covering a smaller area, and fewer spermatogenic cells. Moreover, the qRT-PCR analysis indicated that DM was associated with high expression of the pro-apoptotic factor Bax and low expression of the anti-apoptotic factor Bcl-2 in the testes. The qRT-PCR and immunohistochemical analysis clarified that DM was also associated with low testicular expression of the Sertoli cell (SC) markers GATA-4, WT1, and vimentin, and genes encoding the glycolytic rate-limiting enzymes LDHA, PKM2, and HK2. DNP treatment increased the body and testicular masses, sperm count, and number of spermatogenic cells of the mice, and reduced the proportion of abnormal sperm. DNP also reduced the expression of Bax, and increased that of Bcl-2, GATA-4, WT1, vimentin, LDHA, PKM2, and HK2, in the testes of the diabetic mice. Thus, DNP protects against spermatogenic dysfunction in diabetic mice by inhibiting apoptosis and activating the glycolytic pathway in their testes.

Pectin supplementation ameliorates intestinal epithelial barrier function damage by modulating intestinal microbiota in lipopolysaccharide-challenged piglets.

During weaning, infants and young animals are susceptible to severe enteric infections, thus inducing intestinal microbiota dysbiosis, intestinal inflammation, and impaired intestinal barrier function. Pectin (PEC), a prebiotic polysaccharide, enhances intestinal health with the potential for a therapeutic effect on intestinal diseases. One 21-d study was conducted to investigate the protective effect of pectin against intestinal injury induced by intraperitoneal injection of Escherichia coli lipopolysaccharide (LPS) in a piglet model. A total of 24 piglets (6.77±0.92 kg BW; Duroc × Landrace × Large White; barrows; 21 d of age) were randomly assigned into three groups: control group, LPS-challenged group, and PEC + LPS group. Piglets were administrated with LPS or saline on d14 and d21 of the experiment. All piglets were slaughtered and intestinal samples were collected after 3 h administration on d21. Pectin supplementation ameliorated the LPS-induced inflammation response and damage to the ileal morphology. Meanwhile, pectin also improved intestinal mucin barrier function, increased the mRNA expression of MUC2, and improved intestinal mucus glycosylation. LPS challenge reduced the diversity of intestinal microbiota and enriched the relative abundance of Helicobacter. Pectin restored alpha diversity and improved the structure of the gut microbiota by enriching anti-inflammatory bacteria and short-chain fatty acids (SCFAs)-producing bacteria, and increased the concentrations of acetate. In addition, Spearman rank correlation analysis also revealed the potential relationship between intestinal microbiota and intestinal morphology, intestinal inflammation, and intestinal glycosylation in piglets. Taken together, these results indicate that pectin enhances intestinal integrity and barrier function by altering intestinal microbiota composition and their metabolites, which subsequently alleviates intestinal injury and finally improves the growth performance of piglets.

A Memetic Algorithm Based on Probability Learning for Solving the Multidimensional Knapsack Problem.

The multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with many real-life applications. In this article, a memetic algorithm based on probability learning (MA/PL) is proposed to solve MKP. The main highlights of this article are two-fold: 1) problem-dependent heuristics for MKP and 2) a novel framework of MA/PL. For the problem-dependent heuristics, we first propose two kinds of logarithmic utility functions (LUFs) based on the special structure of MKP, in which the profit value and weight vector of each item are considered simultaneously. Then, LUFs are applied to effectively guide the repair operator for infeasible solutions and the local search operator. For the framework of MA/PL, we propose two problem-dependent probability distributions to extract the special knowledge of MKP, that is, the marginal probability distribution (MPD) of each item and the joint probability distribution (JPD) of two conjoint items. Next, learning rules for MPD and JPD, which borrow ideas from competitive learning and binary Markov chain, are proposed. Thereafter, we generate MA/PL's offspring by integrating MPD and JPD, such that the univariate probability information of each item as well as the dependency of conjoint items can be sufficiently used. Results of experiments on 179 benchmark instances and a real-life case study demonstrate the effectiveness and practical values of the proposed MKP.

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Nutritional diet plan for patients with dysphagia.

BACKGROUND AND OBJECTIVES: To present a new method of text-modified food management for patients with dysphagia that integrates the idea of food exchange. In addition to prioritizing nutrition in each recipe, the diet plan emphasizes straightforward preparation methods that balance nutrition and palatability. METHODS AND STUDY DESIGN: On the basis of the recommended intake in the Expert Consensus, the design of the texture-modified food incorporates the concept of equivalent food exchange. The plan consists of a staple food, a meat, a vegetable, a snack, and a fruit as the base units, and the volume and nutrient density of each unit is modified to meet the needs of patients with dysphagia. RESULTS: Five categories of standard portions were established, the standard portion of staple foods, milk should be used instead of water during preparation, and carbohydrate components (dextrin) should be added so that each portion provides approximately 200 Kcal of energy. The standard portion of meat, protein components (90% whey) should be added to provide approximately 14 g of protein and 150 Kcal of energy per portion. Two types of standard snacks are recommended, each serving provides 250 Kcal of energy. Vegetables and fruits provide 70 Kcal and 90 Kcal of energy. We compiled 11 recipes representative of the food exchange system and our recipe design priorities (texture modification, sufficient nutrition, color, fragrance and taste). CONCLUSIONS: The method is combined theory and practice and can be applied to clinical nutrition work to promote the nutritional intake of patients with dysphagia.

Efficacy and Safety of Emricasan in Liver Cirrhosis and/or Fibrosis.

This study aimed to perform a meta-analysis to determine the efficacy and safety of emricasan. Nine databases were searched for clinical trials investigating the efficacy of emricasan treatment in patients with liver cirrhosis or fibrosis. A manual search was conducted to identify the missing trials. The quality of the included studies was assessed using the revised Cochrane risk of bias tool. Efficacy of emricasan treatment was defined as a positive change in apoptosis-related parameters from baseline to the last follow-up visit. Overall, emricasan treatment is more effective in patients with liver cirrhosis or fibrosis than placebo (standardized mean difference [SMD] [95% confidence intervals (CI)]=0.28 [0.14; 0.41]). No significant change in model for end-stage liver disease (MELD) score between the emricasan and placebo groups was noted (SMD [95% CI]=0.18 [-0.01; 0.36]; p=0.058). A 50 mg dose of emricasan had the highest efficacy rate compared to placebo (SMD [95% CI]=0.28 [0.06; 0.50]; p=0.012), followed by the 5 mg dosing regimen (SMD [95% CI]=0.28 [0.06; 0.50]; p=0.012). Treatment with emricasan resulted in significant reductions in ALT (mean difference (MD) [95% CI]=-5.89 [-10.59; -1.20]; p=0.014) and caspase3/7 levels (MD [95%CI]=-1215.93 [-1238.53; -1193.33]; p<0.001), respectively. No significant increase in the rate of overall adverse events was noted (OR [95% CI]=1.52 [0.97; 2.37]; p=0.069). Treatment with emricasan is more effective in improving liver function and apoptosis parameters compared to placebo, with a well-tolerated safety profile. However, due to the poor quality of the analyzed studies, the small number of trials and patients, and the short follow-up periods, more robust trials are still warranted.

Long Non-coding RNA LINC00847 Induced by E2F1 Accelerates Non-small Cell Lung Cancer Progression Through Targeting miR-147a/IFITM1 Axis.

Background: Long non-coding RNAs (lncRNAs) can remarkably regulate human malignancies in terms of the development and the progression. Previously, lncRNA LINC00847 (LINC00847) has been reported to present dysregulation in several tumors. However, the expression and function of LINC00847 in non-small cell lung cancer (NSCLC) have not been investigated. Methods: RT-qPCR was performed to determine the expressions of LINC00847 in collected tissue samples and cell lines. The clinical significance of LINC00847 was statistically analyzed. CCK-8 test, cell scratch test and trans-well test were used to evaluate the proliferation, invasion and migration abilities of NSCLC cells, respectively. The xenograft tumor model was constructed to confirm the effects of LINC00847 knockdown on NSCLC in vivo. Further, luciferase reporter assays and Western blot were performed to explore molecular mechanisms underlying the functions of LINC00847. Results: Increased expressions of LINC00847 were observed in NSCLC samples as well as cell lines. Additionally, E2F1 could be capable of directly binding to the LINC00847 promoter region, followed by promoting its expression. Clinically, LINC00847 high-expression could lead to poor prognosis of NSCLC patients. Functionally, LINC00847 knockdown noticeably repressed NSCLC cell growth and metastasis. Mechanically, miR-147a/IFITM1 axis was a downstream target of LINC00847, and silencing of miR-147a could rescue the anti-cancer effects of LINC00847 knockdown on NSCLC cell behaviors. Conclusion: Overall, up regulation of LINC00847 induced by E2F1 promoted the progression of NSCLC by modulating miR-147a/IFITM1 axis, representing a novel regulatory mechanism for NSCLC progression.

Replacing white rice bars with peanuts as snacks in the habitual diet improves metabolic syndrome risk among Chinese adults: a randomized controlled trial.

BACKGROUND: Observational studies have suggested that intake of nuts is associated with lower risk of cardiometabolic diseases, whereas refined grain consumption has been linked to higher risk. Little is known about whether substituting white rice, a refined grain, with nuts may confer benefit among adults at high risk of cardiometabolic diseases. OBJECTIVES: We aimed to evaluate isocaloric substitution of peanuts for white rice bars as snacks on changes in fasting glucose, lipid profile, body weight, as well as changes in metabolic syndrome (MetS) status among participants with MetS or at risk of MetS. METHODS: This parallel-arm randomized controlled trial included 224 participants either with MetS (according to the International Diabetes Federation criteria for Chinese adults, n = 163) or at risk of MetS (central obesity plus 1 additional MetS risk factor, n = 61). Participants were randomly assigned to either the peanut arm (56 g/d as snacks, n = 113) or the control arm (isocaloric white rice bars, n = 111) for 12 wk. RESULTS: A total of 209 participants (93.3%) completed the 12-wk intervention with a compliance rate > 85% among all participants. No between-group differences were found for improvements in fasting glucose, HDL cholesterol, waist circumference, and body weight. Participants in the peanut group had a significantly higher MetS reversion rate (no longer meeting MetS criteria after the 12-wk trial) than those in the control group (RR: 2.33; 95% CI: 1.10, 4.89; P = 0.026). CONCLUSIONS: Including peanuts as a snack in the habitual diet in place of a refined-grain snack did not significantly change glycemic or lipid parameters, but improved overall MetS risk without promoting weight gain among Chinese adults at high risk of cardiometabolic diseases. Further larger-scale trials are needed to confirm these findings and elucidate underlying biological mechanisms.This trial was registered at clinicaltrials.gov as NCT03194152.

Efficacy and Safety of Emricasan in Liver Cirrhosis and/or Fibrosis

This study aimed to perform a meta-analysis to determine the efficacy and safety of emricasan. Nine databases were searched for clinical trials investigating the efficacy of emricasan treatment in patients with liver cirrhosis or fibrosis. A manual search was conducted to identify the missing trials. The quality of the included studies was assessed using the revised Cochrane risk of bias tool. Efficacy of emricasan treatment was defined as a positive change in apoptosis-related parameters from baseline to the last follow-up visit. Overall, emricasan treatment is more effective in patients with liver cirrhosis or fibrosis than placebo (standardized mean difference [SMD] [95% confidence intervals (CI)]=0.28 [0.14; 0.41]). No significant change in model for end-stage liver disease (MELD) score between the emricasan and placebo groups was noted (SMD [95% CI]=0.18 [-0.01; 0.36]; p=0.058). A 50 mg dose of emricasan had the highest efficacy rate compared to placebo (SMD [95% CI]=0.28 [0.06; 0.50]; p=0.012), followed by the 5 mg dosing regimen (SMD [95% CI]=0.28 [0.06; 0.50]; p=0.012). Treatment with emricasan resulted in significant reductions in ALT (mean difference (MD) [95% CI]=-5.89 [-10.59; -1.20]; p=0.014) and caspase3/7 levels (MD [95%CI]=-1215.93 [-1238.53; -1193.33]; p<0.001), respectively. No significant increase in the rate of overall adverse events was noted (OR [95% CI]=1.52 [0.97; 2.37]; p=0.069). Treatment with emricasan is more effective in improving liver function and apoptosis parameters compared to placebo, with a well-tolerated safety profile. However, due to the poor quality of the analyzed studies, the small number of trials and patients, and the short follow-up periods, more robust trials are still warranted.

Acupuncture for poststroke hemiplegia focusing on cerebral bilateral connections: study protocol for a randomised controlled neuroimaging trial.

INTRODUCTION: Acupuncture is safe and effective for improving the motor function of poststroke hemiplegic patients, but there still exists a certain gap between clinical practice and understanding its neural mechanisms. The cerebral functional reconstruction after unilateral motor pathway injury exhibits a bilateral tendency, however current studies seldom pay attention to it. Hence, based on cerebral bilateral connections, the underlying mechanism of acupuncture in stroke rehabilitation remains an area for further research. The results of this study will increase our understanding of acupuncture-induced motor recovery in patients who had suffered a stroke and demonstrate the differences in brain response and clinical assessments. METHODS AND ANALYSIS: This is a single-centre, randomised controlled, paralleled neuroimaging trial, with patients and outcome assessors blinded. Thirty patients who had a stroke with motor dysfunction meeting the inclusion criteria will be randomly assigned (2:1) to receive either 10 sessions true or sham acupoints treatments (five sessions per week for 2 weeks). All the participants will receive conventional standard medical care and rehabilitation. Motor function assessments and neuroimaging scanning will be conducted before and after the entire acupuncture treatment. The clinical and neuroimaging data will be analysed, respectively. The voxel-mirrored homotopic connectivity will be the primary outcome and the primary effect indicator. The secondary outcomes comprise clinical evaluations and neuroimaging assessments, which include Fugl-Meyer Assessment, the National Institutes of Health Stroke Scale, fractional anisotropy and gray matter volume. The Needle Sensation Assessment Scale is an additional outcome. The correlation analysis will be explored between the neuroimaging indicators, clinical motor assessments and needle sensation. ETHICS AND DISSEMINATION: The protocol has been approved by the ethics committee of Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine (DZMEC-KY-2018-04). The results of the neuroimaging trial will be disseminated through peer-reviewed publications and conferences. TRIAL REGISTRATION NUMBER: Chinese Clinical Trials Registry (ChiCTR 1800016263).

Acupuncture Enhances Communication between Cortices with Damaged White Matters in Poststroke Motor Impairment.

Stroke is a leading cause of motor disability. Acupuncture is an effective therapeutic strategy for poststroke motor impairment. However, its mechanism is still elusive. Twenty-two stroke patients having a right-hemispheric subcortical infarct and 22 matched healthy controls were recruited to undergo diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) scanning. The resting-state fMRI was implemented before and after needling at GB34 (Yanglingquan). The stroke patients presented a substantially reduced fractional anisotropy value in the right superior longitudinal fasciculus (SLF), corticospinal tract, and corpus callosum. The structural integrity of the frontoparietal part of the SLF (SLF-FP) correlated with the motor scores of lower limbs in stroke patients. This corticocortical association bundle originated from the premotor cortex (PM) and the adjacent supplementary motor area (SMA), known as secondary motor areas, and terminated in the supramarginal gyrus (SMG). After acupuncture intervention, the corresponding functional connectivity between the PM/SMA and SMG was enhanced in stroke patients compared with healthy controls. These findings suggested that the integrity of the SLF is a potential neuroimaging biomarker for motor disability of lower limbs following a stroke. Acupuncture could increase the communication between the cortices connected by the impaired white matter tracts, implying the neural mechanism underlying the acupuncture intervention.