Combining various acupuncture therapies with multimodal analgesia to enhance postoperative pain management following total knee arthroplasty: a network meta-analysis of randomized controlled trials

ObjectiveThis study aims to evaluate the efficacy and safety of various acupuncture treatments in conjunction with multimodal analgesia (MA) for managing postoperative pain and improving knee function in patients undergoing total knee arthroplasty (TKA), based on the findings from clinical research indicating the potential benefits of acupuncture-related therapies in this context.MethodsWe searched Web of Science, PubMed, SCI-hub, Embase, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang Data, and Chinese Scientific Journal Database (VIP) to collect randomized controlled trials of acupuncture-related therapies for post-TKA pain. After independent screening and data extraction, the quality of the included literature was evaluated. The potential for bias in the studies incorporated in the analysis was assessed according to the guidelines outlined in the Cochrane Handbook 5.1. Network meta-analysis (NMA) was conducted using RevMan 5.4 and Stata 16.0 software, with primary outcome measures including visual analog scale (VAS), pain pressure threshold (PPT), hospital for special surgery knee score (HSS), and knee joint range of motion (ROM). Furthermore, the interventions were ranked based on the SUCRA value.ResultsWe conducted an analysis of 41 qualifying studies encompassing 3,003 patients, examining the efficacy of four acupuncture therapies (acupuncture ACU, electroacupuncture EA, transcutaneous electrical acupoint stimulation TEAS, and auricular acupoint therapy AAT) in conjunction with multimodal analgesia (MA) and MA alone. The VAS results showed no significant difference in efficacy among the five interventions for VAS-3 score. However, TEAS+MA (SMD: 0.67; 95%CI: 0.01, 1.32) was more effective than MA alone for VAS-7 score. There was no significant difference in PPT score among the three interventions. ACU + MA (SMD: 6.45; 95%CI: 3.30, 9.60), EA + MA (SMD: 4.89; 95%CI: 1.46, 8.32), and TEAS+MA (SMD: 5.31; 95%CI: 0.85, 9.78) were found to be more effective than MA alone for HSS score. For ROM score, ACU + MA was more efficacious than EA + MA, TEAS+MA, and AAT + MA, MA. Regarding the incidence of postoperative adverse reactions, nausea and vomiting were more prevalent after using only MA. Additionally, the incidence of postoperative dizziness and drowsiness following ACU + MA (OR = 4.98; 95%CI: 1.01, 24.42) was observed to be higher compared to that after AAT + MA intervention. Similarly, the occurrence of dizziness and drowsiness after MA was found to be significantly higher compared to the following interventions: TEAS+MA (OR = 0.36; 95%CI: 0.18, 0.70) and AAT + MA (OR = 0.20; 95%CI: 0.08, 0.50). The SUCRA ranking indicated that ACU + MA, EA + MA, TEAS+MA, and AAT + MA displayed superior SUCRA scores for each outcome index, respectively.ConclusionFor the clinical treatment of post-TKA pain, acupuncture-related therapies can be selected as a complementary and alternative therapy. EA + MA and TEAS+MA demonstrate superior efficacy in alleviating postoperative pain among TKA patients. ACU + MA is the optimal choice for promoting postoperative knee joint function recovery in TKA patients. AAT + MA is recommended for preventing postoperative adverse reactions.Systematic review registrationhttps://www.crd.york.ac.uk/, identifier (CRD42023492859)

The epigenetic regulator SIRT6 protects the liver from alcohol-induced tissue injury by reducing oxidative stress in mice

BACKGROUND & AIMS: As a nicotinamide adenine dinucleotide-dependent deacetylase and a key epigenetic regulator, sirtuin 6 (SIRT6) has been implicated in the regulation of metabolism, DNA repair, and inflammation. However, the role of SIRT6 in alcohol-related liver disease (ALD) remains unclear. The aim of this study was to investigate the function and mechanism of SIRT6 in ALD pathogenesis. METHODS: We developed and characterized Sirt6 knockout (KO) and transgenic mouse models that were treated with either control or ethanol diet. Hepatic steatosis, inflammation, and oxidative stress were analyzed using biochemical and histological methods. Gene regulation was analyzed by luciferase reporter and chromatin immunoprecipitation assays. RESULTS: The Sirt6 KO mice developed severe liver injury characterized by a remarkable increase of oxidative stress and inflammation, whereas the Sirt6 transgenic mice were protected from ALD via normalization of hepatic lipids, inflammatory response, and oxidative stress. Our molecular analysis has identified a number of novel Sirt6-regulated genes that are involved in antioxidative stress, including metallothionein 1 and 2 (Mt1 and Mt2). Mt1/2 genes were downregulated in the livers of Sirt6 KO mice and patients with alcoholic hepatitis. Overexpression of Mt1 in the liver of Sirt6 KO mice improved ALD by reducing hepatic oxidative stress and inflammation. We also identified a critical link between SIRT6 and metal regulatory transcription factor 1 (Mtf1) via a physical interaction and functional coactivation. Mt1/2 promoter reporter assays showed a strong synergistic effect of SIRT6 on the transcriptional activity of Mtf1. CONCLUSIONS: Our data suggest that SIRT6 plays a critical protective role against ALD and it may serve as a potential therapeutic target for ALD. LAY SUMMARY: The liver, the primary organ for ethanol metabolism, can be damaged by the byproducts of ethanol metabolism, including reactive oxygen species. In this study, we have identified a key epigenetic regulator SIRT6 that plays a critical role in protecting the liver from oxidative stress-induced liver injury. Thus, our data suggest that SIRT6 may be a potential therapeutic target for alcohol-related liver disease

Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Purpose: Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach: Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT masked language model (MLM), we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. Then, we compare our model with HSLN-RNN, BERT-based and SciBERT using the same dataset.Findings: Compared with the BERT-based and SciBERT models, the F1 score of our model outperforms them by 4.96% and 4.34%, respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present.Research limitations: The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed, which is a typical biomedical database, to fine-tune our model.Practical implications The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value: The study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way. The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Automatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research. We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models. Compared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement. We just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases. We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at: https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction. By designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.</p

Moves recognition in abstract of research paper based on deep learning

The purpose of this work is to explore the applicability and effectiveness of deep learning methods for the task-moves recognition in abstract of research paper. We firstly build a large corpus for moves recognition. Then we choose the traditional machine learning method SVM as a benchmark, and develop four moves recognition methods based on DNN, LSTM, Attention-BiLSTM and BERT. Finally, we design two groups of experiments with sample size 10,000 and 50,000 and then compare experimental results. The results show that most of the deep learning methods outperform the traditional machine learning method SVM especially in large-scale sample experiments, in which the BERT with a re-pre-trained model achieves the best results in both groups of experiments. Deep learning methods are proved applicable and effective for moves recognition in research paper abstracts.</p

Purification and characterization of a new β-glucosidase from Penicillium piceum and its application in enzymatic degradation of delignified corn stover

A new β-glucosidase (Cel3B) was first isolated from cellulytic fungi, designated as PpCel3B. Although PpCel3B was classified to GH family 3 based on the homology sequence, PpCel3B had different biological functions in cellulose degradation and signaling molecules production. PpCel3B was constitutive and could form multiple soluble lignocellulose inducers for cellulase and hemicellulase synthesis via high tranglycosylation activity and new enzymatic activity. Moreover, PpCel3B showed apparent synergism with cellulases by removing several inhibitors. Supplementing low doses of PpCel3B (52 μg/g substrate) increased saccharification efficiency of cellulase produced by Trichoderma reesei and Penicillium piceum by 15% and 35%, respectively on delignified corn stover. PpCel3B had important application in boosting cellulase yield and efficiency

Degradation Behavior in vitro of Carbon Nanotubes (CNTs)/Poly(lactic acid) (PLA) Composite Suture

Poly(lactic acid) (PLA) suture can be absorbed by the human body, and so have wide applications in modern surgery operations. The degradation period of PLA suture is expected to meet with the healing time of different types of wounds. In order to control the degradation period of the PLA suture, the carbon nanotubes (CNTs) were composited with PLA suture, and the degradation experiment in vitro was performed on sutures. The structure and properties of sutures during degradation, such as surface morphology, breaking strength, elongation, mass and chemical structure, were tracked and analyzed. The results indicated that the degradation brought about surface defects and resulted in 13.5 weeks for the strength valid time of the original PLA suture. By contrast, the strength valid time of the CNTs/PLA suture was increased to 26.6 weeks. Whilst the toughness of both the pure PLA and CNTs/PLA sutures decreased rapidly and almost disappeared after 3 to 4 weeks of degradation. The mass loss demonstrated that the time required for complete degradation of the two sutures was obviously different, the pure PLA suture 49 weeks, while CNTs/PLA sutures 63 to 73 weeks. The research proved that CNTs delayed PLA degradation and prolonged its strength valid time in degradation

Fullerene-Based Macro-Heterocycle Prepared through Selective Incorporation of Three N and Two O Atoms into C-60

A 14-membered heterocycle is created on the C-60 cage skeleton through a multistep procedure. Key steps involve repeated PCl5-induced hydroxylamino N-O bond cleavage leading to insertion of nitrogen atoms, and also piperidine-induced peroxo O-O bond cleavage leading to insertion of oxygen atoms. The hetero atoms form one pyrrole, two pyran, and one diazepine rings in conjunction with the C-60 skeleton carbon atoms. The fullerene-based macrocycle showed unique reactivities towards fluoride ion and copper salts