Microarray analysis of tRNA-derived small RNA (tsRNA) in LPS-challenged macrophages treated with metformin.

Metformin, a widely used anti-diabetic drug, has demonstrated its efficacy in addressing various inflammatory conditions. tRNA-derived small RNA (tsRNA), a novel type of small non-coding RNA, exhibits diverse regulatory functions and holds promise as both a diagnostic biomarker and a therapeutic target for various diseases. The purpose of this study is to investigate whether the abundance of tsRNAs changed in LPS versus LPS + metformin-treated cells, utilizing microarray technology. Firstly, we established an in vitro lipopolysaccharide (LPS)-induced inflammation model using RAW264.7 macrophages and assessed the protective effects of metformin against inflammatory damage. Subsequently, we extracted total RNA from both LPS-treated and metformin + LPS-treated cell samples for microarray analysis to identify differentially abundant tsRNAs (DA-tsRNAs). Furthermore, we conducted bioinformatics analysis to predict target genes for validated DA-tsRNAs and explore the biological functions and signaling pathways associated with DA-tsRNAs. Notably, metformin was found to inhibit the inflammatory response in RAW264.7 macrophages. The microarray results revealed a total of 247 DA-tsRNAs, with 58 upregulated and 189 downregulated tsRNAs in the Met + LPS group compared to the LPS group. The tsRNA-mRNA network was visualized, shedding light on potential interactions. The results of bioinformatics analysis suggested that these potential targets of specific tsRNAs were mainly related to inflammation and immunity. Our study provides compelling evidence that metformin exerts anti-inflammatory effects and modulates the abundance of tsRNAs in LPS-treated RAW264.7 macrophages. These findings establish a valuable foundation for using tsRNAs as potential biomarkers for metformin in the treatment of inflammatory conditions.

Lever sign test for anterior cruciate ligament injuries: a diagnostic meta-analysis.

BACKGROUND: Sports-related ACL (anterior cruciate ligament) injuries are frequent. Successful management requires early diagnosis and treatment. One of the clinical tests used to identify ACL damage is the lever sign test. This meta-analysis aimed to assess the lever sign test's diagnostic efficacy for ACL injuries. METHODS: An extensive investigation of the Cochrane Library, Embase, and PubMed databases was conducted until April 2023. Studies assessing the lever sign test's diagnostic efficacy for ACL injuries were also included. A bivariate random-effects model was employed to acquire the pooled estimates of diagnostic odds ratios, specificity, positive and negative likelihood ratios, sensitivity, and curves of the summary receiver operating characteristic (SROC). RESULTS: The meta-analysis comprised twelve investigations with a total of 1365 individuals. The lever sign test's combined sensitivity and specificity for the purpose of diagnosing injuries to the ACL were 0.810 (95% confidence interval [CI] 0.686-0.893) and 0.784 (95% CI 0.583-0.904), respectively. The positive and negative likelihood ratios were 3.148 (95% CI 1.784-5.553) and 0.210 (95% CI 0.084-0.528), respectively. The study revealed a diagnostic odds ratio of 17.656, with a 95% CI ranging from 4.800 to 64.951. The SROC curve's area was determined to be 0.912 (95% CI 0.857-0.967). CONCLUSION: With high specificity and sensitivity, the lever sign test is a reliable diagnostic modality for ACL injuries. However, the test should be used in combination with other diagnostic tests to increase the accuracy of the diagnosis. Further investigations are warranted to assess the clinical practicability of the lever sign test in various populations and settings.

Associated predictors of prolonged length of stay in patients surviving extensive burns: A large multicenter retrospective study.

BACKGROUND: Patients with extensive burns are critically ill and have long treatment periods. Length of stay (LOS) is a good measure for assessing treatment. This study sought to identify predictors of prolonged LOS in patients with extensive burns (≥50% TBSA). METHODS: This retrospective multicenter cohort study included adults aged ≥ 18 years who survived extensive burns in three burn centers in Eastern China between January 2016 and June 2022. Epidemiological, demographic and clinical outcomes data were extracted from electronic medical records and compared between patients with/without prolonged LOS, which was defined as LOS greater than the median. Logistic regression analysis was used to identify predictors of prolonged LOS. RESULTS: The study sample included 321 patients, of whom 156 (48.6%) had an LOS of 58 days (IQR 41.0-77.0). Univariate regression analysis showed that increased total burn area and increased full-thickness burn area; electrical, chemical and other burns; increased erythrocytes, leukocytes, platelets or serum creatinine within 24 h of admission; concomitant inhalation injury, pulmonary edema, sepsis, bloodstream infection, wound infection, pulmonary infection, urinary tract infection, or HB < 70 g/L during hospitalization were associated with prolonged LOS in patients with extensive burns. Increased number of surgical operations, mechanical ventilation and renal replacement therapy were also associated with prolonged LOS (P < 0.05 or P < 0.001). Multivariate regression analysis revealed that increased total burn area (ratio 1.032, 95%CI 1.01-1.055; P = 0.004), electrical and chemical or other burns (3.282, 1.335-8.073; P = 0.01), development of wound infection (2.653 1.285-5.481; P = 0.008) and increased number of operative procedures (1.714, 1.388-2.116, P < 0.001) were significant predictors. CONCLUSIONS: Increased area of full-thickness burn,occurrence of electrical and chemical or other burns,occurrence of wound infection and increased number of surgeries are the best predictors of prolonged LOS in patients with extensive burns. Clarifying relevant predictors of burn patients' LOS provides a reliable reference for clinical treatment.

Effectiveness of a Quality Improvement Intervention on Reperfusion Treatment for Patients With Acute Ischemic Stroke: A Stepped-Wedge Cluster Randomized Clinical Trial.

Importance: Reperfusion therapy is the most effective treatment for acute ischemic stroke but remains underused in China. Objective: To evaluate the effect of a problem-oriented, culturally adapted, targeted quality improvement intervention on reperfusion therapy for patients with acute ischemic stroke in China. Design, Setting, and Participants: In this stepped-wedge cluster randomized clinical trial, patients from 16 secondary and 33 tertiary hospitals in China with acute ischemic stroke within 6 hours of symptom onset were consecutively recruited between July 1, 2018, and June 30, 2020. Interventions: Hospitals were randomly assigned to 1 of 3 sequences to receive the targeted quality improvement intervention (n = 5689), in which workflow reconstruction was promoted to reduce in-hospital reperfusion treatment delays, or usual care (n = 6443), in which conventional stroke care was left to the discretion of the stroke team. Main Outcomes and Measures: The primary outcome was the reperfusion therapy rate, a composite outcome of intravenous recombinant tissue plasminogen activator (IV rtPA) or endovascular thrombectomy (EVT) for eligible patients who arrived within 3.5 or 4.5 hours of symptom onset. Secondary outcomes were the IV rtPA administration rate among eligible patients who arrived within 3.5 hours of symptom onset, the EVT rate among eligible participants who arrived within 4.5 hours of symptom onset, the proportion of patients with door-to-needle time within 60 minutes, the proportion of patients with door-to-puncture time within 90 minutes, in-hospital mortality, and 3-month disability as measured by a modified Rankin Scale score greater than 2. Results: All 12 132 eligible patients (mean [SD] age, 66 [12.1] years; 7759 male [64.0%]) completed the trial. The reperfusion rate was 53.5% (3046 of 5689) for the eligible patients in the intervention period and 43.9% (2830 of 6443) in the control period. No significant improvement in primary outcomes was found for the intervention after adjusting for cluster, period, and imbalanced baseline covariates (adjusted risk difference [ARD], 5.5%; 95% CI, -8.0% to 19.0%; adjusted odds ratio [AOR], 1.26; 95% CI, 0.72-2.21) or for the secondary outcomes. However, significant improvements were found in secondary hospitals for reperfusion therapy (1081 of 1870 patients [57.8%] vs 945 of 2022 patients [42.9%]; ARD, 19.0%; 95% CI, 6.4%-31.6%; AOR, 2.24; 95% CI, 1.29-3.88), IV rtPA administration (1062 of 1826 patients [58.2%] vs 916 of 2170 patients [42.2%]; ARD, 20.3%; 95% CI, 7.4%-33.1%; AOR, 2.37; 95% CI, 1.34-4.19), and EVT (51 of 231 patients [22.1%] vs 37 of 259 patients [14.3%]; ARD, 13.6%; 95% CI, 1.0%-26.3%; AOR, 3.03; 95% CI, 1.11-8.25) in subgroup analyses. Conclusions and Relevance: In this stepped-wedge cluster randomized clinical trial of patients with acute ischemic stroke in China, the use of a targeted quality improvement intervention compared with usual care did not improve the reperfusion therapy rate. However, the intervention may be effective in secondary hospitals. Trial Registration: ClinicalTrials.gov Identifier: NCT03578107.

Attention Regularized Laplace Graph for Domain Adaptation.

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. However, current graph embedding DA methods suffer from two issues: 1). they are only concerned with preservation of the underlying data structures in the embedding and ignore sub-domain adaptation, which requires taking into account intra-class similarity and inter-class dissimilarity, thereby leading to negative transfer; 2). manifold learning is proposed across different feature/label spaces separately, thereby hindering unified comprehensive manifold learning. In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely A ttention R egularized Laplace G raph-based D omain A daptation (ARG-DA), to remedy the aforementioned issues. Specifically, by weighting the importance across different sub-domain adaptation tasks, we propose the A ttention R egularized Laplace Graph for class aware DA, thereby generating the attention regularized DA. Furthermore, using a specifically designed FEEL strategy, our approach dynamically unifies alignment of the manifold structures across different feature/label spaces, thus leading to comprehensive manifold learning. Comprehensive experiments are carried out to verify the effectiveness of the proposed DA method, which consistently outperforms the state of the art DA methods on 7 standard DA benchmarks, i.e., 37 cross-domain image classification tasks including object, face, and digit images. An in-depth analysis of the proposed DA method is also discussed, including sensitivity, convergence, and robustness.

Social experiences switch states of memory engrams through regulating hippocampal Rac1 activity.

In pathological or artificial conditions, memory can be formed as silenced engrams that are unavailable for retrieval by presenting conditioned stimuli but can be artificially switched into the latent state so that natural recall is allowed. However, it remains unclear whether such different states of engrams bear any physiological significance and can be switched through physiological mechanisms. Here, we show that an acute social reward experience switches the silent memory engram into the latent state. Conversely, an acute social stress causes transient forgetting via turning a latent memory engram into a silent state. Such emotion-driven bidirectional switching between latent and silent states of engrams is mediated through regulation of Rac1 activity­dependent reversible forgetting in the hippocampus, as stress-activated Rac1 suppresses retrieval, while reward recovers silenced memory under amnesia by inhibiting Rac1. Thus, data presented reveal hippocampal Rac1 activity as the basis for emotion-mediated switching between latent and silent engrams to achieve emotion-driven behavioral flexibility.

Disturbance observer based adaptive model predictive control for uncalibrated visual servoing in constrained environments.

This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method.

The effectiveness of lateral ankle ligament reconstruction when treating chronic ankle instability: A systematic review and meta-analysis.

Chronic ankle instability predominantly occurs due to multiple exercise-related diseases. Conservative treatment methods regarding this condition have not effectively improved in recent years, which is why more focus has been put on exploring different novel reconstruction procedures of the lateral ankle ligament for the treatment of chronic ankle instability. OBJECTIVES: This study aims to obtain the overall effectiveness of various lateral ankle ligament reconstruction methods for chronic ankle ligament instability. METHODS: We gathered data from PubMed and EMBASE databases using the keywords: ankle, malleolar, and reconstruction. Newcastle - Ottawa quality assessment was carried out for the obtained studies; effect volume combination and image drawing were performed by Stata14, and Excel was used for data statistics. RESULTS: A total of 12 articles were included in the quantitative analysis by performing full-text reading and data inclusion. Among them, 476 patients (485 ankle joints) were treated. The results showed that the overall valid efficiency of "excellent" was 59% and "good" lateral ligament reconstruction was 26%, I2=87.3%, P = 0.000; the subgroup analysis anatomic reconstruction group I2=0.0%, P = 0.993; the autograft group I2=0.0%, P = 1.000; allograft group I2=0.0%, P = 0.993. CONCLUSION: Reconstruction of the lateral ankle ligament is a relatively stable treatment for chronic ankle instability.

Surgical hand rubbing versus surgical hand scrubbing: Systematic review and meta-analysis of efficacy.

BACKGROUND: Surgical hand rubbing (SHR) and surgical hand scrubbing (SHS) are two common methods used by surgeons to reduce surgical site infections. To date, the optimal method that can effectively reduce these infections remains unknown. In this study, we performed a comprehensive statistical analysis to compare the efficacy of these two methods in effectively controlling surgical site infections. METHODS: A systemic review and meta-analysis was performed by mining literature from major databases, including Pubmed, Embase, Cochrane library, Ovid and Google Scholar, and recruiting studies published between 1980 and 1st April 2019. Analysis was performed using Revman, version 5.3, software, and focused on primary outcomes that included colony-forming unit (CFU) counts and logarithmic reduction of CFU after hand antisepsis and after surgery. RESULTS: Seven clinical trials met our inclusion criteria, with a total of 764 healthcare workers analyzed. We found no statistically significant differences between the two methods with regards to CFU counts and logarithmic reduction of CFU after hand antisepsis and surgery, as well as antisepsis and surgery times. CONCLUSION: From the literature, it was evident that SHR had similar efficacy to SHS, without necessarily increasing costs. Owing to advantages such as ease of application, exposure to less dermal irritation, and less time consumption, SHR is recommended as a cost-effective alternative for management of surgical site infections.

Discriminative and Geometry-Aware Unsupervised Domain Adaptation.

Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation of the upper error bound, we argue, in this article, that an effective DA method for classification should: 1) search a shared feature subspace where the source and target data are not only aligned in terms of distributions as most state-of-the-art DA methods do but also discriminative in that instances of different classes are well separated and 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain. In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models for classification, namely, close yet discriminative DA (CDDA), geometry-aware DA (GA-DA), and discriminative and GA-DA (DGA-DA), to highlight the contribution of CDDA based on 1), GA-DA based on 2), and, finally, DGA-DA implementing jointly 1) and 2). Using both the synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms the state-of-the-art DA methods over 49 image classification DA tasks through eight popular benchmarks. We further carry out an in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data.

Object Tracking Based On Huber Loss Function.

In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by Principle Component Analysis (PCA) basis vectors and row group sparsity. This method takes advantage of the strengths of sub-space representation and explicitly takes the underlying relationship between particle candidates into consideration in the tracker. The representation of each particle is learned via the multi-task sparse learning method. Huber loss function is employed to model the error between candidates and templates, yielding robust tracking. We utilize the Alternating Direction Method of Multipliers (ADMM) to solve the proposed representation model. In experiments we tested sixty representative video sequences that reflect the specific challenges of tracking and used both qualitative and quantitative metrics to evaluate the performance of our tracker. The experiment results demonstrated that the proposed tracking algorithm achieves superior performance compared to nine state-of-the-art tracking methods.

[PRELIMINARY APPLICATION OF VIRTUAL PREOPERATIVE RECONSTRUCTION PLANNING IN Pilon FRACTURES].

OBJECTIVE: To investigate the application value of three-dimensional reconstruction and virtual preoperative planning for Pilon fractures. METHODS: Between July 2010 and June 2014, 16 patients with closed Pilon fracture were treated, including 12 males and 4 females with an average age of 36.5 years (range, 22-53 years) and a mean disease duration of 10.2 days (range, 6-14 days). According to AO/Orthopaedic Trauma Association (AO/OTA) typing, 2 cases were rated as 43.B2 type, 3 cases as 43.B3 type, 3 cases as 43.C1 type, 2 cases as 43.C2 type, and 6 cases as 43.C3 type. The preoperative CT data from 16 patients were imported into Mimics10.01 software to establish the detailed fracture three-dimensional digital models. Virtual operation of fracture reduction and implanting internal fixation was performed on the models, and the optional surgical planning was made. Based on the virtual preoperative planning, operations were performed. RESULTS: Established detailed three-dimensional Pilon fracture digital models could perfectly reflect the fracture characteristics, could be observed at any direction, and aided for fracture classification accurately. Virtual fracture operations of reduction, internal fixation and other could be performed to simulate the clinical operation, which could assist the surgeon better preoperative planning in achieving visual presentation and improving the communication. The operation time was 70-130 minutes (mean, 87.8 minutes); intraoperative blood loss volume was 30-150 mL (mean, 71.9 mL). The wounds healed by first intension in all patients. The mean follow-up time was 11.6 months (range, 8-18 months). Postoperative radiological results at 3 groups showed good anatomic reduction according to the Burwell-Charnley criteria, and the fracture healing time was 3-6 months (mean, 3.7 months). There was no complication of internal fixation loosening or breakage during follow-up. The American Orthopedic Foot and Ankle Society (AOFAS) score was 71-100 (mean, 92.3); the results were excellent in 10 cases, good in 5 cases, and fair in 1 case, with an excellent and good rate of 93.8% at last follow-up. No loss of fracture reduction was observed on the X-ray film. CONCLUSION: The clinical feasibility of virtual reconstruction preoperative planning is good in the treatment of Pilon fractures, which helps surgeons better understanding Pilon fracure and making the surgical planning.

SIFT flow for large-displacement object tracking.

Traditional tracking methods place an emphasis on how to cope with the variations in target appearance effectively. However, when the motion displacement of the target between image frames becomes larger, these methods may be unstable. This paper presents a novel (to our knowledge) visual object tracking method. In this method, we first introduce scale-invariant feature transform (SIFT) flow into the tracking problem and develop a real-time motion prediction method to capture large displacement between consecutive image frames. Then we use belief propagation (BP) to convert the problem of finding maximum a posteriori probability (MAP) to globally minimizing an energy function to get the best matching pairs of points for producing good candidate regions of the target. And last, the refined point trajectories are obtained according to the bidirectional flow field consistency estimation and covariance region descriptor matching, which can update model states efficiently so as to achieve enhanced robustness for visual tracking. Compared with the state-of-art tracking methods, the experimental results demonstrate that the proposed algorithm shows favorable performance when the object undergoes large motion displacement between image frames.

Anomaly detection based on local nearest neighbor distance descriptor in crowded scenes.

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

[Studies on fingerprints of Centella asiatica by HPLC].

OBJECTIVE: To study the HPLC fingerprints and establish a sensitive and specific method for controlling the quality of Centella asiatica. METHODS: HPLC gradient elution was applied for the fingerprints of Centella asiatica. All 16 samples are collected from different habitats of China. The columni was Alltech C18 column (250 mm x 4.6 mm, 5 microm), mobile phase was acetonitrile-water, flow rate was 1.0 ml/min, wavelength was 205 nm. RESULTS: The fingerprint of Centella asiatica was established, 16 samples of different areas of Centella asiatica were detected. There were 15 common peaks in the HPLC fingerprints of Centella asiatica. By comparison with the reference standards and using LC-ESI-MS(n) to corroborate the structure, 5-10 peaks were identified as madecassoside, asiaticoside, quercetin, kaemperol, madecassic acid and asiatic acid respectively. After calculating the similarity of the HPLC fingerprints of 16 habitants, the similarity of different habitats has been bad quite. CONCLUSION: The method is accurate, reliable and good repeatability. This chromatographic fingerprint method can be used to controll the quality of Centella asiatica.