Surface Reconstruction from Point Clouds: A Survey and a Benchmark.

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem in computer vision and graphics research. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identity the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular in the research community, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research. We make the benchmark publicly accessible at https://Gorilla-Lab-SCUT.github.io/SurfaceReconstructionBenchmark.

The beneficial effects of neutrophil elastase inhibitor on gastrointestinal dysfunction in sepsis.

To investigate the effects of neutrophil elastase inhibitor (sivelestat sodium) on gastrointestinal function in sepsis. A reanalysis of the data from previous clinical trials conducted at our center was performed. Septic patients were divided into either the sivelestat group or the non-sivelestat group. The gastrointestinal dysfunction score (GIDS), feeding intolerance (FI) incidence, serum levels of intestinal barrier function and inflammatory biomarkers were recorded. The clinical severity and outcome variables were also documented. A total of 163 septic patients were included. The proportion of patients with GIDS ≥2 in the sivelestat group was reduced relative to that in the non-sivelestat group (9.6% vs. 22.5%, p = 0.047) on the 7th day of intensive care unit (ICU) admission. The FI incidence was also remarkably reduced in the sivelestat group in contrast to that in the non-sivelestat group (21.2% vs. 37.8%, p = 0.034). Furthermore, the sivelestat group had fewer days of FI [4 (3, 4) vs. 5 (4-6), p = 0.008]. The serum levels of d-lactate (p = 0.033), intestinal fatty acid-binding protein (p = 0.005), interleukin-6 (p = 0.001), white blood cells (p = 0.007), C-reactive protein (p = 0.001), and procalcitonin (p < 0.001) of the sivelestat group were lower than those of the non-sivelestat group. The sivelestat group also demonstrated longer ICU-free days [18 (0-22) vs. 13 (0-17), p = 0.004] and ventilator-free days [22 (1-24) vs. 16 (1-19), p = 0.002] compared with the non-sivelestat group. In conclusion, sivelestat sodium administration appears to improve gastrointestinal dysfunction, mitigate dysregulated inflammation, and reduce disease severity in septic patients.

EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation.

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinate using perspective projection. In this way, we can process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network that consists of a LiDAR stream and a camera stream to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU. Compared to PMF, EPMF also achieves 2.06× acceleration with 2.0% improvement in mIoU. Our source code is available at https://github.com/ICEORY/PMF.

Higher serum carotenoid concentrations were associated with the lower risk of cancer-related death: Evidence from the National Health and Nutrition Examination Survey.

The study focuses on the association between serum carotenoids and cancer-related death. Using data from the National Health and Nutrition Examination Survey (2001-2006 and 2017-2018), the study encompasses 10,277 participants older than age 20 years, with recorded baseline characteristics and serum carotenoid concentrations (including α-carotene, trans-ß-carotene, cis-ß-carotene, ß-cryptoxanthin, trans-lycopene, and lutein/zeaxanthin). We hypothesized that serum carotenoid concentrations were negatively associated with cancer-related death. The weighted chi-square analyses indicate significant negative correlations between higher serum concentrations of α-carotene, ß-cryptoxanthin, trans-lycopene, and total carotenoids, and the risk of cancer-related deaths. Using weighted Cox regression analysis, this study confirms that α-carotene, ß-cryptoxanthin, trans-lycopene, and total carotenoids, as continuous or categorical variables, are inversely related to cancer mortality (P < .0001). Furthermore, considering competitive risk events, lower concentrations of serum ß-cryptoxanthin (Fine-Gray P = 1.12e-04), trans-lycopene (P = 5.68e-14), and total carotenoids (P = .03) are associated with an increased risk of cancer-related deaths. The research reveals a crucial inverse relationship between serum carotenoid concentrations and cancer-related death.

FOXO1 reshapes neutrophils to aggravate acute brain damage and promote late depression after traumatic brain injury.

BACKGROUND: Neutrophils are traditionally viewed as first responders but have a short onset of action in response to traumatic brain injury (TBI). However, the heterogeneity, multifunctionality, and time-dependent modulation of brain damage and outcome mediated by neutrophils after TBI remain poorly understood. METHODS: Using the combined single-cell transcriptomics, metabolomics, and proteomics analysis from TBI patients and the TBI mouse model, we investigate a novel neutrophil phenotype and its associated effects on TBI outcome by neurological deficit scoring and behavioral tests. We also characterized the underlying mechanisms both in vitro and in vivo through molecular simulations, signaling detections, gene expression regulation assessments [including dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays], primary cultures or co-cultures of neutrophils and oligodendrocytes, intracellular iron, and lipid hydroperoxide concentration measurements, as well as forkhead box protein O1 (FOXO1) conditional knockout mice. RESULTS: We identified that high expression of the FOXO1 protein was induced in neutrophils after TBI both in TBI patients and the TBI mouse model. Infiltration of these FOXO1high neutrophils in the brain was detected not only in the acute phase but also in the chronic phase post-TBI, aggravating acute brain inflammatory damage and promoting late TBI-induced depression. In the acute stage, FOXO1 upregulated cytoplasmic Versican (VCAN) to interact with the apoptosis regulator B-cell lymphoma-2 (BCL-2)-associated X protein (BAX), suppressing the mitochondrial translocation of BAX, which mediated the antiapoptotic effect companied with enhancing interleukin-6 (IL-6) production of FOXO1high neutrophils. In the chronic stage, the "FOXO1-transferrin receptor (TFRC)" mechanism contributes to FOXO1high neutrophil ferroptosis, disturbing the iron homeostasis of oligodendrocytes and inducing a reduction in myelin basic protein, which contributes to the progression of late depression after TBI. CONCLUSIONS: FOXO1high neutrophils represent a novel neutrophil phenotype that emerges in response to acute and chronic TBI, which provides insight into the heterogeneity, reprogramming activity, and versatility of neutrophils in TBI.

Relation between frailty and adverse outcomes in elderly patients with gastric cancer: a scoping review.

Background: Playing an exemplary role, frailty have crucial effect on the preoperative evaluation of elderly patients. Previous studies have shown that frailty is associated with complications and mortality in patients with gastric cancer (GC). However, with the development of the concept of "patient-centered", the range of health-related outcomes is broad. The differences in relation between frailty and various adverse outcomes will be further explored. Method: The PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wan Fang, and Chinese Biomedical Literature databases were searched for keywords, including frailty (such as frail) and gastric cancer (such as stomach neoplasms or stomach cancer or gastrectomy or gastric surgery). The search period is until August 2023. The included studies were observational or cohort studies with postoperative related adverse outcomes as primary or secondary outcome measures. Valid assessment tools were used. The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess methodological quality in the included literature. Result: Fifteen studies were included, including 4 cross-sectional studies, 8 retrospective cohort studies, and 3 prospective cohort studies. Among them, 6 studies were rated as "Good" and 9 studies were rated as "Fair," indicating that the quality of the literature was high. Then, 10 frailty assessment tools were summarized and classified into two broad categories in accordance with frailty models. Results of the included studies indicated that frailty in elderly patients with GC was associated with postoperative complications, mortality, hospital days, readmissions, quality of life, non-home discharge, and admission to the intensive care unit. Conclusion: This scoping review concludes that high levels of preoperative frailty increase the risk of adverse outcomes in elderly patients with GC. Frailty will be widely used in the future clinical evaluation of elderly gastric cancer patients, precise risk stratification should be implemented for patients, and frailty management should be implemented well to reduce the occurrence of adverse treatment outcomes.

Exploring the Experiences and Support of Nurses as Second Victims After Patient Safety Events in China: A Mixed-Method Approach.

Aim: To investigate the current status of experience and support of nurses as second victims and explore its related factors in nurses. Design: A sequential, explanatory, mixed-method study was applied. Methods: A total of 406 nurses from seven tertiary hospitals in China were chosen as participants between September to October 2023. The Chinese version of the Second Victim Experience and Support Questionnaire (SVEST), Somatic Complaints of Sub-health Status Questionnaire (SCSSQ) and Generalized Anxiety Disorder (GAD-7) were applied to collect quantitative data. Eight nurses were selected for a qualitative study through in-depth interviews. Through interpretive phenomenological analysis, the interview data were analysed to explore the experience and support of nurses as second victims. Results: Practice distress (15.74 ± 4.97) and psychological distress (15.48 ± 3.74) were the highest dimensions, indicating Chinese nurses experienced second victim-related practice and psychological distress. Nurses with different gender, age, education, marital status, income, working hours, professional titles, and unit types have different levels of second victim-related experience and support (p < 0.05). In addition, the score of SVEST was positively associated with SCSSQ (r = 0.444) and GAD-7 (r = 0.490) (p < 0.05). This qualitative study found that the experience and support of nurses as second victims included nurses' perceptions and needs for patient safety events; psychological, physical and practice distress of nurses; and nurses and hospitals coping style after patient safety events. Discussion: Our findings suggest that nurses who are second victims of patient safety events experience severe practice and psychological distress, indicating that nursing managers should pay attention to psychological and practice distress of nurses after patient safety events and provide effective preventive measures.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

Nutrition therapy in critically ill patients with severe acute pancreatitis.

A significant proportion of patients (10%-20%) with acute pancreatitis develop severe acute pancreatitis characterized by pancreatic necrosis, systemic inflammation, and organ failure, commonly requiring intensive care unit (ICU) admission. In this specific population, nutrition therapy is more challenging than that in the general ICU population, primarily because of inevitable gastrointestinal involvement by pancreatic inflammation. In this review, we discussed several key aspects of nutrition therapy in this population, including key pathophysiology that may impede nutrition therapy, the timing and implementation of enteral nutrition and parenteral nutrition, the importance of specific nutrient supplements, and the long-term outcomes that may be addressed by nutrition therapy.

Prevalence and unfavorable outcome of frailty in older adults with gastric cancer: a systematic review and meta-analysis.

BACKGROUND AND AIM: Previous studies reported inconsistent results on the prevalence and prognostic implications of frailty among older adults with gastric cancer. This systematic review synthesized available literature pertaining on this topic to establish the prevalence and unfavorable outcomes of frailty in older adults with gastric cancer. METHODS: A comprehensive search was conducted across multiple English databases including PubMed, Cochrane Library, CINAHL, Embase, and Web of Science as well as Chinese databases, namely, CNKI, Wan Fang, and CBM, from inception to July 4, 2023, to identify potential studies. Data related to the incidence of frailty and its unfavorable outcomes in older adults with gastric cancer were extracted. RevMan5.3 and R 4.2.2 were used to evaluate pooled prevalence, hazard ratios (HR), and 95% confidence interval (CI). RESULTS: This review comprehensively selected 13 studies, comprising 9 cohort studies and 4 cross-sectional studies, on 44,117 older adults diagnosed with gastric cancer. The incidence of frailty among older adults with gastric cancer ranged from 10 to 71%. The pooled prevalence of frailty was 29% (95% CI 0.21-0.39). Frailty was found to be associated with an elevated risk of postoperative complications (HR = 1.99, 95% CI 1.45-2.73), prolonged postoperative hospital stay (HR = 2.68, 95% CI 2.38-3.02), likelihood of readmission (HR = 3.28, 95% CI 1.77-6.08), and an increased mortality risk (HR = 1.60, 95% CI 1.36-1.90). CONCLUSIONS: Frailty was associated with a poor prognosis in older adults with gastric cancer. Clinical medical staff should focus on the frailty of older adults with gastric cancer, conduct large-scale, multicenter, and prospective studies and early screening of patients, and provide guidance for the implementation of prevention and treatment strategies.

Application of the quality of recovery-40 questionnaire to evaluate the effectiveness of enhanced recovery after surgery protocols in gastric cancer.

Patient reported outcomes is currently considered to be an important supplement to evaluate the effectiveness of enhanced recovery after surgery (ERAS) clinical practice. The Quality of Recovery-40 Questionnaire (QoR-40) is one of the most frequently used and validation tool to assess the subjective feelings of quality of life after surgery. The present study aimed to use the QoR-40 to evaluate the effectiveness of ERAS protocols in gastric cancer from the perspective of patient-reported quality of recovery. The study was designed as a prospective, non-randomized clinical trial, conducted in a single center. Patients in our hospital who were scheduled to undergo radical surgery for gastric cancer were divided into ERAS group and control group (Contr group). The QoR-40 were administered one day before surgery (Baseline) and on postoperative day 1, 3, 6, and 30. The difference in QoR-40 scores between the ERAS and Contr groups was compared by repeated-measures ANOVA. A total of 200 patients completed the study, including 100 patients in the ERAS group and 100 patients in the Contr group. The Baseline time point QoR-40 scores of the ERAS and Contr groups were 179.68 ± 14.46 and 180.12 ± 17.12, respectively, and no significant difference was noted between the two groups (p = 0.845). The postoperative QoR-40 score of the ERAS group was significantly higher than that of the Contr group, and the difference was statistically significant (p = 0.006). This study demonstrated that, in terms of patient-reported quality of recovery, the postoperative recovery effect of ERAS protocols in gastric cancer is significantly better than that of the traditional treatment model.

Efficacy and Safety of Different Doses of Rivaroxaban and Risk Factors for Bleeding in Elderly Patients with Venous Thromboembolism: A Real-World, Multicenter, Observational, Cohort Study.

INTRODUCTION: Venous thromboembolism (VTE) consists of deep vein thrombosis (DVT) and pulmonary embolism (PE). Rivaroxaban is a direct oral anticoagulant (DOAC) inhibiting activated coagulation factor X (FXa), and exerts several advantages in the treatment of VTE compared to conventional therapy. However, the efficacy and safety of rivaroxaban in elderly patients with VTE was still poorly understood. METHODS: The study was carried out using an observational and non-interventional approach. A total of 576 patients aged ≥ 60 years with newly diagnosed VTE were included in the study. All patients received rivaroxaban with recommended treatment duration of ≥ 3 months for secondary prevention. In addition, 535 elderly patients with various diseases except VTE were included in the study in a retrospective and randomized way. RESULTS: The total bleeding rate was 12.2% (70/576). Major bleeding and non-major clinically relevant (NMCR) bleeding occurred in 4 (0.69%) patients and 5 (0.87%) patients, respectively. The rate of recurrent VTE was 5.4%. The mean level of D-dimers was increased by 467.2% in the elderly patients with VTE compared with the elderly patients without VTE. The elderly patients with VTE receiving rivaroxaban at a dose of 10 mg once daily (n = 134) had lower risk for bleeding (3.7% vs 14.7%; P = 0.001) and a similar rate of recurrent VTE (4.5% vs 5.7%; P = 0.596) as compared to the elderly patients with VTE receiving rivaroxaban at higher doses including 15 mg once daily and 20 mg once daily (n = 442). In addition, age, concomitant aspirin, hemoglobin, activated partial thromboplastin time (APTT), and rivaroxaban doses were independent predictive factors for bleeding events. CONCLUSIONS: The study suggested that a dose of 10 mg once daily should be the priority in elderly patients with VTE receiving long-term rivaroxaban anticoagulation therapy in view of reduced bleeding risk.

[Retracted] Anti­Semaphorin­7A single chain antibody demonstrates beneficial effects on pulmonary inflammation during acute lung injury.

[This retracts the article DOI: 10.3892/etm.2018.5724.].

Identification and initial validation of maximal tumor area as a novel prognostic factor for overall and disease-free survival in patients with resectable colon cancer: a retrospective study.

BACKGROUND: The tumor area may be a potential prognostic indicator. The present study aimed to determine and validate the prognostic value of tumor area in curable colon cancer. METHODS: This retrospective study included a training and validation cohorts of patients who underwent radical surgery for colon cancer. Independent prognostic factors for overall survival (OS) and disease-free survival (DFS) were identified using Cox proportional hazards regression models. The prognostic discrimination was evaluated using the integrated area under the receiver operating characteristic curves (iAUCs) for prognostic factors and models. The prognostic discrimination between tumor area and other individual factors was compared, along with the prognostic discrimination between the tumor-node-metastasis (TNM) staging system and other prognostic models. Two-sample Wilcoxon tests were carried out to identify significant differences between the two iAUCs. A two-sided P <0.05 was considered statistically significant. RESULTS: A total of 3051 colon cancer patients were included in the training cohort and 872 patients in the validation cohort. Tumor area, age, differentiation, T stage, and N stage were independent prognostic factors for both OS and DFS in the training cohort. Tumor area had a better OS and DFS prognostic discrimination characteristics than T stage, maximal tumor diameter, differentiation, tumor location, and number of retrieved lymph nodes. The novel prognostic model of T stage + N stage + tumor area (iAUC for OS, 0.714, P <0.001; iAUC for DFS, 0.694, P <0.001) showed a better prognostic discrimination than the TNM staging system (T stage + N stage; iAUC for OS, 0.664; iAUC for DFS, 0.658). Similar results were observed in an independent validation cohort. CONCLUSIONS: Tumor area was identified as an independent prognostic factor for both OS and DFS in curable colon cancer patients, and in cases with an adequate number of retrieved lymph nodes. The novel prognostic model of combining T stage, N stage, and tumor area may be an alternative to the current TNM staging system.

Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization.

Learning invariant (causal) features for out-of-distribution (OOD) generalization have attracted extensive attention recently, and among the proposals, invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, the IB-IRM approach has demonstrated its power to solve these challenges. In this paper, we further improve IB-IRM from two aspects. First, we show that the key assumption of support overlap of invariant features used in IB-IRM guarantees OOD generalization, and it is still possible to achieve the optimal solution without this assumption. Second, we illustrate two failure modes where IB-IRM (and IRM) could fail in learning the invariant features, and to address such failures, we propose a Counterfactual Supervision-based Information Bottleneck (CSIB) learning algorithm that recovers the invariant features. By requiring counterfactual inference, CSIB works even when accessing data from a single environment. Empirical experiments on several datasets verify our theoretical results.

Correlates of stigma for patients with breast cancer: a systematic review and meta-analysis.

OBJECTIVE: This study was conducted to examine the factors associated with stigma in breast cancer women. METHODS: PubMed, Embase, the Cochrane Library, Web of Science, and two Chinese electronic databases were electronically searched to identify eligible studies that reported the correlates of stigma for patients with breast cancer from inception to July 2022. Two researchers independently performed literature screening, data extraction, and risk of bias assessment. R4.1.1 software was used for statistical analysis. RESULTS: Twenty articles including 4161 patients were included in the systematic review and meta-analysis. Results showed that breast cancer stigma was positively correlated with working status, type of surgery, resignation coping, depression, ambivalence over emotional expression, and delayed help-seeking behavior and negatively correlated with age, education, income, quality of life, social support, confrontation coping, psychological adaptation, self-efficacy, and self-esteem. Descriptive analysis showed that breast cancer stigma was positively correlated with intrusive thoughts, body image, anxiety, and self-perceived burden but negatively correlated with a sense of coherence, personal acceptance of the disease, sleep quality, cancer screening attendance and doctor's empathy. CONCLUSION: Many demographic, disease-related, and psychosocial variables are related to breast cancer stigma. Our view can serve as a basis for health care professionals to develop health promotion and prevention strategies for patients with breast cancer.

Convolutional Fine-Grained Classification With Self-Supervised Target Relation Regularization.

Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such target coding schemes are less flexible to model inter-class correlation and are sensitive to sparse and imbalanced data distribution as well. In light of this, this paper introduces a novel target coding scheme - dynamic target relation graphs (DTRG), which, as an auxiliary feature regularization, is a self-generated structural output to be mapped from input images. Specifically, online computation of class-level feature centers is designed to generate cross-category distance in the representation space, which can thus be depicted by a dynamic graph in a non-parametric manner. Explicitly minimizing intra-class feature variations anchored on those class-level centers can encourage learning of discriminative features. Moreover, owing to exploiting inter-class dependency, the proposed target graphs can alleviate data sparsity and imbalanceness in representation learning. Inspired by recent success of the mixup style data augmentation, this paper introduces randomness into soft construction of dynamic target relation graphs to further explore relation diversity of target classes. Experimental results can demonstrate the effectiveness of our method on a number of diverse benchmarks of multiple visual classification, especially achieving the state-of-the-art performance on three popular fine-grained object benchmarks and superior robustness against sparse and imbalanced data. Source codes are made publicly available at https://github.com/AkonLau/DTRG.

Muscone improves hypoxia/reoxygenation (H/R)-induced neuronal injury by blocking HMGB1/TLR4/NF-κB pathway via modulating microRNA-142.

Previous reports have indicated that natural muscone has neuroprotective effects against cerebral hypoxia injury; however, little is known in regards to its pharmacological mechanism. In this study, we tried to evaluate the neuroprotective effects and mechanisms of muscone against cerebral hypoxia injury using an in vitro model. The cerebral hypoxia injury cell model was produced by hypoxia/reoxygenation (H/R). The cell viability and apoptosis were measured using the cell counting Kit-8 and the Annexin V-FITC/PI Apoptosis Detection kit, respectively. To screen microRNAs regulated by muscone, we analyzed the gene expression datasets of GSE84216 retrieved from gene expression omnibus (GEO). Here, it was demonstrated that muscone treatment significantly alleviated the cell apoptosis, oxidative stress and inflammation in H/R-exposed neurons. Subsequently, through analyzing GSE84216 from the GEO database, miR-142-5p was markedly upregulated by treatment of muscone in this cell model of cerebral hypoxia injury. Further experiments revealed that downregulation of miR-142-5p eliminated the neuroprotective effects of muscone against H/R induced neuronal injury. Additionally, high mobility group box 1 (HMGB1), an important inflammatory factor, was identified as a direct target of miR-142-5p in neurons. Meanwhile, we further demonstrated that muscone could reduce the expression of HMGB1 by upregulating miR-142-5p expression, which subsequently resulted in the inactivation of TLR4/NF-κB pathway, finally leading to the improvement of cell injury in H/R-exposed neurons. Overall, we demonstrate for the first time that muscone treatment alleviates cerebral hypoxia injury in in vitro experiments through blocking activation of the TLR4/NF-κB signaling pathway by targeting HMGB1, suggesting that muscone may serve as a potential therapeutic drug for treating cerebral hypoxia injury.