A Method for Reconstructing Background from RGB-D SLAM in Indoor Dynamic Environments.

Dynamic environments are challenging for visual Simultaneous Localization and Mapping, as dynamic elements can disrupt the camera pose estimation and thus reduce the reconstructed map accuracy. To solve this problem, this study proposes an approach for eliminating dynamic elements and reconstructing static background in indoor dynamic environments. To check out dynamic elements, the geometric residual is exploited, and the static background is obtained after removing the dynamic elements and repairing images. The camera pose is estimated based on the static background. Keyframes are then selected using randomized ferns, and loop closure detection and relocalization are performed according to the keyframes set. Finally, the 3D scene is reconstructed. The proposed method is tested on the TUM and BONN datasets, and the map reconstruction accuracy is experimentally demonstrated.

Triplet versus doublet neoadjuvant chemotherapy regimens for locally advanced gastric cancer: a propensity score matching analysis.

BACKGROUND: To investigate the differences between doublet and triplet neoadjuvant chemotherapy (NAC) regimens in efficacy and safety profile. METHODS: A total of 227 locally advanced gastric cancer (LAGC) patients who received NAC and sequential radical gastrectomy were reviewed. After propensity score matching (PSM), 140 patients with similar baseline characteristics were selected. Among them, 70 received doublet NAC regimens consisted of platinum and fluorouracil; the other 70 received triplet NAC regimens consisted of docetaxel, platinum, and fluorouracil. RESULTS: The efficacy of doublet and triplet regimens was comparable after propensity score matching in terms of tumor regression (pathological complete response, Doublet 11.4% vs. Triplet 15.7%, p = 0.642), achieving of R0 resection (Doublet 88.6% vs. Triplet 88.6%, p = 1), 1-year disease-free survival (DFS) (Doublet 77.1% vs. Triplet 68.6%, p = 0.178), 3-years overall survival (OS) (Doublet 54.3% vs. Triplet 60.9%, p = 0.941). Post-surgery complications were more common in the triplet cohort (Doublet 5.7% vs. Triplet 27.1%, p = 0.001), especially abdominal infection (Doublet 0% vs. Triplet 11.1%, p = 0.001). CONCLUSIONS: A more intense preoperative triplet NAC regimen does not bring extra downstage effect and survival benefit compared to a doublet regimen. It may even result in a higher risk of post-surgery complications.

[Numerical simulation study of fracture mechanics of the atherosclerotic plaque].

Atherosclerotic plaque rupture is the main cause of many cardiovascular diseases, and biomechanical factors play an important role in the process of plaque rupture. In the study of plaque biomechanics, there are relatively few studies based on fatigue fracture failure theory, and most of them mainly focus on the whole fatigue propagation process from crack initiation to plaque rupture, while there are few studies on the influence of crack on plaque rupture at a certain time in the process of fatigue propagation. In this paper, a two-dimensional plaque model with crack was established. Based on the theory of fracture mechanics and combined with the finite element numerical simulation method, the stress intensity factor (SIF) and related influencing factors at the crack tip in the plaque were studied. The SIF was used to measure the influence of crack on plaque rupture. The results show that the existence of crack can lead to local stress concentration, which increases the risk of plaque rupture. The SIF at the crack tip in the plaque was positively correlated with blood pressure, but negatively correlated with fibrous cap thickness and lipid pool stiffness. The effect of the thickness and angle of lipid pool on the SIF at the crack tip in the plaque was less than 4%, which could be ignored. This study provides a theoretical basis for the risk assessment of plaque rupture with cracks.

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model.

Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction networks. As the association between disordered proteins and severe diseases continues to be discovered, identifying MoRFs has gained growing significance. Due to the limited number of experimentally validated MoRFs, the performance of existing MoRF's prediction algorithms is not good enough and still needs to be improved. In this research, we present a model named MoRF_ESM, which utilizes deep-learning protein representations to predict MoRFs in disordered proteins. This approach employs a pretrained ESM-2 protein language model to generate embedding representations of residues in the form of attention map matrices. These representations are combined with a self-learned TextCNN model for feature extraction and prediction. In addition, an averaging step was incorporated at the end of the MoRF_ESM model to refine the output and generate final prediction results. In comparison to other impressive methods on benchmark datasets, the MoRF_ESM approach demonstrates state-of-the-art performance, achieving [Formula: see text] higher AUC than other methods when tested on TEST1 and achieving [Formula: see text] higher AUC than other methods when tested on TEST2. These results imply that the combination of ESM-2 and TextCNN can effectively extract deep evolutionary features related to protein structure and function, along with capturing shallow pattern features located in protein sequences, and is well qualified for the prediction task of MoRFs. Given that ESM-2 is a highly versatile protein language model, the methodology proposed in this study can be readily applied to other tasks involving the classification of protein sequences.

AAindex-PPII: Predicting polyproline type II helix structure based on amino acid indexes with an improved BiGRU-TextCNN model.

The polyproline-II (PPII) structure domain is crucial in organisms' signal transduction, transcription, cell metabolism, and immune response. It is also a critical structural domain for specific vital disease-associated proteins. Recognizing PPII is essential for understanding protein structure and function. To accurately predict PPII in proteins, we propose a novel method, AAindex-PPII, which only adopts amino acid index to characterize protein sequences and uses a Bidirectional Gated Recurrent Unit (BiGRU)-Improved TextCNN composite deep learning model to predict PPII in proteins. Experimental results show that, when tested on the same datasets, our method outperforms the state-of-the-art BERT-PPII method, achieving an AUC value of 0.845 on the strict data and an AUC value of 0.813 on the non-strict data, which is 0.024 and 0.03 higher than that of the BERT-PPII method. This study demonstrates that our proposed method is simple and efficient for PPII prediction without using pre-trained large models or complex features such as position-specific scoring matrices.

Red cell distribution width: a novel predictive biomarker for stroke risk after transient ischaemic attack.

OBJECTIVE: Predicting the prognosis of transient ischaemic attack (TIA) is difficult for many frontline clinicians. The purpose of this study was to determine whether subsequent stroke in TIA patients can be predicted via red blood cell distribution width (RDW). MATERIAL AND METHODS: A total of 360 consecutive patients with new-onset TIA in our stroke centre, were enrolled over the period studied. The patients were divided into three groups: 103 TIA patients, 206 ischaemic stroke (IS) patients and 51 patients with haemorrhagic stroke (HS) within 7 days after TIA. Complete blood count, biochemical parameters and brain imaging were performed on all patients. RESULTS: The mean RDW values of patients with IS and HS after TIA were significantly higher than patients with TIA (13.35 ± 1.59 vs 12.84 ± 1.19, 13.32 ± 1.08 vs 12.84 ± 1.19, respectively, all p ≤ .001). In a multivariate model, RDW was independently associated with stroke after TIA (IS: odds ratio (OR) = 2.52, 95% confidence interval (CI) = 1.46-3.35, p = .002; HS: OR = 1.511, 95% CI = 1.101-2.074, p = .011). Compared to ABCD2 scores, the diagnostic power of RDW in the differentiation of patients with IS after TIA was better (area under curve (AUC): 0.731 vs 0.613, p = .015). When an RDW cut-off value of 13.95% was accepted for differentiating patients with IS from TIA, the sensitivity and specificity were 73.7% and 74.3%, respectively. However, the AUC for the ability of the RDW to predict HS was 0.653 (95% CI = 0.589-0.716; p < .001). CONCLUSIONS: The early determination of RDW is a promising, rapid, easy and inexpensive biomarker to predict the subsequent stroke in TIA patients, especially for IS. KEY MESSAGESThe most important result of our study is to show that (1) the higher RDW, the earlier the stroke onset and (2) RDW ≥13.95% has a 2.52-fold risk of ischaemic stroke in TIA patients, and RDW ≥12.85% has a 1.51-fold risk of haemorrhagic stroke.As an economic and accessible hematological marker, baseline RDW may serve as a useful biomarker for risk stratification in TIA patients.

Elevated red cell distribution width predicts residual dizziness in patients with benign paroxysmal positional vertigo.

Objective: The present study aimed to determine whether residual dizziness (RD) after successful repositioning treatment in benign paroxysmal positional vertigo (BPPV) patients could be predicted by red blood cell distribution width (RDW). Materials and methods: In this study, a total of 303 BBPV patients hospitalized at the neurology department were investigated. The enrolled patients were divided into two groups after successful repositioning treatment: non-RD group included patients who were completely cured, and RD group included patients with RD. We collected data on all subjects, including general information, blood routine examination, blood biochemical examination, and magnetic resonance imaging tests. Results: The mean RDW values of patients in the RD group were significantly higher than that in the non-RD group (13.63 ± 1.8 vs. 12.5 ± 0.8; p < 0.001). In subsequent multivariate analysis, elevated RDW levels were a statistically significant risk factor associated with the occurrence of RD [odds ratio = 2.62, 95% confidence interval (CI) 1.88-3.64, p < 0.001]. The area under the ROC curve was 0.723 in terms of its predictive ability to distinguish patients with RD. A cut-off point of 12.95% of RDW predicted RD with a sensitivity of 75.6% and a specificity of 69.5%. Moreover, the AUC for the ability of the RDW to predict recurrence were 0.692 (95% CI = 0.561-0.831; p < 0.014). Conclusions: Elevated RDW level was related to increased risk of RD among BPPV patients, requiring further efforts to clarify the actual underlying pathophysiology.

Low Antioxidant Status of Serum Uric Acid, Bilirubin, Albumin, and Creatinine in Patients With Benign Paroxysmal Positional Vertigo.

Objective: To investigate the roles of serum uric acid (UA), bilirubin (BIL), albumin (ALB), and creatinine (CRE) as major intravascular antioxidants, in benign paroxysmal positional vertigo (BPPV). Methods: The serum levels of UA, BIL, ALB, and CRE were retrospectively analyzed in 70 patients with new-onset idiopathic BPPV and 140 age- and sex-matched healthy controls (HCs). Results: Serum UA, BIL, ALB, and CRE levels were significantly lower in the BPPV group than the HC group. Furthermore, serum levels of BIL and ALB were significantly lower in the BPPV group when compared by sex. Multiple stepwise logistic regression revealed that a reduction in serum ALB was independently related to BPPV (odds ratio = 0.688; 95% confidence interval = 0.607- 0.780). Receiver operating characteristic analyses revealed a cut-off value of 45.15 g/L for ALB with a sensitivity of 74.29% (62.97- 83.07%) and specificity of 73.57% (65.71- 80.18%). Conclusions: Serum levels of UA, BIL, ALB, and CRE were lower in BPPV patients, indicating a lower antioxidant status. Furthermore, a reduction in serum ALB was independently associated with BPPV. These results provide insights into the possible roles of oxidative stress in the pathogenesis of BPPV.

Percutaneous transhepatic biliary drainage and stenting for malignant obstructive jaundice: A report of two cases.

Malignant obstructive jaundice comprises a group of diseases that can be caused by primary biliary and extra-biliary carcinomas. Generally, surgical resection is the primary treatment for malignant obstructive jaundice; however, for the patients that are unable to undergo surgery, urgent treatment is required to improve hepatic function. Percutaneous transhepatic biliary drainage (PTBD) and stenting are emerging alternative treatments for malignant obstructive jaundice. PTBD and stenting have exhibited good efficacy for the treatment of malignant obstructive jaundice, with few complications and reduced associated pain.