Search

BackgroundClinically useful predictors for fatal toxoplasmosis are lacking. We investigated the value of serological assays for antibodies to whole Toxoplasma antigens and to peptide antigens of the Toxoplasma cyst matrix antigen 1 (MAG1), for predicting incident toxoplasmic encephalitis (TE) in people living with human immunodeficiency virus (HIV; PLWH).MethodsWe performed a nested case control study, conducted within the Multicenter AIDS Cohort Study (MACS), using serum samples obtained 2 years prior to diagnosis of TE from 28 cases, and 37 HIV disease-matched Toxoplasma seropositive controls at matched time-points. Sera were tested for Toxoplasma antibodies using a commercial assay and for antibodies to MAG1_4.2 and MAG1_5.2 peptides in enzyme-linked immunosorbent assay (ELISA).ResultsTwo years prior to clinical diagnosis, 68% of TE cases were MAG1_4.2 seropositive compared with 16% of controls (odds ratio [OR] 25.0, 95% confidence interval [CI] 3.14-199.18). Corresponding results for MAG1_5.2 seropositivity were 36% and 14% (OR 3.6, 95% CI .95-13.42). Higher levels of antibody to MAG1_4.2 (OR 18.5 per doubling of the optical density [OD] value, 95% CI 1.41-242) and to Toxoplasma (OR 2.91 for each OD unit increase, 95% CI 1.48-5.72) were also associated with the risk of TE. When seropositivity was defined as the presence of MAG1 antibody or relatively high levels of Toxoplasma antibody, the sensitivity was 89% and specificity was 68% for subsequent TE.ConclusionsAntibodies to MAG1 showed predictive value on the occurrence of TE in PLWH, and the predictive performance was further improved by adding the levels of Toxoplasma antibody. These measures could be clinically useful for predicting subsequent diseases in multiple at-risk populations.

Structural damage detection is crucial for maintaining the health and safety of buildings. However, achieving high accuracy in damage detection remains challenging, especially in noisy environments. To improve the accuracy and noise robustness of damage detection, this study proposes a novel method that combines the Conformer model and the dual-channel pseudo-supervised (DCPS) learning strategy for structural damage detection. The DCPS learning strategy improves the stability and accuracy of the model in noisy environments. It enables the model to input acceleration signals with different noise levels into each branch of the dual-channel network, thereby learning noise-robust features. The Conformer model, as the backbone network, integrates the advantages of convolutional neural networks (CNNs) and Transformers to effectively extract both local and global features from acceleration signals. The proposed method is validated using a four-story single-span steel-frame building model and the IASC-ASCE simulated benchmark structure. The results show that the proposed method achieves a higher classification accuracy than existing structural damage detection methods. Compared to the single Conformer-based method, this method improves the accuracy by 1.57% and 4.93% for the two validation structures, respectively. Moreover, the proposed method benefits from the DCPS learning strategy's ability to achieve superior noise robustness compared to other methods. The proposed method holds potential value for improving the accuracy of damage detection and noise robustness in scenarios such as maintenance and extreme events. [ABSTRACT FROM AUTHOR]

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions. [ABSTRACT FROM AUTHOR]

Aim: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels. Methods: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 in cell proliferation, drug resistance, migration and invasion in vivo and vitro. Results: The results indicated that low hENT1 expression indicated a significantly poor outcome in PC patients, including shortened DFS (21.6± 2.8 months versus 36.9± 4.0 months, p< 0.001) and OS (33.6± 3.9 versus 39.6± 3.9, p=0.004). Meanwhile, patients in stage I/II of TNM stage had a longer OS (40.2± 3.4 versus 15.4± 1.7, p=0.002) and DFS (31.0± 3.1 versus 12.4± 1.9, p=0.016) than patients in stage III/IV. Patients in M0 stage had a longer OS (39.7± 3.4 versus 16.2± 1.9, p=0.026) and DFS(30.7± 3.0 versus 11.8± 2.2, p=0.031) than patients in M1 stage, and patients with tumors not invading the capsule had a better DFS than those with tumor invasion into the capsule (30.8± 3.0 versus 12.6± 2.3, p=0.053). Patients with preoperative CA19-9 values ≤ 467 U/mL have longer DFS than that of patients who had preoperative CA19-9 values > 467 U/mL (37.9± 4.1 versus 22.9± 4.0, p=0.04). In the subgroup analysis, a high hENT1 expression level was related to a longer OS(39.4± 4.0 versus 31.5± 3.9, p=0.001) and DFS(35.7± 4.0 versus 20.6± 2.7; p< 0.0001) in the Gemcitabine subgroup. Conclusion: PC patients with high hENT1 expression have a better survival outcomes when receiving Gemcitabine. hENT1 expression can be a great prognostic indicator for PC patients to receive Gemcitabine treatment. [ABSTRACT FROM AUTHOR]

Background and Purpose: Diagnosis of retroperitoneal schwannoma (RS), especially cystic RS, is frequently missed or delayed owing to its rarity, location, nonspecific symptoms, and similarities with other tumors on various imaging modalities. This study aimed to determine associations between clinical, radiological, and histopathologic features and outcome. Materials and Methods: Seventy‐nine patients with pathologically confirmed RS who underwent tumor resection between June 2010 and June 2020 were retrospectively reviewed and analyzed. Patients were stratified into three groups according to degree of tumoral cystic degeneration. Results: Cystic degeneration was significantly associated with multiple foci (p = 0.025), calcification (p = 0.012), and hemorrhage (p = 0.000), but not size (p = 0.08), high Ki‐67 (p = 0.094), malignancy (p = 0.115; prevalence of cystic degeneration in the benign and malignant groups were 53.9% vs 100%), rough margin (p = 0.162), or irregular shape (p = 0.369). Malignant RS was significantly associated with multiple lymph nodes enlargement (p = 0.034). Tumor size, margins, shape, or/and multiplicity did not significantly differ between benign and malignant tumors. No recurrence occurred in patients with benign RS (mean follow‐up, 45 months). All malignant tumors recurred; mean time to recurrence was 11.4 months (mean follow‐up, 33 months). Conclusion: Since RS is misdiagnosed mostly as malignancy and diagnosis is often delayed, a suspicion is necessary for diagnosis when atypical features are present. In RS, cystic degeneration was not associated with tumor size, Ki‐67, or malignancy; however, it was significantly associated with multiple foci, calcification, and hemorrhage. Cystic degeneration and related factors are useful for the diagnosis of RS. Malignant RS should be considered when a mass involves multiple lymph nodes. Margins, morphology, and size are not associated with malignancy. Pathological tumor type, tumor location, and adjacent anatomic structures are associated with outcome. [ABSTRACT FROM AUTHOR]