A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model.

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment's smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.

The Effectiveness and Cost-effectiveness of a Parenting Intervention Integrated with Primary Health Care on Early Childhood Development: a Cluster-Randomized Controlled Trial.

Developing countries require interventions that can sustainably improve early childhood development (ECD) at scale because hundreds of millions of children are at risk of poor development. This study examined the effectiveness and cost-effectiveness of a parenting intervention integrated with primary health care in terms of ECD. A cluster-randomized controlled trial was conducted in 20 urban communities in China, with 82 and 86 children aged 1-2 months enrolled in the intervention and control groups, respectively, and 71 and 69 children, respectively, followed to 14 months of age. All children in both groups received routine primary health care services. Intervention caregivers received a parenting pamphlet and two parenting training sessions during well-child clinic visits; those with children with suspected developmental delay received additional parenting guidance by telephone. Compared with controls, children receiving the intervention had similar developmental outcomes, measured with the Chinese version of the Ages & Stages Questionnaires third edition (ASQ-C), at baseline, but had significantly higher communication (adjusted mean difference = 0.26; 95% CI 0.03, 0.51), fine motor (adjusted mean difference = 0.19; 95% CI 0.01, 0.37), and overall (adjusted mean difference = 0.25; 95% CI 0.10, 0.41) ASQ-C z-scores after 12 months of the intervention. The intervention cost per child was $50.87, and the costs for increasing the communication, fine motor, and overall ASQ-C scores by one SD were $195.65, $267.74, and $203.48, respectively. Our findings indicate that the integration of a parenting intervention with existing primary health care is a cost-effective way to improve ECD.

TMT-based quantitative proteomics revealed follicle-stimulating hormone (FSH)-related molecular characterizations for potentially prognostic assessment and personalized treatment of FSH-positive non-functional pituitary adenomas.

BACKGROUND: Non-functional pituitary adenoma (NFPA) is highly heterogeneous with different hormone expression subtypes. Of them, follicle-stimulating hormone (FSH)-positive expression is an important subtype of NFPAs. It is well-known that FSH exerted its functions through binding its receptor. However, the expression rate of FSH receptor was significantly higher in aggressive pituitary adenomas. This study aimed to investigate the molecular characteristics of FSH-positive NFPAs for effective stratification of patient, target treatment, prognostic assessment, and personalized treatment of FSH-positive NFPAs. METHODS: Tandem mass tag (TMT)-based quantitative proteomics was used to investigate differentially expressed proteins (DEPs) between FSH-positive and negative NFPAs. Gene ontology and KEGG pathway enrichment analyses were used to analyze the DEPs. Differentially expressed genes (DEGs) between invasive and non-invasive NFPAs from GEO database were analyzed with pathway enrichment analysis. Protein-protein interaction (PPI) networks were constructed based on DEPs in excetral cellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt pathways. Cytoscape was used to obtain most significant modules. Western blot was used to validate the expressions of upregulated proteins (ITGA1, ITGA6, and ITGB4), the expression and phosphorylated status of Akt in PI3K-Akt pathway, and the expression of FSH receptors in FSH-positive relative to negative NFPAs. RESULTS: A total of 594 DEPs (374 upregulated and 220 downregulated) were identified between FSH-positive and negative NFPAs. Nineteen KEGG pathway networks were identified to involve DEPs, and reveal molecular differences between FSH-positive and negative NFPAs, including three important pathways that were significantly associated with tumor invasiveness and aggressiveness: ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways. Further, focal adhesion pathway was also confirmed with invasiveness-related NFPA DEG data that were derived from GEO database. Moreover, the significantly upregulated DEPs (ITGA1, ITGA6, and ITGB4) that were associated with tumor invasiveness and aggressiveness were confirmed by immunoaffinity analysis in FSH-positive vs. negative NFPAs. Also, the phosphorylation level but not its expression level of AKT in PI3K-AKT signaling was significantly increased, and the expression level of FSH receptor was significantly increased in FSH-positive relative to negative NFPAs. Also, overlapping analysis of 594 DEPs and 898 DEGs revealed 45 invasiveness-related DEPs, including 11 upregulated DEPs (ITGA6, FARP1, PALLD, PPBP, LIMA1, SCD, UACA, BAG3, CLU, PLEC, and GATM) that were also upregulated genes in invasive NFPAs, and 8 downregulated DEPs (ALCAM, HP, FSTL4, IL13RA2, NPTX2, DPP6, CRABP2, and SLC27A2) that were also downregulated genes in invasive NFPAs. CONCLUSIONS: FSH-positive expression was an important NFPA subtype. It was the first time for this study to reveal FSH-related proteomic variations and the corresponding molecular network alterations in FSH-positive relative to negative NFPAs. Also, three signaling pathways (ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways) and involved upregulated proteins (ITGA1, ITGA6, ITGB4, pAKT, and FSHR) were significantly associated with tumor invasiveness and aggressiveness, and a set of invasiveness-related DEPs were identified with overlapping analysis of 594 DEPs in FSH-positive vs. negative NFPAs and 898 DEGs in invasive vs. non-invasive NFPAs. These findings offered the scientific evidence to in-depth understand molecular characteristics of FSH-positive NFPAs, and effectively stratify the post-surgery patients for personalized prognostic assessment and targeted treatment of FSH-positive NFPAs.

Assessment of the optimal cutoff value of fasting plasma glucose to establish diagnosis of gestational diabetes mellitus in Chinese women.

Our aim is to assess the optimal cutoff value of fasting plasma glucose (FPG) in Chinese women at 24-28 weeks' gestation by performing oral glucose tolerance test (OGTT) to improve diagnostic rate of gestational diabetes mellitus (GDM). Data were derived from the Medical Birth Registry of Xiamen. A FPG cutoff value of 5.1 mmol/L confirmed the diagnosis of GDM in 4,794 (6.10%) pregnant women. However, a FPG cutoff value of 4.5 mmol/L should rule out the diagnosis of GDM in 35,932 (45.73%) pregnant women. If we use this cutoff value, the diagnosis of GDM to about 27.3% of pregnant women will be missed. Additionally, a 75-g OGTT was performed in pregnant women with FPG values between 4.5 and 5.1 mmol/L, avoiding the performance of formal 75-g OGTT in about 50.37% pregnant women. Meanwhile, according to maternal age and pre-pregnancy BMI categories, with FPG values between 4.5 mmol/L and 5.1 mmol/L, which had high sensitivity, to improve the diagnostic rate of GDM in all groups. Further researches are needed to present stronger evidences for the screening value of FPG in establishing the diagnosis of GDM in pregnant women.

Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). METHODS: Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. RESULTS: The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. CONCLUSIONS: Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

Assessment of care pattern and outcome in hemangioblastoma.

Due to its rarity, current literature assessing prognostic factors and survival outcomes of hemangioblastoma is limited. Patients with histologically confirmed hemangioblastoma were identified from the US National Cancer Data Base. 1488 patients met inclusion criteria. 644 patients underwent gross total resection (GTR), 220 subtotal resection (STR)/biopsy, 60 stereotactic radiosurgery (SRS), 15 external beam radiotherapy (EBRT), 51 surgery followed by radiotherapy (SR + RT) and 498 no treatment. Independent predictors of shorter OS included age ≥ 40 (HR, 3.897; 95% CI, 2.341-6.487; p < 0.001), Charlson-Deyo score ≥ 1(HR, 1.756; 95% CI, 1.213-2.544; p = 0.003), tumor location in the brainstem (HR, 1.955; 95% CI, 1.129-3. 384; p = 0.017) compared to cerebellum, no treatment (HR, 2530; 95% CI, 1.533-4.177; p < 0.001) and receipt of EBRT (HR, 2.860; 95% CI, 1.073-7.618; p = 0.036) compared to STR/biopsy. GTR was associated with longer OS (HR 0.617; 95% CI, 0.391-0.974; p = 0.038), while SRS had comparable OS to STR/biopsy. The overall trend of OS by treatment modality was consistent after matching to age- and sex-matched US population data. In patients younger than 40 years, treatment was not a significant predictor of OS. In conclusion, GTR remained the optimal treatment for hemangioblastoma. SRS may perform similarly to surgery alone. Treatment was not a significant predictor of survival in younger patients.

Evaluation of the three-in-one team-based care model on hierarchical diagnosis and treatment patterns among patients with diabetes: a retrospective cohort study using Xiamen's regional electronic health records.

BACKGROUND: Xiamen is a pilot city in China for hierarchical diagnosis and treatment reform of non-communicable diseases, especially diabetes. Since 2012, Xiamen has implemented a program called the "three-in-one", a team-based care model for the treatment of diabetes, which involves collaboration between diabetes specialists, general practitioners, and health managers. In addition, the program provides financial incentives to improve care, as greater accessibility to medications through community health care centers (CHCs). The aim of this study was to evaluate the effectiveness of these policies in shifting visits from general hospitals to CHCs for the treatment of type 2 diabetes mellitus (T2DM). METHOD AND MATERIALS: A retrospective observational cohort study was conducted using Xiamen's regional electronic health record (EHR) database, which included 90% of all patients registered since 2012. Logistic regression was used to derive the adjusted odds ratio (OR) for patients shifting from general hospitals to CHCs. Among patients treated at hospitals, Kaplan-Meier(KM) curves were constructed to evaluate the time from each policy introduction until the switch to CHCs. A k-means clustering analysis was conducted to identify patterns of patient care-seeking behavior. RESULTS: In total, 89,558 patients and 2,373,524 visits were included. In contrast to increased outpatient visits to general hospitals in China overall, the percentage of visits to CHCs in Xiamen increased from 29.7% in 2012 to 66.5% in 2016. The most significant and rapid shift occurred in later periods after full policy implementation. Three clusters of patients were identified with different levels of complications and health care-seeking frequency. All had similar responses to the policies. CONCLUSIONS: The "three-in-one" team-based care model showed promising results for building a hierarchical health care system in China. These policy reforms effectively increased CHCs utilization among diabetic patients.

Correlation of early postoperative blood glucose levels with postoperative complications, hospital costs, and length of hospital stay in patients with gastrointestinal malignancies.

Early postoperative hyperglycemia in non-diabetic patients is an important risk factor affecting postoperative complications and mortality. This study aimed at investigating the effects of early postoperative hyperglycemia on postoperative complications, hospital costs, and length of hospital stay in non-diabetic patients with gastrointestinal malignancies; data of 1,015 non-diabetic patients with gastrointestinal malignancies, who underwent surgical intervention between January 2010 and January 2012, were retrospectively evaluated. Records on fasting plasma glucose (FPG), liver function, and kidney function were collected before and one day after surgery. Correlation of early postoperative FPG levels with postoperative complications, hospital costs, and length of hospital stay was further assessed in non-diabetic patients with gastrointestinal malignancies. One day after surgery, FPG results were significantly increased compared to preoperative values. FPG levels greater than or equal to 9.13 mmol/L (or 164.34 mg/dL) were associated with significant increases in the incidence of postoperative complications, length of hospital stay, and hospital costs. An association is shown between FPG and postoperative hyperglycemia in non-diabetic patients undergoing surgery for gastrointestinal malignancies. Significant increases in postoperative complications among these patients suggest that measurement of early postoperative FPG levels is critical to identify patients with postoperative hyperglycemia.