Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.

UNASSIGNED: There is considerable diversity of outcome selections and methodologies for handling the multiple outcomes across all systematic reviews (SRs) of Interdisciplinary Pain Treatment (IPT) due to the complexity. This diversity presents difficulties for healthcare decision makers. Better recommendations about how to select outcomes in SRs (with or without meta-analysis) are needed to explicitly demonstrate the effectiveness of IPT.
UNASSIGNED: This overview systematically collates the reported outcomes and measurements of IPT across published SRs and identifies the methodological characteristics. Additionally, we provide some suggestions on framing the selection of outcomes and on conducting SRs of IPT.
UNASSIGNED: Three electronic databases (PubMed, Cochrane Library, and Epistemonikos) and the PROSPERO registry for ongoing SR were supplemented with hand-searching ending on 30 September 2021.
UNASSIGNED: We included 18 SRs with data on 49007 people from 356 primary randomised controlled trials (RCTs); eight were followed by meta-analysis and ten used narrative syntheses of data. For all the SRs, pain was the most common reported outcome (72%), followed by disability/functional status (61%) and working status (61%). Psychological well-being and quality of life were also reported in half of the included SR (50%). The core outcome domains according to VAPAIN, IMMPACT, and PROMIS were seldom met. The methodological quality varied from critically low to moderate according to AMSTAR2. The AMSTAR2 rating was negatively correlated to the number of outcome domains in PROMIS, and VAPAIN was positively correlated with IMMPACT and PROMIS, indicating the intercorrelations between the reported outcomes.
UNASSIGNED: This systematic overview showed wide-ranging disparity in reported outcomes and applied outcome domains in SRs evaluating IPT interventions for chronic pain conditions. The intercorrelations between the reported outcomes should be appropriately handled in future research. Some approaches are discussed as well.

Plasma concentrations of branched-chain amino acids (BCAAs) are known to exhibit strong intercorrelations; however, the associated regulatory mechanism is not sufficiently understood. Furthermore, the mechanisms underlying the intercorrelation changes in metabolic disorders with the disease are unclear. Therefore, plasma BCAAs in patients with chronic kidney disease (CKD) were examined.
This study included a healthy group of older participants (Group C; n = 87, 46 males, 41 females) who had undergone health examinations at Sanyudo Hospital and a group of CKD patients (Group CKD; n = 71, 49 males, 22 females) receiving maintenance hemodialysis at the same hospital. Samples from Group C were collected 12 h after fasting. CKD samples were collected before and after hemodialysis (pre-HD and post-HD, respectively), without 12 h fasting. The samples were analyzed for 38 amino acids by SRL Inc., using liquid chromatography-mass spectrometry (LC-MS).
Differences between the plasma BCAA concentrations of Group C and pre-HD were determined. Dialysis-induced BCAA losses were different for each BCAA. However, strong intercorrelations between the plasma concentrations of each BCAA were maintained. In addition, the regression lines did not converge at the origin and were in different positions for groups C, pre-HD, and post-HD. A different distribution of the constant (b) for each group was observed for each BCAA correlation when a in the regression line (Y = aX + b) was fixed at the value for Group C, and b was calculated.
Strong intercorrelations among plasma concentrations of BCAAs were maintained in CKD patients both pre- and post-dialysis, whereas the changes in the plasma concentrations of each BCAA were different. We speculate that there is a novel mechanism that selectively regulates each BCAA and suggest that changes in the constant of the regression formula for correlation may have a novel function as an index for renal contribution to BCAA metabolism.

Hair steroids and their ratios are believed to be reliable biomarkers reflecting the long-term exposure of circulating steroids. Hereinto, two underlying assumptions are that hair biomarkers have consistency with traditional biomarkers in saliva or urine, and good long-term intraindividual stability across a long time. However, these two assumptions have not been well verified for most of hair biomarkers except for hair cortisol. Thus, this study aimed to verify the two issues on eight hair biomarkers: cortisol, cortisone, dehydroepiandrosterone (DHEA), testosterone, progesterone, the ratios of cortisol to cortisone, DHEA and testosterone. The five steroids in hair, saliva and urine were measured with high performance chromatography tandem mass spectrometry. The results revealed that the hair biomarkers had significant correlations with the salivary biomarkers calculated by the mean area under curve (AUCg) in a matched time span (ps < 0.05) where the coefficients of correlations (r) were >0.3 (r = 0.322-0.616) except cortisone and progesterone (r = 0.177 and 0.212, respectively). It indicated that hair biomarkers had weak to moderate consistency with salivary ones. But only three biomarkers showed the consistency between hair and urine, such as testosterone (r = 0.352, p < 0.01), progesterone (r = 0.228, p < 0.05) and the ratio of cortisol to testosterone (r = 0.502, p < 0.01). Hair biomarkers showed no absolute stability, but moderate to high long-term relative stability across 12 months where interclass correlation coefficients ranged between 0.356 and 0.678 (ps < 0.01). These results implied that the eight biomarkers in hair could retrospectively reflect their cumulative exposure in vivo. Therefore, the hair biomarkers would be considerable reliable long-term biomarkers for psychological and physiological research.

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.

Some immunological biomarkers have been reported to be associated with job-related stress. This study was conducted to explore the intercorrelation between the psychosocial components of job stress and various immunological biomarkers among female nurses. To assess monthly and weekly job stress, 41 nurses have repeatedly completed questionnaires such as the National Institute for Occupational Safety and Health General Job Stress Questionnaire, the profile of mood states short version and the Center for Epidemiologic Studies-Depression scale. Using flow cytometry and radioimmunoassay, the number of white blood cells, lymphocytic proliferation to mitogens, and toxoid were measured. Moreover, levels of hydrocortisol, interleukin-β, interferon-γ, and tumor necrosis factor-α and salivary immunoglobulin A were evaluated by enzyme-linked immunosorbent assay. When the Pearson correlation coefficients between job stress and immunological biomarkers were estimated after adjusting for age and smoking status, \"Clashes: conflict at work\" was significantly related to the number of CD4 cells (r = 0.36, p-value <0.05), CD4 to CD8 ratio (0.35; <0.05), response to concanavalin A (0.42; <0.05), and phytohemagglutinin (0.35; <0.05). Additionally, the level of hydrocortisol was significantly related to seven psychosocial measures; i.e., role conflict (-0.47; <0.01), role ambiguity (-0.39; <0.05), clashes at work (-0.38; <0.05), control and influence at work (0.53; <0.01), task control (0.55; <0.001), resources at work (0.35; <0.05), and skill underutilization (0.43; <0.05). The results indicate that (1) the psychosocial job stress is associated with the levels of some immunological biomarkers in nurses; and in particular, (2) hydrocortisol shows a remarkable relationship with diverse job stress indicators.