Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data ...analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
•A novel quantitative measure reflecting functional connectivity asymmetry of brain was employed.•The largest resting-state fMRI dataset of major depressive disorder in China was used.•Many brain ...networks contribute to broad clinical pathophysiology of MDD.•A lateralized, efficient and economical brain information processing system is disrupted in MDD.
: Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied.
: Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes).
: Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities.
: Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results.
: Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
While gastrointestinal (GI) symptoms are very common in patients with major depressive disorder (MDD), few studies have investigated the neural basis behind these symptoms. In this study, we sought ...to elucidate the neural basis of GI symptoms in MDD patients by analyzing the changes in regional gray matter volume (GMV) and gray matter density (GMD) in brain structure.
Subjects were recruited from 13 clinical centers and categorized into three groups, each of which is based on the presence or absence of GI symptoms: the GI symptoms group (MDD patients with at least one GI symptom), the non-GI symptoms group (MDD patients without any GI symptoms), and the healthy control group (HCs). Structural magnetic resonance images (MRI) were collected of 335 patients in the GI symptoms group, 149 patients in the non-GI symptoms group, and 446 patients in the healthy control group. The 17-item Hamilton Depression Rating Scale (HAMD-17) was administered to all patients. Correlation analysis and logistic regression analysis were used to determine if there was a correlation between the altered brain regions and the clinical symptoms.
There were significantly higher HAMD-17 scores in the GI symptoms group than that of the non-GI symptoms group (P < 0.001). Both GMV and GMD were significant different among the three groups for the bilateral superior temporal gyrus, bilateral middle temporal gyrus, left lingual gyrus, bilateral caudate nucleus, right Fusiform gyrus and bilateral Thalamus (GRF correction, cluster-P < 0.01, voxel-P < 0.001). Compared to the HC group, the GI symptoms group demonstrated increased GMV and GMD in the bilateral superior temporal gyrus, and the non-GI symptoms group demonstrated an increased GMV and GMD in the right superior temporal gyrus, right fusiform gyrus and decreased GMV in the right Caudate nucleus (GRF correction, cluster-P < 0.01, voxel-P < 0.001). Compared to the non-GI symptoms group, the GI symptoms group demonstrated significantly increased GMV and GMD in the bilateral thalamus, as well as decreased GMV in the bilateral superior temporal gyrus and bilateral insula lobe (GRF correction, cluster-P < 0.01, voxel-P < 0.001). While these changed brain areas had significantly association with GI symptoms (P < 0.001), they were not correlated with depressive symptoms (P > 0.05). Risk factors for gastrointestinal symptoms in MDD patients (p < 0.05) included age, increased GMD in the right thalamus, and decreased GMV in the bilateral superior temporal gyrus and left Insula lobe.
MDD patients with GI symptoms have more severe depressive symptoms. MDD patients with GI symptoms exhibited larger GMV and GMD in the bilateral thalamus, and smaller GMV in the bilateral superior temporal gyrus and bilateral insula lobe that were correlated with GI symptoms, and some of them and age may contribute to the presence of GI symptoms in MDD patients.
•The present study is the first to investigate the GMV and GMD changes of the regional brain in MDD patients with GI symptoms through multicenter clinical data, based on the VBM of structural MRI.•The study found MDD patients with GI symptoms have more severe depressive symptoms.•The study pointed out that the dysfunction of CNS may be one of the important pathophysiologic mechanisms causing GI symptoms in MDD patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Human and animal fungal pathogens are a growing threat worldwide leading to emerging infections and creating new risks for established ones. There is a growing need for a rapid and accurate ...identification of pathogens to enable early diagnosis and targeted antifungal therapy. Morphological and biochemical identification methods are time-consuming and require trained experts. Alternatively, molecular methods, such as DNA barcoding, a powerful and easy tool for rapid monophasic identification, offer a practical approach for species identification and less demanding in terms of taxonomical expertise. However, its wide-spread use is still limited by a lack of quality-controlled reference databases and the evolving recognition and definition of new fungal species/complexes. An international consortium of medical mycology laboratories was formed aiming to establish a quality controlled ITS database under the umbrella of the ISHAM working group on “DNA barcoding of human and animal pathogenic fungi.” A new database, containing 2800 ITS sequences representing 421 fungal species, providing the medical community with a freely accessible tool at http://www.isham.org/ and http://its.mycologylab.org/ to rapidly and reliably identify most agents of mycoses, was established. The generated sequences included in the new database were used to evaluate the variation and overall utility of the ITS region for the identification of pathogenic fungi at intra-and interspecies level. The average intraspecies variation ranged from 0 to 2.25%. This highlighted selected pathogenic fungal species, such as the dermatophytes and emerging yeast, for which additional molecular methods/genetic markers are required for their reliable identification from clinical and veterinary specimens.
Nonalcoholic fatty liver disease (NAFLD) had become the most prevalent liver disease worldwide. Early diagnosis could effectively reduce NAFLD-related morbidity and mortality. This study aimed to ...combine the risk factors to develop and validate a novel model for predicting NAFLD.
We enrolled 578 participants completing abdominal ultrasound into the training set. The least absolute shrinkage and selection operator (LASSO) regression combined with random forest (RF) was conducted to screen significant predictors for NAFLD risk. Five machine learning models including logistic regression (LR), RF, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and support vector machine (SVM) were developed. To further improve model performance, we conducted hyperparameter tuning with train function in Python package ‘sklearn’. We included 131 participants completing magnetic resonance imaging into the testing set for external validation.
There were 329 participants with NAFLD and 249 without in the training set, while 96 with NAFLD and 35 without were in the testing set. Visceral adiposity index, abdominal circumference, body mass index, alanine aminotransferase (ALT), ALT/AST (aspartate aminotransferase), age, high-density lipoprotein cholesterol (HDL-C) and elevated triglyceride (TG) were important predictors for NAFLD risk. The area under curve (AUC) of LR, RF, XGBoost, GBM, SVM were 0.915 95% confidence interval (CI): 0.886–0.937, 0.907 (95% CI: 0.856–0.938), 0.928 (95% CI: 0.873–0.944), 0.924 (95% CI: 0.875–0.939), and 0.900 (95% CI: 0.883–0.913), respectively. XGBoost model presented the best predictive performance, and its AUC was enhanced to 0.938 (95% CI: 0.870–0.950) with further parameter tuning.
This study developed and validated five novel machine learning models for NAFLD prediction, among which XGBoost presented the best performance and was considered a reliable reference for early identification of high-risk patients with NAFLD in clinical practice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
BACKGROUNDEarly identification of metabolic-associated fatty liver disease (MAFLD) is urgent. Atherogenic index of plasma (AIP) is a reference predictor of obesity-related diseases, but its ...predictive value for MAFLD remains unclear. No studies have reported whether its combination with waist circumference (WC) and body mass index (BMI) can improve the predictive performance for MAFLD. AIMTo systematically explore the relationship between AIP and MAFLD and evaluate its predictive value for MAFLD and to pioneer a novel noninvasive predictive model combining AIP, WC, and BMI while validating its predictive performance for MAFLD. METHODSThis cross-sectional study consecutively enrolled 864 participants. Multivariate logistic regression analysis and receiver operating characteristic curve were used to evaluate the relationship between AIP and MAFLD and its predictive power for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI to predict MAFLD was established, and internal verification was completed by magnetic resonance imaging diagnosis. RESULTSSubjects with higher AIP exhibited a significantly increased risk of MAFLD, with an odds ratio of 12.420 (6.008-25.675) for AIP after adjusting for various confounding factors. The area under receiver operating characteristic curve of the A-W-B model was 0.833 (0.807-0.858), which was significantly higher than that of AIP, WC, and BMI (all P < 0.05). Subgroup analysis illustrated that the A-W-B model had significantly higher area under receiver operating characteristic curves in female, young and nonobese subgroups (all P < 0.05). The best cutoff values for the A-W-B model to predict MAFLD in males and females were 0.5932 and 0.4105, respectively. Additionally, in the validation set, the area under receiver operating characteristic curve of the A-W-B model to predict MAFLD was 0.862 (0.791-0.916). The A-W-B level was strongly and positively associated with the liver proton density fat fraction (r = 0.630, P < 0.001) and significantly increased with the severity of MAFLD (P < 0.05). CONCLUSIONAIP was strongly and positively associated with the risk of MAFLD and can be a reference predictor for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI can significantly improve the predictive ability of MAFLD and provide better services for clinical prediction and screening of MAFLD.
Abstract
Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD ...patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a ‘suicidality gradient’. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (
p
< 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (
p
< 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73–0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.
Osteoarthritis (OA) is a common degenerative joint disease. Inflammation may exaggerate the catabolism and degeneration in the pathogenesis of OA. Hydroxytyrosol (HT) has been used in the management ...of inflammatory diseases. In addition, reports have revealed that autophagy was a therapeutic target of diseases caused by inflammation. Sirtuin 6 (SIRT6) has also been demonstrated to prevent OA development by reducing both the inflammatory response and chondrocyte senescence. However, the roles of SIRT6 and autophagy in cartilage and its underlying anti‑inflammatory mechanism are unknown. Therefore, the present study aimed to determine the effects of HT on autophagy and inflammation in chondrocytes, and clarify whether HT regulates the inflammatory response through SIRT6‑mediated autophagy. The expression of protein and mRNA were determined by western blot analysis and reverse transcription‑quantitative polymerase chain reaction. The production of cytokines was detected by ELISA. It was demonstrated that HT inhibited the levels of interleukin (IL)‑1β and IL‑6 in tumor necrosis factor (TNF)‑α‑stimulated chondrocytes in a concentration‑dependent manner. In addition, HT promoted cell autophagy and increased the mRNA and protein expression levels of SIRT6 in chondrocytes stimulated with TNF-α. Autophagy inhibitor 3-methyladenine or knockdown of SIRT6 decreased the inhibitory effects of HT on the inflammatory response in chondrocytes. In addition, knockdown of SIRT6 attenuated the expression of microtubule-associated protein 1A/1B‑light chain 3 and Beclin1 in chondrocytes. Overall, these findings suggested that HT inhibits the inflammatory response of chondrocytes through SIRT6‑mediated autophagy. The present study provided a new drug target for the clinical treatment of inflammatory diseases.
Objective
To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images.
Methods
Healthy controls and volunteers confirmed to have ...NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.
Results
A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 95% confidence interval (CI) 0.68–0.98. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66–0.92) in validation set.
Conclusions
The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, ...inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.
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