Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. METHODS: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. RESULTS: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. CONCLUSION: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

The activation of RARα prevents surgery-induced cognitive impairments via the inhibition of neuroinflammation and the restoration of synaptic proteins in elderly mice.

Post-operative cognitive dysfunction (POCD) is a multi-etiological symptom mainly occurred in elderly people after surgery. The activation of retinoic acid receptor α (RARα), a transcriptional factor, was previously predicated to be negatively associated with the occurrence of POCD. However, the mechanisms underlying anti-POCD effects of RARα were still unclear. In this study, AM580, a selective agonist of RARα, and all-trans-retinoic acid (ATRA), a pan agonist of RAR, significantly alleviated cognitive dysfunction and increased the expression of RARα in elderly mice after surgery, which was decreased by RO41-5253, an antagonist of RARα. A bioinformatic study further predicted that the activation of RARα might produce anti-POCD effects via the restoration of synaptic proteins. Both agonists inhibited the expression of Toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (Myd88) and the phosphorylation of nuclear factorkappa-B (NF-κB), leading to the prevention of microglial over-activation and pro-inflammatory cytokines secretion in the hippocampal regions of elderly mice after surgery. Moreover, AM580 and ATRA increased the expression of brain-derived neurotrophic factor (BDNF) and postsynaptic density protein 95 (PSD95), and the phosphorylation of extracellular signal-regulated kinase (ERK) and cAMP-response element binding protein (CREB). All these results suggested that the activation of RARα prevented surgery-induced cognitive impairments via the inhibition of neuroinflammation by the reduction of the TLR4/Myd88/NF-κB pathway and the restoration of synaptic proteins by the activation of the BDNF/ERK/CREB pathway, providing a further support that RARα could be developed as a therapeutic target for POCD.

Retinoic Acid Receptor Is a Novel Therapeutic Target for Postoperative Cognitive Dysfunction.

Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was established by using differently expressed landmark genes in the serum samples of POCD and non-POCD patients from the only human transcriptome study. The predictability and reliability of this model were further supported by the positive CMap scores of known POCD inducers and the negative CMap scores of anti-POCD drug candidates. Most retinoic acid receptor (RAR) agonists were negatively associated with POCD in this CMap model, suggesting that RAR might be a novel target for POCD. Most importantly, acitretin, a clinically used RAR agonist, significantly inhibited surgery-induced cognitive impairments and prevented the reduction in RARα and RARα-target genes in the hippocampal regions of aged mice. The study denotes a reliable CMap bioinformatics model of POCD for future use and establishes that RAR is a novel therapeutic target for treating this clinical syndrome.

Fucoxanthin, a marine derived carotenoid, attenuates surgery-induced cognitive impairments via activating Akt and ERK pathways in aged mice.

BACKGROUND: Fucoxanthin is the most abundant marine carotenoid derived from brown seaweeds, possesses antioxidant, anti-inflammatory, and neuroprotective properties, and might be benefit for the treatment of neurological disorders. Post-operative cognitive dysfunction (POCD) is a neurological symptom with learning and memory impairments, mainly affecting the elderly after surgery. However, there is no effective treatments for this symptom. PURPOSES: In this study, we evaluated the neuroprotective effects of fucoxanthin against POCD in aged mice after surgery. STUDY DESIGN AND METHODS: The animal model of POCD was established in 12 - 14 month aged mice with a laparotomy. Curcumin was used as a positive control. The beneficial effects of fucoxanthin on POCD was analyzed by behavioral tests. Pro-inflammatory cytokines were measured by Enzyme-linked Immunosorbent Assay (ELISA). And the expressions of key proteins in the Akt and ERK signaling pathways were analyzed by Western blotting analysis. The morphology of microglial cells and astrocytes was explored by immunohistochemical staining. The activity of antioxidant superoxide dismutase (SOD) and catalase (CAT) were measured by anti-oxidative enzyme activity assays. RESULTS: Fucoxanthin at 100 - 200 mg/kg significantly attenuated cognitive dysfunction, with a similar potency as curcumin, in aged mice after surgery. In addition, fucoxanthin and curcumin significantly increased the expression of pAkt, prevented the activation of microglial cells and astrocytes, and inhibited the secretion of pro-inflammatory interleukin-1ß (IL - 1ß) and tumor necrosis factor-α (TNF-α). Furthermore, fucoxanthin and curcumin elevated the ERK pathway and potently increased the activity of antioxidant enzymes. Most importantly, U0126, an inhibitor of the ERK pathway, and wortmannin, an inhibitor of the Akt pathway, significantly abolished the cognitive-enhancing effects, as well as the inhibition of neuroinflammation and the reduction of oxidative stress, induced by fucoxanthin in aged mice after surgery. CONCLUSION: Fucoxanthin might be developed as a functional food or drug for the treatment of POCD by inhibiting neuroinflammation and enhancing antioxidant capacity via the activation of the Akt and ERK signaling pathways.

Involvement of the SIRT1-NLRP3 pathway in the inflammatory response.

The silent information regulator 2 homolog 1-NACHT, LRR and PYD domains-containing protein 3 (SIRT1-NLRP3) pathway has a crucial role in regulation of the inflammatory response, and is closely related to the occurrence and development of several inflammation-related diseases. NLRP3 is activated to produce the NLRP3 inflammasome, which leads to activation of caspase-1 and cleavage of pro-interleukin (IL)-1ß and pro-IL-18 to their active forms: IL-1ß and IL-18, respectively. They are proinflammatory cytokines which then cause an inflammatory response.SIRT1 can inhibit this inflammatory response through nuclear factor erythroid 2-related factor 2 and nuclear factor-kappa B pathways. This review article focuses mainly on how the SIRT1-NLRP3 pathway influences the inflammatory response and its relationship with melatonin, traumatic brain injury, neuroinflammation, depression, atherosclerosis, and liver damage. Video Abstract.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Background and objective: Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. Results: All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. Conclusion: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.

Background: Although depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET. Methods: Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance. Results: The MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity. Conclusion: Our findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.

Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential tremor.

Essential tremor (ET) is the most common tremor disorder, and the intrinsic brain activity changes and diagnostic biomarkers of ET remain unclear. Combined multivariate pattern analysis (MVPA) with resting-state functional MRI (Rs-fMRI) data provides the most promising way to identify individual subjects, reveal brain activity changes, and further establish diagnostic biomarkers in neurological diseases. Using voxel-level amplitude of low-frequency fluctuations (ALFF) and local (regional homogeneity, ReHo) and global (degree centrality, DC) brain connectivity mappings based on three frequency bands (classical band: 0.01-0.10 Hz; slow-5: 0.01-0.023 Hz; slow-4: 0.023-0.073 Hz) of 162 ET patients and 153 well-matched healthy controls (HCs) as input features, MVPA (binary support vector machine, SVM) was performed to differentiate ET from HCs. Each modality achieved good classification performance, except for ReHo based on the slow-4 band with a sensitivity, specificity and total accuracy of 58.64%, 65.36%, 61.90%, respectively (P < 0.05). The classification performance with slow-4 bands was poorer than that with slow-5 and classical bands, but slow-4 bands could be used to reveal the spatial distribution changes in subcortical structures, especially the thalamus. The significant discriminative features were mostly located in the cerebello-thalamo-cortical pathway, and partial correlation analyses showed that significant discriminative features in the cerebello-thalamo-cortical pathway could be used to explain the clinical features of tremor in ET patients. Our findings revealed that voxel-level frequency-dependent ALFF, ReHo and DC could be used to discriminate ET from HCs and help to reveal intrinsic brain activity changes, further acting as potential diagnostic biomarkers.

Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance. Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET. Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

Altered regional homogeneity and connectivity in cerebellum and visual-motor relevant cortex in Parkinson's disease with rapid eye movement sleep behavior disorder.

OBJECTIVE: Rapid eye movement sleep behavior disorder (RBD) frequently occurs in Parkinson's disease (PD), however, the exact pathophysiological mechanism underlying its occurrence is not clear. In this study, we explored whether there is abnormal spontaneous neuronal activities and connectivity maps in some brain areas under resting-state in PD patients with RBD. METHODS: We recruited 38 PD patients (19 PD with RBD and 19 PD without RBD), and 20 age- and gender-matched normal controls. We used resting-state functional magnetic resonance imaging (RS-fMRI) to analyze regional homogeneity (ReHo) and functional connectivity (FC), and further to reveal the neuronal activity in all subjects. RESULTS: Compared with the PD without RBD patients, the PD with RBD patients showed a significant increase in regional homogeneity in the left cerebellum, the right middle occipital region and the left middle temporal region, and decreased regional homogeneity in the left middle frontal region. The REM sleep behavioral disorders questionnaire scores were significantly positively correlated with the ReHo values of the left cerebellum. The functional connectivity analysis in which the four regions described above were used as regions of interest revealed increased functional activity between the left cerebellum and bilateral occipital regions, bilateral temporal regions and bilateral supplementary motor area. CONCLUSION: The pathophysiological mechanism of PD with RBD may be related to abnormal spontaneous neuronal activity patterns with strong synchronization of cerebellar and visual-motor relevant cortex, and the increased connectivity of the cerebellum with the occipital and motor regions.

First Report of Wilt of European hornbeam (Carpinus betulus L.) Caused by Fusarium oxysporum in China.

European hornbeam (Carpinus betulus L.) has been used as an important ornamental species for urban landscaping since the Italian Renaissance (Rocchi et al. 2010). In May 2019, 15% of 3000 C. betulus trees with wilted leaves and root rot were observed in a field (about 26 ha) in Pizhou, Jiangsu Province, China. Internal discoloration of the stem began with brown to black discoloration of the vascular system and gradually spread to inward areas. Roots and stems from symptomatic plants were washed free of soil, surface sterilized with 0.8% NaOCl, rinsed three times in sterile H2O, and blotted dry with a paper towel. Small segments (0.5-cm-long) were cut from the discolored vascular tissues, and then put on potato dextrose agar (PDA) at 25°C in darkness. After 4 days, fungal colonies were observed on the PDA. Pure cultures were obtained by monosporic isolation, and 9 morphologically similar fungal isolates (EJ-1 to EJ-9) were obtained. All purified cultures were incubated on PDA at 25°C in darkness as the initial isolation. Colonies of the 9 isolates on PDA displayed entire margins and showed abundant pink aerial mycelia initially and turned to light violet with age. Microconidia were elliptical or oval in shape, 0 septate, (5.2-)8.7(-12.5) × (3.5-)3.6(-5.5) µm. Macroconidia were falciform, 0-4 septate, and straight to slightly curved with a notched foot cell, (17.1-)20.5(-28.4) × (3.8-)4.1(-4.6) µm. These morphological characteristics resemble Fusarium oxysporum (Leslie and Summerell 2006). Genomic DNA of each isolate was extracted from mycelia using a CTAB method (Mo¨ller et al. 1992). The RPB2, TEF1 and cmdA genes were amplified and sequenced with the primers 5f2/7c (Liu et al. 2000), EF-1Ha/EF-2Tb (Carbone and Kohn 1999) and Cal228F/CAL2Rd (Groenewald et al. 2013), respectively. The sequences were deposited in GenBank (Table 1). A maximum likelihood phylogenetic analysis based on RPB2, TEF1 and cmdA sequences using MEGA7 revealed that the isolates were placed in the F. oxysporum species complex with 98% bootstrap support. Based on the morphological and molecular characters, all 9 isolates were identified as F. oxysporum. A pathogenicity experiment was conducted using 30 2-year-old C. betulus seedlings potted in sterile peat, 27 for inoculation (3 replicate plants per isolate) and 3 for a negative control. The treated plants were planted in the peat mixed with 50 ml of a conidial suspension of each isolate respectively. The negative control was inoculated with sterilized water. Conidia were harvested from colonized plates of PDA using sterilized water and adjusted to a concentration of 1×107 conidia/ml. All 30 seedlings were incubated in a greenhouse at 25°C with a relative humidity of 80% and a 12-h photoperiod. The inoculated seedlings displayed wilt symptoms within 30 to 40 days, and eventually died within 75 to 85 days after inoculation. Control plants remained symptomless. F. oxysporum was successfully reisolated from the vascular tissues of symptomatic plants, and sequences of RPB2, TEF1 and cmdA of re-isolates matched those of the original isolates. No pathogen was isolated from the tissues of control plants. The experiment was repeat twice with the similar results, fulfilling Koch's postulates. F. oxysporum is an important soil-borne pathogen and can cause disease in many economic plants, such as yellowwood (Graney et al. 2016), hickory (Zhang et al. 2015) and larch (Rolim et al. 2020). To our knowledge, this is the first report of wilt on C. betulus caused by F. oxysporum in China.

Altered local and matrix functional connectivity in depressed essential tremor patients.

BACKGROUND: Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided a promising way to observe spontaneous brain activity. METHODS: Local FC analyses were performed in forty-one depressed ET patients, 49 non-depressed ET patients and 43 healthy controls (HCs), and then matrix FC and clinical depression severity correlation analyses were further performed to reveal spontaneous neural activity changes in depressed ET patients. RESULTS: Compared with the non-depressed ET patients, the depressed ET patients showed decreased ReHo in the bilateral cerebellum lobules IX, and increased ReHo in the bilateral anterior cingulate cortices and middle prefrontal cortices. Twenty-five significant changes of ReHo clusters were observed in the depressed ET patients compared with the HCs, and matrix FC analysis further revealed that inter-ROI FC differences were also observed in the frontal-cerebellar-anterior cingulate cortex pathway. Correlation analyses showed that clinical depression severity was positively correlated with the inter-ROI FC values between the anterior cingulate cortex and bilateral middle prefrontal cortices and was negatively correlated with the inter-ROI FC values of the anterior cingulate cortex and bilateral cerebellum lobules IX. CONCLUSION: Our findings revealed local and inter-ROI FC differences in frontal-cerebellar-anterior cingulate cortex circuits in depressed ET patients, and among these regions, the cerebellum lobules IX, middle prefrontal cortices and anterior cingulate cortices could function as pathogenic structures underlying depression in ET patients.

First report of leaf spot disease caused by Stemphylium eturmiunum on American sweetgum (Liquidambar styraciflua L.) in China.

American sweetgum (Liquidambar styraciflua L.) is an important tree for landscaping and wood processing. In recent years, leaf spots on American sweetgum with disease incidence of about 53% were observed in about 1200 full grown plants in a field (about 8 ha) located in Pizhou, Jiangsu Province, China. Initially, dense reddish-brown spots appeared on both old and new leaves. Later, the spots expanded into dark brown lesions with yellow halos. Symptomatic leaf samples from different trees were collected and processed in the laboratory. For pathogen isolation, leaf sections (4×4mm) removed from the lesion margin were surface sterilized with 75% ethanol for 20s and then sterilized in 2% NaOCl for 30s, rinsed three times in sterile distilled water, incubated on potato dextrose agar (PDA) at 25 °C in the darkness. After 5 days of cultivation, the pure culture was obtained by single spore separation. 6 isolate samples from different leaves named FXA1 to FXA6 shared nearly identical morphological features. The isolate FXA1 (codes CFCC 54675) was deposited in the China Center for Type Culture Collection. On the PDA, the colonies were light yellow with dense mycelium, rough margin, and reverse brownish yellow. Conidiophores (23-35 × 6-10 µm) (n=60) were solitary, straight to flexuous. Conidia (19-34 × 10-21 µm) (n=60) were single, muriform, oblong, mid to deep brown, with 1 to 6 transverse septa. These morphological characteristics resemble Stemphylium eturmiunum (Simmons 2001). Genomic DNA was extracted from mycelium following the CTAB method. The ITS region, gapdh, and cmdA genes were amplified and sequenced with the primers ITS5/ITS4 (Woudenberg et al. 2017), gpd1/gpd2 (Berbee et al. 1999), and CALDF1/CALDR2 (Lawrence et al. 2013), respectively. A maximum likelihood phylogenetic analysis based on ITS, gapdh and cmdA (accession nos. MT898502-MT898507, MT902342-MT902347, MT902336-MT902341) sequences using MEGA 7.0 revealed that the isolates were placed in the same clade as S. eturmiunum with 98% bootstrap support. All seedlings for pathogenicity tests were enclosed in plastic transparent incubators to maintain high relative humidity (90%-100%) and incubated in a greenhouse at 25°C with a 12-h photoperiod. For pathogenicity, the conidial suspension (105 spores/ml) of each isolate was sprayed respectively onto healthy leaves of L. styraciflua potted seedlings (2-year-old, 3 replicate plants per isolate). As a control, 3 seedlings were sprayed with sterile distilled water. After 7 days, dense reddish-brown spots were observed on all inoculated leaves. In another set of tests, healthy plants (3 leaves per plant, 3 replicate plants per isolate) were wound-inoculated with mycelial plugs (4×4mm) and inoculated with sterile PDA plugs as a control. After 7 days, brown lesions with light yellow halo were observed on all inoculation sites with the mycelial plugs. Controls remained asymptomatic in the entire experiment. The pathogen was reisolated from symptomatic tissues and identified as S. eturmiunum but was not recovered from the control. The experiment was repeated twice with the similar results, fulfilling Koch's postulates. S. eturmiunum had been reported on tomato (Andersen et al. 2004), wheat (Poursafar et al. 2016), garlic (L. Fu et al. 2019) but not on woody plant leaves. To our knowledge, this is the first report of S. eturmiunum causing leaf spot on L. styraciflua in the world. This disease poses a potential threat to American sweetgum and wheat in Pizhou.

Altered resting-state voxel-level whole-brain functional connectivity in depressed Parkinson's disease.

BACKGROUND: Depression is one of the most common non-motor symptoms in Parkinson's disease (PD), but its pathogenesis is still not very clear. Recently, degree centrality, a voxel-level whole-brain functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided the most promising way to explore the neural network mechanisms underlying depressed PD. METHODS: Degree centrality, voxel-wise image and clinical symptoms correlation and secondary seed-based FC analyses were performed in twenty-seven drug-naïve, early stage depressed PD patients, 27 non-depressed PD patients and 27 healthy controls (HCs) to reveal voxel-level whole-brain FC changes in depressed PD. RESULTS: Compared with the HCs, depressed PD and non-depressed PD patients shared similar brain degree centrality abnormalities mainly in the basal ganglia, insular cortex, motor cortices, default mode network, prefrontal gyrus and the cerebellum. However, compared with non-depressed PD, depressed PD showed degree centrality abnormalities in the right middle prefrontal gyrus, anterior cingulate cortices, supplementary motor cortices and cerebellum lobule VI. The right middle prefrontal gyrus degree centrality abnormalities were correlated with the clinical depression severity, and using it as a seed, a secondary seed-based FC analysis further revealed the FC changes in the anterior cingulate cortices and the cerebellum lobule VI. CONCLUSIONS: Our findings revealed that dysfunction in extensive brain areas were involved in depressed PD, and among these regions, the right middle prefrontal gyrus, anterior cingulate cortices and the cerebellum may pose as pathogenesis hubs underlying depressed PD.

Role and mechanism of insulin-like growth factor 2 on the proliferation of human trophoblasts in vitro.

AIM: To study the effect and relevant molecular mechanisms of insulin-like growth factor 2 (IGF2) on the proliferative activity of first trimester human trophoblasts in vitro. MATERIALS AND METHODS: Extravillous cytotrophoblasts (EVCTs) were isolated and cultured. Cells were cultured with IGF2 at different concentrations and the proliferative activity was measured using methyl thiazolyl tretrazolium assay. LY294002, a specific inhibitor of the phosphatidylinositol 3-kinase (PI3K), was used as an indirect indicator of the possible involvement of the PI3K signal pathway. We tested the apoptosis rate using flow cytometry technology influenced by IGF2 with or without LY294002. The effects of IGF2 on phosphorylation of key cell signaling proteins (protein kinase B [AKT] and phosphorylated AKT) in EVCTs were examined by western blot analysis with or without LY294002. RESULTS: There was a significant difference between the IGF2 group above 10 nM and the control group (P < 0.05). LY294002 (10 µM) not only inhibited the proliferative activity of EVCT, but also significantly restrained the effect on EVCTs (P < 0.05). In vitro data proved that the apoptosis rate decreased when IGF2 was added (P < 0.05), but increased when inhibited by LY294002 (P < 0.05). After incubation with IGF2, AKT phosphorylation increased compared to incubation without IGF2 treatment (P < 0.05). LY294002 activation reduced the IGF2-induced effects (P < 0.05). CONCLUSIONS: Our data suggest that IGF2 enhances EVCT proliferation and inhibits apoptosis. The PI3K/AKT pathway is an important signaling pathway in the proliferative activity of EVCTs on early human pregnancy in vitro.

Essential tremor is associated with disruption of functional connectivity in the ventral intermediate Nucleus--Motor Cortex--Cerebellum circuit.

The clinical benefits of targeting the ventral intermediate nucleus (VIM) for the treatment of tremors in essential tremor (ET) patients suggest that the VIM is a key hub in the network of tremor generation and propagation and that the VIM can be considered as a seed region to study the tremor network. However, little is known about the central tremor network in ET patients. Twenty-six ET patients and 26 matched healthy controls (HCs) were included in this study. After considering structural and head-motion factors and establishing the accuracy of our seed region, a VIM seed-based functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging (RS-fMRI) data was performed to characterize the VIM FC network in ET patients. We found that ET patients and HCs shared a similar VIM FC network that was generally consistent with the VIM anatomical connectivity network inferred from normal nonhuman primates and healthy humans. Compared with HCs, ET patients displayed VIM-related FC changes, primarily within the VIM-motor cortex (MC)-cerebellum (CBLM) circuit, which included decreased FC in the CBLM and increased FC in the MC. Importantly, tremor severity correlated with these FC changes. These findings provide the first evidence that the pathological tremors observed in ET patients might be based on a physiologically pre-existing VIM - MC - CBLM network and that disruption of FC in this physiological network is associated with ET. Further, these findings demonstrate a potential approach for elucidating the neural network mechanisms underlying this disease.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

INTRODUCTION: The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. METHODS: Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. RESULTS: Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. CONCLUSIONS: The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome.