Nonmuscle myosin heavy chain IIA facilitates SARS-CoV-2 infection in human pulmonary cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), binds to host receptor angiotensin-converting enzyme 2 (ACE2) through its spike (S) glycoprotein, which mediates membrane fusion and viral entry. However, the expression of ACE2 is extremely low in a variety of human tissues, especially in the airways. Thus, other coreceptors and/or cofactors on the surface of host cells may contribute to SARS-CoV-2 infection. Here, we identified nonmuscle myosin heavy chain IIA (MYH9) as an important host factor for SARS-CoV-2 infection of human pulmonary cells by using APEX2 proximity-labeling techniques. Genetic ablation of MYH9 significantly reduced SARS-CoV-2 pseudovirus infection in wild type (WT) A549 and Calu-3 cells, and overexpression of MYH9 enhanced the pseudovirus infection in WT A549 and H1299 cells. MYH9 was colocalized with the SARS-CoV-2 S and directly interacted with SARS-CoV-2 S through the S2 subunit and S1-NTD (N-terminal domain) by its C-terminal domain (designated as PRA). Further experiments suggested that endosomal or myosin inhibitors effectively block the viral entry of SARS-CoV-2 into PRA-A549 cells, while transmembrane protease serine 2 (TMPRSS2) and cathepsin B and L (CatB/L) inhibitors do not, indicating that MYH9 promotes SARS-CoV-2 endocytosis and bypasses TMPRSS2 and CatB/L pathway. Finally, we demonstrated that loss of MYH9 reduces authentic SARS-CoV-2 infection in Calu-3, ACE2-A549, and ACE2-H1299 cells. Together, our results suggest that MYH9 is a candidate host factor for SARS-CoV-2, which mediates the virus entering host cells by endocytosis in an ACE2-dependent manner, and may serve as a potential target for future clinical intervention strategies.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder.

AIM: Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD. METHODS: The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test. RESULTS: Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks. CONCLUSIONS: Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

Revealing the characteristics of ZIKV infection through tissue-specific transcriptome sequencing analysis.

BACKGROUND: Recently, Zika virus (ZIKV) re-emerged in India and was potentially associated with microcephaly. However, the molecular mechanisms underlying ZIKV pathogenesis remain to be explored. RESULTS: Herein, we performed a comprehensive RNA-sequencing analysis on ZIKV-infected JEG-3, U-251 MG, and HK-2 cells versus corresponding uninfected controls. Combined with a series of functional analyses, including gene annotation, pathway enrichment, and protein-protein interaction (PPI) network analysis, we defined the molecular characteristics induced by ZIKV infection in different tissues and invasion time points. Data showed that ZIKV infection and replication in each susceptible organ commonly stimulated interferon production and down-regulated metabolic-related processes. Also, tissue-specific immune responses or biological processes (BPs) were induced after ZIKV infection, including GnRH signaling pathway in JEG-3 cells, MAPK signaling pathway in U-251 MG cells, and PPAR signaling pathway in HK-2 cells. Of note, ZIKV infection induced delayed antiviral interferon responses in the placenta-derived cell lines, which potentially explains the molecular mechanism by which ZIKV replicates rapidly in the placenta and subsequential vertical transmission occurs. CONCLUSIONS: Together, these data may provide a systemic insight into the pathogenesis of ZIKV infection in distinct human tissue-derived cell lines, which is likely to help develop prophylactic and therapeutic strategies against ZIKV infection.

An Investigation into the Association Between Dopamine Receptor D1 Multilocus Genetic Variation, Multiparametric Magnetic Resonance Imaging, and Antidepressant Treatment.

BACKGROUND: Combining genetic variants with neuroimaging phenotypes may facilitate understanding of the biological mechanisms for the etiology and pharmacology of antidepressant treatment of major depressive disorder (MDD). PURPOSE: To explore the latent pathway of dopamine gene-hierarchical brain network-antidepressant treatment. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty-eight MDD inpatients divided into responders (N = 98) or nonresponders (N = 70) based on the treatment outcome of antidepressant. FIELD STRENGTH/SEQUENCE: Diffusion tensors imaging and resting-state functional magnetic resonance imaging at 3.0T using echo-planar sequence. ASSESSMENT: Four genetic variations of the dopamine receptor D1 (DRD1) were genotyped. Strengths of rich-club, feeder, and local connections were calculated based on the rich-club organizations of structural and functional brain networks at baseline and following 4 weeks of selective serotonin reuptake inhibitor (SSRI) therapy. STATISTICAL TESTS: Logistic and linear regressions were used to analyze the impact of DRD1 multilocus genetic profile score on the treatment response of SSRI, and their associations with strengths of rich-club, feeder, and local connections. Mediation models were developed to explore the mediation role of rich-club organizations on the relationship between DRD1 and SSRI therapy response. A P value <0.05 was considered to be statistically significant. RESULTS: Multiple genetic variations of DRD1 were significantly related to the strengths of feeder connections both in structural and functional networks, and to the treatment response of SSRI. Furthermore, the strength of the structural feeder connection significantly modulated the effect of DRD1 variants on SSRI treatment outcome. DATA CONCLUSION: DRD1 displayed close connections both with SSRI treatment outcome and rich-club organizations of structural and functional data. Moreover, structural feeder connection played a mediating role in the relationship between DRD1 and antidepressant therapy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 4.

Functional impairment-based segmentation of anterior cingulate cortex in depression and its relationship with treatment effects.

In major depressive disorder (MDD), the anterior cingulate cortex (ACC) is widely related to depression impairment and antidepressant treatment response. The multiplicity of ACC subdivisions calls for a fine-grained investigation of their functional impairment and recovery profiles. We recorded resting state fMRI signals from 59 MDD patients twice before and after 12-week antidepressant treatment, as well as 59 healthy controls (HCs). With functional connectivity (FC) between each ACC voxel and four regions of interests (bilateral dorsolateral prefrontal cortex [DLPFC] and amygdalae), subdivisions with variable impairment were identified based on groups' dissimilarity values between MDD patients before treatment and HC. The ACC was subdivided into three impairment subdivisions named as MedialACC, DistalACC, and LateralACC according to their dominant locations. Furthermore, the impairment pattern and the recovery pattern were measured based on group statistical analyses. DistalACC impaired more on its FC with left DLPFC, whereas LateralACC showed more serious impairment on its FC with bilateral amygdalae. After treatment, FCs between DistalACC and left DLPFC, and between LateralACC and right amygdala were normalized while impaired FC between LateralACC and left amygdala kept dysfunctional. Subsequently, FC between DistalACC and left DLPFC might contribute to clinical outcome prediction. Our approach could provide an insight into how the ACC was impaired in depression and partly restored after antidepressant treatment, from the perspective of the interaction between ACC subregions and critical frontal and subcortical regions.

Sub-second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model.

The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient-activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal-spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0-270 ms) was mainly manifested by state 1, and the emotional information processing stage (270-600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10-5 ) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis.

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries-both in two and three dimensions-namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation-called the Digital Confocal Microscopy Suite or DCMS-can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.

Insights into the interactions of plant-associated bacteria and their role in the transfer of antibiotic resistance genes from soil to plant.

This study discussed the role of plant-associated microbiome in regulating ARG transfer in soil-plant systems. Results showed that target ARGs in plants were mainly derived from rhizosphere soil. Cooperative interactions among bacteria in rhizosphere soil, plant-roots, plant-shoots, and soil-roots-shoots systems occurred during ARG transfer. The number of modules and keystone taxa identified as positively correlated with ARG transfer in rhizosphere soil, roots, and shoots was 3 and 49, 3 and 41, 2 and 5, respectively. Among these modules, module 3 in roots was significantly positively correlated with module 3 in rhizosphere soils and module 2 in shoots, indicating that module 3 in roots played central hub roles in ARG transfer from rhizosphere soil to roost and shoots. This may be because module 3 in roots increased cell motility and xenobiotics biodegradation and metabolism. These keystone taxa mainly belonged to Proteobacteria that can carry ARGs to transfer in soil-plant systems, especially Clostridium-sensu_stricito and Pseudomonas in rhizosphere soil carried ARGs into the shoot. Additionally, they promoted ARG transfer by increasing plant biomass, net photosynthetic rate and water use efficiency. The findings helped reveal the mechanism of plant-associated bacterial interactions and provided understanding for potential risks of ARG transfer from soil to plants.

Analysis of flow cytometry data from ultrasound-guided lymph node biopsies with two types of needles.

BACKGROUND: In this study, we combined two techniques, ultrasound-guided needle biopsy and flow cytometry (FCM), to explore their value in patients with enlarged lymph nodes. METHODS: We compared the results of 198 needle biopsies on FCM and pathology. Forty-two were done by (fine needle aspiration, FNA), and the remaining 156 with (core needle biopsy, CNB), in 36 of 156 patients, a FNA was performed in the same lymph node after completion of the CNB. Except for five types of pathological entities, the rest were differentiated only detected or undetected tumours as the outcome distinction. RESULTS: Among the 198 needle biopsies, 13 were inadequate specimens, while the remaining 185 had pathological findings, including 47 benign and 138 neoplastic findings. Thirty-six patients underwent puncture with both FNA and CNB, both needles produced identical results by FCM, but more cells were obtained by FNA. Among the pathologically positive results, there were 23 missed diagnoses in FCM, in contrast, evidence of tumours was observed in the FCM images of 15 needle biopsies that reported benign or findings that were inconsistent with pathology, and the final diagnosis was consistent with the FCM in 10 cases. FCM detected haematolymphoid tumours with a sensitivity of 87.8% and a specificity of 91.9%. CONCLUSION: The combination of FCM and ultrasound-guided lymph node needle biopsy can quickly provide guidance for clinical decision-making. We recommend that all lymph node needle biopsies be sent for FCM, the specimen can be obtained by the last puncture with FNA.

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder.

OBJECTIVE: Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD. METHODS: 532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF. RESULTS: Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%. CONCLUSIONS: The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

Safety and feasibility of anti-CD19 CAR T cells expressing inducible IL-7 and CCL19 in patients with relapsed or refractory large B-cell lymphoma.

Although CD19-specific chimeric antigen receptor (CAR) T cells are curative for patients with relapsed or refractory large B-cell lymphoma (R/R LBCL), disease relapse with tumor antigen-positive remains a challenge. Cytokine/chemokine-expressing CAR-T cells could overcome a suppressive milieu, but the clinical safety and efficacy of this CAR-T therapy remain unclear. Here we report the preclinical development of CD19-specific CAR-T cells capable of expressing interleukin (IL)-7 and chemokine (C-C motif) ligand (CCL)-19 upon CD19 engagement (referred to as 7 × 19 CAR-T cells) and results from a phase 1 and expansion phase trial of 7 × 19 CAR-T cell therapy in patients with R/R LBCL (NCT03258047). In dose-escalation phase, there were no dose-limiting toxicities observed. 39 patients with R/R LBCL received 7 × 19 CAR-T with doses ranged from 0.5 × 106-4.0 × 106 cells per kg body weight. Grade 3 cytokine release syndrome occurred in 5 (12.8%) patients and ≥ grade 3 neurotoxicity in 4 (10.3%) patients. The overall response rate at 3 months post-single infusion was 79.5% (complete remission, 56.4%; partial response, 23.1%). With a median follow-up of 32 months, the median progression-free survival was 13 months, and median overall survival was not reached, with an estimated rate of 53.8% (95% CI, 40.3% to 72.0%) at two years. Together, these long-term follow-up data from the multicenter clinical study suggest that 7 × 19 CAR-T cells can induce durable responses with a median overall survival of greater than 2 years, and have a manageable safety profile in patients with R/R LBCL.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder.

BACKGROUND: Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response. METHODS: Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions. RESULTS: After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969). LIMITATIONS: Relatively small sample size and not a double-blind design. CONCLUSIONS: The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Nonmuscle myosin IIA promotes the internalization of influenza A virus and regulates viral polymerase activity through interacting with nucleoprotein in human pulmonary cells.

Influenza A virus (IAV), responsible for seasonal epidemics and recurring pandemics, represents a global threat to public health. Given the risk of a potential IAV pandemic, it is increasingly important to better understand virus-host interactions and develop new anti-viral strategies. Here, we reported nonmuscle myosin IIA (MYH9)-mediated regulation of IAV infection. MYH9 depletion caused a profound inhibition of IAV infection by reducing viral attachment and internalization in human lung epithelial cells. Surprisingly, overexpression of MYH9 also led to a significant reduction in viral productive infection. Interestingly, overexpression of MYH9 retained viral attachment, internalization, or uncoating, but suppressed the viral ribonucleoprotein (vRNP) activity in a minigenome system. Further analyses found that excess MYH9 might interrupt the formation of vRNP by interacting with the viral nucleoprotein (NP) and result in the reduction of the completed vRNP in the nucleus, thereby inhibiting subsequent viral RNA transcription and replication. Together, we discovered that MYH9 can interact with IAV NP protein and engage in the regulation of vRNP complexes, thereby involving viral replication. These findings enlighten new mechanistic insights into the complicated interface of host-IAV interactions, ultimately making it an attractive target for the generation of antiviral drugs.

Altered resting-state brain activity in major depressive disorder comorbid with subclinical hypothyroidism: A regional homogeneity analysis.

BACKGROUND: Major depressive disorder (MDD), a common mental disorder worldwide, frequently coexists with various physical illnesses, and recent studies have shown an increased prevalence of subclinical hypothyroidism (SHypo) among MDD patients. However, the neural mechanisms shared and unique to these disorders and the associated alterations in brain function remain largely unknown. This study investigated the potential brain function mechanisms underlying comorbid MDD and SHypo. METHOD: Thirty MDD patients (non-comorbid group), 30 MDD patients comorbid with SHypo (comorbid group), 26 patients with SHypo, and 30 healthy controls were recruited for resting-state functional magnetic resonance imaging (rs-fMRI). We used regional homogeneity (ReHo) to examine differences in internal cerebral activity across the four groups. RESULTS: Compared with the non-comorbid group, the comorbid group exhibited significantly higher ReHo values in the right orbital part of the middle frontal gyrus (ORBmid) and bilateral middle frontal gyrus; decreased ReHo values in the right middle temporal gyrus, right thalamus, and right superior temporal gyrus, and right insula. Within the comorbid group, serum TSH levels were negatively associated with the ReHo values of the right insula; the ReHo values of the right Insula were negatively associated with the retardation factor score; the ReHo values of the right ORBmid were positively correlated with the anxiety/somatization factor scores. CONCLUSIONS: These findings provide valuable clues for exploring the shared neural mechanisms between MDD and SHypo and have important implications for understanding the pathophysiological mechanisms of the comorbidity of the two disorders.

Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study.

BACKGROUND: Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments. METHODS: Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis. RESULTS: Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed. CONCLUSIONS: Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Gray matter reduction is associated with cognitive dysfunction in depressed patients comorbid with subclinical hypothyroidism.

Introduction: To explore the association between regional gray matter volume (GMV) and cognitive impairments and ascertain whether the regional brain alterations related to cognitive impairments occur in major depressive disorder (MDD) patients with comorbid subclinical hypothyroidism (SHypo). Methods: We enrolled 32 MDD patients, 32 MDD patients with comorbid SHypo, and 32 normal controls and subjected them to thyroid function tests, neurocognitive tests, and magnetic resonance imaging (MRI). Using voxel-based morphometry (VBM) analysis, we examined the pattern of gray matter (GM) in these participants. We also used ANOVA to detect group differences and partial correlation to explore the potential association between GMV alterations and cognitive tests in comorbid patients. Results: The comorbid patients exhibited significantly smaller GMV in the right middle frontal gyrus (MFG) than the non-comorbid group. Furthermore, the partial correlation analysis showed that GMV of the right MFG was associated with poor executive function (EF) performance in comorbid patients. Conclusion: These findings provide valuable insight into the relationship between the alteration of GMV and cognitive dysfunction of MDD patients with comorbid SHypo.

[Effects of shikonin on the proliferation and apoptosis of HL-60 cells].

OBJECTIVE: To explore the mechanism of shikonin for inducing the apoptosis of human promyelocytic leukemia cell HL-60. METHODS: The effects of shikonin on the HL-60 cell proliferation were detected using MTT. The apoptosis rate was analyzed by Annexin-V/PI double staining. The expression level of the bcl-2 gene was detected using semi-quantitative reverse transcriptase PCR (RT-PCR), thus analyzing the correlation between the bcl-2 expression level and the apoptosis of HL-60. RESULTS: Shikonin could inhibit the proliferation of HL-60 cells with the concentration range of 1-8 microg/mL in a time- and concentration-dependent manner. Two microg/mL shikonin could induce the apoptosis of HL-60 cells in a time-dependent manner. The expression level of bcl-2 was obviously down-regulated at 2 microg/mL shikonin. CONCLUSIONS: Shikonin could induce the apoptosis of HL-60 cells. Its mechanism was correlated with down-regulation of the expression level of bcl-2.