Unraveling a Concerted Proton-Coupled Electron Transfer Pathway in Atomically Precise Gold Nanoclusters.

Metal nanoclusters (MNCs) represent a promising class of materials for catalytic carbon dioxide and proton reduction as well as dihydrogen oxidation. In such reactions, multiple proton-coupled electron transfer (PCET) processes are typically involved, and the current understanding of PCET mechanisms in MNCs has primarily focused on the sequential transfer mode. However, a concerted transfer pathway, i.e., concerted electron-proton transfer (CEPT), despite its potential for a higher catalytic rate and lower reaction barrier, still lacks comprehensive elucidation. Herein, we introduce an experimental paradigm to test the feasibility of the CEPT process in MNCs, by employing Au18(SR)14 (SR denotes thiolate ligand), Au22(SR)18, and Au25(SR)18- as model clusters. Detailed investigations indicate that the photoinduced PCET reactions in the designed system proceed via an CEPT pathway. Furthermore, the rate constants of gold nanoclusters (AuNCs) have been found to be correlated with both the size of the cluster and the flexibility of the Au-S framework. This newly identified PCET behavior in AuNCs is prominently different from that observed in semiconductor quantum dots and plasmonic metal nanoparticles. Our findings are of crucial importance for unveiling the catalytic mechanisms of quantum-confined metal nanomaterials and for the future rational design of more efficient catalysts.

Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease.

The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.

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.

Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma.

OBJECTIVES: To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort. RESULTS: The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report. CONCLUSIONS: The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC. KEY POINTS: • The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

Paeonol improves renal and vascular angiotensin II type 1 receptor function via inhibiting oxidative stress in spontaneously hypertensive rats.

BACKGROUND: Oxidative stress has been shown to play a critical role in the pathogenesis of hypertension. Paeonol, a major phenolic component extracted from Moutan Cortex, exerts a beneficial effect in preventing cardiovascular disease via reducing oxidative stress. The present study investigated the protective mechanism of paeonol against high blood pressure in spontaneous hypertension rats (SHRs). METHODS: Wistar-Kyoto (WKY) rats and SHRs received vehicle or peaonol in the drinking water for 5 weeks. Blood pressure was measured by tail-cuff plethysmography and oxidative stress in kidney and vascular tissue was examined by enzyme-linked immunosorbed assay. The functions of angiotensin II type 1 receptors (AT1R) in the kidney and mesenteric artery were measured by natriuresis and vasoconstrictor response, respectively. RESULTS: Compared with vehicle-treated WKY rats, vehicle-treated SHRs exhibited higher blood pressure, increased oxidative stress, accompanied by exaggerated diuretic and natriuretic responses to candesartan (AT1 receptor antagonist) and vasoconstrictor responses to angiotensin II (Ang II). Moreover, SHRs had higher ACE and AT1R in the kidney and mesenteric artery, and higher Ang II and lower renin levels. Interestingly, paeonol treatment reduced the candesartan-induced increase in diuresis and natriuresis and vasoconstrictor responses to Ang II, and lowered blood pressure in SHRs, accompanied by reducing AT1R protein expression in the kidney and mesenteric artery of SHR, and Ang II levels in plasma and increasing renin levels in renal cortex. In addition, these changes were associated with reducing oxidative stress. CONCLUSIONS: The present study suggests that paeonol improves renal and vascular AT1R functions by inhibition of oxidative stress, thus lowering blood pressure in SHRs.

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.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

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.

Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression.

BACKGROUND: Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy. PURPOSE: To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment. STUDY TYPE: Prospective. SUBJECTS: A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site. FIELD STRENGTH/SEQUENCE: A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence. ASSESSMENT: Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site. STATISTICAL TESTS: Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run. RESULTS: Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%). DATA CONCLUSION: These findings support future research on clinical antidepressant selection for MDD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Differences in verbal and spatial working memory in patients with bipolar II and unipolar depression: an MSI study.

BACKGROUND: Depressive symptoms could be similarly expressed in bipolar and unipolar disorder. However, changes in cognition and brain networks might be quite distinct. We aimed to find out the difference in the neural mechanism of impaired working memory in patients with bipolar and unipolar disorder. METHOD: According to diagnostic criteria of bipolar II disorder of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and assessments, 13 bipolar II depression (BP II), 8 unipolar depression (UD) patients and 15 healthy controls (HC) were recruited in the study. We used 2-back tasks and magnetic source imaging (MSI) to test working memory functions and get the brain reactions of the participants. RESULTS: Compared with HC, only spatial working memory tasks accuracy was significantly worse in both UD and BP II (p = 0.001). Pearson correlation showed that the stronger the FCs' strength of MFG-IPL and IPL-preSMA, the higher accuracy of SWM task within left FPN in patients with UD (r = 0.860, p = 0.006; r = 0.752, p = 0.031). However, the FC strength of IFG-IPL was negatively correlated with the accuracy of SWM task within left FPN in patients with BP II (r = - 0.591, p = 0.033). CONCLUSIONS: Our study showed that the spatial working memory of patients with whether UD or BP II was impaired. The patterns of FCs within these two groups of patients were different when performing working memory tasks.

Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.

Caudothalamic dysfunction in drug-free suicidally depressed patients: an MEG study.

Major depressive disorder (MDD), characterized by low mood or anhedonia, is commonly associated with a greater suicidal susceptibility. There are numerous suicide-related findings pertaining to the dorsolateral prefrontal cortex (DLPFC), caudate nucleus and thalamus, which form a cortico-striato-thalamo-cortical (CSTC) circuit responsible for executive function and working memory. An aberrant CSTC circuitry is hypothesized to be implicated in depressed patients with a high suicidal risk. 27 MDD patients were assessed with the Nurses Global Assessment of Suicide Risk (NGASR), following which 14 patients were classified into a high suicide risk group (NGASR ≥ 12) and 13 patients were assigned to a low suicide risk group (NGASR < 6). All 27 patients were enrolled with 25 healthy controls for resting-state magnetoencephalography (MEG). Cross-frequency coupling (CFC) measured the phase of alpha-band (8-13 Hz) as it modulated to cortical gamma-band (30-48 Hz). There was a significantly lower alpha-to-gamma phase-amplitude coupling (PAC) between the right caudate and left thalamus in high-risk suicide group compared to both the low-risk suicide group and healthy controls. The presence of a weaker coupling between the right caudate and left thalamus is indicative of a caudothalamic abnormality in suicidally depressed patients. This implies that a disruption of CSTC loop could result in executive dysfunction and working memory impairment, leading to an increased suicidal risk in MDD patients. In the future, this preliminary study has the possibility of being replicated on a larger scale, and hence validates caudothalamic dysfunction as a reliable neuroimaging biomarker for suicide in depression.

An Effective Method Combining Various Endoscopes in the Treatment of Intravesical Migrated Intrauterine Device.

OBJECTIVE: To introduce an effective method combining various endoscopes in the treatment of intravesical migrated intrauterine device (IUD). DESIGN: A step-by-step explanation of the surgery using video, approved by the Shengjing Hospital of China Medical University. SETTING: Shengjing Hospital of China Medical University. INTERVENTIONS: A 39-year-old young woman, in whom an IUD was inserted 2 months prior, presented with frequent urination after IUD insertion. Cystoscope and pelvic computed tomography were performed, and the results showed an IUD in the bladder. The migrated IUD was found partly in the uterus and partly in the bladder by hysteroscope and cystoscope. Management of the migrated IUD consists of 4 steps: (1) lysing the adhesion between the bladder and uterus, (2) suturing the bladder and taking the IUD part out of the bladder, (3) removing the IUD part in the uterus, and (4) suturing the bladder again to reinforce it and suturing the uterus. CONCLUSION: The migrated IUD in the bladder was successfully and completely extracted by the method combining various endoscopes; operative time was 56 minutes. In the follow-up period the patient did not report any symptoms of frequency urination. This surgical process has the following characteristics: Preoperative examination should be performed to clarify the ectopic site of the IUD, various endoscopes should be combined for diagnosis and treatment, and endoscopic surgery is an effective treatment method for migrated IUD.

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI.

OBJECTIVE: Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD. METHOD: The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD. RESULTS: Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN). CONCLUSION: The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Evaluation of specific neural marker GAP-43 and TH combined with Masson-trichrome staining for forensic autopsy cases with old myocardial infarction.

It has been a puzzling forensic task to determine the cause of death as a result of old myocardial infarction (OMI) in the absence of recognizable acute myocardial infarction. Recent studies indicated that the heterogeneous cardiac nerve sprouting and sympathetic hyperinnervation at border zones of the infarcted site played important roles in sudden cardiac death (SCD). So, the present study explored the value of growth associated protein-43 (GAP-43) and tyrosine hydroxylase (TH) as objective and specific neural biomarkers combined with Masson-trichrome staining for forensic autopsy cases. Myocardium of left ventricle of 58 medicolegal autopsy cases, 12 OMI cases, 12 acute/OMI cases, and 34 control cases, were immunostained with anti-GAP-43 and anti-TH antibodies. Immunoreactivity of GAP-43 and TH identified nerve fibers and vascular wall in OMI cases and acute/OMI cases. Specifically, TH-positive nerve fibers were abundant at border zones of the infarcted site. There were a few GAP-43 and TH expressions in the control cases. With Masson-trichrome staining, collagen fibers were blue and cardiac muscle fibers were pink in marked contrast with the surrounding tissue, which improved the location of nerve fibers. Thus, these findings suggest that immunohistochemical detection of GAP-43 and TH combined with Masson-trichrome staining can provide the evidence for the medicolegal expertise of SCD due to OMI, and further demonstrate a close relationship between sympathetic hyperinnervation and SCD.

Time-dependent appearances of myofibroblasts during the repair of contused skeletal muscle in rat and its application for wound age determination.

OBJECTIVE: To research the relation between the time-dependent appearances of myotibroblasts during the repair of contused skeletal muscle in rat and wound age determination. METHODS: A total of 35 SD male rats were divided into the control and six injured groups according to wound age as follows: 12 h, 1 d, 5 d, 7 d, 10 d and 14 d after injury. The appearances of myofibroblasts were detected by HE staining, immunohistochemistry and confocal laser scanning microscopy. Masson's trichrome staining was utilized to examine collagen accumulation in the contused areas. RESULTS: Immunohistochemical staining showed that α-SMA+ myofibroblasts were initially observed at 5 d post-injury. The average ratio of myofibroblasts was highest at 14 d post-injury, with all samples, ratios more than 50%. In the other five groups, the average of α-SMA positive ratios were less than 50%. The collagen stained areas in the contused zones, concomitant with myofibroblast appearance, were increasingly augmented along with advances of posttraumatic interval. CONCLUSION: The immunohistochemical detection of myofibroblasts can be applied to wound age determination. The myofibroblasts might be involved in collagen deposition during the repair of contused skeletal muscle in rat.

[Correlation between percentages of PMN, MNC, FBC and wound age after skeletal muscle injury in rats].

OBJECTIVE: To study the percentages of polymorphonuclear leukocytes (PMN), mononuclear cells (MNC) and fibroblastic cells (FBC) in different post-traumatic intervals after skeletal muscle mechanical injury in rats. METHODS: The rat model of skeletal muscle mechanical injury was established. The rats were divided into injured groups (6 h, 12 h, 1 d, 3 d, 7 d, 10 d and 14 d after injury) and control group. The percentages of PMN, MNC and FBC in different post-traumatic intervals after skeletal muscle mechanical injury were assessed with HE staining and image analysis. RESULTS: At post-injury 6-12h, the percentages of PMN and MNC infiltration appeared in injured sites and that of PMN reached peak. At 1 d, the percentage of MNC infiltration appeared and reached peak, while that of PMN decreased. At 3-7 d, the percentage of FBC gradually increased, while that of PMN and MNC decreased. At 10-14d, the percentage of FBC reached peak. CONCLUSION: The percentages of PMN, MNC and FBC in injured zones showed time-dependent changes, which might be used as reference index for determination of age of skeletal muscle injury.

[Progress in myofibroblast and its application in forensic medicine].

The myofibroblasts have dual characteristics of smooth muscle cells and fibroblasts. In repairing tissular wound, myofibroblasts are involved in fibrogenesis and remodeling the extracellular matrix of the fibrotic cascades reaction. The review describes the morphological characteristics and biological behaviors of myofibroblasts and the application of skin wound age determination, which may provide reference for research in forensic medicine.

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.