Neurophysiological consequences of synapse loss in progressive supranuclear palsy.

Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.

Causal Evidence for the Multiple Demand Network in Change Detection: Auditory Mismatch Magnetoencephalography across Focal Neurodegenerative Diseases.

The multiple demand (MD) system is a network of fronto-parietal brain regions active during the organization and control of diverse cognitive operations. It has been argued that this activation may be a nonspecific signal of task difficulty. However, here we provide convergent evidence for a causal role for the MD network in the "simple task" of automatic auditory change detection, through the impairment of top-down control mechanisms. We employ independent structure-function mapping, dynamic causal modeling (DCM), and frequency-resolved functional connectivity analyses of MRI and magnetoencephalography (MEG) from 75 mixed-sex human patients across four neurodegenerative syndromes [behavioral variant fronto-temporal dementia (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), posterior cortical atrophy (PCA), and Alzheimer's disease mild cognitive impairment with positive amyloid imaging (ADMCI)] and 48 age-matched controls. We show that atrophy of any MD node is sufficient to impair auditory neurophysiological response to change in frequency, location, intensity, continuity, or duration. There was no similar association with atrophy of the cingulo-opercular, salience or language networks, or with global atrophy. MD regions displayed increased functional but decreased effective connectivity as a function of neurodegeneration, suggesting partially effective compensation. Overall, we show that damage to any of the nodes of the MD network is sufficient to impair top-down control of sensation, providing a common mechanism for impaired change detection across dementia syndromes.SIGNIFICANCE STATEMENT Previous evidence for fronto-parietal networks controlling perception is largely associative and may be confounded by task difficulty. Here, we use a preattentive measure of automatic auditory change detection [mismatch negativity (MMN) magnetoencephalography (MEG)] to show that neurodegeneration in any frontal or parietal multiple demand (MD) node impairs primary auditory cortex (A1) neurophysiological response to change through top-down mechanisms. This explains why the impaired ability to respond to change is a core feature across dementias, and other conditions driven by brain network dysfunction, such as schizophrenia. It validates theoretical frameworks in which neurodegenerating networks upregulate connectivity as partially effective compensation. The significance extends beyond network science and dementia, in its construct validation of dynamic causal modeling (DCM), and human confirmation of frequency-resolved analyses of animal neurodegeneration models.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy.

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

Adiabatic dynamic causal modelling.

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

vmPFC Drives Hippocampal Processing during Autobiographical Memory Recall Regardless of Remoteness.

Our ability to recall past experiences, autobiographical memories (AMs), is crucial to cognition, endowing us with a sense of self and underwriting our capacity for autonomy. Traditional views assume that the hippocampus orchestrates event recall, whereas recent accounts propose that the ventromedial prefrontal cortex (vmPFC) instigates and coordinates hippocampal-dependent processes. Here we sought to characterize the dynamic interplay between the hippocampus and vmPFC during AM recall to adjudicate between these perspectives. Leveraging the high temporal resolution of magnetoencephalography, we found that the left hippocampus and the vmPFC showed the greatest power changes during AM retrieval. Moreover, responses in the vmPFC preceded activity in the hippocampus during initiation of AM recall, except during retrieval of the most recent AMs. The vmPFC drove hippocampal activity during recall initiation and also as AMs unfolded over subsequent seconds, and this effect was evident regardless of AM age. These results recast the positions of the hippocampus and the vmPFC in the AM retrieval hierarchy, with implications for theoretical accounts of memory processing and systems-level consolidation.

Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG.

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain - using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison - asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.

Bayesian fusion and multimodal DCM for EEG and fMRI.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

A guide to group effective connectivity analysis, part 2: Second level analysis with PEB.

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.

A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI.

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.

Structure learning in coupled dynamical systems and dynamic causal modelling.

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Absence of FLT3 mutations in Iranian adult T-cell leukemia/lymphoma patients.

Background: Adult T cell leukemia lymphoma (ATLL) is a rare disease, significantly linked to the infection by the human T-cell lymphotropic virus 1(HTLV-1). ATLL is typically preceded by decades of clinical latency during which infected cells accumulate selectable traits leading to a malignant transformation. Amongst all the HTLV-1 infected carriers only about 3-5% will develop ATLL. Despite the intensive attempt to improve the overall survival, ATLL remains one of worse prognosis among the hematologic malignancies. FMS like tyrosine kinase 3 internal tandem duplication (FLT3-ITD) mutations are mutations which are frequent among leukemic patients. We aimed to investigate the frequency of FLT3 mutation status in patients with acute type of ATLL which has not been studied yet. Methods: In this case control study 38 patients with acute type of ATLL were retrospectively analyzed between February 2015 and February 2017. Forty HTLV-1 positive patients were also used as control cases. Genomic DNA was extracted according to phenolchloroform protocol and two restriction fragment length polymorphism (RFLP) PCR reactions were set up to detect FLT3/ ITD and FLT3/TKD mutations. Differences between variables were evaluated by the chi-square test and t test for categorical and continuous variables, respectively. SPSS software v. 15 was used for statistical analysis. All P values were two sided and values less than 0.05 were considered to be significant. Results: No FLT3 mutations were detected in acute type of ATLL patients. So far, not many studies have shown the frequency of FLT3 mutation in ATLL patients Conclusion: Therefore, we conclude that although FLT3 mutations are rather unusual in the acute type of ATLL patients, but other alternative mechanisms associated with ATLL remain to be further investigated. This study was a novel project regarding the analysis of FLT3 mutation in the field of ATLL research.

Histological Evaluation of the Effect of Platelet-rich Plasma on Pulp Regeneration in Nonvital Open Apex Teeth: An Animal Study.

AIM: Platelet-rich plasma (PRP), which is a concentration of growth factors found in platelets, may be a suitable material for pulp regeneration. The aim of this animal study was a histological evaluation of PRP on pulp regeneration in nonvital teeth with immature apices. MATERIALS AND METHODS: A total of 40 premolar dogs' teeth were chosen for this study. After general anesthesia, the teeth were exposed, and subsequently, pulps were removed and the cavities were opened to the oral cavity. After 2 weeks, root canals were irrigated and disinfected with sodium hypochlorite with noninstrumentation technique, and triple antibiotic paste was placed inside the canals. Cavities were sealed with a temporary restoration. About 4 weeks later, canals were irrigated again and the teeth were randomly divided into three groups. Bleeding was evoked with overinstrumentation, then experimental materials for each group [PRP, mineral trioxide aggregate (MTA), and parafilm respectively] were placed over the bleeding, and orifices were sealed with MTA and glass ionomer. After 3 months, dogs were sacrificed and the teeth were separated from the jaws and sections prepared for histological evaluation. RESULTS: Regeneration was shown in 44.7% of the samples. About 47.3% of the samples in the MTA group and 42.1% of the samples in the PRP group showed regeneration; however, no regeneration was observed in the parafilm group. Chi-square test showed no significant difference between groups I and II. The soft regenerative tissue included pulp-like tissue and vessels. Mineralized regenerative tissue included cementum-like, periodontal ligament-like, and bone-like tissues. No normal pulp and nerve tissue were observed. CONCLUSION: Both PRP and MTA may be ideal scaffolds to accelerate the regeneration process. CLINICAL SIGNIFICANCE: Pulp repair in immature permanent teeth with weak roots has a better outcome than replacement of the pulp with gutta-percha or biomaterials.

Primary Undifferentiated Pleomorphic Sarcoma of the Breast: A Case Report with Literature Review of Similar Cases.

Breast sarcoma is a rare but aggressive tumor. There are few case reports in the literature and several aspects of this disease are still not completely comprehended. Therefore, reporting new cases can help to enrich the literature. We report a patient with breast mass and pus secretion from her right breast, misdiagnosed as an abscess and mistreated by antibiotics. The patient was referred for an ultrasound examination and mammography, and a needle biopsy was performed that suggested an aggressive tumor. By the pathologist's suggestion, a total mastectomy of the right breast was performed with the excision of sentinel nodes. A pathological examination revealed a high-grade undifferentiated pleomorphic sarcoma (UPS) without vascular or lymph node invasion as the final diagnosis. The patient underwent postoperative chemotherapy and is currently in good condition. This case emphasizes considering this rare tumor when approaching a breast mass. Performing surgery with adequate resection margin can improve the patient's prognosis. Some suggested breast UPS cases with lung and brain metastasis would be more aggressive tumors than other breast sarcomas. Total mastectomy with negative margins and free-of-tumor lymph nodes may be the key to improve prognosis in such patients.

Protective role of alpha-lipoic acid against rhabdomyolysis-induced acute kidney injury in rats.

Objectives: Rhabdomyolysis, a potentially life-threatening condition, occurs when myoglobin is released from damaged muscle cells, leading to acute kidney injury (AKI). Alpha lipoic acid (ALA), an organosulfur compound known for its anti-oxidant and anti-inflammatory properties, was examined in this study for its potential impact on rhabdomyolysis-induced AKI in rats. Materials and Methods: Six groups of rats were included in the study, with each group consisting of six rats (n=6): Control, rhabdomyolysis, rhabdomyolysis treated with different doses of ALA (5, 10, and 20 mg/kg), and ALA alone (20 mg/kg) groups. Rhabdomyolysis was induced by intramuscular injection of glycerol on the first day of the experiment, while ALA was administered intraperitoneally for four consecutive days. Renal function parameters, oxidative stress markers, and histological changes in the kidneys were evaluated. Western blot analysis was performed to measure the levels of neutrophil gelatinase-associated lipocalin (NGAL) and tumor necrosis factor-alpha (TNF-α) proteins. Results: A significant increase in serum urea, creatinine, renal malondialdehyde, NGAl, and TNF-α protein levels was observed in glycerol-injected rats. In addition, a significant decrease in glutathione was recorded. Compared to the rhabdomyolysis group, treatment with ALA recovered kidney histological and biochemical abnormalities. Conclusion: Results suggest that rhabdomyolysis-induced AKI is associated with increased oxidative stress and inflammation. Treatment with ALA improved kidney histological abnormalities and reduced oxidative stress markers in rats. Therefore, ALA may have a potential protective effect against rhabdomyolysis-induced AKI.

Effect of verbascoside against acute kidney injury induced by rhabdomyolysis in rats.

Rhabdomyolysis is a pathological condition caused by muscle tissue degradation. In this condition, intracellular contents enter the bloodstream, and acute kidney injury (AKI) develops. Verbascoside (VB) is one of the most common phenylethanoid glycosides and has antioxidant and anti-inflammatory effects. This study investigated the effects of VB on AKI induced by rhabdomyolysis in rats. Male Wistar rats were divided into six groups (n = 6): (1) control group (normal saline), (2) 50% glycerol (10 ml/kg, IM, single injection, only on the first day), (3)-(5) 50% glycerol (same as group 2) + VB (30, 60, and 100 mg/kg, IP, 4 days), and (6) VB (100 mg/kg). Serum and kidney tissue samples were collected on day 5. Subsequently, serum creatinine (Cr), blood urea nitrogen (BUN), renal glutathione (GSH), malondialdehyde (MDA), lipocalin associated with neutrophil gelatinase (NGAL), tumor necrosis factor-alpha (TNF-α), and pathological changes were investigated. The injection of glycerol elevated levels of kidney damage markers, including Cr and BUN in serum, MDA, TNF-α, and NGAL, along with a reduction in GSH levels in the kidney tissue. The administration of VB (100 mg/kg) significantly lowered the levels of these markers, indicating the therapeutic effect of VB against AKI caused by rhabdomyolysis. Histopathological examinations revealed enhanced myoglobin cast formation and tubular necrosis in the glycerol group, which was reduced in rats that received VB, although this reduction did not reach statistical significance. VB can reduce rhabdomyolysis-induced AKI through its anti-inflammatory and antioxidant effects and decrease kidney damage severity.

Renoprotective effect of thymoquinone against rhabdomyolysis-induced acute kidney injury in the rat model.

Objectives: Rhabdomyolysis leads to the release of myoglobin, sarcoplasmic proteins, and electrolytes into the blood circulation causing acute kidney injury (AKI). Thymoquinone, a natural compound found in Nigella sativa seeds, has antioxidant and anti-inflammatory effects. This investigation assessed the renoprotective effect of thymoquinone on rhabdomyolysis-induced AKI in rats. Materials and Methods: Male Wistar rats were categorized into six groups (n = 6): 1. Control: (normal saline), 2. Glycerol (50 ml/kg, single dose, IM), 3-5: Glycerol + thymoquinone (1, 2.5 and 5 mg/kg, 4 days, IP), 6. Thymoquinone (5 mg/kg). On day 5, serum and kidney tissue were isolated and the amounts of serum creatinine and blood urea nitrogen (BUN), renal malondialdehyde (MDA), glutathione (GSH.), tumor necrosis factor-alpha (TNF-α), neutrophil gelatinase-associated lipocalin (NGAL), and pathological changes were evaluated. Results: Glycerol increased creatinine, BUN, MDA, TNF-α, and NGAL levels. It decreased GSH amounts and caused renal tubular necrosis, glomerular atrophy, and myoglobin cast in kidney tissue. Co-administration of glycerol and thymoquinone reduced creatinine, BUN, histopathological alterations, and MDA levels, and enhanced GSH amounts. Administration of glycerol and thymoquinone (5 mg/kg) had no significant effect on TNF-α amount but decreased NGAL protein levels. The administration of thymoquinone (5 mg/kg) alone did not display a significant difference from the control group. Conclusion: Rhabdomyolysis from glycerol injection in rats can cause kidney damage. Thymoquinone may attenuate renal dysfunction and oxidative stress. However, the TNF-α level was not significantly affected. Further studies are needed to explore the potential therapeutic effects of thymoquinone in managing AKI.

Comparison of FNA-based conventional cytology specimens and digital image analysis in assessment of pancreatic lesions.

Objectives: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is one of the most important diagnostic tools for investigation of suspected pancreatic masses, although the interpretation of the results is controversial. In recent decades, digital image analysis (DIA) has been considered in pathology. The aim of this study was to assess the DIA in the evaluation of EUS-FNA based cytopathological specimens of pancreatic masses and comparing it with conventional cytology analysis by pathologist. Material and Methods: This study was performed using cytological slides related to EUS-FNA samples of pancreatic lesions. The digital images were prepared and then analyzed by ImageJ software. Factors such as perimeter, circularity, area, minimum, maximum, mean, median of gray value, and integrated chromatin density of cell nucleus were extracted by software ImageJ and sensitivity, specificity, and cutoff point were evaluated in the diagnosis of malignant and benign lesions. Results: In this retrospective study, 115 cytology samples were examined. Each specimen was reviewed by a pathologist and 150 images were prepared from the benign and malignant lesions and then analyzed by ImageJ software and a cut point was established by SPSS 26. The cutoff points for perimeter, integrated density, and the sum of three factors of perimeter, integrated density, and circularity to differentiate between malignant and benign lesions were reported to be 204.56, 131953, and 24643077, respectively. At this cutting point, the accuracy of estimation is based on the factors of perimeter, integrated density, and the sum of the three factors of perimeter, integrated density, and circularity were 92%, 92%, and 94%, respectively. Conclusion: The results of this study showed that digital analysis of images has a high accuracy in diagnosing malignant and benign lesions in the cytology of EUS-FNA in patients with suspected pancreatic malignancy and by obtaining cutoff points by software output factors; digital imaging can be used to differentiate between benign and malignant pancreatic tumors.

Molecular Status of BRAF Mutation in Epithelial Ovarian Cancer: An Analysis of 57 Cases in the Northeast of Iran.

Background & Objective: Epithelial ovarian cancer (EOC) is the most prevalent type of ovarian cancer. Previous studies have elucidated different pathways for the progression of this malignancy. The mutation in the B-Raf proto-oncogene, serine/threonine kinase (BRAF) gene, a member of the MAPK/ERK signaling pathway, plays a role in the development of EOC. The current study aimed to determine the frequency of the BRAF V600E mutation in ovarian serous and mucinous tumors, including borderline and carcinoma subtypes. Methods: A total of 57 formalin-fixed paraffin-embedded samples, including serous borderline tumors (SBTs), low-grade serous carcinomas (LGSCs), high-grade serous carcinomas (HGSCs), mucinous borderline tumors (MBTs), and mucinous carcinomas, and 57 normal ovarian tissues were collected. The BRAF V600E mutation was analyzed using polymerase chain reaction (PCR) and sequencing. Results: While 40% of the SBT harbor BRAF mutation, we found no BRAF mutation in the invasive serous carcinoma (P=0.017). Also, there was only 1 BRAF mutation in MBT and no mutation in mucinous carcinomas. In addition, we found no mutation in the control group. Conclusion: The BRAF mutation is most frequent in borderline tumors but not in invasive serous carcinomas. It seems that 2 different pathways exist for the development of ovarian epithelial neoplasms: one for borderline tumors and the other for high-grade invasive carcinomas. Our study supports this hypothesis. The BRAF mutation is rare in mucinous neoplasms.

Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures.

We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous-discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.

Slow-Fast Duffing Neural Mass Model.

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions.