Reporting Pancreatic FNAC using the Papanicolaou System: Still a Diagnostic Challenge.

Introduction: The Papanicolaou Society of Cytopathology System for reporting Pancreaticobiliary Cytology (PSCPC) is a reliable method to classify pancreatic fine needle aspiration cytology (FNAC) smears. However, it is not without practical problems which can diminish the diagnostic accuracy of the cytological diagnosis. Aims and Objectives: To determine the diagnostic pitfalls while reporting cytomorphology of pancreatic lesions according to PSCPC on correlating FNAC findings with histopathology. Materials and Methods: Retrospective analysis of pancreatic FNAC smears received in the Department of Pathology of our tertiary care institute over a period of 2 years was done. The cytological diagnoses were classified according to the Papanicolaou Society of Cytopathology system of reporting pancreaticobiliary cytology and correlated with histopathology. The reasons of cyto-histological discordance were analyzed. Results: Out of 50 cases in which both FNAC and biopsy of pancreatic lesions were done, 34 cases were positive/malignant (Category VI), eight cases were suspicious for malignancy (Category V), three cases were neoplastic (Category IV), two cases were atypical (Category III), two cases were negative for malignancy (Category II), and one case was non-diagnostic (Category I). Out of 50 cases, histopathology was non-diagnostic due to inadequate material in six cases. The cytological diagnoses were compared with histopathology in the remaining 44 cases. Categories III, IV V, and VI were considered as positive for neoplastic pathology. The sensitivity of FNAC to predict neoplastic pathology was 97.5%, while the specificity was 25%. The positive predictive value was 92.9%. Two cases reported as atypical (Category III) turned out to be adenocarcinoma on histopathology. One case reported as neuroendocrine tumor and two cases reported as adenocarcinoma on cytology displayed features of chronic pancreatitis on histology. One case reported as neoplastic mucinous cyst (Category IV) turned out to be adenocarcinoma on histology (limited concordance). Conclusion: The cytopathologist needs to be wary of the potential pitfalls to improve the diagnostic accuracy of FNACs.

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI.

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS: SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS: Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS: These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Association of pruritus with sleep in patients with psoriasis and chronic spontaneous urticaria: A cross-sectional study.

Background: Pruritus is a frequent complaint associated with various inflammatory dermatoses. Sleep is often disturbed because of pruritus but the impact of severity and diurnal pattern of pruritus has not been studied so far. Objectives: To estimate the prevalence of nocturnal itch (NI) and its association with itch severity, sleep disturbance and quality of life (QoL) compared with non-NI in chronic plaque psoriasis (CPP) and chronic spontaneous urticaria (CSU). Methods: We performed a cross-sectional study in patients aged ≥18 years with CPP or CSU for at least 6 weeks. A comprehensive in-house questionnaire designed for study formed the basis for categorizing patients into NI and non-NI. Validated instruments like visual analog scale, pruritus grading system, General Sleep Disturbance Scale, and Dermatology life quality index were used to assess itch severity, sleep, and QoL. Results: A total of 255 patients (CPP: 131; CSU: 124) were included in this study. Prevalence of NI was 43.5% (95% confidence interval: 34.9%-52.4%) in CPP and 29% (95% confidence interval: 21.2%-37.9%) in CSU. NI was strongly associated with higher pruritus grading system scores in CSU and CPP (regression coefficient = 1.5, P =0.004 and regression coefficient = 1.3, P =0.004, respectively), with impaired sleep (OR = 2.97, P = 0.025) in CPP and with itch-affected sleep in CSU. Itch severity was associated with impaired sleep; however, the association was modified by the presence of NI in CSU patients. Conclusion: Nocturnal itch is prevalent in chronic dermatoses and significant for sleep deficit and impaired QoL. Early screening and management of sleep disturbance among patients presenting with nocturnal itch should be routinely undertaken.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Bayesian inference of a spectral graph model for brain oscillations.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Stability and dynamics of a spectral graph model of brain oscillations.

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Identification of Eight High Yielding Strains via Morpho-Molecular Characterization of Thirty-Three Wild Strains of Calocybe indica.

Calocybe indica, generally referred as milky mushroom, is one of the edible mushroom species suitable for cultivation in the tropical and sub-tropical regions of the world. However, lack of potential high yielding strains has limited its wider adaptability. To overcome this limitation, in this study, the germplasms of C. indica from different geographical regions of India were characterized based on their morphological, molecular and agronomical attributes. Internal transcribed spacers (ITS1 and ITS4)-based PCR amplification, sequencing and nucleotide analysis confirmed the identity of all the studied strains as C. indica. Further, evaluation of these strains for morphological and yield parameters led to the identification of eight high yielding strains in comparison to the control (DMRO-302). Moreover, genetic diversity analysis of these thirty-three strains was performed using ten sequence-related amplified polymorphism (SRAP) markers/combinations. The Unweighted Pair-group Method with Arithmetic Averages (UPGMA)-based phylogenetic analysis categorized the thirty-three strains along with the control into three clusters. Cluster I possesses the maximum number of strains. Among the high yielding strains, high antioxidant activity and phenol content was recorded in DMRO-54, while maximum protein content was observed in DMRO-202 and DMRO-299 as compared with the control strain. The outcome of this study will help the mushroom breeders and growers in commercializing C. indica.

Bayesian Inference of a Spectral Graph Model for Brain Oscillations.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify abnormal biophysical mechanisms underlying these abnormal electrophysiological patterns, we estimated the parameters of a spectral graph-theory model (SGM). SGM is an analytic model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. The long-range excitatory time scale was associated with greater deficits in global cognition and was able to distinguish AD patients from controls with high accuracy. These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the spatiospectral signatures and cognition in AD.

Mucocutaneous manifestations of COVID-19-associated mucormycosis: A retrospective cross-sectional study.

Background Cutaneous mucormycosis has shown a significant upsurge during the COVID-19 pandemic. Due to the rapid progression and high mortality of cutaneous mucormycosis in this context, it is important to identify it early. However, very few studies report detailed clinical descriptions of cutaneous mucormycosis in COVID-19 patients. Objectives To describe mucocutaneous lesions of COVID-19-associated mucormycosis based on clinical morphology and attempt to correlate them with radiological changes. Methods A retrospective cross-sectional study was conducted at a tertiary care centre from 1st April to 31st July 2021. Eligibility criteria included hospitalised adult patients of COVID-19-associated mucormycosis with mucocutaneous lesions. Results All subjects were recently recovering COVID-19 patients diagnosed with cutaneous mucormycosis. One of fifty-three (2%) patients had primary cutaneous mucormycosis, and all of the rest had secondary cutaneous mucormycosis. Secondary cutaneous mucormycosis lesions presented as cutaneous-abscess in 25/52 (48%), nodulo-pustular lesions in 1/52 (2%), necrotic eschar in 1/52 (2%) and ulcero-necrotic in 1/52 (2%). Mucosal lesions were of three broad sub-types: ulcero-necrotic in 1/52 (2%), pustular in 2/52 (4%) and plaques in 1/52 (2%). Twenty out of fifty-two patients (38%) presented with simultaneous mucosal and cutaneous lesions belonging to the above categories. Magnetic resonance imaging of the face showed variable features of cutaneous and subcutaneous tissue involvement, viz. peripherally enhancing collection in the abscess group, "dot in circle sign" and heterogeneous contrast enhancement in the nodulo-pustular group; and fat stranding with infiltration of subcutaneous tissue in cases with necrotic eschar and ulcero-necrotic lesions. Limitations The morphological variety of cutaneous mucormycosis patients in a single-centre study like ours might not be very precise. Thus, there is a need to conduct multi-centric prospective studies with larger sample sizes in the future to substantiate our morphological and radiological findings. Conclusions COVID-19-associated mucormycosis patients in our study presented with a few specific types of mucocutaneous manifestations, with distinct magnetic resonance imaging findings. If corroborated by larger studies, these observations would be helpful in the early diagnosis of this serious illness.

Biomimetic Approach toward Visible Light-Driven Hydrogen Generation Based on a Porphyrin-Based Coordination Polymer Gel.

There has been a widespread interest in developing self-assembled porphyrin nanostructures to mimic nature's light-harvesting processes. Herein, porphyrin-based coordination polymer gel (CPG) has been developed as a "soft" photocatalyst material for hydrogen (H2) production from water under visible light. The CPG offers a hierarchical nanofibrous network structure obtained through self-assembly of a terpyridine alkyl-amide appended porphyrin (TPY-POR)-based low molecular weight gelator with ruthenium ions (RuII) and produces H2 with a rate of 5.7 mmol g-1 h-1 in the presence of triethylamine (TEA) as a sacrificial electron donor. Further, the [Fe2(bdt)(CO)6] (dbt = 1,2-benzenedithiol) cocatalyst, which can mimic the activity of iron hydrogenase, is coassembled in the CPG and shows remarkable improvement in H2 evolution (catalytic activity; rate ∼10.6 mmol g-1 h-1 and turnover number ∼1287). The significant enhancement in catalytic activity was supported by several controlled experiments, including femtosecond transient absorption (TA) spectroscopy and also DFT calculation. The TA study supported the cascade electron transfer process from porphyrin core to [Ru(TPY)2]2+ center, and subsequently, the electron transfers to the cocatalyst [Fe2(bdt)(CO)6] for H2 production.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis.

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aß) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

An overview of mycetoma and its diagnostic dilemma: Time to move on to advanced techniques.

The neglected tropical disease mycetoma can become extremely devastating, and can be caused both by fungi and bacteria; these are popularly known as eumycetoma and actinomycetoma respectively. The classical triad of the disease is subcutaneous swelling, multiple discharging sinuses and the presence of macroscopic granules. The present study aims to highlight the existing diagnostic modalities and the need to incorporate newer and more advanced laboratory techniques like pan fungal/pan bacterial 16S rRNA gene polymerase chain reaction (PCR) and sequencing, Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA). It is important for the medical team to be aware of the various diagnostic options (both existing and future), so that diagnosis of such a debilitating disease is never missed, both by clinicians and microbiologists/pathologists. The newer diagnostic methods discussed in this article will help in rapid, accurate diagnosis thus facilitating early treatment initiation, and decreasing the overall morbidity of the disease. In the Indian context, newer technologies need to be made available more widely. Making clinicians aware and promoting research and development in mycetoma diagnostics is the need of the hour.

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging.

We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.

Reticular Skin Rash as an Adverse Effect of 5-Azacitidine.

Azacitidine is a hypomethylating agent used for the treatment of patients with myelodysplastic syndrome (MDS). It has been approved by the Food and Drug Administration (FDA) and the European Medicines Agency for the treatment of MDS and is also indicated for the treatment of acute myeloid leukemia (AML). Injection site erythema, ecchymosis, and petechiae are some of the common cutaneous adverse reactions associated with azacitidine. This article describes a rare adverse cutaneous drug reaction with azacitidine in the form of a reticular generalized skin rash in a 28-year-old female with AML.

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease.

Background: Neuronal- and circuit-level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aß) in preclinical models of Alzheimer's disease (AD). These relationships remain poorly understood in patients with AD. Methods: Using empirical spectra from magnetoencephalography and computational modeling (neural mass model), we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aß, measured by positron emission tomography, in patients with AD. Results: Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aß depositions. Conclusions: Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aß in patients with AD. Funding: This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM and GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer's Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL (KGR), 2019-A-013-SUP (KGR); grants from the Alzheimer's Association: AARG-21-849773 (KGR); PCTRB-13-288476 (KAV), and made possible by Part the CloudTM (ETAC-09-133596); a grant from Tau Consortium (GDR and WJJ), and a gift from the S. D. Bechtel Jr. Foundation.