Direct Immunofluorescence in Oral Lichen Planus and Related Lesions: Sensitivity, Specificity, and Diagnostic Accuracy in a Single Diagnostic Center in Poland.

Our study aimed to establish the basic reliability parameters of direct immunofluorescence test results in patients with oral lichen planus. We conducted an evaluation of individual antibody classes in the DIF and ELISA (BP180 antigen), comparing these results with the classical histopathological (HP) examination in a group of patients treated within the standard healthcare in our clinic. Among 66 participants with oral changes indicative of LP, only 50% received histopathological confirmation of the LP diagnosis.

Molecular analysis of inherited disorders of cornification in polish patients show novel variants and functional data and provokes questions on the significance of secondary findings.

Background: The Mendelian Disorders of Cornification (MeDOC) comprise a large number of disorders that present with either localised (palmoplantar keratoderma, PPK) or generalised (ichthyoses) signs. The MeDOC are highly heterogenic in terms of genetics and phenotype. Consequently, diagnostic process is challenging and before implementation of the next generation sequencing, was mostly symptomatic, not causal, which limited research on those diseases.

Classification-Predictive Model Based on Artificial Neural Network Validated by Histopathology and Direct Immunofluorescence for the Diagnosis of Oral Lichen Planus.

The diagnosis of oral lichen planus (OLP) poses many challenges due to its nonspecific clinical symptoms and histopathological features. Therefore, the diagnostic process should include a thorough clinical history, immunological tests, and histopathology. Our study aimed to enhance the diagnostic accuracy of OLP by integrating direct immunofluorescence (DIF) results with clinical data to develop a multivariate predictive model based on the Artificial Neural Network.

Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion.

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno-occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes.

A parcellation-based connectomic model of hemispatial neglect.

Background And Purpose: Hemispatial neglect is characterized by a reduced awareness to stimuli on the contralateral side. Current literature suggesting that damage to the right parietal lobe and attention networks may cause hemispatial neglect is conflicting and can be improved by investigating a connectomic model of the "neglect system" and the anatomical specificity of regions involved in it.

Methods: A meta-analysis of voxel-based morphometry magnetic resonance imaging (MRI) studies of hemispatial neglect was used to identify regions associated with neglect.

Prevalence of Taurodontism in Contemporary and Historical Populations from Radom: A Biometric Analysis of Radiological Data.

Taurodontism is a morphological anomaly of multirooted molars characterized by apical displacement of the pulp chamber, shortened roots, and the absence of constriction at the dentoenamel junction. It can negatively impact the outcome of dental treatment plans. This study aimed to compare the prevalence of taurodontism among contemporary and historical populations from Radom, Poland.

Functional connectivity of the language area in migraine: a preliminary classification model.

Background: Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment.

Real world demonstration of hand motor mapping using the structural connectivity atlas.

Background: Locating the hand-motor-cortex (HMC) is an essential component within many neurosurgeries. Despite advancements in these localization methods there are still downfalls for each. Additionally, the importance of presurgical planning calls for increasingly accurate and efficient methods of locating specific cortical regions.

An agile, data-driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets.

Introduction: Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient-specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study.

Methods: We used the resting-state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN).

Symptom-circuit mappings of the schizophrenia connectome.

Objective: This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology.

Methods: T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.

Connectomics underlying motor functional outcomes in the acute period following stroke.

Objective: Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes.

Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke.

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.

Objective: Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.

Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC).

Fatty acid profiles in various lipid fractions in the female epidermis. Does the body site and age matter?

Background: The epidermis forms the barrier between an organism and its external environment. Although one of the major functional elements of the epidermis is the lipid-enriched extracellular matrix, containing mainly ceramides, cholesterol (CHOL) and free fatty acids, the data are limited regarding the lipid profile in the epidermis. The aim of the study was to determine the whole profile of fatty acids (FAs) in the epidermis and to examine any dependence according to the age of the subject and the site on the epidermis.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response.

Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy.

Materials And Methods: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI).

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment.

Cortical semantization of autobiographical memory over subjective chronological time: An fMRI study.

The content and neural representation of autobiographical memories change over time; however, these changes are poorly understood. We hypothesize that the content of memories becomes semanticized, while the neural representation moves from mesial to cortical structures. We conducted an fMRI (functional magnetic resonance imaging) study on the effects of time on autobiographical memory retrieval.

Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight.

Humans undergo extreme physiological changes when subjected to long periods of weightlessness, and as we continue to become a space-faring species, it is imperative that we fully understand the physiological changes that occur in the human body, including the brain. In this study, we present findings of brain structural changes associated with long-duration spaceflight based on diffusion magnetic resonance imaging (dMRI) data. Twelve cosmonauts who spent an average of six months aboard the International Space Station (ISS) were scanned in an MRI scanner pre-flight, ten days after flight, and at a follow-up time point seven months after flight.

Bullous diseases caused by gene mutations: from epidermolytic hyperkeratosis to a novel variant of epidermolysis bullosa simplex.

Introduction: Mutations in the gene encoding keratin 1 cause epidermolytic hyperkeratosis characterized by blistering in the neonatal period followed by ichthyotic hyperkeratosis in childhood and adolescent life. We observed a spectrum of clinical manifestations of blistering disorders caused by different mutations in the same gene.

Aim: To analyse the phenotypic spectrum of blistering disorders caused by the mutations.

Efficacy of gentamicin 0.3% solution of oral erosions healing in patients with severe generalized recessive dystrophic epidermolysis bullosa and its impact on the expression of type VII collagen.

Introduction: Epidermolysis bullosa (EB) is a rare genetic skin disorder inherited either in autosomal recessive (AR) or autosomal dominant (AD) manner and characterized by blistering of the skin and mucous membranes. According to a subtype of EB, the oral manifestations and dental involvement vary in frequency and in severity. The most severe dental problems occur in patients with junctional epidermolysis bullosa (JEB) and severe generalized dystrophic epidermolysis bullosa (RDEB) and involve enamel erosion and development of blisters followed by painful oral wounds.

The analysis of echocardiographic results in patients suffering from epidermolysis bullosa.

Introduction: Cardiac abnormalities revealed in patients suffering from epidermolysis bullosa (EB) include dilated cardiomyopathy (DC) and aortopathy. DC is a rare but serious complication associated with an increased mortality, predominantly observed in recessive dystrophic EB. Echocardiography is the most available diagnostic tool used to detect heart disease in EB patients.

Novel and recurrent variants of ATP2C1 identified in patients with Hailey-Hailey disease.

Hailey-Hailey disease (HHD) is a rare, late-onset autosomal dominant genodermatosis characterized by blisters, vesicular lesions, crusted erosions, and erythematous scaly plaques predominantly in intertriginous regions. HHD is caused by ATP2C1 mutations. About 180 distinct mutations have been identified so far; however, data of only few cases from Central Europe are available.