Epilepsy treatments for patients with mechanistic target of rapamycin (mTOR) disorders, such as tuberous sclerosis complex (TSC) or focal cortical dysplasia type II (FCDII), are urgently needed. In these patients, the presence of focal cortical malformations is associated with the occurrence of lifelong epilepsy, leading to severe neurological comorbidities. Here, we show that the expression of the actin cross-linking protein filamin A (FLNA) is increased in resected cortical tissue that is responsible for seizures in patients with FCDII and in mice modeling TSC and FCDII with mutations in phosphoinositide 3-kinase (PI3K)–ras homolog enriched in brain (Rheb) pathway genes. Normalizing FLNA expression in these mice through genetic knockdown limited cell misplacement and neuronal dysmorphogenesis, two hallmarks of focal cortical malformations. In addition, Flna knockdown reduced seizure frequency independently of mTOR signaling. Treating mice with a small molecule targeting FLNA, PTI-125, before the onset of seizures alleviated neuronal abnormalities and reduced seizure frequency compared to vehicle-treated mice. In addition, the treatment was also effective when injected after seizure onset in juvenile and adult mice. These data suggest that targeting FLNA with either short hairpin RNAs or the small molecule PTI-125 might be effective in reducing seizures in patients with TSC and FCDII bearing mutations in PI3K-Rheb pathway genes.

Tremendous efforts have been made to explore biomarkers for classification and grading on gliomas. The goal of this study was to identify more molecular features that are associated with clinical outcomes by comparing the genomic profiles of primary and recurrent gliomas and determine potential recurrence leading factors that are significantly enriched in relapse tumors. Hybrid capture based next generation sequencing (NGS) analysis was performed on 64 primary and 17 recurrent glioma biopsies. Copy number variation (CNV) was more frequent in recurrent tumors and CDKN2A/B loss was significantly enriched. In addition, overall mutations in cell cycle pathway are more common in relapse tumors. The patterns of gene sets, including IDH1/TERT and IDH1/TP53 exhibited significant difference between the groups. Survival analysis uncovered the worse disease-free survival (DFS) and overall survival (OS) associated with altered copy number and excessive activation of CELL CYCLE pathway. High Tumor Mutation Burden (TMB) was also a biomarker with great potential for poor prognosis. The assessment of genomic characteristics in primary versus recurrent gliomas aids the discovery of potential predictive biomarkers. The prognostic value of TMB in gliomas was raised for the first time.

Objectives Patients with mammalian target of rapamycin (mTOR)–dependent malformations of cortical development (MCDs) associated with seizures display hyperperfusion and increased vessel density of the dysmorphic cortical tissue. Some studies have suggested that the vascular defect occurred independently of seizures. Here, we further examined whether hypervascularization occurs in animal models of global and focal MCD with and without seizures, and whether it is sensitive to the mTOR blocker, rapamycin, that is approved for epilepsy treatment in tuberous sclerosis complex. Methods We used two experimental models of mTOR‐dependent MCD consisting of conditional transgenic mice containing Tsc1null cells in the forebrain generating a global malformation associated with seizures and of wild‐type mice containing a focal malformation in the somatosensory cortex generated by in utero electroporation (IUE) that does not lead to seizures. Alterations in blood vessels and the effects of a 2‐week–long rapamycin treatment on these phenotypes were assessed in juvenile mice. Results Blood vessels in both the focal and global MCDs of postnatal day 14 mice displayed significant increase in vessel density, branching index, total vessel length, and decreased tissue lacunarity. In addition, rapamycin treatment (0.5 mg/kg, every 2 days) partially rescued vessel abnormalities in the focal MCD model, but it did not ameliorate the vessel abnormalities in the global MCD model that required higher rapamycin dosage for a partial rescue. Significance Here, we identified hypervascularization in mTOR‐dependent MCD in the absence of seizures in young mice, suggesting that increased angiogenesis occurs during development in parallel to alterations in corticogenesis. In addition, a predictive functional outcome is that dysplastic neurons forming MCD will have better access to oxygen and metabolic supplies via their closer proximity to blood vessels. Finally, the difference in rapamycin sensitivity between a focal and global MCD suggest that rapamycin treatment will need to be titrated to match the type of MCD.

As our understanding of the genetic underpinnings of systemic sclerosis (SSc) increases, questions regarding the environmental trigger(s) that induce and propagate SSc in the genetically predisposed individual emerge. The interplay between the environment, the immune system, and the microbial species that inhabit the patient’s skin and gastrointestinal tract is a pathobiological frontier that is largely unexplored in SSc. The purpose of this review is to provide an overview of the methodologies, experimental study results, and future roadmap for elucidating the relationship between the SSc host and his/her microbiome.

Background Although treatments have been proposed for calcinosis cutis (CC) in patients with systemic sclerosis (SSc), a standardized and validated method for CC burden quantification is necessary to enable valid clinical trials. We tested the hypothesis that computer vision applied to dual-energy computed tomography (DECT) finger images is a useful approach for precise and accurate CC quantification in SSc patients. Methods De-identified 2-dimensional (2D) DECT images from SSc patients with clinically evident lesser finger CC lesions were obtained. An expert musculoskeletal radiologist confirmed accurate manual segmentation (subtraction) of the phalanges for each image as a gold standard, and a U-Net Convolutional Neural Network (CNN) computer vision model for segmentation of healthy phalanges was developed and tested. A validation study was performed in an independent dataset whereby two independent radiologists manually measured the longest length and perpendicular short axis of each lesion and then calculated an estimated area by assuming the lesion was elliptical using the formula long axis/2 × short axis/2 × π, and a computer scientist used a region growing technique to calculate the area of CC lesions. Spearman’s correlation coefficient, Lin’s concordance correlation coefficient with 95% confidence intervals (CI), and a Bland-Altman plot (Stata V 15.1, College Station, TX) were used to test for equivalence between the radiologists’ and the CNN algorithm-generated area estimates. Results Forty de-identified 2D DECT images from SSc patients with clinically evident finger CC lesions were obtained and divided into training (N = 30 with image rotation × 3 to expand the set to N = 120) and test sets (N = 10). In the training set, five hundred epochs (iterations) were required to train the CNN algorithm to segment phalanges from adjacent CC, and accurate segmentation was evaluated using the ten held-out images. To test model performance, CC lesional area estimates calculated by two independent radiologists and a computer scientist were compared (radiologist 1 vs. radiologist 2 and radiologist 1 vs. computer vision approach) using an independent test dataset comprised of 31 images (8 index finger and 23 other fingers). For the two radiologists’, and the radiologist vs. computer vision measurements, Spearman’s rho was 0.91 and 0.94, respectively, both p < 0.0001; Lin’s concordance correlation coefficient was 0.91 (95% CI 0.85–0.98, p < 0.001) and 0.95 (95% CI 0.91–0.99, p < 0.001); and Bland-Altman plots demonstrated a mean difference between radiologist vs. radiologist, and radiologist vs. computer vision area estimates of − 0.5 mm2 (95% limits of agreement − 10.0–9.0 mm2) and 1.7 mm2 (95% limits of agreement − 6.0–9.5 mm2, respectively. Conclusions We demonstrate that CNN quantification has a high degree of correlation with expert radiologist measurement of finger CC area measurements. Future work will include segmentation of 3-dimensional (3D) images for volumetric and density quantification, as well as validation in larger, independent cohorts.