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Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Background and purpose: Excitotoxicity in nerve cells is a type of neurotoxicity in which excessive stimulation of receptors (such as N-methyl-d-aspartate glutamate receptors (NMDAR)) leads to the influx of high-level calcium ions into cells and finally cell damage or death. This complication can occur after taking some of the plasminogen activators like tissue plasminogen activator and reteplase. The interaction of the kringle2 domain in such plasminogen activator with the amino-terminal domain (ATD) of the NR1 subunit of NMDAR finally leads to excitotoxicity. In this study, we assessed the interaction of two new chimeric reteplase, mutated in the kringle2 domain, with ATD and compared the interaction of wild-type reteplase with ATD, computationally. Experimental approach: Homology modeling, protein docking, molecular dynamic simulation, and molecular dynamics trajectory analysis were used for the assessment of this interaction. Findings/Results: The results of the free energy analysis between reteplase and ATD (wild reteplase: -2127.516 ± 0.0, M1-chr: -1761.510 ± 0.0, M2-chr: -521.908 ± 0.0) showed lower interaction of this chimeric reteplase with ATD compared to the wild type. Conclusion and implications: The decreased interaction between two chimeric reteplase and ATD of NR1 subunit in NMDAR which leads to lower neurotoxicity related to these drugs, can be the start of a way to conduct more tests and if the results confirm this feature, they can be considered potential drugs in acute ischemic stroke treatment. [ABSTRACT FROM AUTHOR]

Glutamate decarboxylase enzyme produces γ-aminobutyric acid (GABA) in a non-reversible decarboxylation reaction of glutamate. GABA is a major inhibitory neurotransmitter of the brain and it is also present at high concentration in other organs such as pancreatic islets. GABA has effects on blood pressure, diabetes, inflammation, sleeplessness and depression. Some bacteria such as Lactobacillus strains are capable of GABA production. Identification of these bacteria is important both for researchers and industry. The aim of this study was molecular gene cloning and sequencing of glutamate decarboxylase (gad) from Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842 and Lactobacillus reuteri ATCC 23272. These strains were cultured in MRS medium at 37°C for 24 hours. For cloning gad gene from these strains, polymerase chain reaction (PCR) was performed using specific primers designed by Oligo7 software. PCR production was extracted from agarose gel and was inserted into PGEM-T vector using T4 DNA ligase enzyme and then it was transformed to E. coli XL1Blue. In the final step, white colonies were selected and after plasmid extraction, the existence of gad gene in recombinants was confirmed by PCR. Gad gene was cloned from Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842 and Lactobacillus reuteri ATCC 23272. It is for the first time that the gad gene sequences from these bacteria were registered on NCBI with accession numbers KF751355 and KF751352 respectively. The result of this research indicates that the two aforementioned bacteria contain glutamate decarboxylase gene and therefore they possibly can be used for industrial γ-aminobutyric acid production. [ABSTRACT FROM AUTHOR]

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.