Rare and New Mutations of B-Globin in Azari Population of Iran, a Considerable Diversity.

Background: Thalassemia, as the most common single-gene genetic disorder, is related to a defect in the synthesis of one or more hemoglobin chains. More than 200 mutations have been identified in the ß-globin gene. Globally, every susceptible racial group has its own specific spectrum of the common mutations that are well-known to a particular geographic region. On the other hand, varying numbers of diverse rare mutations may occur. Materials and Methods: The subjects of the study included 2113 heterozygote or homozygote ß-thalassemia cases selected among couples who participated in the Iranian national thalassemia screening program from January 2011 to November 2019. Molecular characterization of the ß-thalassemia mutation was initially carried out by the amplification-refractory mutation system-polymerase chain reaction (ARMS-PCR) technique for common mutations, followed by sequencing, Gap PCR, and Multiple ligation-dependent probe amplification (MLPA) methods - in cases not detected by the ARMS-PCR. Results: The existence of 39 rare and new point mutations and 4 large deletions were described in our cohort. Sicilian (-13,337bp) deletion, CD36/37 (-T), and CD15 TGG>TGA were encountered more often than the others in a decreasing order, in terms of frequency. The least frequent mutations/deletions were deletion from HBD exon 1 to HBB promoter, 619 bp deletion, Deletion from up HBBP1-Exon3 HBBP1 and up HBB-0.5Kb down HBB, CAP+8 C>A, CD37 (G>A), CD6 (-A), IVSI-2 (T>C), IVSII-705 T>G, and IVSII-772 (G>A). Each occurred once. Five mutations/variants were also determined which have not been reported previously in Iran. Conclusion: According to the findings of the study, the Northwestern Iranian population displayed a wide variety of thalassemia allelic distributions. Identification of rare and new mutations in the ß-thalassemia in the national population is beneficial for screening programs, genetic counseling, and prenatal diagnosis.

Non-invasive assessment of disease progression and neuroprotective effects of dietary coconut oil supplementation in the ALS SOD1G93A mouse model: A 1H-magnetic resonance spectroscopic study.

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disease primarily characterized by progressive degeneration of motor neurons in the motor cortex, brainstem and spinal cord. Due to relatively fast progression of ALS, early diagnosis is essential for possible therapeutic intervention and disease management. To identify potential diagnostic markers, we investigated age-dependent effects of disease onset and progression on regional neurochemistry in the SOD1G93A ALS mouse model using localized in vivo magnetic resonance spectroscopy (MRS). We focused mainly on the brainstem region since brainstem motor nuclei are the primarily affected regions in SOD1G93A mice and ALS patients. In addition, metabolite profiles of the motor cortex were also assessed. In the brainstem, a gradual decrease in creatine levels were detected starting from the pre-symptomatic age of 70 days postpartum. During the early symptomatic phase (day 90), a significant increase in the levels of the inhibitory neurotransmitter γ- aminobutyric acid (GABA) was measured. At later time points, alterations in the form of decreased NAA, glutamate, glutamine and increased myo-inositol were observed. Also, decreased glutamate, NAA and increased taurine levels were seen at late stages in the motor cortex. A proof-of-concept (PoC) study was conducted to assess the effects of coconut oil supplementation in SODG93A mice. The PoC revealed that the coconut oil supplementation together with the regular diet delayed disease symptoms, enhanced motor performance, and prolonged survival in the SOD1G93A mouse model. Furthermore, MRS data showed stable metabolic profile at day 120 in the coconut oil diet group compared to the group receiving a standard diet without coconut oil supplementation. In addition, a positive correlation between survival and the neuronal marker NAA was found. To the best of our knowledge, this is the first study that reports metabolic changes in the brainstem using in vivo MRS and effects of coconut oil supplementation as a prophylactic treatment in SOD1G93A mice.

Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas.

Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Tensor based tumor tissue type differentiation using magnetic resonance spectroscopic imaging.

Magnetic resonance spectroscopic imaging (MRSI) has the potential to characterise different tissue types in brain tumors. Blind source separation techniques are used to extract the specific tissue profiles and their corresponding distribution from the MRSI data. A 3-dimensional MRSI tensor is constructed from in vivo 2D-MRSI data of individual tumor patients. Non-negative canonical polyadic decomposition (NCPD) with common factor in mode-1 and mode-2 and l(1) regularization on mode-3 is applied on the MRSI tensor to differentiate various tissue types. Initial in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in high grade glioma patients compared to previous matrix-based decompositions, such as non-negative matrix factorization and hierarchical non-negative matrix factorization.

Different quantification algorithms may lead to different results: a comparison using proton MRS lipid signals.

Proton magnetic resonance spectroscopy (MRS) is a sensitive method for investigating the biochemical compounds in a tissue. The interpretation of the data relies on the quantification algorithms applied to MR spectra. Each of these algorithms has certain underlying assumptions and may allow one to incorporate prior knowledge, which could influence the quality of the fit. The most commonly considered types of prior knowledge include the line-shape model (Lorentzian, Gaussian, Voigt), knowledge of the resonating frequencies, modeling of the baseline, constraints on the damping factors and phase, etc. In this article, we study whether the statistical outcome of a biological investigation can be influenced by the quantification method used. We chose to study lipid signals because of their emerging role in the investigation of metabolic disorders. Lipid spectra, in particular, are characterized by peaks that are in most cases not Lorentzian, because measurements are often performed in difficult body locations, e.g. in visceral fats close to peristaltic movements in humans or very small areas close to different tissues in animals. This leads to spectra with several peak distortions. Linear combination of Model spectra (LCModel), Advanced Method for Accurate Robust and Efficient Spectral fitting (AMARES), quantitation based on QUantum ESTimation (QUEST), Automated Quantification of Short Echo-time MRS (AQSES)-Lineshape and Integration were applied to simulated spectra, and area under the curve (AUC) values, which are proportional to the quantity of the resonating molecules in the tissue, were compared with true values. A comparison between techniques was also carried out on lipid signals from obese and lean Zucker rats, for which the polyunsaturation value expressed in white adipose tissue should be statistically different, as confirmed by high-resolution NMR measurements (considered the gold standard) on the same animals. LCModel, AQSES-Lineshape, QUEST and Integration gave the best results in at least one of the considered groups of simulated or in vivo lipid signals. These outcomes highlight the fact that quantification methods can influence the final result and its statistical significance.

Ex vivo high resolution magic angle spinning metabolic profiles describe intratumoral histopathological tissue properties in adult human gliomas.

In gliomas one can observe distinct histopathological tissue properties, such as viable tumor cells, necrotic tissue or regions where the tumor infiltrates normal brain. A first screening between the different intratumoral histopathological tissue properties would greatly assist in correctly diagnosing and prognosing gliomas. The potential of ex vivo high resolution magic angle spinning spectroscopy in characterizing these properties is analyzed and the biochemical differences between necrosis, high cellularity and border tumor regions in adult human gliomas are investigated. Statistical studies applied on sets of metabolite concentrations and metabolite ratios extracted from 52 high resolution magic angle spinning recordings coming from patients with different grades of glial tumors show a strong correlation between the histopathological tissue properties and the considered metabolic profiles, regardless of the malignancy grade. The results are in agreement with the pathology obtained by the histopathological examination that succeeded the high resolution magic angle spinning measurements. The metabolite concentration set can better differentiate between the considered histopathological tissue properties compared to the ratios. Representative reference tissue models describing the metabolic behavior are extracted for characterizing the intratumoral tissue properties. The proposed metabolic profiles reflect that the metabolites behavior is interconnected, and typical biochemical patterns emerge for each histopathological tissue property.

Non-negative blind source separation techniques for tumor tissue typing using HR-MAS signals.

Given High Resolution Magic Angle Spinning (HR-MAS) signals from several glioblastoma tumor subjects, the goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, high cellular tumor and border tumor tissue, and providing the contribution (abundance) of each tumor tissue to the profile of the spectra. The problem is formulated as a non-negative source separation problem. We illustrate the effectiveness of the proposed methods and we analyze to which extent the dimension of the input space could influence the performance by comparing the results on the full magnitude signals and on dimensionally reduced spaces.

Model order selection for quantification of a multi-exponential magnetic resonance spectrum.

Magnetic resonance spectroscopic signals analyzed by time-domain models in order to retrieve estimates of the model parameters usually require prior knowledge about the model order. For multi-exponential signals where a superposition of peaks occurs at the same resonance frequency, but with different damping values, model order selection criteria from information theory can be used. In this study, several generalized versions of information criteria are compared using Monte-Carlo simulation signals. The best criterion is further applied for selecting the model order of experimental glycogen signals.