Eye lens and thyroid gland radiation exposure for patients undergoing brain computed tomography examination

This study aims to estimate the effective radiation dose and organ dose from head CT procedures. It was conducted in three main private hospitals in Khartoum State-Sudan, using Toshiba machines with 64 slices. The total number of patients included in this study was 142 patients (82 males and 60 females). The effective dose and organ dose were calculated by CT Expo software. The effective dose slightly varied among patients according to gender and age. The effective dose for female patients (5.99 mSv) was higher than that for male patients (5.84 mSv), and the pediatric dose (5.46 mSv) was lower than the adults’ dose (5.94 mSv).The dose for eye lens was found lower for male patients (89.117 mSv) than the dose for female patients (94.62) mSv). According to patients’ age: the dose received by the lens of the eye was much lower in pediatric (79.93 mSv) than the adults (92.41 mSv). The dose for thyroid in female patients (33.52 mSv) was higher than the male patients (28 mSv). The pediatric dose (28.34 mSv) was lower than the adults’ dose (30.64 mSv).Departmental imaging protocol and lack of training among hospital staff are expected to be responsible for these variations. Therefore, this study recommends that the CT technologists be trained on suitable strategies to achieve dose optimization. Moreover, patients’ doses must be monitored regularly.     

Recognition of brain tumors in MRI images using texture analysis

ObjectivesBrain neoplasms or intracranial tumors, which are more common in older adults, can affect individuals of any age including pediatric and children. Exposure to carcinogenic agents including ionizing radiation and family history is one of the main causes of the disease. Early diagnosis is crucial to avoid prolonged. patients' suffering. The aim of the study was to efficiently recognize the brain tumors from the other brain tissues which include grey and white matter as well as cerebrospinal fluid (CSF).Materials and methodsThis study was performed using axial, sagittal and coronal views for fifty brain tumor patients randomly selected from a set of 200 patients, with a “control” set consisting of images showing no sign of disease; and the “test” brain MRI images for patients diagnosed with brain tumor. The study includes both genders with age ranging from 18 years to 83 years old, (56.5 ± 17.2). The brain images were acquired using a standard head coil Philips Intera 1.5 Tesla machine (USA). The thickness of each section in the entire sequence was 8 mm. Acquisition of T2-weighted and T1-weighted were performed. Interactive Data Language software was used to analyze the data.ResultsThe results of this study showed that: the overall accuracy of classification process was 94.8%, and for the tumor; the sensitivity was 97.3%. White matter and grey matter showed a classification accuracy of 95.7% and 89.7% and for CSF the accuracy was 94.3%.ConclusionThe results showed that brain tumor can be classified successfully and delineated using texture analysis with minimum efforts and with high accuracy for brain tumors.     

A Novel p53 Mutant Found in Iatrogenic Urothelial Cancers Is Dysfunctional and Can Be Rescued by a Second-site Global Suppressor Mutation

Exposure to herbal remedies containing the carcinogen aristolochic acid (AA) has been widespread in some regions of the world. Rare A→T TP53 mutations were recently discovered in AA-associated urothelial cancers. The near absence of these mutations among all other sequenced human tumors suggests that they could be biologically silent. There are no cell banks with established lines derived from human tumors with which to explore the influence of the novel mutants on p53 function and cellular behavior. To investigate their impact, we generated isogenic mutant clones by integrase-mediated cassette exchange at the p53 locus of platform (null) murine embryonic fibroblasts and kidney epithelial cells. Common tumor mutants (R248W, R273C) were compared with the AA-associated mutants N131Y, R249W, and Q104L. Assays of cell proliferation, migration, growth in soft agar, apoptosis, senescence, and gene expression revealed contrasting outcomes on cellular behavior following introduction of N131Y or Q104L. The N131Y mutant demonstrated a phenotype akin to common tumor mutants, whereas Q104L clone behavior resembled that of cells with wild-type p53. Wild-type p53 responses were restored in double-mutant cells harboring N131Y and N239Y, a second-site rescue mutation, suggesting that pharmaceutical reactivation of p53 function in tumors expressing N131Y could have therapeutic benefit. N131Y is likely to contribute directly to tumor phenotype and is a promising candidate biomarker of AA exposure and disease. Rare mutations thus do not necessarily point to sites where amino acid exchanges are phenotypically neutral. Encounter with mutagenic insults targeting cryptic sites can reveal specific signature hotspots.     

Y chromosome lineage- and village-specific genes on chromosomes 1p22 and 6q27 control visceral leishmaniasis in Sudan

Familial clustering and ethnic differences suggest that visceral leishmaniasis caused by Leishmania donovani is under genetic control. A recent genome scan provided evidence for a major susceptibility gene on Chromosome 22q12 in the Aringa ethnic group in Sudan. We now report a genome-wide scan using 69 families with 173 affected relatives from two villages occupied by the related Masalit ethnic group. A primary ten-centimorgan scan followed by refined mapping provided evidence for major loci at 1p22 (LOD score 5.65; nominal p = 1.72 × 10⁻⁷; empirical p < 1 × 10⁻⁵; λ_S = 5.1) and 6q27 (LOD score 3.74; nominal p = 1.68 × 10⁻⁵; empirical p < 1 × 10⁻⁴; λ_S = 2.3) that were Y chromosome–lineage and village-specific. Neither village supported a visceral leishmaniasis susceptibility gene on 22q12. The results suggest strong lineage-specific genes due to founder effect and consanguinity in these recently immigrant populations. These chance events in ethnically uniform African populations provide a powerful resource in the search for genes and mechanisms that regulate this complex disease.     

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.