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Abstract Background Alfalfa mosaic virus (AMV) is an important virus affecting many vegetable crops in Egypt. In this study, virus isolates were collected from naturally infected potato, tomato, alfalfa and clover plants that showed suspected symptoms of AMV in different locations of Beheira and Alexandria governorates during the 2019–2020 growing season. The relative incidence of the virus ranged from 11–25% based on visual observations of symptoms and ELISA testing. A total of 41 samples were tested by ELISA using polyclonal antisera for AMV. Four AMV isolates collected from different host plants, named AM1 from potato, AM2 from tomato, AM3 from alfalfa and AM4 from alfalfa, were maintained on Nicotiana glutinosa plants for further characterization of AMV. Results Electron micrographs of the purified viral preparation showed spheroidal particles with a diameter of 18 nm and three bacilliform particles with lengths of roughly 55, 68, and 110 nm and diameters identical to those of the spheroidal particles. The CP gene sequence comparisons of four AMV isolates (AM1, AM2, AM3 and AM4) showed the highest nucleotide identity of 99.7% with the Gomchi isolate from South Korea infecting Gomchi (Ligularia fischeri) plants. Phylogenetic analysis showed that the present isolates were grouped together into a distinct separate clade (GPI) along with the Gomchi isolate from South Korea. Similarly, the deduced amino acid sequence comparisons of Egyptian AMV isolates revealed that amino acids Q29, S30, T34, V92 and V175 were conserved among the Egyptian isolates in GPI. Conclusion The present study found strong evolutionary evidence for the genetic diversity of AMV isolates by the identification of potential recombination events involving parents from GPI and GPII lineages. Additionally, the study found that Egyptian AMV isolates are genetically stable with low nucleotide diversity. Genetic analysis of the AMV population suggested that the AMV populations differ geographically, and AMV CP gene is under mild purifying selection. Furthermore, the study proposed that the Egyptian AMV population had common evolutionary ancestors with the Asian AMV population. Antioxidant enzymes activity was assessed on N. glutinosa plants in response to infection with each AMV isolate studied, and the results revealed that the enzyme activity varied.

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (

Abstract This open‐label, phase Ib study (NCT02346370) assessed the effect of pegvorhyaluronidase alfa (PVHA; PEGPH20) on the plasma pharmacokinetics (PKs) and safety of docetaxel in 15 patients with stage IIIB/IV non‐small cell lung cancer (NSCLC). The docetaxel PK profile from this study was consistent with simulations from a published docetaxel population PK model, and did not demonstrate an effect of PVHA on docetaxel PK. A maximum a posteriori Bayesian fit of the literature PK model to the docetaxel PK appeared unbiased. Adverse events (AEs) were generally consistent with previous reports for docetaxel monotherapy in NSCLC, except for higher incidence of musculoskeletal events, including myalgias, with PVHA plus docetaxel. The most common AEs were fatigue (87%), muscle spasms (60%), and myalgia (53%). Four patients experienced thromboembolic events (27%), three leading to treatment discontinuation. PVHA appeared to demonstrate an acceptable safety profile when given with docetaxel without significantly changing the plasma PK of docetaxel in patients with stage IIIB/IV NSCLC.

Preparation of polyvinyl-formaldehyde (PVF)/multi walled carbon nanotubes (MWCNTs) foams as an efficient heat sink nanocomposites was presented here. The PVF foam was loaded with 1 wt%, 2 wt%, and 4 wt% of MWCNTs. Thermal conductivity of the PVF/MWCNTs foams are investigated within the temperature range 25–200 °C. The structure of the prepared foams were characterized by Fourier transform infrared (FTIR), Raman spectroscopy, and differential scanning calorimetry (DSC). The results show that the MWCNTs are homogeneously distributed inside the PVF foam and attached to the PVF matrix via van der Waals forces. The addition of the MWCNTs increased the thermal stability of the PVF foam through connecting and filling the pores of the PVF foam. The maximum thermal conductivity value was found to be ≈5 W/mK for 4 wt% of PVF/MWCNTs foam at room temperature. This value is 166 times higher than the thermal conductivity of neat PVF foam. In addition, this value is higher than that of the previously published values for many carbon nanomaterials (CNMs)/polymer nanocompsites heat sinks. The thermal conductivity of the 4 wt% of PVF/MWCNTs reached 65 W/mK at 200 °C. The 4 wt% PVF/MWCNTs foam lowered the temperature of a hot surface from ≈ 40 to 30 °C, leading to cooling efficiency of 40%. Based on the experimental results, the PVF/MWCNTs foams show an improved capability for heat dissipation through easily and scalable preparation method.

Abstract Background Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically “re-wired” over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of molecular networks, such as sparsity. Results To address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the Drosophila Melanogaster. Conclusions We show that the networks inferred by the AKRON-Kalman filter are sparse and can detect more known gene-to-gene interactions for the Drosophila melanogaster than the Lasso-Kalman filter. Finally, all of the code reported in this contribution will be publicly available.

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance (p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor (p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.

BackgroundLow-grade gliomas cause significant neurological morbidity by brain invasion. There is no universally accepted objective technique available for detection of enlargement of low-grade gliomas in the clinical setting; subjective evaluation by clinicians using visual comparison of longitudinal radiological studies is the gold standard. The aim of this study is to determine whether a computer-assisted diagnosis (CAD) method helps physicians detect earlier growth of low-grade gliomas.Methods and findingsWe reviewed 165 patients diagnosed with grade 2 gliomas, seen at the University of Alabama at Birmingham clinics from 1 July 2017 to 14 May 2018. MRI scans were collected during the spring and summer of 2018. Fifty-six gliomas met the inclusion criteria, including 19 oligodendrogliomas, 26 astrocytomas, and 11 mixed gliomas in 30 males and 26 females with a mean age of 48 years and a range of follow-up of 150.2 months (difference between highest and lowest values). None received radiation therapy. We also studied 7 patients with an imaging abnormality without pathological diagnosis, who were clinically stable at the time of retrospective review (14 May 2018). This study compared growth detection by 7 physicians aided by the CAD method with retrospective clinical reports. The tumors of 63 patients (56 + 7) in 627 MRI scans were digitized, including 34 grade 2 gliomas with radiological progression and 22 radiologically stable grade 2 gliomas. The CAD method consisted of tumor segmentation, computing volumes, and pointing to growth by the online abrupt change-of-point method, which considers only past measurements. Independent scientists have evaluated the segmentation method. In 29 of the 34 patients with progression, the median time to growth detection was only 14 months for CAD compared to 44 months for current standard of care radiological evaluation (p < 0.001). Using CAD, accurate detection of tumor enlargement was possible with a median of only 57% change in the tumor volume as compared to a median of 174% change of volume necessary to diagnose tumor growth using standard of care clinical methods (p < 0.001). In the radiologically stable group, CAD facilitated growth detection in 13 out of 22 patients. CAD did not detect growth in the imaging abnormality group. The main limitation of this study was its retrospective design; nevertheless, the results depict the current state of a gold standard in clinical practice that allowed a significant increase in tumor volumes from baseline before detection. Such large increases in tumor volume would not be permitted in a prospective design. The number of glioma patients (n = 56) is a limitation; however, it is equivalent to the number of patients in phase II clinical trials.ConclusionsThe current practice of visual comparison of longitudinal MRI scans is associated with significant delays in detecting growth of low-grade gliomas. Our findings support the idea that physicians aided by CAD detect growth at significantly smaller volumes than physicians using visual comparison alone. This study does not answer the questions whether to treat or not and which treatment modality is optimal. Nonetheless, early growth detection sets the stage for future clinical studies that address these questions and whether early therapeutic interventions prolong survival and improve quality of life.

A simulation study for the production of 2 keV filters of quasi-mono-energetic neutrons based on the deep interference minima in the 45Sc total cross-section was carried out. A computer code QMENF-II was adapted to calculate the optimum amounts of the 45Sc as a main filter element and additional component ones to obtain sufficient intensity at high resolution and purity of the filtered quasi-mono-energetic neutrons. The emitted neutron spectrum from nuclear reactor and from the reaction of 2.6 MeV protons on a lithium fluoride target at the accelerator beam port, are used for simulation.

Glioblastoma (GBM) is a malignant brain tumor that continues to be associated with neurological morbidity and poor survival times. Brain invasion is a fundamental property of malignant glioma cells. The Go-or-Grow (GoG) phenotype proposes that cancer cell motility and proliferation are mutually exclusive. Here, we construct and apply a single glioma cell mathematical model that includes motility and angiogenesis and lacks the GoG phenotype. Simulations replicate key features of GBM including its multilayer structure (i.e.edema, enhancement, and necrosis), its progression patterns associated with bevacizumab treatment, and replicate the survival times of GBM treated or untreated with bevacizumab. These results suggest that the GoG phenotype is not a necessary property for the formation of the multilayer structure, recurrence patterns, and the poor survival times of patients diagnosed with GBM.

BackgroundThis study was designed to measure in vivo effects of propofol, isoflurane and sevoflurane on apoptosis by measuring caspase-3 and tumor necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) blood level as apoptotic markers.MethodsAfter obtaining ethical committee approval and informed written consents, sixty adult patients ASA I scheduled for open cholecystectomy participated in this study. They were randomally allocated into one of three equal groups to receive propofol infusion, low-flow isoflurane or sevoflurane for maintenance of anesthesia. Venous blood samples were collected preoperatively, immediately postoperative and after 24 hours to measure hemoglobin, hematocrit, creatinine, liver enzymes, serum TRAIL and caspase-3 levels.ResultsThere was no significant difference in hematological markers and serum creatinine. Liver enzymes showed significant postoperative rise (P < 0.05). In Propofol group, TRAIL and caspase-3 levels were significantly elevated immediately postoperative then decreased significantly after 24-hours (P < 0.05). In Isoflurane group, immediate postoperative level of TRAIL was significantly higher than 24 hours reading and significantly lower than its level in Propofol group at the same timing meanwhile caspase-3 levels were comparable at different timings. In Sevoflurane group, TRAIL and caspase-3 levels increased significantly in both postoperative samples than preoperative level and than those of Isoflurane and Propofol groups after 24 hours concerning TRAIL (P & 0.05).ConclusionsThis study concluded that isoflurane is superior and sevoflurane is the least effective among the three anesthetics in protection against apoptosis. This study neither proved nor excluded propofol-induced apoptosis. Further studies are required during lengthy procedure and in compromised patients.