GaInNAs-based Hellish-vertical cavity semiconductor optical amplifier for 1.3 μm operation

Hot electron light emission and lasing in semiconductor heterostructure (Hellish) devices are surface emitters the operation of which is based on the longitudinal injection of electrons and holes in the active region. These devices can be designed to be used as vertical cavity surface emitting laser or, as in this study, as a vertical cavity semiconductor optical amplifier (VCSOA). This study investigates the prospects for a Hellish VCSOA based on GaInNAs/GaAs material for operation in the 1.3-μm wavelength range. Hellish VCSOAs have increased functionality, and use undoped distributed Bragg reflectors; and this coupled with direct injection into the active region is expected to yield improvements in the gain and bandwidth. The design of the Hellish VCSOA is based on the transfer matrix method and the optical field distribution within the structure, where the determination of the position of quantum wells is crucial. A full assessment of Hellish VCSOAs has been performed in a device with eleven layers of Ga0.35In0.65N0.02As0.08/GaAs quantum wells (QWs) in the active region. It was characterised through I-V, L-V and by spectral photoluminescence, electroluminescence and electro-photoluminescence as a function of temperature and applied bias. Cavity resonance and gain peak curves have been calculated at different temperatures. Good agreement between experimental and theoretical results has been obtained

Preoperative glioma grading by MR diffusion and MR spectroscopic imaging

Objectives: To assess the diagnostic accuracy of DW-MRI, ADC value, and MRS in preoperative glioma grading because of its importance in treatment planning. Patients and methods: A prospective study included 30 patients with gliomas, based on CT and cMRI findings, referred from Neurosurgery Department for DWI and MRS. Results were correlated with histopathological diagnosis after surgical resection. Results: ADC values were significantly higher in low grade gliomas relative to high grade ones. The lowest value was for GBM, 100% sensitivity, specificity, PPV and NPP in glioma grading. MRS revealed higher Cho/NAA and Cho/Cr ratios in high grade neoplasms and characteristic elevated lipid peak with statistical significant difference between low grade and high grade gliomas. MRS was more accurate than ADC value in detecting peritumoral infiltration of high grade gliomas, but there were no statistically significant differences between anaplastic and GBM in tumoral and peritumoral regions. Conclusion: DW-MRI had higher sensitivity, specificity and accuracy than cMRI and MRS in glioma grading while MRS is more accurate than ADC value in assessing peri-tumoral infiltration based on high metabolite ratios in peri-tumoral tissue for anaplastic glioma and GBM, but there were no statistically significant differences between high grade groups

Clinical diagnosis and surgical approaches of vaginal hyperplasia in bitches

Objective: To describe the breed predisposition, clinical diagnosis, pathological findings and the surgical approach through excision of the hyperplasic mass from the vagina. Methods: Twenty five bitches of different breeds suffering from a protruded mass from the vulva were examined clinically, blood samples were collected to ensure the phase of estrus that were determined by evaluating the exfoliative vaginal epithelium, and a histopathological examination of the hyperplastic mass was done after its surgical excision. Results: The current work revealed that the maximum value of estradiol 17-B was in Alabai breed while the maximum value of progesterone was in Pit bull breed. And Pit bull breed showed cornification and stratification of the vaginal mucosa with abundant eosinophilic cytoplasm and regular round nuclei. Conclusions: As the vaginal hyperplasia is a crucial gynaecological problem that affects different breeds of bitches, the current work provides a comprehensive diagnosis of the case and illustrates the surgical interference for its excision

Activated-carbon nanofibers/graphene nanocomposites and their adsorption performance towards carbon dioxide

Activated-carbon nanofibers (ACNFs) provide a relatively new, modified structure of carbon-based adsorbents that have the ability to adsorb carbon dioxide due to their high specific surface area, wide distribution of porous structures, and high volume of active sites. In this study, cost-effective agricultural waste-based graphene synthesized from rice husk ashes was used as additive to enhance the ACNF properties. ACNF/graphene (gACNF) is still a relatively unexplored adsorbent. The resultant gACNF exhibited better thermal stability properties, with higher yield, larger specific surface area, and higher micropore volume. These properties are the main factors contributing to their enhanced adsorption performance towards CO2

Damage in rheumatic diseases: Contemporary international standpoint and scores emerging from clinical, radiological and machine learning

In rheumatic diseases, damage is a major concern and reflects irreversible organ scarring or tissue degradation. Quantifying damage or measuring its severity is an indispensable concern in determining the overall outcome. Damage considerably influences both longterm prognosis and quality of life. Rheumatic diseases (RD) represent a significant health burden. Organ damage is consistently associated with increased mortality. Monitoring damage is critical in the evaluation of patients and in appraising treatment efficacy. Proper assessment and early detection of damage paves way for modifying the disease course with effective medications and regimens may reduce organ damage, improve outcomes and decrease mortality. With the exception of systemic lupus erythematosus and vasculitis, most RDs lack an established damage index making it an ongoing demand to develop effective scores and prediction models for damage accrual early in the disease course. A better understanding of machine learning with the increasing availability of medical large data may facilitate the development of meaningful precision medicine for patients with RDs. An updated spectrum of clinical and radiological damage scores and indices as well as the role of machine learning are presented in this review for the key RDs