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Purpose: Postoperative radiation therapy (RT) is commonly used for World Health Organization grade II-III intracranial ependymoma. Clinicians generally aim to begin RT ≤5 weeks after surgery, but postoperative recovery and need for second look surgery can delay the initiation of adjuvant therapy. On ACNS 0831, patients were required to enroll ≤8 weeks after initial surgery and begin adjuvant therapy within 3 weeks after enrollment. The purpose of this study was to determine the optimal timing of RT after surgery. Methods and Materials: The National Cancer Database was queried for patients (aged 1-39 years) with localized World Health Organization grade II-III intracranial ependymoma treated with surgery and postoperative RT. Overall survival (OS) curves were plotted based on RT timing (≤5 weeks, 5-8 weeks, and >8 weeks after surgery) and were compared by log-rank test. Factors associated with OS were identified by multivariate analysis. After 2009, complete data were available on whether patients underwent gross total resection or subtotal resection. Planned subset analysis was performed to examine the effect of RT timing on OS in patients with known extent of resection. Results: In the final analytical data set of 1043 patients, no difference in 3-year OS was observed in patients who initiated RT ≤5 weeks, 5 to 8 weeks, and >8 weeks after surgery (89.8% vs 89.1% vs 88.4%; P = .796). On multivariate analysis, grade III tumors (hazard ratio, 2.752; 95% confidence interval, 1.969-3.846, P < .001) and subtotal resection (hazard ratio, 2.253; 95% confidence interval, 1.405-3.611, P < .001) were significantly associated with reduced OS. Timing of RT, total RT dose, age, and other factors were not significant. These findings were affirmed in the subset of patients treated between 2010 and 2016, when extent of resection was routinely recorded. Conclusions: Delayed postoperative RT was not associated with inferior survival in patients with intracranial ependymoma. Delayed RT initiation may be acceptable in patients who require longer postoperative recovery or referral to an appropriate RT center, but should be minimized whenever practical.

Background: Levofloxacin has been recommended by WHO for the treatment of pulmonary tuberculosis and inhalable delivery of levofloxacin can be advantageous over conventional delivery. Objective: This study aimed to develop and optimize inhalable levofloxacin loaded chitosan nanoparticles (LCN). The objective was to achieve the mean particle size of LCN less than 300nm, sustain the drug release up to 24 h and achieve MMAD of LCN of less than 5μm. Methods: LCN were prepared by ionic gelation of chitosan with sodium tripolyphosphate (STPP) and subsequent lyophilization. A Plackett Burman screening design, 32 full factorial design, and overlay plots were sequentially employed to optimize the formulation. The mean particle size, % entrapment efficiency, in vitro drug release, and minimum inhibitory concentration were all evaluated. Results: The Pareto chart from the Placket Burman screening design revealed that the concentrations of chitosan and STPP was found to be significant (p < 0.05). Further analysis by 32 full factorial design revealed that F-ratio for each model generated was found to be greater than the theoretical value (p < 0.05), confirming the significance of each model. Conclusion: The optimized formulation showed a mean particle size of 171.5 nm, sustained the drug release up to 24 h in simulated lung fluid, and revealed MMAD of 3.18 μm, which can confirm delivery of the drug to the deep lung region. However, further in vivo studies are required to design a suitable dosage regimen and establish the fate of nanoparticles for safe and efficacious delivery of the drug.

Objectives The aim of this study was to develop and optimize levofloxacin loaded PLGA nanoparticles (LN) for pulmonary delivery employing screening and experimental design and evaluate their in-vitro and in-vivo performance. The objective was to achieve Mass Median Aerodynamic Diameter (MMAD) of LN of less than 5μm, sustain the drug release up to 120 h and a higher AUC/MIC at the site of action. Methods LN were prepared by modified emulsion solvent evaporation technique employing high speed homogenization, probe sonication and subsequent lyophilization. Key Findings The Pareto chart from Placket Burman screening design revealed that homogenization speed and amount of PLGA were found to be significant (P < 0.05). Further analysis by 3 full-factorial design revealed that F-ratio was found to be far greater than the theoretical value (P < 0.05) for each regression model. Conclusion The optimized formulation with desirability value 0.9612 showed mean particle size of 146 nm, MMAD of 4.40 μm and sustained the drug release up to 120 h in simulated lung fluid. Augmentation in Cmax (1.71-fold), AUC 0-∞ (5.46-fold), Mean Residence Time (6.64-fold) and AUC/MIC (6.21-fold) of LN through pulmonary route was found to significantly higher (P < 0.05) than levofloxacin (p. o.).

Dual-energy CT (DECT) can improve the accuracy of myocardial perfusion CT with projection-based monochromatic (DECT-MCE) and quantification of myocardial iodine in material decomposition (DECT-MD) reconstructions. However, evaluation of multiple reconstructions is laborious and the optimal reconstruction to detect myocardial perfusion defects is unknown.Left ventricular (LV) phantoms with artificial perfusion defects were scanned using DECT and single energy cardiac computed tomography angiography (SECT). Reconstructions of DECT-MCE at 40, 70, 100 and 140 keV, DECT-MD pairs of water, iodine, iron and fat, and SECT were evaluated using a 17-segment myocardial model. The diagnostic performance of each reconstruction was calculated on a per-segment basis and compared across DECT reconstructions.Over 34 phantoms with artificial perfusion defects were found in 64/578 (11%) of segments, the sensitivity of DECT-MCE at 40, 70, 100, and 140 keV was 100% (95% confidence interval (CI): 93-100), 100% (95% CI: 93-100), 71% (95% CI: 56-83), and 25% (95% CI: 14-40), respectively, with a significant decline between 70 keV and 100 keV (p 0.001). The specificity of DECT-MCE was 100% at all energies (95% CI: 99-100). As a group, the DECT-MD iodine background reconstructions had significantly lower sensitivity than the remaining modes (2.1% [95% CI, 0.05-11.1], vs. 100% [95% CI, 92.6-100], p 0.001). Specificity of all material pair modes remained 100%.Using LV phantom models, the approach with the best sensitivity and specificity to assess myocardial perfusion defects with DECT are reconstructions of DECT-MCE at 40 or 70 KeV and DECT-MD without iodine background.

INTRODUCTION Postoperative radiotherapy is commonly given for WHO Grade 2–3 intracranial ependymoma. Clinicians generally aim to begin radiotherapy ≤5 weeks following surgery, but the optimal timing remains uncertain. METHODS The National Cancer Database was queried for patients (age ≤39 years) with localized WHO Grade 2–3 intracranial ependymoma treated with surgery and postoperative radiotherapy. Multivariable logistic regression was used to identify factors associated with delayed postoperative radiotherapy, defined as starting >8 weeks after surgery. Overall survival (OS) curves were plotted based on radiotherapy timing (≤5 weeks, 5–8 weeks, and >8 weeks after surgery) and compared by log-rank test. Multivariate analysis (MVA) was used to identify factors associated with OS. RESULTS In the final analytic set of 1,043 patients, age ≥21 years (OR 2.07, 95% CI 1.56–2.74) and WHO Grade 2 tumors (OR 1.41, 95% CI 1.08–1.85) were significantly associated with delayed time to adjuvant radiotherapy. No difference in 3-year OS was observed in patients who initiated radiotherapy ≤5 weeks, 5–8 weeks, and >8 weeks after surgery (89.8% vs. 89.1% vs. 88.4%; p= 0.796). On MVA, anaplastic histology (HR 2.414, 95% CI 1.784–3.268, p CONCLUSION Delayed postoperative radiotherapy was not associated with inferior survival in patients with intracranial ependymoma, suggesting delayed radiotherapy initiation may be considered in patients requiring longer postoperative recovery or referral to an appropriate radiotherapy center.

Alcohol misuse is a common social issue related to the central nervous system. Electroencephalogram (EEG) signals are used to depict electrical activities of the brain. In the proposed study, a new computer-aided diagnosis (CAD) has been developed to recognize alcoholic and normal EEG patterns, accurately. In this paper, we present an automatic system for the classification of normal and alcoholic EEG signals using orthogonal wavelet filter bank (OWFB). First, we derive sub-bands (SBs) of EEG signals. Then, we compute logarithms of the energies (LEs) of the SBs. The LEs are employed as the discriminating features for the separation of alcoholic and normal EEG signals. A supervised machine learning algorithm called K nearest neighbor (KNN) has been employed to classify normal and alcoholic patterns. The proposed model has yielded very good classification results. We have achieved a classification accuracy (CA) of 94.20% with tenfold cross-validation (CV).

Ezetimibe (EZE), a water insoluble drug, depicts variable bioavailability. The objective of the present investigation was to improve dissolution characteristics of EZE, which might offer improved bioavailability. The solid dispersions were prepared using poloxamer 407 (L 127) and polyvinyl pyrrolidone by melt and solvent method, respectively. Phase solubility studies indicated linear relationship between drug solubility and carrier concentration. In vitro release studies revealed improvement in the dissolution characteristics of EZE in solid dispersions. Solid dispersion with L 127 gave better rate and extent of dissolution. The best fit model indicating the probable mechanism of drug release from solid dispersions was found to be Korsemeyer–Peppas. The results of characterization of solid dispersions by Fourier transform infrared spectroscopy, differential scanning calorimetry, and powder X-ray diffraction revealed reduction in drug crystallinity which might be responsible for improved dissolution properties. The tablets of solid dispersion, containing L 127 prepared by direct compression, exhibited better drug release as compared to marketed formulation.

Identification of seizure using electroencephalogram (EEG) signal is crucial to detect diseases like epilepsy. Manual epilepsy detection is time consuming and prone to errors. Various techniques have been developed to get quick and accurate results for epilepsy detection. We propose a novel approach to detect epileptic seizure using bi-orthogonal wavelet filters. The bi-orthogonal wavelet filters divide the EEG signal into different sub-bands. Then different features, i.e., Shannon entropy (ShEn), Renyi entropy (RenEn), fractal dimension (FD), energy and fuzzy entropy (FE) are extracted from the sub-bands. The [Formula: see text]-values of the features are used to evaluate the discriminating ability of the features. These features are given as input to the support vector machine (SVM). The EEG signals are then classified into the following classes: (i) normal versus seizure, (ii) seizure-free versus seizure, (iii) normal versus seizure-free versus seizure, and (iv) normal versus seizure-free. We have used two independent datasets: a public dataset for training and validation of the model, and a private dataset for evaluating the model’s performance. The ten-fold cross-validation method is used here to reduce the chances of over fitting. The SVM classifier yielded 100% accuracy in discriminating both seizure-free and normal patients for public dataset, and inter-ictal and ictal for private dataset. It also gave very good classification accuracies for the other classification problems for both the datasets. The proposed method is ready for the clinical trial to be tested with huge databases before the actual practical usage.

The present work is aimed to develop and optimize pulsatile delivery during dissolution of an improved formulation of valsartan to coordinate the drug release with circadian rhythm. Preliminary studies suggested that β cyclodextrin could improve the solubility of valsartan and showed AL type solubility curve. A 1:1 stoichiometric ratio of valsartan to β cyclodextrin was revealed from phase solubility studies and Job's plot. The prepared complex showed significantly better dissolution efficiency (p