Transnasal targeted delivery of therapeutics in central nervous system diseases: a narrative review.

Currently, neurointervention, surgery, medication, and central nervous system (CNS) stimulation are the main treatments used in CNS diseases. These approaches are used to overcome the blood brain barrier (BBB), but they have limitations that necessitate the development of targeted delivery methods. Thus, recent research has focused on spatiotemporally direct and indirect targeted delivery methods because they decrease the effect on nontarget cells, thus minimizing side effects and increasing the patient's quality of life. Methods that enable therapeutics to be directly passed through the BBB to facilitate delivery to target cells include the use of nanomedicine (nanoparticles and extracellular vesicles), and magnetic field-mediated delivery. Nanoparticles are divided into organic, inorganic types depending on their outer shell composition. Extracellular vesicles consist of apoptotic bodies, microvesicles, and exosomes. Magnetic field-mediated delivery methods include magnetic field-mediated passive/actively-assisted navigation, magnetotactic bacteria, magnetic resonance navigation, and magnetic nanobots-in developmental chronological order of when they were developed. Indirect methods increase the BBB permeability, allowing therapeutics to reach the CNS, and include chemical delivery and mechanical delivery (focused ultrasound and LASER therapy). Chemical methods (chemical permeation enhancers) include mannitol, a prevalent BBB permeabilizer, and other chemicals-bradykinin and 1-O-pentylglycerol-to resolve the limitations of mannitol. Focused ultrasound is in either high intensity or low intensity. LASER therapies includes three types: laser interstitial therapy, photodynamic therapy, and photobiomodulation therapy. The combination of direct and indirect methods is not as common as their individual use but represents an area for further research in the field. This review aims to analyze the advantages and disadvantages of these methods, describe the combined use of direct and indirect deliveries, and provide the future prospects of each targeted delivery method. We conclude that the most promising method is the nose-to-CNS delivery of hybrid nanomedicine, multiple combination of organic, inorganic nanoparticles and exosomes, via magnetic resonance navigation following preconditioning treatment with photobiomodulation therapy or focused ultrasound in low intensity as a strategy for differentiating this review from others on targeted CNS delivery; however, additional studies are needed to demonstrate the application of this approach in more complex in vivo pathways.

CD4+/CD8+ Ratio and Growth Differentiation Factor 8 Levels in Peripheral Blood of Large Canine Males Are Useful Parameters to Build an Age Prediction Model.

PURPOSE: To build an age prediction model, we measured CD4+ and CD8+ cells, and humoral components in canine peripheral blood. MATERIALS AND METHODS: Large Belgian Malinois (BGM) and German Shepherd Dog (GSD) breeds (n=27), aged from 1 to 12 years, were used for this study. Peripheral bloods were obtained by venepuncture, then plasma and peripheral blood mononuclear cells (PBMCs) were separated immediately. Six myokines, including interleukin (IL)-6, IL-8, IL-15, leukemia inhibitory factor (LIF), growth differentiation factor 8 (GDF8), and GDF11 were measured from plasma and CD4+/CD8+ T-lymphocytes ratio were measured from PBMC. These parameters were then tested with age prediction models to find the best fit model. RESULTS: We found that the T-lymphocyte ratio (CD4+/CD8+) was significantly correlated with age (r=0.46, p=0.016). Among the six myokines, only GDF8 showed a significant correlation with age (r=0.52, p=0.005). Interestingly, these two markers showed better correlations in male dogs than females, and BGM breed than GSD. Using these two age biomarkers, we could obtain the best fit in a quadratic linear mixed model (r=0.77, p=3×10-6). CONCLUSIONS: Age prediction is a challenging task because of complication with biological age. Our quadratic linear mixed model using CD4+/CD8+ ratio and GDF8 level showed a meaningful age prediction.

Bioactivity-Guided Extract Optimization of Osmanthus fragrans var. aurantiacus Leaves and Anti-Inflammatory Activities of Phillyrin.

The aim of this study was to identify the optimal extraction conditions for leaves of Osmanthus fragrans var. aurantiacus. Inhibitory effects of various extracts on NO production were compared. Antioxidant evaluations for total phenol and flavonoid contents were carried out using various extracts of O. fragrans var. aurantiacus leaves obtained under optimal extraction conditions that showed the greatest effect on NO production. The optimal method for extracting O. fragrans var. aurantiacus leaves resulted in an extract named OP OFLE. OP OFLE showed DPPH and ABTS radical scavenging activities in a concentration-dependent manner. Phillyrin (PH) was isolated as a major compound from OP OFLE by HPLC/DAD analysis. OP OFLE and PH reduced inducible nitric oxide (iNOS) and cyclooxygenase (COX)-2 protein expression and downregulated proinflammatory cytokines such as interleukin (IL)-1ß, IL-6, IL-8, and tumor necrosis factor (TNF)-α in LPS-stimulated RAW 264.7 and HT-29 cells. To determine the signal pathway involved in the inhibition of NO production, a Western blot analysis was performed. Results showed that OP OFLE decreased phosphorylation of extracellular regulated kinase (pERK) 1/2 and the expression of nuclear factor-kappa B (NF-κB). Our results suggest that extracts of O. fragrans var. aurantiacus leaves and its major components have biological activities such as antioxidative and anti-inflammatory properties.

Application of N Descriptors Proposed by the International Association for the Study of Lung Cancer in Clinical Staging.

Background Patients with N1 or N2 non-small cell lung cancer exhibit prognostic heterogeneity. To refine the current N staging system, new N stages were proposed by the International Association for the Study of Lung Cancer. However, those proposed new N stages have not been validated. Purpose To evaluate the prognostic performance of the proposed N descriptors for clinical staging. Materials and Methods Participants with non-small cell lung cancer without distant metastasis from January 2010 to December 2014 were retrospectively included. Each patient's clinical N (cN) stage was assigned to one of seven categories (cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, cN3). The 5-year overall survival rates were estimated with the Kaplan-Meier method. The adjusted hazard ratios (HRs) and their 95% CIs were estimated by using a multivariable Cox proportional hazard model. Ad hoc analyses according to lymph node (LN) size were performed. Results A total of 1271 patients (median age, 66 years; interquartile range, 59-73 years; 812 men) were included. The 5-year overall survival rates were 77.3%, 53.7%, 36.0%, 29.2%, 34.4%, 18.0%, and 12.4% for stages cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, and cN3, respectively. Patients with cN2b disease had a worse prognosis than patients with cN2a disease (HR, 1.53; 95% CI: 1.06, 2.22; P = .02). There was no prognostic difference between cN1b and cN1a (HR, 1.13; 95% CI: 0.61, 2.09; P = .71); however, there was a difference between cN1 subgroups when stratified by LN size (≥2 cm; HR, 2.26; 95% CI: 1.16, 4.44; P = .02). Within cN2a disease, there were no differences between cN2a1 and cN2a2 (HR, 0.98; 95% CI: 0.61, 1.56; P = .93) or between subgroups according to LN size (HR, 0.74; 95% CI: 0.40, 1.37; P = .34). Conclusion A survival difference was observed between single- and multistation involvement among cN2 disease. The number of involved lymph node stations in patients with cN1 disease and the presence of skip metastasis in patients with cN2 disease were not associated with survival differences. © RSNA, 2021 Online supplemental material is available for this article.

Accuracy and complications of percutaneous transthoracic needle lung biopsy for the diagnosis of malignancy in patients with idiopathic pulmonary fibrosis.

OBJECTIVES: To determine the accuracy of CT-guided percutaneous transthoracic needle lung biopsy (PTNB) for the diagnosis of malignancy and the associated complication rates in patients with idiopathic pulmonary fibrosis (IPF). METHODS: This retrospective study included 91 CT-guided PTNBs performed in 80 patients with IPF from April 2003 through December 2016. Data regarding patients, target lesions, procedures, complications, and pathological reports were collected, and the final diagnosis was made. The diagnostic accuracy, sensitivity, specificity, percentage of nondiagnostic results, and complication rates were determined. Multivariable logistic regression analyses were performed to identify risk factors for nondiagnostic results and major complications. RESULTS: Three biopsies (technical failure [n = 2] and undetermined final diagnosis [n = 1]) were excluded from the diagnostic accuracy calculation. The diagnostic accuracy, sensitivity, and specificity were 89% (78/88), 90% (62/69), and 84% (16/19), respectively. The percentage of nondiagnostic results was 34% (30/88). Lesion size ≤ 3 cm (odds ratio [OR], 8.8; 95% confidence interval [CI], 2.5-31.2; p = 0.001) and needle tip placement outside the target lesion (OR, 13.7; 95% CI, 1.4-132.2; p = 0.02) were risk factors for nondiagnostic results. The overall and major complication rates were 51% (46/91) and 12% (11/91), respectively. The presence of honeycombing along the path of the needle (OR, 11.2; 95% CI, 1.4-89.1; p = 0.02) was an independent risk factor for major complications. CONCLUSIONS: CT-guided PTNB shows a relatively reasonable accuracy in diagnosing malignancy in patients with IPF. The complication rate may be high, especially when the needle passes through honeycomb lesions. KEY POINTS: • In patients with idiopathic pulmonary fibrosis (IPF), CT-guided percutaneous transthoracic needle lung biopsy (PTNB) showed a relatively reasonable accuracy for the diagnosis of malignancy. • Target lesion size ≤ 3 cm and biopsy needle tip placement outside the target lesion were risk factors for nondiagnostic results of CT-guided PTNB. • The complication rate may be high, especially in cases where the biopsy needle passes through honeycomb lesions.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers.

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. For the healthy control group, 234 patients (median age, 58 years; IQR, 48-68 years; 123 women) with normal chest radiographs were randomly selected. Nine observers reviewed each chest radiograph, with and without a DLAD. They detected potential lung cancers and determined whether they would recommend chest CT for follow-up. Observer performance was compared with use of the area under the alternative free-response receiver operating characteristic curve (AUC), sensitivity, and rates of chest CT recommendation. Results In total, 105 of the 117 patients had lung cancers that were overlooked on their original radiographs. The average AUC for all observers significantly rose from 0.67 (95% confidence interval [CI]: 0.62, 0.72) without a DLAD to 0.76 (95% CI: 0.71, 0.81) with a DLAD (P < .001). With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.