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The formulation F8 showed high dissolution performance (% DE30 value of 80.65 ± 3.05) among the other formulations. Optimized Gelucire®48/16-based SDs formulation suggested improved oral absorption of atorvastatin as evidenced with improved pharmacokinetic parameters (Cmax 2864.33 ± 573.86 ng/ml; AUC0-t 5594.95 ± 623.3 ng/h ml) as compared to ATV suspension (Cmax 317.82 ± 63.56 ng/ml; AUC0-t 573.94 ± 398.9 ng/h ml) and marketed tablets (Cmax 852.72 ± 42.63 ng/ml; 4837.4 ± 174.7 ng/h ml). Conclusively, solid dispersion-based oral formulation of atorvastatin could be a promising approach for enhanced drug solubilization, dissolution, and subsequently improved absorption.This research aimed to develop a novel drug delivery system to improve treatment of skin disorders. The system is comprised of a Carbopol 980-based nanoemulgel (NE-gel) containing a desonide (DES; 0.05%, w/w) nanoemulsion (NE), which has a small particle size, high encapsulation efficiency, good thermodynamic stability, good permeation ability, and high skin retention. DES-loaded NE (DES-NE) was prepared by high-pressure homogenization. The developed formulation was characterized by differential scanning calorimetry (DSC), X-ray diffraction, drug release, skin permeation, and drug retention. DES in vitro release and skin permeation studies with different formulations of artificial membrane and rat abdominal skin were performed with the Franz diffusion cell system. Confocal laser scanning microscopy (CLSM) was used to detect the localization and permeation pathways of drugs in the skin. Compared with commercially available gel (CA-gel) and NE, the NE-gel release process conformed to the Higuchi release model (R2 = 0.9813). NE-gel prolonged the drug release time and allowed for reduced administration dose and frequency. The unit cumulative permeation of NE and NE-gel through the skin for 12 h was 63.13 ± 2.78 and 42.53 ± 2.06 μg/cm2, respectively, values significantly higher (p less then 0.01) than that of the CA-gel (30.65 ± 1.25 μg/cm2) and CA-cream (15.21 ± 0.97 μg/cm2). The DES-NE and DES NE-gel skin drug retention was significantly higher than commercially available formulations (p less then 0.01). Hence, the prepared NE-gel is a potential vehicle for improved topical DES delivery for better treatment of skin disorders.Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age. The pathology of OA disease is still unclear, and there are no effective interventions that can completely alter the OA disease process. Magnetic resonance (MR) image evaluation is sensitive for depicting early changes of knee OA, and therefore important for early clinical intervention for relieving the symptom. Automated cartilage segmentation based on MR images is a vital step in experimental longitudinal studies to follow-up the patients and prospectively define a new quantitative marker from OA progression. In this paper, we develop a deep learning-based coarse-to-fine approach for automated knee bone, cartilage, and meniscus segmentation with high computational efficiency. The proposed method is evaluated using two-fold cross-validation on 507 MR volumes (81,120 slices) with OA from the Osteoarthritis Initiative (OAI)1 dataset. [https://www.selleckchem.com/products/aebsf-hcl.html see more] The mean dice similarity coefficients (DSCs) of femoral bone (FB), tibial bone (TB), femoral cartilage (FC), and tibial cartilage (TC) separately are 99.1%, 98.2%, 90.9%, and 85.8%. The time of segmenting each patient is 12 s, which is fast enough to be used in clinical practice. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of OA images.Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods. With the rising attention for personalized and precision medicine, scientists and clinicians have now turned to artificial intelligence systems to provide them with solutions for therapeutics development. CNNs have already provided valuable insights into biological data transformation. Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional biological data, such as nucleotide and protein sequences, as well as small molecular data, e.g., simplified molecular-input line-entry specification, InChI, binary fingerprints, etc., to categorize the models based on their objective and also highlight various challenges. The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.Papaverine, a poorly soluble opium alkaloid, has recently been shown to reduce retinal inflammation due to which it may have therapeutic application in the management of Leber's hereditary optic neuropathy. In this study, papaverine eyedrops based on medium chain triglycerides were prepared and the effect of diethyl glycol monoethyl ether (DGME) on their ocular distribution was evaluated using an ex vivo porcine eye model. The route of drug penetration was also studied by orienting the eye to expose either only the cornea or the sclera to the formulation. Furthermore, in vivo studies were performed to confirm ocular tolerability and evaluate ocular drug distribution. Our results showed increased papaverine concentrations in the cornea and sclera in the presence of DGME but with a slight reduction in the retina-choroid (RC) drug concentration when administered via the corneal route, suggesting that DGME enhances drug accumulation in the anterior ocular tissues but with little effect on posterior drug delivery. In vivo, the papaverine eyedrop with DGME showed good ocular tolerability with the highest drug concentration being observed in the cornea (1.53 ± 0.28 μg/g of tissue), followed by the conjunctiva (0.74 ± 0.18 μg/g) and sclera (0.25 ± 0.06 μg/g), respectively. However, no drug was detected in the RC, vitreous humor or plasma. Overall, this study highlighted that DGME influences ocular distribution and accumulation of papaverine. Moreover, results suggest that for hydrophobic drugs dissolved in hydrophobic non-aqueous vehicles, transcorneal penetration via the transuveal pathway may be the predominant route for drug penetration to posterior ocular tissues. Graphical abstract.