Objective: The aim of this study was to investigate the in vitro and in vivo performance of self-nanoemulsifying drug delivery systems (SNEDDSs) of talinolol (TAL), a poorly water-soluble drug. Methods: Self-nanoemulsifying drug delivery systems of TAL were prepared using various oils, non-ionic surfactants and/or water-soluble co-solvents and assessed visually/by droplet size measurement. Equilibrium solubility of TAL in the anhydrous and diluted SNEDDS was conducted to achieve the maximum drug loading. The in vitro dissolution experiments and human red blood cells (RBCs) toxicity test, ex vivo gut permeation studies, and bioavailability of SNEDDS in rats were studied to compare the representative formulations with marketed product Cordanum^® 50 mg and raw drug. Results: The results from the characterization and solubility studies showed that SNEDDS formulations were stable with lower droplet sizes and higher TAL solubility. From the dissolution studies, it was found that the developed SNEDDS provided significantly higher rate of TAL release (>97% in 2.0 h) compared to raw TAL and marketed product Cordanum^®. The RBC lysis test suggested negligible toxicity of the formulation to the cells. The ex vivo permeability assessment and in vivo pharmacokinetics study of a selected SNEDDS formulation (F6) showed about four-fold increase in permeability and 1.58-fold enhanced oral bioavailability of TAL in comparison to pure drug, respectively. Conclusion: Talinolol loaded SNEDDS formulations could be a potential oral pharmaceutical product with high drug-loading capacity, improved drug dissolution, increased gut permeation, reduced/no human RBC toxicity, and enhanced oral bioavailability.

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.

Early detection, right therapeutic intervention, and simultaneous effectiveness mapping are considered the critical factors in successful cancer therapy. Nevertheless, these factors experience the limitations of conventional cancer diagnostics and therapeutics delivery approaches. Along with providing the targeted therapeutics delivery, advances in nanomedicines have allowed the combination of therapy and diagnostics in a single system (called cancer theranostics). This paper discusses the progress in the pre-clinical and clinical development of therapeutics, diagnostics, and theranostics cancer nanomedicines. It has been well evident that compared to the overabundance of works that claimed success in pre-clinical studies, merely 15 and around 75 cancer nanomedicines are approved, and currently under clinical trials, respectively. Thus, we also brief the critical bottlenecks in the successful clinical translation of cancer nanomedicines.

Apigenin (APG) is a very well-known flavonoid for its anti-inflammatory and anticancer properties. The purpose of this study is to improve the solubility and bioavailability of APG using a stable bioactive self-nanoemulsifying drug delivery system (Bio-SNEDDS). APG was incorporated in an oil phase comprising coconut oil fatty acid, Imwitor 988, Transcutol P, and HCO30 to form a Bio-SNEDDS. This preparation was characterized for morphology, particle size, and transmission electron microscopy (TEM). The APG performance was investigated in terms of loading, precipitation, release and stability tests from the optimal Bio-SNEDDS. An antimicrobial test was performed to investigate the activity of the Bio-SNEDDS against the selected strains. Bioavailability of the Bio-SNEDDS was evaluated using Wister rats against the commercial oral product and the pure drug. The results demonstrated the formation of an efficient nanosized (57 nm) Bio-SNEDDS with a drug loading of 12.50 mg/gm which is around 500-fold higher than free APG. TEM analysis revealed the formation of spherical and homogeneous nanodroplets of less than 60 nm. The dissolution rate was faster than the commercial product and was able to maintain 90% APG in gastro intestinal solution for more than 4 h. A stability study demonstrated that the Bio-SNEDDS is stable at a harsh condition. The in vivo pharmacokinetics parameters of the Bio-SNEDDS formulation in comparison to the pure drug showed a significant increase in maximum concentration (C_max) and area under the curve (AUC _(0–t)) of 105.05% and 91.32%, respectively. Moreover, the antimicrobial study revealed moderate inhibition in the bacterial growth rate. The APG-Bio-SNEDDS could serve as potential carrier aimed at improving the clinical application of APG.