Objective: Development of effective drug delivery in the treatment of psoriasis is the major challenge for its successful management. To develop and assess the potential of Nanostructured Lipid Carriers (NLCs) enriched with the powdered leaves extracts of Azadirachta indica (AE), Lawsonia inermis (LE) and fruit extract of Mallotus philippensis (ME) in the management of psoriasis. Methods: Drug loaded NLCs were prepared via hot homogenization technique by adopting 2³ factorial design with factors X1 as the concentration of lipids, X2 concentration of surfactants and X3 being the number of homogenization cycle. The responses Y1 and Y2 were particle size and zeta potential. The optimized batch was obtained from Surface response plot and was evaluated for zeta potential, % entrapment efficiency, % drug loading, Scanning Electron Microscopy(SEM), % in vitro diffusion of drugs from the NLCs, anti-lipid peroxidation and nitric oxide scavenging activities, cytotoxicity on HaCat cell lines, Mouse Tail and Rat ultraviolet ray B photodermatitis models for Psoriasis. Results: The optimized batch of NLCs was found within the nanosized range with a relatively low polydispersity index and zeta potential of-20mV. The %EE for an optimized batch of NLCs was found to be 98.97±0.83%, 96.99±0.56% and 99.25±0.55% and the %DL of 21.84±0.15%, 8.55±045%, and 87.91±0.38% respectively for AE, LE and ME. The SEM images showed the spherical vesicular structures of drugs loaded NLCs. The in-vitro diffusion of drugs from the NLCs followed initial burst release thereafter sustained release for 24 h. The AE, LE and ME loaded NLCs proved to possess anti-lipid peroxidation and nitric oxide scavenging activities, cytotoxicity on HaCat cell lines, DNA fragmentation on HaCat cell lines which are biomarkers in the pathogenesis of psoriasis. The results of Mouse Tail and Rat ultraviolet ray B photodermatitis models for Psoriasis supported the anti-psoriatic potential of AE, LE and ME loaded NLCs. Conclusion: AE, LE and ME loaded NLCs can be used for prolonged topical delivery to the psoriatic skin for an effective treatment.

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement.