Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

The purpose of current research work was to formulate and typify gelatin and poly(vinyl) alcohol (Gel/PVA) hydrogel which would be highly pH-responsive and can able to accomplish targeted delivery of methotrexate in order to treat the colo-rectal pathologies. The primed gel/pva hydrogel discs were subjected to various physicochemical techniques i.e. swelling, diffusion co-efficient, sol-gel analysis and porosity using three altered sorts of pH (1.2, 6.8 & 7.4) phosphate buffer solutions for assessment/evaluation, and their characterization was done through Fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analysis (TGA). Shape alteration and controlled methotrexate of release of Gel/PVA hydrogel have been done using three type of pH (1.2, 6.8 & 7.4) phosphate buffer mediums. Methotrexate was loaded through in-situ drug loading method due to hydrophobicity. Different kinetic models (first order & zero order kinetic), Higuchi model and Krosmere peppas model/Power law were applied to manipulate the drug release data. Physicochemical evaluation tests and drug release profile results were found insignificant (p< 0.05) in various pH mediums and dependent upon polymers concentration pH of medium and cross-linker amount. Kinetic model disclosed that release of methotrexate from Gel/PVA hydrogel follow non-Fickian diffusion method. It became concluded from this research work that release of methotrexate Gel/PVA hydrogel in targeted colon area can be achieved for treating colo-rectal disorders.

The aim of the current study was to develop and evaluate the therapeutic potential of apocynin (APO) loaded pH-sensitive nanoparticles (NPs) based transdermal hydrogel for management of rheumatoid arthritis (RA). Slightly modified nanoprecipitation technique was used for preparation of polymeric nanoparticles. Optimization was done through design expert software. Optimized APO-NPs were loaded into carbopol-934 based hydrogel as final dosage form and further studied for physicochemical properties. Optimized APO-NPs formulation had a minimum particle size 63.44 nm, polydisperibility index 0.161, and zeta potential -15mV with a maximum encapsulation efficiency of 89%. In-vitro and ex-vivo studies of APO-NPs based hydrogel was performed at pH 5.5 (pH of normal skin) and 6.8 (pH of inflammed joint) showed a pH-responsive sustained drug release and increased penetration in comparison to free APO based hydrogel. The stability studies of APO-NPs based hydrogel were done to strengthen the potential use of the prepared formulation through transdermal route. Assessment and therapeutic efficacy of the prepared pH-sensitive nanocarriers system was evaluated in chronic inflammatory RA mice model. Parameters associated with chronic inflammation were investigated including behavioral changes and histopathological, and radiological x-rays images of joints of mice paws. In-vivo study depicts improvement in behavioral parameter, decline in synovial hyperplasia and bone structure restoration. In conclusion, APO loaded pH-sensitive NPs based transdermal is a promising carrier system that can effectively manage RA.