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An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

Given the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. Accompanying semantic annotations with a growing volume biomedical content on the internet is critical to enhancing search engines’ context-aware indexing, improving search speed and retrieval accuracy. We propose a novel methodology for annotation recommendation in the biomedical content authoring environment by introducing the socio-technical approach where users can get recommendations from each other for accurate and high quality semantic annotations. We performed experiments to record the system level performance with and without socio-technical features in three scenarios of different context to evaluate the proposed socio-technical approach. At a system level, we achieved 89.98% precision, 89.61% recall, and an 89.45% F1-score for semantic annotation recollection. Similarly, a high accuracy of 90% is achieved with the socio-technical approach compared to without, which obtains 73% accuracy. However almost equable precision, recall, and F1- score of 90% is gained by scenario-1 and scenario-2, whereas scenario-3 achieved relatively less precision, recall and F1-score of 88%. We conclude that our proposed socio-technical approach produces proficient annotation recommendations that could be helpful for various uses ranging from context-aware indexing to retrieval accuracy. 

Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable accuracy of ≈90% for the semantic optimization approach. 

Several studies have highlighted the positive effects that active learning may have on student engagement and performance. However, the influence of active learning strategies is mediated by several factors, including the nature of the learning environment and the cognitive level of in-class tasks. These factors can affect different dimensions of student engagement such as the nature of social processing in student groups, how knowledge is used and elaborated upon by students during in-class tasks, and the amount of student participation in group activities. In this study involving four universities in the US, we explored the association between these different dimensions of student engagement and the cognitive level of assigned tasks in five distinct general chemistry learning environments where students were engaged in group activities in diverse ways. Our analysis revealed a significant association between task level and student engagement. Retrieval tasks often led to a significantly higher number of instances of no interaction between students and individualistic work, and a lower number of knowledge construction and collaborative episodes with full student participation. Analysis tasks, on the other hand, were significantly linked to more instances of knowledge construction and collaboration with full group participation. Tasks at the comprehension level were distinctive in their association with more instances of knowledge application and multiple types of social processing. The results of our study suggest that other factors such as the nature of the curriculum, task timing, and class setting may also affect student engagement during group work. 

Three new organotin( iv ) carboxylate compounds were synthesized and structurally characterized by elemental analysis and FT-IR and multinuclear NMR ( 1 H, 13 C, 119 Sn) spectroscopy. Single X-ray crystallography reveals that compound C2 has a monoclinic crystal system with space group P 2 1 / c having distorted bipyramidal geometry defined by C 3 SnO 2 . The synthesized compounds were screened for drug-DNA interactions via UV-Vis spectroscopy and cyclic voltammetry showing good activity with high binding constants. Theoretical investigations also support the reactivity of the compounds as depicted from natural bond orbital (NBO) analysis using Gaussian 09. Synthesized compounds were initially evaluated on two cancer (HeLa and MCF-7) cell lines and cytotoxicity to normal cells was evaluated using a non-cancerous (BHK-21) cell line. All the compounds were found to be active, with IC 50 values less than that of the standard drug i.e. cisplatin. The cytotoxic effect of the most potent compound C2 was confirmed by LDH cytotoxicity assay and fluorescence imaging after PI staining. Apoptotic features in compound C2 treated cancer cells were visualized after DAPI staining while regulation of apoptosis was observed by reactive oxygen species generation, binding of C2 with DNA, a change in mitochondrial membrane potential and expression of activated caspase-9 and caspase-3 in cancer cells. Results are indicative of activation of the intrinsic pathway of apoptosis in C2 treated cancer cells.