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In an era where social media platforms burgeon with diverse content, compelling moderation is imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, in response to the pressing need for effective social media content moderation, we introduce a pioneering approach that combines Convolutional Neural Networks (CNNs) and Transformers. We aim to enhance accuracy and computational efficiency in filtering harmful multimodal content prevalent on social media platforms. By integrating CNNs and Transformers, we achieve nuanced visual content extraction and contextual textual understanding, thus improving the identification of harmful content. Additionally, our model utilises a Bi-directional Attention Mechanism (BAM) and Genetic Algorithms (GAs) for efficient text-visual fusion and hyper-parameter optimisation, respectively. Empirical testing on datasets from Google, Facebook, and Kaggle demonstrates the superior performance of our model in terms of precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful content. The proposed Multimodal Social Media Content Moderation Using Hybrid Graph Theory & Bio-inspired Optimization (MSCMGTB) model consistently achieves superior precision, accuracy, recall, AUC, and specificity with rates ranging from 86.78% to 98.82% across varying dataset sizes, highlighting its efficacy in content moderation as well as reduced the delay time to classify social media contents as compared to Social Graph Neural Network (SGNN), CrediBot, and Adaptive LDA (ALDA) techniques. The model also preempts potentially harmful content posters, offering enhanced pre-emption metrics.

Inflammation in ulcerative colitis is typically restricted to the mucosal layer of distal gut. Disrupted mucus barrier, coupled with microbial dysbiosis, has been reported to occur prior to the onset of inflammation. Here, we show the involvement of vesicular trafficking protein Rab7 in regulating the colonic mucus system. We identified a lowered Rab7 expression in goblet cells of colon during human and murine colitis. In vivo Rab7 knocked down mice (Rab7KD) displayed a compromised mucus layer, increased microbial permeability, and depleted gut microbiota with enhanced susceptibility to dextran sodium-sulfate induced colitis. These abnormalities emerged owing to altered mucus composition, as revealed by mucus proteomics, with increased expression of mucin protease chloride channel accessory 1 (CLCA1). Mechanistically, Rab7 maintained optimal CLCA1 levels by controlling its lysosomal degradation, a process that was dysregulated during colitis. Overall, our work establishes a role for Rab7-dependent control of CLCA1 secretion required for maintaining mucosal homeostasis.

Mechanistic understanding of antibiotic persistence is a prerequisite in controlling the emergence of MDR cases in Tuberculosis (TB). We have reported that the cholesterol-induced activation of VapC12 ribonuclease is critical for disease persistence in TB. In this study, we observed that relative to the wild type, mice infected with ΔvapC12 induced a pro-inflammatory response, had a higher pathogen load, and responded better to the anti-TB treatment. In a high-dose infection model, all the mice infected with ΔvapC12 succumbed early to the disease. Finally, we reported that the above phenotype of ΔvapC12 was dependent on the presence of the TLR4 receptor. Overall, the data suggests that failure of a timely resolution of the early inflammation by the ΔvapC12 infected mice led to hyperinflammation, altered T-cell response and high bacterial load. In conclusion, our findings suggest the role of the VapC12 toxin in modulating the innate immune response of the host in ways that favor the long-term survival of the pathogen inside the host.