Toll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-friendly, and easily accessible machine learning-based web application for predicting inhibitors against TLR4 by targeting the TLR4-myeloid differentiation primary response 88 (MyD88) interaction. MyD88 is a crucial adaptor protein in the TLR4-induced hyper-inflammation pathway. Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. A 10-fold cross-validation strategy was used to evaluate model performance based on metrics including accuracy, balanced accuracy, and recall. The XGBoost model demonstrated superior performance with accuracy, balanced accuracy, and recall scores of 0.994, 0.958, and 0.917, respectively, on the test. The AdaBoost and decision tree models also excelled with accuracies ranging from 0.981 to 0.992, balanced accuracies between 0.921 and 0.944, and recall scores between 0.845 and 0.891 on both training and test datasets. The XGBoost model was deployed as AICpred and was used to screen compounds that have been reported to have positive effects on mitigating the hyperinflammation-associated cytokine storm, which is a key factor in COVID-19. The models predicted Baricitinib, Ibrutinib, Nezulcitinib, MCC950, and Acalabrutinib as anti-TLR4 compounds with prediction probability above 0.90. Additionally, compounds known to inhibit TLR4, including TAK-242 (Resatorvid) and benzisothiazole derivative (M62812), were predicted as bioactive agents within the applicability domain with probabilities above 0.80. Computationally inferred compounds using AICpred can be explored as potential starting skeletons for therapeutic agents against hyperinflammation. These predictions must be consolidated with experimental screening to enhance further optimisation of the compounds. AICpred is the first of its kind targeting the inhibition of TLR4-MyD88 binding and is freely available at http://197.255.126.13:8080.

Organoids present immense promise for studying organ systems and their functionality. Recently, they have become the subject of exploration outside of purely biomedical uses in multiple directions. We will explore the rapidly evolving landscape of organoid research over the 21st century, discussing significant advancements in organoid research and highlighting breakthroughs, methodologies, and their transformative impact on our understanding of physiology and modeling. In addition, we will explore their potential use for biocomputing and harnessing organoid intelligence, investigate how these miniaturized organ-like structures promise to create novel computational models and processing platforms allowing for innovative approaches in drug discovery, personalized medicine, and disease prediction. Lastly, we will address the ethical dilemmas surrounding organoid research by dissecting the intricate ethical considerations related to the creation, use, and potential implications of these in vitro models. Through this work, the goal of this paper is to provide introductory perspectives and bridges that will connect organoids to cybersecurity applications and the imperative ethical discourse accompanying its advancements with commentary on future uses.

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI) project demonstrates the importance of sound data work as a precondition for the delivery of useful, localized datacentric solutions for public good tasks such as agricultural productivity and food security. Drone collected data and machine learning are utilized to determine crop stressors. Data, model and the final app are developed jointly and made available to local farmers via a desktop application.

In neuroscience, understanding how single-neuron firing contributes to distributed neural ensembles is crucial. Traditional methods of analysis have been limited to descriptions of whole population activity, or, when analyzing individual neurons, criteria for response categorization varied significantly across experiments. Current methods lack scalability for large datasets, fail to capture temporal changes and rely on parametric assumptions. There’s a need for a robust, scalable, and non-parametric functional clustering approach to capture interpretable dynamics. To address this challenge, we developed a model-based, statistical framework for unsupervised clustering of multiple time series datasets that exhibit nonlinear dynamics into an a-priori- unknown number of parameterized ensembles called Functional Encoding Units (FEUs). FEU outperforms existing techniques in accuracy and benchmark scores. Here, we apply this FEU formalism to single-unit recordings collected during social behaviors in rodents and primates and demonstrate its hypothesis-generating and testing capacities. This novel pipeline serves as an analytic bridge, translating neural ensemble codes across model systems.

The rapid expansion of mobile technology in West Africa presents significant opportunities for socio-economic development, yet cybersecurity concerns increasingly threaten sustainable mobile app adoption. This study examines the intersection of mobile app implementation strategies and cybersecurity challenges in Ghana and Nigeria. Through systematic literature analysis and implementation case studies, this research identifies critical vulnerabilities in mobile app ecosystems that impede adoption and threaten user security. Findings reveal that while mobile penetration exceeds 67% in Ghana and 49% in Nigeria, over 78% of users remain unaware of basic security practices. The research proposes an integrated framework balancing accessibility with security, contributing a security-focused implementation strategy tailored to the vulnerabilities of developing economies.