Claude Fiifi Hayford

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.

Osteoporosis results in low-trauma fractures affecting millions globally, in particular elderly populations. Despite the inclusion of physical activity in fracture prevention strategies, the optimal bone-strengthening exercises remain uncertain, highlighting the need for a deeper understanding of lower limb joint loading dynamics across various exercise types and levels. This study examines lower limb joint loading during high-impact exercises across different intensities. A total of 40 healthy, active participants were recruited (mean ± SD: age of 40.3 ± 13.1 years; height 1.71 ± 0.08 m; and mass 68.44 ± 11.67 kg). Motion capture data and ground reaction forces of six different exercises: a self-selected level of walking, running, counter-movement jump, squat jump, unilateral hopping, and bilateral hopping were collected for each participant. Joint reaction forces were estimated using lower body musculoskeletal models developed in OpenSim. Running and hopping increased joint forces compared to walking, notably at the hip (83% and 21%), knee (134% and 94%), and ankle (94% and 77%), while jump exercises reduced hip and ankle loading compared to walking (36% and 19%). Joint loading varied with exercise type and intensity with running faster increasing forces on all joints, particularly at the hip. Sprinting increased forces at the hip but lowered knee and ankle forces. Higher jumps intensified forces on all joints, while faster hopping reduced forces. The wide variation of lower limb joint loading observed across the exercises tested in this study underscores the importance of implementing diverse exercise routines to optimize overall bone health and strengthen the musculoskeletal structure. Practitioners must therefore ensure that exercise programs include movements that are specifically suitable for their intended purpose.

A novel hydroxyapatite [HAp: Ca₁₀ (PO₄)₆(OH)₂] material for defluoridation was prepared from Achatina achatina (AA) snail shells using a modified chemical precipitation method. X-ray diffractometry and atomic absorption spectrometry revealed carbonate substitution as a function of stirring conditions. Stirring time was varied to control crystallite size and trace element concentrations. In addition, Infrared spectra, cyclic voltammograms, and ion exchange profiles confirmed the functional groups, the surface mass concentration and the fluoride removal efficiency, respectively. It was observed that the samples prepared after 1 hr optimal stirring times reduced fluoride concentration from to without affecting the overall pH conditions of the water, whereas beyond this time frame, low uptake of the fluoride ions was obtained with increasing pH conditions. It was also observed that crystallite size did not affect the removal capacity of the samples. The results demonstrated herein the possibility of using locally prepared AA shells for water purification and other environmental remediation applications.