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Several extraction steps involved while analyzing metformin in plasma necessitates the need to develop a simple less tedious RP-HPLC method for metformin analysis in human saliva.Blank saliva (2 mL) was spiked with 2 mL solution (12.5 µg mL-1) of metformin and 1 mL solution (0.5 µg mL-1) of caffeine as internal standard (IS). The mixture was vortex mixed and centrifuged at 3000 rpm for 10 minutes. A portion (0.5 mL) of the resultant solution was injected into the HPLC machine (Agilent 1260 infinity). The optimized conditions included a mobile phase of methanol:water (80:20 v/v) containing 0.1 % orthophosphoric acid, isocratic elusion mode, an injection volume of 10 µL, flow rate of 1 mLmin-1, at 35°C and detection wavelength of 232nm. Calibration curve(1.25to25.0 µg mL-1) was prepared by plotting the peak height ratios of metformin Vs IS against their corresponding concentrations. The method was validated according to ICH guidelines. Metformin and caffeine eluted at 1.6 and 2.6 minutes respectively. The method was precise (

Abstract Background The plant Olax subscorpioidea Oliv (Olacaceae) is a shrub that is widely available in Africa. It has been used in traditional medicine to treat various diseases including asthma, pain, inflammation, gastrointestinal and central nervous system (CNS) disorders, cough, diabetes mellitus, cancer, infectious diseases, hepatic diseases, and many other diseases. Several phytochemical and pharmacological investigations were conducted on this plant. However, comprehensive information on this medicinally important plant is not available in the literature. Therefore, in this review, we aimed to provide comprehensive and critical information on all the reported ethnomedicinal uses, phytochemistry, pharmacological activities, and potential toxicity of Olax subscorpioidea to highlight its therapeutic potentials based on traditional usage and identify research gaps as a basis for further investigations to develop novel therapeutic compounds. Main body The available information about the plant was retrieved from the online bibliographic databases (PubMed and Google Scholar) and published PhD dissertation using the search terms Olax subscorpioidea, traditional uses, ethnomedicinal uses, phytochemistry, pharmacology, toxicology, and safety. Phytochemical studies have shown that the plant contains several bioactive compounds such as rutin, morin, quercetin, caffeic acid, santalbic acid, n-hexadecanoic acid, squalene, nonacosane, hentriacontane, and many more compounds. Also, pharmacological investigations revealed that Olax subscorpioidea has antidepressant, antiepileptic, anti-Alzheimer’s, cytotoxic, antioxidant, antihyperlipidemic, analgesic, antiinflammatory, antiarthritic, antidiabetic, anticancer, antiulcer, antimicrobial, hepatoprotective, apoptotic, antiprotease, and other CNS effects. Conclusion Several pharmacological studies on Olax subscorpioidea have established its ethnopharmacological uses. However, there are limited phytochemical and pharmacological studies to validate other folkloric claims of the plant. Therefore, extensive phytochemical and further pre-clinical efficacy and safety evaluations to fully establish its therapeutic potentials and elucidate its mechanisms of pharmacological actions could be necessary. Graphical abstract

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Despite recent advances in deep learning, the rise of edge devices, and the exponential growth of Internet of Things (IoT) connected devices undermine the performance of deep learning models. It is clear that the future of computing is moving to edge devices. Autonomous vehicles and self-driving cars have leveraged the power of computer vision, especially object detection to navigate through traffic safely. Nevertheless, to be able to drive on all types of roads, these new vehicles have to be equipped with a road anomaly detection system, which strikes the need for small deep learning models of detecting road anomalies that can be deployed on these vehicles for safer driving experience. However, the current deep learning models are not practical on embedded devices due to the heavy resource requirements of the models, as such cannot be deployed on embedded devices. This paper proposes a theoretical approach to building a lightweight model from a cumbersome pothole detection model that is suitable on edge devices using knowledge distillation. It presents the theoretical approach of knowledge distillation, why it is a better technique of model compression compared to the rest. It shows that a cumbersome model can be made lightweight without sacrificing accuracy and with a reduced time complexity and faster training time.

Emotion intelligence is a fundamental aspect of human communication, enabling individuals to perceive and respond to the emotional states of others. In the realm of humanrobot interaction, the ability of robots to recognize and respond to human emotions is of paramount importance for fostering natural and effective communication. This paper introduces EmoNet, a novel facial emotion recognition model, designed to significantly enhance the quality of human-robot interactions. EmoNet leverages deep learning techniques to achieve impressive accuracy, reaching 92% in recognizing a wide range of human emotions, including happiness, sadness, fear, surprise, anger, and disgust. The results indicate that EmoNet not only excelsin recognizing emotions but also offers a promising avenue for applications in e-learning, social robotics, and automated decision-making. This research represents a significant step towards creating emotionally intelligent robots that can bridge the gap between humans and machines, paving the way for a more connected and emotionally attuned future. [ABSTRACT FROM AUTHOR]

During biodegradation of petroleum products which are the main sources of aromatic compounds, optimization is the major strategy that enhances the performance of the participating organisms. This research aimed at optimizing diesel degrading conditions using the response surface methodology (RSM) based on full factorial central composite design (CCD). Bacteria were isolated from the diesel contaminated soil which were screened based on diesel oil tolerance. Bacteria resting cells (1.5 × 105 cells/ml) of the screened strains were used to formulate consortia using mathematical permutation approach on Bushnell Haas Agar before being tested on heavy metals co-contaminants. Optimization based on CCD was carried out to pH, temperature, salinity, and substrate concentration. The results indicate a total of 47 bacteria were isolated where 4 were finally screened based on their survival at the sole expense of 4% v/v diesel with high growth populations. These bacteria were identified as Alcaligenes sp., Ochrobactrum sp., Alcaligenes aquatilis, and Alcaligenes faecalis UMYU001 (MN519483.1) which were used for the formulation and selection of effective degrading consortium. The selected consortium performed better even in the presence of more than 1 g/L of different heavy metals co-contaminants producing population higher than 1.6 × 104cfu/mL (p-value = 0.0037). Based on the CCD results, parameter values of 7.14 pH, 32 °C temperature, 2.58 g/L NaCl, 3.74% v/v diesel substrate were found to be the optimum conditions for the diesel biodegradation. The interactions among these parameters generated 8.4 × 106cfu/ml which were validated by correlating the predicted and actual experimental results. [ABSTRACT FROM AUTHOR]