College of Pure and Applied Sciences

Antimicrobial and Immunomodulatory Effects of Aqueous Extracts of Edible Mushrooms Termitomyces striatus in Infected BALB/c Mice

Mon, 11 Aug 2025 00:00:00 GMT

Antimicrobial and Immunomodulatory Effects of Aqueous Extracts of Edible Mushrooms Termitomyces striatus in Infected BALB/c Mice Sitati, Concepta Nekesa Wafula The worldwide increase in infectious diseases has resulted in the overuse of available antibiotics. The fight to effectively reduce morbidity and mortality has increased due to multidrug resistance. This calls for further investigation and the discovery of new antibiotics, as well as vaccines. Studies over the past few decades have proven that mushrooms and their active ingredients have positive impacts on a number of biological systems, with some mushrooms exhibiting a variety of characteristics. Synthetic immunomodulatory and antibiotic medications used to treat bacterial infections have been linked to a number of adverse effects, making alternative therapeutic agents necessary. This has necessitated the search for antimicrobial drugs with a new mechanisms of action that include immunomodulation. Immunomodulatory drugs from natural products like mushrooms would be a new novel drug in combating infections since they would work on the immune system without facing the problem of drug resistance. Termitomyces striatus is a wild edible mushroom that grows in many places worldwide Kenya included. T. striatus is used as food by people living in western Kenya. However, scientific data to validate the presence of phytochemical compounds, antimicrobial activity and immunomodulatory effects is lacking. The objective of this study was to determine the antimicrobial and immunomodulatory characteristics of T. striatus extract. The T. striatus mushrooms were collected from different parts of Kakamega county. They were dried, ground into a powder, and an aqueous extract was prepared using a freeze-drier. Standard laboratory procedures were used to determine phytochemical families of secondary metabolites. From powdered T. striatus, the aqueous extract was prepared. The disc-diffusion method was used to assess the antibacterial and antifungal properties. To determine the immunomodulatory effect of the mushroom extract, mice were grouped into four groups of twelve mice each as follows; normal control, positive control (levamisole), and extract-treated groups of 200 and 400mg/kg body weight (bw). To determine the effect of the extract on immunosuppression E. coli-infected mice were divided into five groups of fiteen mice each. They were negative control, positive control, and two extract-treated groups of mice were orally administered with 200μl of 2 ×108 CFU/mL of enterohemorrhagic Escherichia coli (EHEC). Blood was collected on different days and then total and differential WBC count analyzed, immunoglobulin G (IgG), interleukin-4 (IL-4) and interferon-gamma (INF-γ) levels were quantified using enzyme-linked immunosorbent assays (ELISA). In evaluation of reversal of experimentally induced leucopenia, mice were injected with cyclophosphamide and left for 3 days. The extracts (200 and 400mg/kg bw) and levamisole were administered, blood was collected for total and differential WBC counts for analysis. Mice were immunized by injecting 20µl of sheep red blood cells (SRBC) subcutaneously into the right foot pad in order to assess the impact of the extract on the immune response in delayed type hypersensitivity using SRBC as an antigen. After 7 days of treatment with different concentrations of extract, the left foot pad was injected with the SRBC. Left foot pad thickness measured before administration of extract and for a period of 48 hours. Data was analyzed using one-way analysis of variance and Bonferroni multiple comparisons. Level of significance was computed at p<0.05. The experiments for phytochemical profile revealed the presence of alkaloids, flavonoids, steroids, sterols, saponins and phenols. The aqueous extract of T. striatus exhibited antibacterial and antifungal activity against E. coli, P. auraginosa, S. aureus and B. subtilis, as well as C. albican. For immunomodulatory effect in BALB/c mice, the extract significantly (p<0.05) increased the levels of neutrophils and monocytes. When BALB/c mice were infected with EHEC, the extract significantly (p<0.05) reduced elevated levels of WBCs, neutrophils, monocytes, eosinophils and basophils, and significantly enhanced the levels of lymphocytes. Similarly, the extract significantly (p<0.05) reduced the levels of INF-γ, as well as significantly (p<0.05) elevated the levels of IL-4 and IgG. In the evaluation of immunosuppression, treatment with the extract significantly (p<0.05) ameliorated reduced levels of total and differential WBC counts following cyclophosphamide-induced immunosuppression in mice. Treatment using the extract also showed an increase in delayed hypersensitivity reaction following injection of SRBC in mice. The study concluded that the extract had phytochemicals associated with potent anti-microbial and immunomodulatory effects in EHEC-infected mice. The extract may therefore be used in the development of alternative antimicrobial and immunomodulatory agents. I have made a few changes in the abstract, though editorials PhD in Zoology (Immunology)

Artificial Intelligence-Based Chatbot Model Providing Expert Advice to Potato Farmers in Kenya

Mon, 04 Aug 2025 00:00:00 GMT

Artificial Intelligence-Based Chatbot Model Providing Expert Advice to Potato Farmers in Kenya Korir, Meshack Kiplangat Potato is the second most important food crop in Kenya after maize, and has been used to address food security challenges with declining yield in cereals. The yield in potato farming has been noted to decline gradually in the last few years due to limited supply of quality seeds as well as limited access to expert advice. This research addressed this challenge by designing, developing, and evaluating an AI-based chatbot model built using IBM Watson Assistant. The chatbot was intended to provide personalized expert advice and connect potato farmers with certified seed producers in a scalable and accessible manner. The specific objectives of the study were to review literature on chatbot technologies to inform the design and development of an AI-based potato chatbot model, to design and develop an AI-based chatbot model tailored for potato farmers, and to evaluate the model's overall effectiveness in addressing the needs of potato farmers in Kenya. This research presented a solution that offered expert advice and a link to quality seed producers through the use of Watson Assistant, which is an artificial intelligence-based chatbot framework. First, an introduction to potato farming in Kenya was presented. A quick review and the evolution of chatbot models marked the end of the first chapter. Chatbot theory was discussed in the second chapter, with a primary focus on its classifications, the general architecture of chatbots, and the Watson Assistant architecture. The methodology used in the research was discussed, with a primary focus on the four significant steps taken, which were data collection, data preprocessing, system development, and the model testing, training, and evaluation phase. The next section then presented the performance indicators that were used to evaluate the chatbot model in detail. The results were then interpreted and discussed, giving rise to the last section where conclusions were drawn. The first conclusion drawn was that the potato farming chatbot model achieved a score of 97.7% in message coverage, a score of 78.4% in conversation containment, and was found to have an overall effectiveness of 88.05%. User acceptance was at 60%, while the adoption rate was at 80%. Finally, recommendations on future work were presented based on user feedback and input from chatbot modeling experts. MSc in Software Engineering

Characterization of P-type SnO2:Ga and Sb-co-doped SnO2:Ga Thin Films for Potential Application in Transparent Electronic Industry

Tue, 15 Jul 2025 00:00:00 GMT

Characterization of P-type SnO2:Ga and Sb-co-doped SnO2:Ga Thin Films for Potential Application in Transparent Electronic Industry Gichana, Sally Kemuma Transparent conductive oxides (TCOs) are a unique class of materials which exhibit appreciable level of electrical conductivity, high optical transparency in the visible range and high infrared reflectivity in a single material. Their uniqueness has demonstrated to be of great importance to many optoelectronic applications. However, compared to the prosperous n-type TCOs, the synthesis, performance and development of existing p-type TCOs has impeded the advancement in technology based on their applications. This comes about from their unique electronic configuration which makes the VBM localized and anisotropic in nature. To address this challenge, Tin have been predicted to demonstrate promising results when doped with group (III) atoms with Sb-co-doping enhancing stability of films. In this study, p-type SnO2:Ga and Sb-co-doped SnO2:Ga thin films have been successfully prepared on blue plus microscope glass substrates using the sol-gel dip-coating method. The concentrations of gallium and antimony composition have been varied and their effects investigated. The transmittance spectra of undoped SnO2 film is transparent with an average transmittance ranging between 61.1 - 81.1 %, SnO2:Ga films at 50.4 – 72.6 % and Sb-co-doped SnO2:Ga films at 53.6 - 78.1 % when set at a wavelength range of 400 nm to 900 nm respectively. Undoped SnO2 has a direct band gap value of 3.89 eV, SnO2:Ga films at values of between 4.07 eV - 4.15 eV and Sb-co doped SnO2:Ga films at 4.10 eV - 4.16 eV. Optical band gap widening and the narrowing is observed for all SnO2:Ga and Sb-co-doped SnO2:Ga. On the conductivity types, all the prepared films are p-type conductive except at higher co-doping levels of Sb. SnO2:Ga thin films resistivity is of order 1.6 x 10-2 to 3.44 x 10-3 Ω cm and Sb-co-doped SnO2:Ga order 4.55 x 10-3 to 6.92 x 10-3 Ω cm. I-V characteristics demonstrate an ohmic behaviour for all films. Morphological analysis reveals films exhibiting smooth surfaces, devoid of cracks. Average crystallite sizes and FWHM values defines a better crystallinity of the films. Combinations of these properties in a single film contribute a valuable insight into p-type TCOs of SnO2:Ga and Sb-co-doped SnO2:Ga films, addressing the limitations associated with their characterization and suitable for optoelectronic applications. MSc in Physics

Deep Gaussian Process Models in the Classification of Cassava Diseases

Thu, 03 Jul 2025 00:00:00 GMT

Deep Gaussian Process Models in the Classification of Cassava Diseases Ahishakiye, Emmanuel Machine learning continues to transform how plant diseases are identified and classified, offering scalable solutions for timely crop health monitoring. Despite significant progress, many conventional models struggle with quantifying predictive uncertainty, a critical requirement in decision-sensitive domains such as agriculture. Gaussian Processes (GPs), known for their ability to provide calibrated predictions, offer a principled way to address this gap. This study leverages the strengths of Deep Gaussian Processes (DGPs), Convolutional Neural Networks (CNNs), and Transfer Learning to improve cassava disease classification. The research aimed to address the following objectives: To Evaluate existing Gaussian Process models and identify their architectural strengths, limitations, and suitability for cassava disease classification; to design and implement a Deep Gaussian Convolutional Neural Network (DGCNN) that integrates GPs and CNNs; to develop a Deep Gaussian Transfer Learning (DGTL) model combining DGPs with pre-trained CNN architectures; and to assess the performance of the proposed models against standard machine learning baselines. A hybrid kernel, formed by combining rational quadratic and squared exponential kernels, was also introduced to enhance model accuracy and expressiveness. Experimental results showed that the DGCNN achieved an accuracy of 90.1%, outperforming models using standard kernels. The DGTL model, when integrated with MobileNetV2 and the hybrid kernel, achieved 90.11% accuracy, surpassing other configurations. Although computational constraints were encountered due to limited hardware resources, the proposed models demonstrated strong predictive performance while maintaining the probabilistic interpretability of Gaussian Processes. These findings contribute to the advancement of intelligent and reliable tools for precision agriculture. Key words: Cassava Disease Classification, Deep Gaussian Processes (DGPs), Convolutional Neural Networks (CNNs), Transfer Learning, Precision Agriculture. PhD in Information Technology