http://hdl.handle.net/123456789/6 2026-06-25T07:37:17Z 2026-06-25T07:37:17Z Chika Egawa, Sankaran K. V., Andy W. Sheppard, Evangelina Schwindt, Llewellyn C. Foxcroft, Sonia Vanderhoeven, Lora Peacock, Maria L. Castillo, Rafael D. Zenni, Jana Müllerová, Ana Isabel González Martínez, John K. Bukombe, Wyclife Wanzala, Dongang C. Mangwa, Tom August, Helen E. Roy, Aníbal Pauchard, Peter Stoett, Tanara Renard Truong http://hdl.handle.net/123456789/19832 2026-06-24T08:33:32Z 2026-04-01T00:00:00Z

The IPBES invasion‑management framework supports informed decision‑making for managing biological invasions Chika Egawa, Sankaran K. V., Andy W. Sheppard, Evangelina Schwindt, Llewellyn C. Foxcroft, Sonia Vanderhoeven, Lora Peacock, Maria L. Castillo, Rafael D. Zenni, Jana Müllerová, Ana Isabel González Martínez, John K. Bukombe, Wyclife Wanzala, Dongang C. Mangwa, Tom August, Helen E. Roy, Aníbal Pauchard, Peter Stoett, Tanara Renard Truong The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control presents a “conceptual diagram of management-invasion continuum”, introducing a versatile framework to support decision-making on the management of biological invasions. Drawing on an extensive synthesis of current knowledge, this IPBES invasion-management framework has been developed to broaden the scope of existing invasion curves—which primarily overlay generic management objectives onto a sigmoid curve depicting the expansion of the afected area over time—and to illustrate the applicability of the concept of efective management at diferent stages of the biological invasion process. To introduce the IPBES invasion-management framework to a wider audience, this paper explains the features of the framework and defnes the invasion-stage-based management approaches and the potential outcomes envisaged therein. Refecting the currently limited management options for biological invasions in marine and other connected-water systems, unlike in terrestrial and closed-water systems, the IPBES invasion-management framework clearly distinguishes between these two groups of systems. For each, it presents management approaches including three key factors that decision-makers should consider concurrently: management objectives, targets, and actions. This framework supports informed decision-making in the management of biological invasions in all ecosystems.

2026-04-01T00:00:00Z John Wamumwe Mwangi, James Ndiritu, Bakari Chaka http://hdl.handle.net/123456789/19831 2026-06-24T08:25:01Z 2026-05-01T00:00:00Z

Phytochemical profling, spectroscopic characterization, and acaricidal activity of Tephrosia vogelii leaf extract from Laikipia county, Kenya John Wamumwe Mwangi, James Ndiritu, Bakari Chaka This study aimed to profle the phytochemical profling constituents, characterize the functional groups spectroscopically, and evaluate the acaricidal activity of Tephrosia vogelii leaf extract from Laikipia County, Kenya. Ultrasound assisted sequential extraction using n-hexane, chloroform, and ethanol was employed to obtain crude leaf extracts, followed by qualitative phytochemical screening, FTIR and GC–MS analysis, and AAS based heavy metal quantifcation. The ethanol extract was further tested for acaricidal activity against Rhipicephalus sanguineus using adult and larval immersion tests. Results revealed a rich spectrum of bioactive compounds, including favonoids and rotenoids, saponins, alkaloids, terpenoids, phenols, and glycosides, with deguelin and tephrosin identifed as key acaricidal agents. FTIR and GC–MS showed characteristic peaks at 3446.79 cm⁻ 1 (O–H/N–H), 1743.65 cm⁻ 1 (C=O lactone), and 1535.34 cm⁻ 1 (aromatic C=C), consistent with rotenoid and favonoid structures. AAS detected elevated levels of heavy metals such as Fe, Mn, and Hg, exceeding WHO limits. Biologically, the ethanol extract produced up to 99.74% larval mortality at 40–50 mg/mL (LC₅₀=8.58 mg/mL) and strongly inhibited egg laying (20.03% at 40 mg/mL). The study concludes that Tephrosia vogelii from Laikipia is a promising natural source of bioactive phytochemicals with potent acaricidal activity against Rhipicephalus sanguineus (R. sanguineus). It is recommended that future work focus on purifcation and fractionation of the ethanol extract to isolate key rotenoids and favonoids and develop standardized, environmentally safe formulations for tick control in livestock and integrated pest-management programs.

2026-05-01T00:00:00Z Kipronoh Theophilus Korir, Jared O. Gwaro, Duke Oeba http://hdl.handle.net/123456789/19830 2026-06-24T08:21:01Z 2025-10-01T00:00:00Z

Forensic Authentication of Paracetamol Using FTIR and UV–Vis Spectroscopy Coupled with Similarity Metrics Kipronoh Theophilus Korir, Jared O. Gwaro, Duke Oeba The global circulation of counterfeit and substandard medicines poses a serious threat to public health and challenges forensic science in generating reliable evidence. Paracetamol, a widely used analgesic and antipyretic, is among the most frequently falsified drugs in low- and middle-income countries. This study employed Ultraviolet-Visible (UV-Vis) and Fourier Transform Infrared (FTIR) spectrophotometry to authenticate paracetamol (PAR). The specific objectives were to examine active pharmaceutical ingredients, and to identify unknown components in paracetamol PAR1– PAR4 samples were analyzed against a certified reference standard. FTIR spectra were obtained using potassium bromide pelletization, while UV–Vis spectra were recorded by dissolving the sample in methanol and phosphate buffer, followed by dilution with distilled water. The data obtained were evaluated by Pearson correlation coefficients (r) and Euclidean distance (ED). Results showed that PAR1 (r ≥ 0.99, ED = 0.051) and PAR3 (r = 0.9983, ED = 0.0389) matched the standard and were classified as authentic. In contrast, PAR2 (r = 0.992, ED = 0.084) exhibited a shifted amide band with reduced absorbance, while PAR4 (r = 0.991, ED = 0.089) showed weakened O–H bands confirming suspect and counterfeit status, respectively. This work addresses a key forensic gap by introducing quantitative, pharmacopeia-compliant thresholds for paracetamol authentication. The novelty lies in combining FTIR and UV–Vis spectra with similarity metrics to deliver objective and legally defensible authentication of paracetamol. The protocol strengthens scientific reliability while offering a scalable, low-cost tool for surveillance and regulatory enforcement in regions most affected by counterfeit medicines.

2025-10-01T00:00:00Z Sankara Aluko Ang’iro, Doryce Ndubi, Duke Ateyh Oeba, Jared Ombiro Gwaro http://hdl.handle.net/123456789/19829 2026-06-24T08:17:29Z 2025-11-01T00:00:00Z

Comparative Analysis of Mask-R CNN and YOLOv8 Models for Automated Detection and Classification of Malaria Parasite in Microscopy Images Sankara Aluko Ang’iro, Doryce Ndubi, Duke Ateyh Oeba, Jared Ombiro Gwaro Accurate and efficient detection of malaria parasites in stained blood smear images remains a critical challenge, particularly in resource-limited settings where expert microscopists may be unavailable. This study compares two deep learning instance segmentation models, YOLOv8 and Mask R-CNN, for automated detection, segmentation, and life-stage classification of malaria parasites in publicly available Giemsa-stained microscopy images. A total of 1,328 annotated images were used to fine-tune YOLOv8n and Mask R-CNN (ResNet-50-FPN backbone). YOLOv8 achieved higher detection performance with bounding-box mAP50 of 0.648, mask mAP50 of 0.624, mean accuracy of 96.7%, and F1-score of 0.71, compared to Mask R-CNN’s mAP50 of 0.511, accuracy of 93.2%, and F1-score of 0.48. Bootstrap resampling (1,000 iterations) confirmed the statistical reliability of performance differences with 95% confidence intervals. YOLOv8 also achieved faster inference (9 ms per image) than Mask R-CNN (93 ms), highlighting its potential for real-time screening. Despite data imbalance among parasite stages, both models produced meaningful segmentation masks enabling quantitative morphological analysis. These results demonstrate that lightweight, statistically validated deep learning architectures can deliver reliable, scalable, and interpretable tools for automated malaria detection and quantification, promoting AI integration into diagnostic microscopy workflows.

2025-11-01T00:00:00Z