Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms.

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

Central contrast sensitivity perimetry discriminates between glaucomatous and non-glaucomatous eyes.

Background: Glaucoma is an optic neuropathy which causes irreversible vision loss. Standard perimetry, which is essential for glaucoma diagnosis, can only detect glaucomatous visual filed loss when considerable structural damage has occurred. Contrast sensitivity is one of the visual function tests that is reduced in eyes with glaucoma. It is known to be affected in pre-perimetric stages of glaucoma. Objective: The objective of this study was to investigate the discriminating ability of central contrast sensitivity perimetry in eyes with and without glaucoma. Design: The study employed a cross-sectional study design. Methods: The study participants were made of two groups; eyes diagnosed with glaucoma by an ophthalmologist based on visual field test and optical coherence tomography (OCT) and age- and sex-matched controls who were declared free from glaucoma. Static contrast sensitivity (CS) was measured in the central 10° of visual field using a custom psychophysical test. Results: There were 45 eyes with glaucoma and 45 age- and sex-matched controls in this study. The static CS in the glaucoma group was significantly reduced in 9 out of the 13 tested locations in the central 10° of the visual field. The mean static CS at 5°, 10°, superior hemifield and inferior hemifield were all significantly reduced in the glaucoma patients compared to the controls. Conclusion: Static CS measurement is a sensitive approach that can be utilized to aid in the detection of glaucoma. The use of static CS can be adopted in the development of a cost-effective yet sensitive screening tool for the detection of glaucoma.

Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques.

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

Computer vision syndrome and its associated ergonomic factors among bank workers.

Objectives. This study aimed to describe the prevalence of ergonomic factors associated with computer vision syndrome (CVS), and knowledge and practices of computer usage among bank workers in the Cape Coast Metropolis, Ghana. Methods. A comprehensive eye examination was performed for 139 bankers in the Cape Coast Metropolis. Measurements of ergonomic practices at workstations were carried out and self-administered questionnaires were used to collect socio-demographic data, symptoms of CVS and associated factors as well as knowledge, attitudes and practices of the bankers on CVS. Descriptive statistics and logistic regression were carried out to compute the different proportions and relevant associations. Results. Headaches (73.4%), eyestrain (65.5%) and itching (63.3%) were the most common reported symptoms. The prevalence of CVS in the study population was 71.2% with a high level of poor ergonomic practices (78.4%) at computer workstations. With regard to knowledge, attitudes and practices of the participants on CVS, 68.3% of the participants had low levels of knowledge on CVS, while 90.6% had negative attitudes towards CVS and 46.0% of the participants had poor practices. Conclusion. Bankers had a high prevalence of CVS. Ergonomic practices, females and computer usage were all associated significantly with the presence of CVS.

Association between Poor Ergophthalmologic Practices and Computer Vision Syndrome among University Administrative Staff in Ghana.

Aim: The aim of this study was to assess the prevalence of computer vision syndrome (CVS) and its associated ergonomic factors among university administrative staff in Ghana. Methods: A cross-sectional survey was conducted among 200 administrative staff of the University of Cape Coast. The procedure included a self-administered questionnaire, comprehensive ocular health examination, and assessment of computer workstation and lighting conditions. The prevalence of CVS among the subjects and the association between CVS and ergonomic practices were determined. Results: The mean age of the study sample was 31.0 ± 4.7 years, and the majority were males (56.0%). The prevalence of CVS was among 103 (51.5%)participants. Over a third of the respondents used computers for 6 or more hours daily. Significant association was found between CVS and poor ergonomic practices (χ = 15.175, p = 0.001). Conclusion: In addition to poor ergonomic office setup, university administrative staff spend several hours behind computer screens leading to the development of CVS. Increased awareness of CVS and adherence to recommended ergonomic practices are necessary to reduce the prevalence of CVS and ultimately enhance work satisfaction and productivity.