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Access status: Open Access ,
Evaluation of mitral annular plane systolic excursion derived ejection fraction to determine left ventricular systolic function
(Central University of Technology, 2024-03) van Rensburg, Hendré
Introduction Left ventricular ejection fraction (LVEF) is an essential measurement in echocardiography used to diagnose, treat, and manage patients. Two dimensional (2D) echocardiography is the most common imaging modality used to determine LVEF. Mitral annular plane systolic excursion (MAPSE) is considered a useful surrogate of 2D longitudinal function and can be converted to an ejection fraction through the formula MAPSE EF = (4.8 x MAPSE) + 5.8. MAPSE EF has been proven to be a valid method to determine ejection fraction in adult males with impaired LV systolic function. Currently, no research has been conducted to determine if this equation could be used in the daily setting. Therefore, the aim of the study was to evaluate mitral annular plane systolic excursion formula for determining ejection fraction in daily practice. MethodsThis prospective descriptive-analytical study was conducted in central South Africa, including 475 male or female adult participants aged 18 and above. Ethical approval (Appendix A) and informed consent (Appendix C) were obtained, and participants were provided with an information guide (Appendix D). The patients were further subdivided according to their primary clinical condition: systemic hypertension, diabetes mellitus type 2, congestive cardiac failure, and ischemic heart disease. Systolic parameters were measured by transthoracic echocardiography. MAPSE was measured and converted to MAPSE EF and correlated with Teichholz EF, modified Simpson’s apical four chamber (AP4CH) and apical two chamber (AP2CH) EF, global longitudinal strain-derived EF, systolic (S’) tissue Doppler imaging of the medial and lateral mitral valve annuli, and LV myocardial performance index (MPI). A correlation value of ≥0.70 was considered a strong clinical correlation. Participants were excluded if they had had aortic-and/or mitral valve replacement surgeries, had mitral valve annular dysfunction, pericardial diseases, tachyarrhythmias, malignant- or uncontrolled hypertension, new or uncontrolled diabetes mellitus type 2, acute ischemic events, or inadequate imaging views. Results Patients presented at a mean age of 61±14.9 years with no gender dominance. Most of the patients were from the Bethlehem region, since the private practice where the study was conducted is situated in Bethlehem (Appendix E). Futhermore,70% of the patients had mild symptoms and were in New York Heart Association (NYHA) class 1 and 2 at presentation (Appendix F). Normal to mildly impaired systolic measurements were noted in the total population with a mean EF:50.2±0.3% (Appendix G). A noteworthy impairment of all systolic parameters was present in the congestive cardiac failure (CCF) group (EF < 30%). Normal to mild impairment was noted in the ischemic group, while normal systolic parameters were found in the hypertension and diabetes mellitus groups. Correlation coefficients for the total study group are illustrated in Appendix H1. Overall, a strong positive correlation was observed between MAPSE EF and all the other systolic parameters (r:>0.82), while LV MPI had a strong negative correlation with MAPSE EF (r:-0.74). MAPSE EF also correlated strongly with the systolic parameters in both genders (r: >±0.73) (Appendix H2 and H3). MAPSE EF and the systolic parameters correlated the best (r:>0.80) in the congestive cardiac failure group. Similarly, in both genders, a strong close correlation was noted between MAPSE EF and the systolic parameters (r:>±0.70), with only LV MPI in the female group, which had low clinical correlation (r:-0.65) (Appendix H4 and H5). In the diabetes mellitus type 2, hypertension, and ischemic heart disease groups, no clinical correlation was noted between MAPSE EF and the systolic parameters. However, a low correlation (r:<0.62) was noted between MAPSE EF and S’ in the hypertension group, and S’ was the only systolic parameter which had a strong, positive correlation (r:>0.74) with MAPSE EF in the ischemic heart disease group. Patients with grade one diastolic dysfunction also demonstrated poor correlations between MAPSE EF and all the systolic parameters (Appendix H6). This improved in grade two diastolic dysfunction (Appendix H7), while patients with grade three diastolic dysfunction had an excellent correlation (r:>±0.84) between MAPSE EF and the other systolic parameters (Appendix H8). Strain-derived EF had a strong, positive correlation (r:>0.81) with modified Simpson’s AP4CH EF in the total population, as well as in all the subgroups (Appendix H9). Strong, negative correlation (r:>-0.76) was observed between LV MPI and Simpson’s AP4CH EF in the total population, while low to weak clinical correlation between these two systolic variables was noted in the subgroups (Appendix H10). Discussion Our patients presented at a mean age of 61±14.9 years with normal or minimally impaired systolic echocardiographic parameters. However, in patients with congestive cardiac failure, echocardiographic parameters were exclusively impaired. Although strong correlations were noted in the total population, subgroup analyses demonstrated several differences between MAPSE EF, and the other methods used to determine systolic LV function. In the congestive cardiac failure group, MAPSE EF correlated superbly with all the systolic parameters, irrespective of gender. It should be noted that this is the first time that MAPSE EF in both genders with CCF was determined. In the hypertension, diabetes mellitus, and ischemic heart disease groups, less favourable correlations were observed between MAPSE EF and the other systolic parameters. MAPSE EF correlated moderately only with S’ in the hypertension group, and strongly in the ischemic heart disease group. We predict that basal segment function was most likely preserved in our patient population study since MAPSE and S' were normal, although left ventriculography was not obtained. Conclusion Results demonstrated that MAPSE EF is a valid method to determine ejection fraction in patients with significantly impaired systolic function, irrespective of gender.
Access status: Open Access ,
Techno-economic assessment of process chains for the manufacturing of external maxillofacial prostheses for the South African ethnic demography using digital and Additive Manufacturing technologies
(Central University of Technology, 2024-11) van Heerden, Izél
Introduction: Maxillofacial trauma can significantly impact a person's life, affecting vital functions such as vision, smell, hearing, speech, breathing, eating, and facial appearance. Treating such trauma is challenging since the face plays a key role in expressing emotions and identity. In South Africa, the impact of facial trauma is extensive, not only for individuals but also for their families. Many patients, especially those reliant on government healthcare, lack the funding to access advanced technologies for the manufacturing of external maxillofacial prostheses. Therefore, given the high costs, research is needed to identify cheaper production options that still produce quality prostheses. To improve access for patients with limited financial resources, identifying cost-effective alternatives is crucial while ensuring the quality of these prostheses matches industry-benchmarks. In this study, process chains incorporating medical image processing (MIP) and computer-aided design (CAD) software using computed tomography (CT) volumetric data were evaluated against the industry-benchmark, which combines Mimics and Geomagic Freeform software applications. Additionally, process chains using surface scan data in conjunction with CAD software were tested and compared to the industry-benchmark. Main research question: The main research question probed in this study was: “Which process chain(s) can produce external maxillofacial prostheses of acceptable quality at a price relevant to the South African demography?” The answer to this question was explored by employing a design science research approach to find practical solutions to real-world problems, aligning with the pragmatic goal of generating useful, actionable knowledge with tangible outcomes. Methods: The objective was to determine which process chains could effectively meet the requirements for designing external maxillofacial prostheses. To systematically explore and validate affordable alternatives to the industry-benchmark, the research was divided into four distinct phases. In Phase 1, MIP and CAD software with potential for inclusion in the test process chains were identified through a comprehensive systematic literature review. In Phase 2, MIP software was selected based on its features and ability to segment CT data obtained from two human subjects. Errors in the segmented geometries were evaluated using Meshmixer, while the alignment of the test geometries with the control mesh was analysed and compared using CloudCompare. In Phase 3, the selected CAD software was tested for its features and sculpting capabilities. Sculpting functionality was evaluated using an anti-oloid mesh as the test object. Meshmixer was also used to detect any errors introduced during the sculpting process. Finally, in Phase 4, 3D models of human ears and noses were designed using the potential alternative process chains, which combined the selected MIP and CAD software and compared them to the industry benchmark. Additionally, the process chains that used surface scan data and CAD software to design ears and noses were also compared to the industry-benchmark. The ears and noses produced by the different process chains were analysed using Meshmixer, CloudCompare, and Geomagic Control X. To further assess the performance of each process chain, an Unweighted Standardised Rating Index was calculated to rate the various process chains based on quality. The process chains were also compared based on cost. Results: Following a thorough examination of over 700 scholarly publications, 73 were found to be suitable for identifying MIP and CAD software that may be suitable for testing in process chains related to the manufacturing of external maxillofacial prostheses. By applying a stepwise process of excluding software that did not fit the criteria, five MIP (out of 21) and nine CAD software applications (out of 37) were chosen for testing besides the industry-benchmarks. After testing the MIP software mainly for their segmenting capabilities, 3D Slicer and InVesalius were selected for testing in process chains. The systematic comparison of CAD software for digital sculpting in the manufacturing of external maxillofacial prostheses revealed that 3D Coat and ZBrush should be tested in process chains. The study's findings showed that the four test process chains combining MIP software with CAD software, and the two chains using scanned data with CAD software, produced products of comparable quality to those created by the control process chains. Among these, the combination of 3D Slicer with either 3D Coat or ZBrush resulted in the highest quality products. In contrast, the combinations of InVesalius with ZBrush, as well as surface scan data with 3D Coat or ZBrush, yielded lower-quality outcomes. In terms of cost, surface scan data combined with 3D Coat was the most affordable option for the South African demographic. For CT data, however, the combination of 3D Slicer or InVesalius with 3D Coat was the most cost-effective. Significance: By identifying alternative, cheaper, and more readily available processes to produce maxillofacial prostheses, more patients suffering from facial trauma will be able to access these life-changing technologies. When patients undergo reconstructive interventions, their self-esteem improves, which in turn helps them to reintegrate into society more quickly.
Access status: Open Access ,
Resistance trends of the Gram-negative ESKAPE pathogens Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. over five years: Northdale, Grey’s and Harry Gwala State Hospitals
(Central University of Technology, 2024-08) Shezi, Balungile
Background and aim Antimicrobial resistance is one of the greatest risks to human health in the modern era and a major health concern worldwide. Previous studies have shown that certain strains of bacteria have developed resistance to almost all antibiotics. The study aimed to investigate the antimicrobial resistance trends of the Gram-negative ESKAPE (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species) pathogens in the current era, determine the most prevalent microorganism, and determine which ward harbors the most microorganisms and specific isolates in the Pietermaritzburg (PMB) area of the KwaZulu- Natal (KZN), South Africa (SA), over five years. Antimicrobial resistance is known to be mostly common in patients of long-term acute care (LTAC) facilities due to a multitude of known risk factors. However, there has not been a thorough description and/or investigation of how antimicrobial resistance is at these selected PMB state hospitals. Methodology Retrospective data from 1 February 2017 to 31 January 2022 from the Pietermaritzburg National Health Laboratory Service (NHLS) hospitals, namely Grey’s, Harry Gwala, and Northdale Hospitals, were collected via the Corporate Data Warehouse (CDW) from 10 303 isolates to assess the resistance trends of Gram-negative ESKAPE (Enterococcus faecium; Staphylococcus aureus; Klebsiella pneumoniae; Acinetobacter baumannii; Pseudomonas aeruginosa; Enterobacter species) pathogens from various clinical samples such as wound swabs, blood, sputum, urine, Central Venous Catheter (CVC) tips, soft tissue, and other body fluids (ascitic and synovial fluid, Cerebrospinal fluid). These samples originated from patients in the hospital wards, intensive care units, and outpatient departments and were previously delivered to the lab for routine diagnostic examinations. Good laboratory practices for microscopy, culture, and susceptibility testing, or isolation, and identification were followed. Once in the NHLS TrakCare database, the test results were retained in the CDW. Information about people diagnosed within the allotted time frame with infections brought on by Enterobacter species, Pseudomonas aeruginosa (P. aeruginosa), Klebsiella pneumoniae (K. pneumoniae), and Acinetobacter baumannii (A. baumannii) complex was sought. The results of microscopy, culture, antibiotic sensitivity testing, and biochemical tests where specimens were tested, were also received. The NHLS laboratory tests for K. pneumoniae, A. baumannii complex, P. aeruginosa, and Enterobacter species were reported. Proper data storage in the Figshare system was always maintained in compliance with the POPIA Act. Results and Discussion Among the 10 303 isolates, the Klebsiella genus was predominant among all ESKAPE isolates, with n = 4 088 (39,7%). The Enterobacter genus played a limited role, with E. cloacae complex being the most frequent in the genus (1 506, 14,6%) and E. aerogenes with the least contribution within the ESKAPE pathogen group (240, 2,3%). Overall, most isolates were resistant to third generation cephalosporins, including ceftazidime and ceftriaxone, indicative of extended spectrum beta lactamase (ESBL) production. The findings of this study correlate well with a study done in 2011– 2015 in the same area (PMB), which states that ceftriaxone (8 326; 58,3%) and ceftazidime (7 412; 51,9%) were among the third generation cephalosporins that more than 50% of isolates were resistant to (Ramsamy et al., 2018). Colistin and tobramycin had the least overall resistance of 2,5% and 8,5%, respectively. This compares well with a 3,1% colistin resistance found in a recent study (Uzairue et al., 2022). Ertapenem and nitrofurantoin were the third-most effective antimicrobial agents, with both an overall resistance percentage of 9,5%. Among drugs of betalactam inhibitor combinations/beta-lactam, piperacillin/tazobactam (TZP) was better active against the isolates, with a resistance rate of 25,2%, which is indicative of the fact that ESBLs were sensitive to TZP. Even though P. aeruginosa showed low resistance to the tested antimicrobial agents during the period of this study, it showed a 98,8% resistance to tigecycline. Tigecycline does not consistently suppress P. aeruginosa; however, it appears to be an effective antimicrobial agent in extremely ill and immunocompromised patients. Regarding the prevalence of these resistant organisms according to wards, our data indicated that the ICU and male surgical ward had the highest rates of resistant isolates at 12,1% and 6,9%, respectively. This is in line with recent research findings, which showed that extended hospital stays, and high usage of invasive procedures contribute to the prevalence of nosocomial and invasive infections in intensive care units (Abubakar and Salman, 2024; Greatorex and Oosthuizen, 2015). The high resistance trend found in this study supports the need for increased surveillance of hospitalized patients. Conclusion This research presents a detailed analysis of the five-year resistance patterns of Gram-negative ESKAPE bacteria in PMB state hospitals. For example, these data show that the A. baumannii Complex shows significant fluctuations in resistance rates to numerous antibiotics. These results emphasize the need for effective antibiotic stewardship, robust infection control techniques, and continuous surveillance as antibiotic resistance grows. PMB state hospitals can enhance patient outcomes and public health by implementing these suggestions and controlling and lowering the prevalence of antibiotic-resistant illnesses. The global threat posed by these resistant pathogens is confirmed by the consistency of our results with earlier research, which highlights the importance of collaboration in addressing this public health emergency. This analysis reveals significant changes in resistance rates for several antibiotics, particularly in the A. baumannii complex. These findings underscore the need for continued surveillance, effective antibiotic stewardship, and robust infection control measures to address the growing challenge of antibiotic resistance.
Access status: Open Access ,
The effect of supplementation of plant and animal- derived dietary oils in pre- and post-parturition Döhne merino ewes on the growth performance of the lambs
(Central University of Technology, 2024-06) Sedupane, Tebogo
The objective of this study was to investigate the effect of dietary supplementation of sunflower oil, olive oil, fish oil, and palm oil to pre- and post-parturition Döhne Merino ewes on lamb growth performance. Birth weight, post-natal growth rate, and weaning weight of lambs suckling on supplemented ewes were compared. Fifty South African Döhne Merino ewes, 2–4 years old (second parity), weighing 46–55 kg, with a mean body condition score (BCS) of 3.5 ± 0.4, were randomly divided into groups of 10 animals each. Each group (n = 10 per group) received a daily dose of 30 ml of either sunflower, olive, fish, or palm oil, while the control group received no oil. The study was conducted over a period of 210 days (i.e., 7 months). Oestrus was synchronised through the use of controlled internal drug release (CIDR) devices inserted intravaginally for a period of 15 days. Oestrus synchronisation and oil supplementation commenced simultaneously. At CIDR withdrawal, ewes were injected intramuscularly with 200 IU Pregnant Mare Serum Gonadotrophin (PMSG). Fixed-time laparoscopic artificial insemination (LAI) was performed with diluted Döhne Merino semen 48 hours following CIDR removal. Two weeks after LAI, follow-up Döhne Merino rams were introduced to all 50 ewes for 2 weeks to mate ewes that exhibited any signs of oestrus, as this is standard procedure of the experimental farm, and it was also importatnt that all selected ewes for the experiment conceive, although not part of the objectives of the present research. The production parameters were expressed as an average per group. The mean conception rate and lambing rate of ewes across all groups were 88%. The olive oil treatment recorded the highest conception rate and lambing rate (100% for both), but did not differ significantly (P> 0.05) from other groups. followed by sunflower oil (90%), fish oil (90%), palm oil (70%); the control group had rates of 90%. post-hoc test using Tukey’s HSD to identify significant differences between treatment groups at specific time points at a 95% confidence interval. The IBM Statistical Package for the Social Sciences (SPSS) 27 software programme was utilised. The shortest gestation length of 150 days was recorded for the control group. The gestation lengths of the sunflower oil, olive oil, fish oil, and palm oil groups were 153, 156, 155, and 156 days, respectively, with no significant differences (P > 0.05) between any groups. The birth weights of female lambs from the control, sunflower oil, olive oil, fish oil, and palm oil groups were 5.41 ± 0.87 kg, 5.70 ± 0.61 kg, 5.8 ± 0.70 kg, 5.8 ± 0.74 kg, and 4.9 ± 0.50 kg, respectively. The birth weights of male lambs from the control, sunflower oil, olive oil, fish oil, and palm oil groups were 5.6 ± 1.19 kg, 4.85 ± 0.70 kg, 5.6 ± 0.84 kg, 5.5 ± 0.49 kg, and 5.4 ± 0.33 kg, respectively. The results indicate no significant difference (P > 0.05) between different treatment groups for birth weights for both male and female lambs. There was improvement or significance (P>0.05) on percentage increment in weight over time in weeks 5,9 and 11. There was significant difference experienced in ADG of lambs among groups in weeks 5,6,10,11 and 13. There was no significant difference (P > 0.05) observed in the final body weight at weaning for both males from the control, sunflower oil, olive oil, fish oil, and palm oil groups (28.8 ± 4.93 kg, 25.5 ± 8.13 kg, 29.0 ± 4.88 kg, 30.2 ± 4.59 kg, and 28.8 ± 5.03, respectively) and females from the control, sunflower oil, olive oil, fish oil, and palm oil groups (28.5 ± 4.58 kg, 28.3 ± 3.81 kg, 25.8 ± 6.93 kg, 28.9 ± 4.51 kg, and 27.6 ± 2.46 kg, respectively) for the lambs of ewes supplemented with different experimental dietary oils. There was difference in the effect of plant and animal-derived dietary oils – namely sunflower oil, olive oil, fish oil, and palm oil – on the percentage increment in weight over time and ADG of lambs among groups. The growth performance of the lambs was similar to the control group. It can thus be concluded that, in this study, diets enriched with plant and animalderived dietary oils pre- and post-mating had an effect on growth performance in Döhne Merino sheep.
Access status: Open Access ,
Evaluating the vulnerability and contamination of groundwater from mining dump in Welkom, Free State, South Africa
(Central University of Technology, 2024-05) Ruzvidzo, Silent
The quality of groundwater in and around Welkom, a city in the Free State Province of South Africa, has been steadily declining over the years. This deterioration has led public and private institutions, including schools, churches, hospitals, and universities, to cease using groundwater for drinking due to health concerns. Previous studies have suggested that contaminants from mine dumps in the Welkom area are the primary cause of groundwater pollution. This study aimed to assess the vulnerability of groundwater to contamination from a specific mine dump in Welkom. A key objective of the research was to develop a point source vulnerability assessment method tailored to identify groundwater vulnerability to contamination from mine dumps. The study introduced a new parametric method called DWAPH, which considers five parameters: Depth to groundwater, Water quality, Aquifer type, Precipitation, and Horizontal distance to the contaminant source. The DWAPH method uses an index with four vulnerability scales: low (5-10), moderate (11-15), high (16-20), and very high (20-25). The study data, informed by the parameters was mainly gathered from five boreholes at the Central University of Technology campus in Welkom and a nearby mine dump. The boreholes were used as proxies to investigate the effects a nearby mine dump on groundwater quality and vulnerability to contamination. Each of the five boreholes was characterised through electrical conductivity and dilution tests to detect the fracture points and type of aquifers. The characterisation also involved the assessment of the depth to groundwater, and the depth of each of the five boreholes using a groundwater level meter. Each of the five boreholes, together with a nearby effluent from a mine stream, were seasonally sampled in the summer and autumn seasons between 2021 and 2023 and the water was tested for physical, chemical, and microbiological elements such as Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), ( Potential of Hydrogen) pH, Sodium( Na), Calcium (Ca), Magnesium (Mg) , Chloride (Cl), Fluoride (F), Sulphate (SO4) and Nitrate (NO3). The Weighted Arithmetic Index method was used to investigate the Water Quality Index using the same set of physical and chemical parameters. Further geological and hydrological characterisation of the study site was conducted through a literature review of previous studies. Results from the characterisation process indicated that all five boreholes were part of an unconfined aquifer with fracture points detected at depths of 10 m for Boreholes 1, 2 and 5; 20 m for Borehole 3; and 24 m for Borehole 4. The results indicated that for Borehole 3, four parameters, EC, TDS, Na, and Cl, exceeded the South African National Standards (SANS 241), while all the other boreholes, except Borehole 1, had parameters that were within the recommended SANS 241 standard limits. Samples from the mine effluent stream had abnormally high levels of EC, TDS, DO, Na, Ca, Mg, Cl, F, SO4 across all the months of the summer season. All boreholes, except Borehole 1 and Borehole 4, had total coliforms and faecal coliform counts that exceeded the SANS 241 drinking water guideline limits, while for the autumn season, the total coliform counts and the E. coli counts in all boreholes, except Borehole 3, were higher than the recommended SANS 241 drinking water guidelines. The results from the Water Quality Index (WQI) calculated through the Weighted Arithmetic Index (WAI) method using the SANS 241 water quality guidelines indicated that three of the five boreholes (B1, B3 and B5) had poor water quality, while only two boreholes (B2 and B4) had good water quality. The overall mean WQI of 68.16 that was calculated from the physical and chemical determinants of all five boreholes indicated that the water quality was poor and posed great health risks when used for drinking purposes. The WQI for the mine dump effluent was 544.31, which was five times beyond the minimum guideline requirements, hence supporting the possibility that it was heavily contaminated and can possibly contaminate nearby groundwater sources. The Welkom area was classified under the Witwatersrand supergroup with shale and sandstone. Using the background knowledge and data from the characterisation process, the DWAPH vulnerability assessment method was applied to assess the extent of groundwater vulnerability of the boreholes to contaminants from mine dumps. Results from the DWAPH index indicated an overall vulnerability score of 13 on a scale ranging from 5 to 25, which indicated moderate vulnerability. The outcomes from the DWAPH vulnerability method were compared against outcomes from the AVI, GOD, and RTt methods. Despite all three methods utilising the same datasets, their outcomes were not the same due to a variety of factors, which were mainly based on their design. The GOD and the DWAPH methods identified the extent of vulnerability of the Welkom area as moderate, while the AVI and RTt methods identified the extent of groundwater vulnerability as very high and low, respectively. The DWAPH method was further validated with NO3- and bacteriological counts of total coliforms and E. coli. The nitrate validation indicated a weak correlation between nitrate concentrations and the DWAPH index of R2 = 0.0894. The total coliform counts showed a weak correlation with the DWAPH index while the E. coli counts indicated an almost perfect relationship with the outcomes from the DWAPH index. The preliminary findings of this study indicate that the groundwater in Welkom exhibits a moderate vulnerability to contamination from nearby mine dumps, with water quality tests revealing varying degrees of contamination. Given these results, it is imperative to exercise extreme caution when using groundwater in the area. Implementing restoration and treatment techniques, such as pump and treat, air stripping, filtration with granulated activated carbon, and air sparging, is strongly recommended before any groundwater is utilized. Future research in the Welkom area should prioritize using alternative parameters to further refine, consolidate, or validate the DWAPH method. Expanding the scope of groundwater assessments will provide a more comprehensive understanding of the contamination risks and help improve the reliability of vulnerability assessments. Additionally, it is crucial for local authorities and mining companies to collaborate in developing sustainable solutions to address the ongoing groundwater contamination issues. By working together, they can mitigate the environmental impact of mining activities and protect the health and well-being of the community.
Access status: Open Access ,
Application of blockchain in managing security challenges confronting the adoption of fourth industrial revolution technologies in smart environments: the case of a multi-hazard early warning system
(Central University of Technology, 2024-09) Ramahlosi, Maneo Ntseliseng
Gaining access to data, both personal and public, has become a source of worry in our digital age, as it poses some security and privacy challenges. This raised consciousness is applicable to all digital applications as well as information systems. Intentions of hacking and violations of privacy have been proven to be a major concern, which has led to digital trust issues. Similarly, this notion is true in smart environments, where datasets that have been recorded and collected are used as inputs for monitoring systems and forecasts, which in turn must adhere to several rules due to the sensitive nature of the predictions. Progression in technology over the years has resulted in geometric enhancements in relation to expertise of monitoring devices and downsizing of devices, encouraging the transition from commonly utilised outdated systems to Internet of Things (IoT) smart devices. In an atmosphere where there is an augmentation of cyber security threats, updating legacy systems is also a great concern. These legacy systems have proven to possess characteristics that pose security vulnerabilities, and they are usually incompatible with current security features, and they also lack sufficient encryption methods. As a result of this shift in paradigms to the IoT age, more attention has been given to integrating various devices, guaranteeing technology compatibility and interoperability, and making the devices smaller over tackling security flaws of the endpoint solutions Therefore, even with the adoption and deployment of the Fourth Industrial Revolution (4IR) technology, security issues still exist. As a case study, a smart environment network of an Early Warning System (EWSs) in a Multi-Hazard Early Warning System (MHEWSs) is not an exception, as it is also susceptible to vulnerabilities; security of data and data pipelines is crucial, and it remains the focus of this study. Its importance is heightened by the integration of 4IR technology and the amalgamation of heterogeneous devices and systems. The objective of this study is to investigate how security issues that afflict smart environments, and consequently, a MHEWSs may be managed and eased by the implementation of Blockchain technology and solutions. This research was carried out with the hypothesis that the implementing Blockchain technology on the MHEWSs will protect the data pipeline. The hypothesis proved to be true as the researcher was able to prove that Blockchain technology can be used to protect the data pipeline of a MHEWS.
Access status: Open Access ,
Local diagnostic reference levels (drls) for paediatric contrasted abdominal computed tomography (ct) examinations for nephroblastoma
(Central University of Technology, 2024-09) Pitso, Tebello
Computed tomography (CT) has become essential to paediatric radiology with the ongoing technological developments, and has been established as an important part of the diagnostic procedure (Hojreh, Weber & Homolka, 2015:1574). However, paediatric CT poses a particular concern, since paediatric patients have a high risk from radiation (Järvinen, Seuri, Kortesniemi, Lanjunen, Hallinen, Savikurki-Heikkilä, Laarne, Perhomaa & Tyrväinen, 2015:86). Therefore, it is important to pay attention to the justification and optimisation of paediatric CT examinations (Järvinen et al., 2015:87). An essential part of optimisation is developing diagnostic reference level (DRL) values (Saravanakumar, Vaideki, Govindarajan, Devenand, Jayakumar & Sharma, 2016:342). The International Commission on Radiological Protection (ICRP) describes DRL values as a form of investigation levels applied to an easily measured quantity (Saravanakumar et al., 2016:342). Furthermore, the ICRP (2017:31) indicated that DRL values are not dose limits and are not based on individual patients but rather on a specific group of patients. According to hospital reports, paediatric patients who presented for abdominal CT examinations at the participating hospitals were most likely to have nephroblastoma (Lesetja, 2021). It was found through a literature evaluation that no documented paediatric local DRL (LDRL) values existed for CT examinations to investigate and stage nephroblastoma in South Africa. The absence of established clinical indication-based paediatric LDRL values in South Africa limits the ability to identify unusually high dose levels, and ensure optimal radiation protection during contrast-enhanced abdominal CT examination of paediatric patients who present with nephroblastoma. The aim of the study was to establish paediatric LDRL values for contrast-enhanced abdominal CT examinations to diagnose and stage nephroblastoma cases. To achieve the aim of this study, three objectives were pursued: (1) to develop paediatric LDRL values that are linked to the patient’s weight and age for contrast-enhanced abdominal CT examination for nephroblastoma cases; (2) to compare LDRL values calculated, using weight versus effective diameter of the patient, and (3) to compare the clinical indication-based LDRL values with other internationally published clinical indication-based LDRL values. The study followed a descriptive research design, and data from paediatric patients who underwent CT abdominal examination to diagnose nephroblastoma from 1 January 2018 to 31 December 2021 were collected retrospectively. In this research study, the researcher used a structured data recording document to descriptively gather dose information from the dose reports on the picture archiving and communication system (PACS) or CT console and the CT register file. Thereby, paediatric LDRL values for contrast-enhanced abdominal CT examinations based on the clinical referral of suspected or confirmed nephroblastoma were established. A structured data recording document was used to obtain the volume computed tomography dose index (CTDIvol), dose length product (DLP), clinical indication, anterior-posterior (AP) dimensions, lateral (LAT) dimensions, effective diameter, age, and weight of the patients. The size-specific dose estimates (SSDE) parameters were calculated using methods provided by the American Association of Physicists in Medicine (AAPM) Report 204. The target population of this study consisted of paediatric patients who presented with nephroblastoma for contrast-enhanced abdominal CT examinations at the participating hospital. The sample size of paediatric patients who formed part of this research study was 120 patients for contrast-enhanced abdominal CT examination. The patients were categorised into five age groups (group 1 [<1 month (M)); group 2 [1M to <4 years (Y)); group 3 [4Y to <10Y); group 4 [10Y to <14Y), and group 5 [14Y to <18Y)) and five weight categories (group 1 [0kg - <5kg), group 2 [5kg - <15kg), group 3 [15kg - <30kg), group 4 [30kg - <50kg) and group 5 [50 kg to <80 kg)), as suggested by the ICRP (2017:93). Furthermore, the sample was also grouped according to five effective diameter groups (group 1 [<13 cm); group 2 [13 cm to <16 cm); group 3 [16 cm to <20 cm); group 4 [20 cm to <24 cm), and group 5 [<24 cm)). The effective diameter was calculated from the square root of the product of the patient’s AP and LAT dimensions’ measurements as suggested by ICRP (2017:97). A minimum of 20 patients for each group was needed to develop LDRL values for this study based on the 75th percentile. The data from the age, weight or effective diameter groups with fewer than 20 patients to establish LDRL values for nephroblastoma were excluded. Therefore, LDRL values were established only for • two weight groups: weight group 2 [5kg - < 15kg), weight group 3 [15kg - < 30kg), three age groups: age group 2 [1M to <4Y), age group 3 [4Y to <10Y) and age group 4 [10y to <14y), and • two effective diameter groups: effective diameter group 2 [13 cm to <16 cm) and effective diameter group 3 [16 cm to <20 cm). The paediatric LDRL values (75th percentile) based on the CTDIvol ranged from 2.7–7.1 mGy, 2.7–3.5 mGy, and 2.8–6.5 mGy for age groups, weight groups and effective diameter groups, respectively. The DLP, 75th percentile paediatric LDRL values for age groups ranged from 98.7–367.9 mGy.cm, 95.7–162.6 mGy.cm for weight groups, and 111.8–325.9 mGy.cm for effective diameter groups. The LDRL values (75th percentile) based on the SSDE parameters (AP, LAT, SUM and effective diameter) ranged from 5.6–12.8 mGy, 5.6–6.9 mGy and 5.9–12.2 mGy for age groups, weight groups and effective diameter groups, respectively. Based on a 95% confidence limit calculated on the 75th percentile, a significant difference was detected between CTDIvol and SSDE parameters, indicating that both dose quantities had different effects on radiation exposure. Since the CTDIvol provides only information about the scanner's output, it cannot estimate size-specific patient doses (AAPM, 2011:2). Therefore, according to AAPM (2011:18), the SSDE should be used as it incorporates corrections for patient sizes. The DLP, CTDIvol, and SSDE parameters did not correlate with weight in weight group 2 (5 kg to <15 kg). The DLP, CTDIvol, and SSDE parameters significantly correlated with weight group 3 (15 kg to <30 kg). The ICRP (2017:93) and European Commission (2018:32) stated that patient weight is the most reliable factor associated with the DRL quantity. Therefore, weight should still be used for categorising, as the ICRP (2017) suggested. The LDRL values calculated using weight were compared with the effective diameter of the patient. The weight and effective diameter LDRL values were comparable, and Spearman’s coefficient correlation indicated a significant correlation between patient weight and effective diameter. Therefore, the weight (kg) or the effective diameter (cm) can be used to categorise the patients. However, the ICRP (2017:97) recommended that the effective diameter should be considered in addition to weight during the development of DRL values. Unlike weight and age, effective diameter represents dimensional measurement at a specific cross-sectional image, while weight and age represent the area being scanned (Cook, Chadalavada & Boom, 2013:2). This study's clinical indication-based LDRL values were also compared with the internationally published LDRL values. No studies could be found in South Africa or internationally that have developed LDRL values, specifically for nephroblastoma in paediatric patients. The LDRL values for paediatrics developed in this study were somewhat lower than most DRL values found in various studies, except those by Hwang, Choi, Yoon, Ryu, Shin, Kim, Lee, You and Park (2021) and Almén, Guðjónsdóttir, Heimland, Højgaard, Waltenburg and Widmark (2022). The radiation dose accumulated by paediatric patients involved in this study was lower due to the number of scan sequences and the use of automatic tube current modulation (ATCM) and iterative reconstruction (IR) algorithms. The established LDRL values in this study will contribute as a guide to optimise the radiation dose received by paediatric patients for nephroblastoma. The findings of this study further support the idea that the effective diameter groups, together with the SSDE dose quantity, should be used to develop paediatric DRL values, as suggested by ICRP (2017) and AAPM (2011).
Access status: Open Access ,
Constructing a microbial diversity profile for red meat abattoir effluent
(Central University of Technology, 2024-03) Phooko, Nthuseng
Abattoirs are critical components of the global food supply chain as they provide essential resources for populations worldwide. However, the environmental footprint of these facilities, particularly through the generation of effluent, poses significant challenges to sustainable water management and environmental conservation. Abattoir effluent is distinguished by its high biological load, which refers to the concentration of organic materials including stomach content, blood, and large amounts of fats, oils, and grease (FOG). While the physicochemical properties of abattoir effluent have been extensively studied, little is known about the microbial diversity within the effluent. This knowledge gap represents a critical barrier to understanding the full environmental and health implications of abattoir effluent discharge, especially in regions where it is released into municipal sewer systems without adequate pre-treatment. Understanding the microbial constituents of abattoir effluent can inform targeted interventions to remove harmful pathogens and reduce the organic load, thereby alleviating the burden on municipal wastewater treatment systems. Ensuring compliance with existing municipal by-laws and regulatory standards that govern effluent discharge is also important in alleviating this burden. This study aimed to construct a detailed profile of the microbial diversity present in red meat abattoir effluent by focusing on both genomic and metabolic capabilities as well as physicochemical compliance with local regulatory standards. The sampling site was a high throughput red meat abattoir in Bloemfontein, Free State. An abattoir is considered to have a high throughput if it processes more than 20 units daily, with the unit count based on the type of animal slaughtered. Red meat abattoirs are facilities where various animals such as cattle, sheep, pigs, or horses are slaughtered. Daily sampling was performed for 10 consecutive days by collecting composite samples from the pipe through which effluent was discharged into the sewer system. Physical and chemical parameters were considered significant when characterising the abattoir effluent environment were analysed. Community Level Physiological Profiles (CLPP) were constructed using BioLog EcoPlate sole-carbon substrate utilisation analysis while culturable bacteria and fungi were quantified. Enumerations of total microbial load, Escherichia coli (E. coli), Pseudomonas aeruginosa, Enterobacter aerogenes, and yeast and mould were performed using rich (Nutrient) and/or selective agars (Harlequin®, Nutrient Agar and Rose Bengal Chloramphenicol). The genetic diversity was determined through 16S- and ITS-targeted metagenomic sequencing on the Illumina MiSeq platform. Physicochemical parameters indicated 37.5% non-compliance with the Mangaung effluent discharge limits. Non-compliance was attributed to high levels of solids, mostly volatile, high chemical oxygen demand (COD) due to the organic loads as well as high alkalinity levels. The environment had a neutral pH and low EC levels that were within the Mangaung effluent discharge limits while sufficient levels of dissolved oxygen (DO) were also detected. The temperature profile was consistent throughout ranging between 24°C-28ºC. Nutrients such as phosphate, total organic carbon (TOC), sulphate, and chloride were also detected at levels that were within the discharge limits. As expected, high microbial loads were noted, with bacteria being the more dominant microorganisms compared to fungi. Bacterial coliforms, Pseudomonas aeruginosa (8.04×105 CFU.mL-1), Enterobacter aerogenes (1.18×105 CFU.mL-1) and E. coli (2.78×104 CFU.mL-1) were present. Low counts of yeast (5.32×103 CFU.mL-1) and mould (7.10×101 CFU.mL-1) were observed. CLPP displayed an abundant, diverse, and functionally active community. Simpson index (0.962) measurements displayed diversity, the evenness index (0.97) depicted the presence of an abundance of species, and the average well colour development symbolised a functionally active consortium. The CLPP showed consistency amongst all the sample days. The relative abundance of metabolic activities was related to polymer, carbohydrate, and amino acid category metabolism. Glycogen and L-asparagine were the most utilised substrates while 2-hydroxy benzoic acid and α-ketobutyric acid were not utilised. The taxonomic bacterial and fungal biodiversity showed a broad similarity. Bacterial diversity showed consistent dominance of Bacteroidetes, Firmicutes, and Proteobacteria phyla. Genus richness varied in dominance distribution, with Acinetobacter, Bacteroides, and Rikenellaceae_RC9_gut_group being the most abundant genera with the dominance varying for each sample. Dominant fungal phyla were either Ascomycota or Neocallimastigomycota. Basidiomycota appeared in small, varied levels for each sample. The distinctive presence of Mucoromycota was observed on days when pigs were slaughtered, which deviated from the normative microbial composition encountered during the other sampling days, thus signifying an exception within the microbial community dynamics. The fungal community was dominated by a particular genus in each sample, namely Neocallimastix, Caecomyces, and Ciliophora respectively. Both the culture-dependent and culture-independent analyses of the microbial content yielded noteworthy findings that indicated an exceptional degree of uniformity across all sample days. The calculated similarity rate ranging from 96-98% highlighted a distinct resemblance in microbial diversity among the samples. The effluent consistently exhibited a comparable microbial content except for sample day 6 phylum discrepancies, which accounted for the 2-4% difference in the similarity rate calculated. These findings strongly indicate a substantial overlap in microbiomes between the meat, stomach content, and hide of different animals, suggesting a commonality in the microbial species present in the effluent. Given the limited information available on the microbial diversity of abattoir effluent, this research filled a significant gap in the environmental science and public health domain as it was able to contribute significant knowledge of the complex microbial ecosystems within abattoir effluent. The findings could pave the way for innovative treatment strategies using indigenous microbes and may thus contribute to more sustainable abattoir effluent discharge practices.
Access status: Open Access ,
An intelligent prediction model for infectious diseases’ outbreaks: an ensemble of machine learning, big climate data and indigenous knowledge
(Central University of Technology, 2024-12) Phoobane, Maingosinathi Paulina
Climate change is real. It affects all spheres of fauna and flora, remarkably increasing human vulnerability to infectious diseases. Infectious diseases are the major causes of death in low-income countries, with Sub-Saharan Africa accounting for a larger proportion of these fatalities. One of the challenges in fighting infectious diseases in developing countries like South Africa is the lack of effective surveillance systems for predicting and monitoring infectious diseases. It is essential to provide an effective, relevant early warning system (EWS) for infectious disease outbreaks to mitigate impact through vaccination and other interventions. Although several studies have developed early warning systems or models to predict and/or monitor infectious disease outbreaks such as malaria, these studies are predominately based only on one knowledge system, primarily focused on scientific approaches and do not account for an African context. The integration of these current approaches with indigenous knowledge (IK) systems could enhance current early warning systems and make them more effective, relevant and understandable to the communities the EWS are intended to serve. The objective of this research study is to develop an intelligent people-centred model for predicting infectious disease outbreaks using IK, big climate data and historical malaria incidence in the Vhembe district of South Africa. Vhembe is a district in the province of Limpopo that is identified as prone to malaria outbreaks and other climate-driven shocks. The lens that guided this research study is the EWS framework termed Malaria Outbreak Early Warning System (MOEWS). MOEWS encompasses the collection and analysis of weather and IK data to predict malaria outbreaks. Malaria outbreak risk knowledge encompasses collecting, understanding and integrating historical weather, malaria and IK data with drought indices. Machine learning (ML) models were used to predict malaria outbreaks using the collected data. Both classification and regression ML algorithms were explored to predict malaria using structured data; weather, malaria and drought indices. Data pre-processing, such as normalization of data, handling outliers, oversampling and feature selections, was performed before modelling the dataset. ML models, developed in Jupiter Notebook using Python, were trained and tested using historical weather, drought indices and malaria data. The Multilayer Perceptron classification showed an optimal performance. MLP model demonstrated an accuracy of 93%, a precision of 95%, a perfect recall of 100%, and an F1 score of 96%, indicating a robust predictive capability. As a result, the MLP model was selected for malaria prediction. The model was subsequently deployed to make malaria predictions using real-time data on weather conditions and reported malaria incidences. A lag of one to two months was applied between the predictors and the malaria outbreak. The results suggest the MLP's robustness in identifying patterns and predicting malaria outbreaks effectively. The deployment of the MLP model with real-time data on weather conditions and reported malaria incidences highlights its practical application in forecasting malaria outbreaks. This implies that the model has the potential to serve as a reliable tool in early warning systems, enabling proactive interventions and informed decision-making for malaria control and prevention. For the malaria outbreak forecast using indigenous knowledge, IK indicators for malaria forecast collected from the local people in Vhembe were formatted and represented as concepts. The relationship/causal effects between the concepts and with malaria were determined and formally represented as adjacency matrices. Adjacency matrices were used to develop fuzzy cognitive maps (FCM) for each of the four seasons of the year, illustrating the concepts and their causal relationships. The importance of each IK indicator within each seasonal FCM was established by analysing the density, in-degree, out-degree and centrality measures. The findings highlight that in Autumn, "autumn heavy rainfalls" and "dirty water in containers/small pools" are major predictors of malaria outbreak while "summer heavy rainfalls," "dirty water in containers" and "summer temperature" are key indicators in summer. In contrast, the main predictors of malaria in winter are 'Fig "Muhuyu" trees not shedding leaves' and 'sight of insects/locusts' while spring rainfalls and '"Mofafa grass" having many ticks' were found important for malaria prediction in spring. These insights imply that malaria forecasting systems can benefit from incorporating seasonally relevant IK indicators to improve accuracy and contextual relevance. The autumn FCM model was utilised to forecast seasonal malaria outbreaks. This was done using real-time indigenous knowledge indicators reported by selected local experts via the mobile MOEWS App. Based on the malaria IK indicators reported by the IK expert for autumn, the malaria outbreak concept had a value of 0.7856 indicating that a low malaria incidence ranging from 400 to 800 cases could be expected. Malaria alerts generated from IK and machine learning-based forecasts were disseminated through the MOEWS mobile App, social media platforms and a web portal. The MOEWS App plays a crucial role in this process, providing a direct and immediate connection between the forecast and the community. The alerts are intended to aid policymakers in developing mitigation strategies and informing decision-making processes related to malaria. This ensures that the policymakers are well-informed and can respond swiftly to potential outbreaks © Central
Access status: Open Access ,
Application of indigenous knowledge, machine learning and satellite imagery to optimize cropping decisions by small-scale farmers: case study of uMgungundlovu District Municipality, South Africa
(Central University of Technology, 2024-09) Nyetanyane, John Makhetha
This thesis presents the methodology to optimise cropping decisions for small-scale farmers who depend primarily on rainfed agriculture for subsistence and market production. The research was motivated by the significant role small-scale farmers, particularly in sub-Saharan Africa (SSA), play in ensuring food security, despite often being overlooked by many technological developments and advancements. Although the use of chemicals to grow crops seems to expand in Africa, many smallholder farmers continue to use the Indigenous Knowledge (IK) system that by its originality is natural, organic and environmentally friendly, and most importantly food produced by using only IK methods is of high quality. Although IK is a crucial resource for small-scale farmers in food production and managing climate-related challenges, its value, accuracy, and presence have diminished over time due to climate change, market competition, deforestation, pollution, modernisation, and many more. This thesis aims to restore and enhance the dignity and effectiveness of indigenous knowledge by integrating it with scientific data and methods to optimise cropping decisions for small-scale farmers. This integration seeks to improve farmers’ traditional methods to predict rainfall patterns and temperature fluctuations throughout the agricultural season. This integration is further expanded to strengthen farmers’ conventional methods of monitoring the health and growth of the crops. While indigenous and scientific knowledge systems have their strengths and limitations, a significant body of literature highlights their collaboration’s benefits in enhancing the resilience of local communities to climate change. However, not much has been done to incorporate these knowledge domains together to optimise food production given that one is qualitative and locally based while the other is quantitative and generic. In this research project, efforts are made to quantify the IK system farmers use to predict rainy season behaviour and monitor the health and growth of crops, then integrate it with scientific data and approaches (satellite imagery, climate data, and machine learning models) to come up with more robust and local-based information that farmers can use to improve their cropping decisions. These knowledge systems are integrated into a mobile-based technology designed for farmers’ use. This hybrid technology is trained to recommend crops a farmer can grow based on anticipated seasonal behaviour. It is then evaluated using a dataset with unknown inputs, achieving an accuracy level of 83.3%.