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Assessing the Coagulation System in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
(Stellenbosch : Stellenbosch University, 2024-12) Nunes, Jean Massimo
Introduction: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating and chronic post-viral disease that is characterized by unresolved fatigue, post-exertional symptom exacerbation (PESE), cognitive dysfunction, orthostatic intolerance, and gastrointestinal disturbances, among other symptoms. ME/CFS shares significant overlap with Long COVID (LC), the post-viral disease associated with SARS-CoV-2 infection, in both disease presentation and etiology. A prominent feature of LC pathology is a dysregulated coagulation system, characterized by anomalous clot formation, hyperactivated platelets, and microclots. In ME/CFS, the coagulation system is understudied, and hence represents a gap in knowledge. Therefore, this study aims to assess whether the clotting pathology present in LC is mirrored in ME/CFS.
Methods: To assess the coagulation system in ME/CFS, 29 ME/CFS (22 females, 7 males, mean age of 45.7 ± 14.9) and 30 age- and gender-matched control (21 females, 9 males, mean age of 49.1 ± 11.3) blood samples were analyzed. Viscoelastic analysis of blood samples was conducted using thromboelastography (TEG®). Platelet activity was assessed via fluorescent microscopy and the use of two fluorescent markers specific for platelet activation markers, glycoprotein IIb/IIIa and P-selectin. Thioflavin T (ThT) was used to visualize microclots using fluorescent microscopy. Representative micrographs and ImageJ processing were used to infer, crudely, concentration values of microclots. To validate this assessment, microclot concentrations were measured by a recently established cell-free flow cytometry technique. To identify differentially expressed proteins, 15 randomly selected ME/CFS and 10 control PPP samples were subject to liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis.
Results: Findings from the TEG® assessment indicate that over half of the ME/CFS WB samples fell out of the standard clinical range, representative of a hypercoagulable profile. This inference is further supported by the analysis of PPP, where the greatest differences were recorded in α-angle (p=0.0006) and maximum rate to thrombus generation (p=0.0001). Roughly half of the ME/CFS cohort demonstrated platelet hyperactivity as determined by spreading, whereas only a quarter of the cohort was positive for significant platelet clumping. Using fluorescent microscopy and ImageJ software, it was inferred that the ME/CFS group contains more than 10x the levels of microclots (0.70 [11.21]) than the controls (0.06 [2.23]) (p<0.0001). Appropriate quantitative analysis with cell-free flow cytometry determined that the ME/CFS group exhibits a microclot burden 5x (27808 [107203]) that of the control group (4898 [20709]) (p<0.0001). Furthermore, ME/CFS PPP sample contain a greater prevalence of large microclots (≤100-400μm2). The proteomics analysis identified 45 differentially expressed proteins. Importantly, proteins related to clotting processes – thrombospondin-1, platelet factor 4, and protein S – were implicated. Complement machinery was also downregulated, whereas lactotransferrin and protein S100-A9 were upregulated. Conclusion: Overall, this study demonstrates that dysregulated clotting processes are an aspect of ME/CFS pathology, and that these abnormalities in coagulation are similar to that observed in LC. These findings provide further overlap between ME/CFS and LC, and has the potential to guide future research and prompt investigations into haematological pathology in ME/CFS. Importantly, this study highlights potential systems and proteins that require further research with regards to their contribution to the pathogenesis of ME/CFS, symptom manifestation, and biomarker potential, and also gives insight into the cardiovascular risk experienced by ME/CFS individuals.
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The size and composition of the expressive vocabularies of monolingual South African English- and Afrikaans-speaking toddlers
(Stellenbosch : Stellenbosch University, 2024-12) Nortje, Mariette; Southwood, Frenette; Brookes, Heather; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of General Linguistics.
It can be said that children’s first words form the foundation of language development, literacy, learning and experiencing life. We use words to organise our thoughts, emotions, and experiences and to interact with those around us. Although children seem to acquire language without intervention, many children are at risk of language disorders or delays. Vocabulary is important, as it has been shown to affect a child’s ability to speak, read, write, think, acquire numeracy skills, process and express emotions, connect socially and even affect their mental health and employment. Therefore, tools are needed with which to evaluate the vocabularies of young children to identify those at risk. The MacArthur-Bates Communicative Development Inventories (MB-CDIs) have been established as reliable and valid parent reporting instruments to assess children’s language development from 8 to 37 months. The South African Child Language Development Node adapted linguistically and culturally equivalent Communicative Development Inventories (CDIs) for all 11 official spoken languages in an effort to create appropriate early child language norms for our contexts. Consequently, the first systematic research into early language development of South African children, using these parent reports, was made possible.
The present study explored two of the first SA-CDI data sets to learn more about the size and composition of the expressive vocabularies of 75 South African English- (SAE) and 113 Afrikaans-speaking toddlers aged 16 to 32 months. The aim was to enhance our knowledge regarding monolingual child first language acquisition in South Africa, especially since languages in the Global South are understudied. The results show that the SAE- and Afrikaans-speaking toddlers had a steady and significant increase in word production as age increased, across all ages, despite large individual differences. At 30 months, the median expressive vocabulary size of SAE-speaking children (526 words) and those of Afrikaans-speaking children (475 words) were within the range reported by other studies (360-630 words). Afrikaans-speaking females had significantly larger vocabularies than their male peers across all ages but there was no significant sex advantage amongst the SAE group. Ecological setting was relevant as the urban Afrikaans-speaking participants had significantly larger vocabularies than their rural peers. As expected, based on other studies’ findings, the concepts presented by the most prevalent words produced by all participants related to familiar people, toys, sounds, routines, animals and body parts. The SAE-speaking participants, however, had more words for toys and animals and fewer for people than their Afrikaans peers. As in other languages, all participants produced more words related to concrete entities (like people and body parts), than those related to abstract concepts (like time and placement). Both languages showed a noun bias in their lexicon, although the Afrikaans-speaking children started using more verbs at a younger age than their SAE-speaking peers. The comparison of lexicons between sexes also yielded qualitative differences in both languages. The study adds value as the first systematic cross-linguistic comparison of male and female participants representing this age group, acquiring Afrikaans and this variety of English.
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Using machine learning to predict the risk severity of late effects o f c hildhood cancer survivors
(Stellenbosch : Stellenbosch University, 2024-12) Nortje, Lene; Grobler, J.; Van Zyl, A.; Kruger, M.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
With improvements in childhood cancer treatment, the number of survivors is continuously increasing. Childhood cancer survivors have a significant risk of developing late effects due to the underlying cancer or the treatment received.
These late effects can affect any organ and may influence the survivors’ health related quality of life from a young age. It is important to identify the risk severity of late effects and diagnose them as soon as possible to plan appropriate
long-term follow-up care, manage late effects early, and potentially improve the health-related quality of life of these survivors. In low-and-middle-income countries, there is often limited access to routine screening for healthcare problems, which makes it challenging to provide adequate long-term follow-up care for childhood cancer survivors. Due to limited access to health care, there is an opportunity for developing other techniques to predict the risk severity of these late effects. This study utilised two datasets: a South African childhood cancer survivor cohort (comprising haematological and solid cancers) and a North American childhood rhabdomyosarcoma survivor cohort. For both datasets, data analysis, and three clustering- and six classification algorithms were applied to select the best strategy for predicting the risk severity of late effects. The clustering was necessary because the target feature required for the supervised
machine learning algorithms was not a single obvious feature already present in the dataset. Therefore, to comprehensively report the extent to which late effects manifested among childhood cancer survivors, the features related to the grade and number of late effects were utilised during the clustering. The indices of these newly created clusters formed the target feature for the supervised machine learning algorithms. Five performance metrics were measured to evaluate the respective classification models. For both the South African and North American cohorts, the gradient boosting model yielded the most promising results across the selected performance metrics of the classification algorithms.
The gradient boosting model of the South African cohort identified anthracycline dose, radiotherapy dose, age at study visit, treatment modalities, body mass index, and age at diagnosis as the most important features for predicting the risk severity of late effects. The gradient boosting model of the North American rhabdomyosarcoma cohort identified age at follow-up, age at diagnosis, neck radiotherapy, participants’ educational status, and head radiotherapy as the most important features for these predictions. Risk stratification into low- or high-risk categories may assist with long-term follow-up care planning for childhood cancer survivors. Predicted risk severity of late effects can assist with providing more intensive follow-up to survivors with a higher risk for late effects and reducing the burden on the healthcare system for the follow-up of survivors with a lower risk of complications. Furthermore, since age at diagnosis, age at follow-up, and treatment modalities (including radiotherapy and chemotherapy) were identified in both cohorts, these risk factors can be important to incorporate in managing and planning appropriate long-term follow-up care for childhood cancer survivors to improve their health-related quality of life potentially.
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Visually grounded speech models for low-resource languages and cognitive modelling
(Stellenbosch : Stellenbosch University, 2024-12) Nortje, Aletta Susanna Elizabeth; Kamper, Herman; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
Visually grounded speech models (VGS) learn from unlabelled speech paired with images. Such models can be valuable to develop speech applications for low-resource languages lacking transcribed data, and understanding how humans acquire language since children learn speech from multimodal cues. This dissertation makes contributions to both of these areas. In the first part of this dissertation, we consider two research questions about using VGS models in low-resource language applications. The first research question asks: can we get a VGS model that can detect and localise a keyword depicted by an image within speech? For this, we propose a new task called visually prompted keyword localisation (VPKL). Here, an image depicting a keyword query should be detected in spoken utterances. A detected query should be localised within the utterance. To do VPKL, we modify a common VGS modelling approach using an acoustic and a vision network connected with a multimodal attention mechanism. On an artificial low-resource language, English, we find that using an ideal tagger to get training pairs outperforms a previous visual Bag-of-Words (BOW) model locating written keywords in spoken utterances. An actual visual tagger results in lower scores than the written keyword baseline. To do VPKL for a real low-resource language, we consider few-shot learning before returning to this problem. In the second research question, we ask if we can get a VGS model to learn words using only a few word-image pairs. We use an architecture similar to the VPKL model’s and combine it with a few-shot learning approach that can learn new classes from fewer natural word-image pairs. Using the few given word-image example pairs, new unsupervised word-image training pairs are mined from large unlabelled speech and image sets. Our approach outperforms an existing VGS few-shot model when the number of examples per class is small. As a result, we apply this approach to an actual low-resource language – Yorùbá. The Yorùbá few-shot model outperforms its English variant. From the few-shot progress we make, we return to the VPKL approach and propose a simpler model similar to our previous VPKL model. Here we assume we have access to a dataset consisting of spoken utterances paired with descriptive images. To mine speech-image training pairs for a keyword, we use a few spoken word examples of the keyword and compare them to the utterances in the dataset’s speech-image pairs. We found that this approach outperforms our previous approach on an English VPKL task and the visual BOW model that detects textual keywords in speech. As a result, we apply this approach to Yorùbá. Since the speech system in the pair mining scheme uses a model trained on English, the precision of the few-shot Yorùbá localisation model is low. However, the ground truth Yorùbá model outperforms the textual keyword localisation model applied to Yorùbá by large margins. In the second part of this dissertation, we ask two more research questions regarding the use of VGS models in computational cognitive studies. Our third research question considers whether a VGS model exhibits the mutual exclusivity (ME) bias which is a word learning constraint used by children. This bias states that a novel word belongs to an unknown object instead of a familiar one. To do this, we use our few-shot object and word learning model and generate a speech-image dataset containing spoken English word and image examples for a set of familiar and novel classes. The model is trained on the word-image pairs for the familiar classes. The model is then prompted with novel English spoken words and asked whether the words belong to unknown or familiar objects. All variants of the model exhibit the ME bias. A model that uses both self-supervised audio and vision initialisations has the strongest ME bias. This makes sense from a cognitive perspective since children are exposed to spoken language and visual stimuli in their surroundings when they begin using the ME bias. Various cognitive ME studies have considered the effect that factors such as multilingualism have on the ME bias. Since this effect has not yet been studied computationally, our fourth research question asks how multilingualism affects the ME bias exhibited by our VGS model. We extend the English ME dataset’s training set to contain spoken Dutch and French words for the familiar classes. We train a trilingual English-Dutch-French model and two bilingual models: an English-Dutch model and an English-French model. These multilingual models are compared to the monolingual English model of the previous research question. We find that the monolingual model has a weaker ME bias than multilingual models. This trend is opposite to the trends seen in children: monolingual children have a stronger ME bias than multilingual children. This study is preliminary and requires further investigation. In summary, we find that VGS models can be used to develop low-resource applications by using only a small set of ground truth examples. We also found that VGS models can
be used to computationally study the ME bias observed in children. Further investigation is required into the effect of multilingualism on the bias in VGS models and comparing it to the effect in children. We believe this dissertation has given enough proof of how valuable VGS models can be and will encourage research in this field to build inclusive speech technology and contribute to understanding human language learning.
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Developing a framework to inform staffing models for long- term care facilities in resource-constrained contexts
(Stellenbosch : Stellenbosch University, 2024-12) Nicholson, Emerentia Cynthia; Van der Heever, Mariana; Young, Cornelle; Van der Merwe, Anita; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Nursing and Midwifery.
Background: The exponential ageing of the world population and corresponding care needs indicate a need to plan for the long-term care of older people. With residents in long-term care facilities (LTCFs) having higher acuity levels, the health workforce must comprise adequate and suitable staff appropriately allocated to meet residents' needs despite scarce resources. However, LTCFs find it difficult to maintain adequate staffing levels and provide a skill mix to ensure quality resident care while staying within the framework of the nurses' scope of practice and caregivers' scope of work. This study focused on exploring the implementation of nurse and caregiver staffing models in LTCFs. The aim of the study was to develop a framework to inform staffing models for LTCFs in resource-constrained contexts.
Methods: The study was conducted in three phases from a critical realism perspective. Phase 1 included the concurrent completion of a scoping review and holistic multiple-case study. In the scoping review, four databases were searched using specific search terms, and the final sample comprised 20 studies. The holistic multiple-case study included a document review and interviews in one state-subsidised and one private for-profit LTCF in the Cape Metropole, South Africa. Purposive sampling was employed to select 45 documents for review and nineteen participants for semi-structured interviews in the two LTCFs. All the data was analysed through an inductive thematic analysis process. The scoping review and holistic multiple-case study findings were triangulated in Phase 2. In Phase 3, the triangulated data were used to develop a framework to inform staffing models for LTCFs in resource-limited contexts. Seven experts were purposefully selected, and they validated the framework.
Findings: More caregivers and fewer nurses were employed in the LTCFs. This led to fewer qualified nurses in the skill mix and shifting tasks to less qualified nurse categories and caregivers beyond their scope of practice and work scope. Consequently, caregivers provided most of the resident care, overburdening ths category. Staff allocation practices did not consider residents’ acuity levels. Thus, residents received the same basic care regardless of needing more skilled care. Managers, nurses, and caregivers seemed oblivious to the legal implications of working beyond a designated scope of practice or job scope or of failing to meet legal staffing requirements. Additional barriers to implementing a staffing model that influenced the nurses’ and caregivers’ wellbeing were overly harsh disciplinary measures, a lack of management support, managers’ verbal communication which suggested bullying, and the absence of staff meetings and in-service training.
Conclusion: The LTCFs implemented aspects of the prescribed staffing model by seemingly using a low-cost one. By over-employing caregivers but fewer nurses, vulnerable older persons were often deprived of care provided by more qualified staff and potentially higher-quality care than they were entitled to. This framework provides a roadmap for role players in LTCFs to ensure adherence to legal requirements, balance cost-effectiveness with quality resident care, and facilitate staff wellbeing.