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1. MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training

Author

Li, Chengyin, Zhu, Hui, Sultan, Rafi Ibn, Ebadian, Hassan Bagher, Khanduri, Prashant, Indrin, Chetty, Thind, Kundan, and Zhu, Dongxiao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomography (CT) scans and Magnetic Resonance (MR) images. This heterogeneity challenges automatic segmentation algorithms to maintain consistent performance across different modalities due to the requirement for spatially aligned and paired images. Typically, segmentation models are trained using a single modality, which limits their ability to generalize to other types of input data without employing transfer learning techniques. Additionally, leveraging complementary information from different modalities to enhance segmentation precision often necessitates substantial modifications to popular encoder-decoder designs, such as introducing multiple branched encoding or decoding paths for each modality. In this work, we propose a simple Multi-Modal Segmentation (MulModSeg) strategy to enhance medical image segmentation across multiple modalities, specifically CT and MR. It incorporates two key designs: a modality-conditioned text embedding framework via a frozen text encoder that adds modality awareness to existing segmentation frameworks without significant structural modifications or computational overhead, and an alternating training procedure that facilitates the integration of essential features from unpaired CT and MR inputs. Through extensive experiments with both Fully Convolutional Network and Transformer-based backbones, MulModSeg consistently outperforms previous methods in segmenting abdominal multi-organ and cardiac substructures for both CT and MR modalities. The code is available in this {\href{https://github.com/ChengyinLee/MulModSeg_2024}{link}}., Comment: Accepted by WACV-2025

Published
2024

2. Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-based VNF Deployments

Author

Chetty, Swarna Bindu, Nag, Avishek, Al-Tahmeesschi, Ahmed, Wang, Qiao, Canberk, Berk, Marquez-Barja, Johann, and Ahmadi, Hamed

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into micro VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated.

Published
2024

3. The Impact of COVID-19 on the Working Environment of Organisations

Author

Laran Chetty

Abstract

This article will review the impact of COVID-19 on the working environment of organisations by critically analysing the following factors: (a) Employee engagement and recruitment; (b) Talent management; and (c) Organisational culture and employees. Communication was identified as an important feature that had to be adapted because of COVID-19. Numerous organisations have developed creative and innovated practices to ensure employee engagement as they begin to experience the impact of COVID-19. The world economy has reduced substantially because of COVID-19. The pandemic changed the way in which work was done. The value and purpose of the performance management systems goes beyond the typical administrative functions such as personnel decisions, salary and promotions, it also provides critical functions that help organisations reach their goals. To embrace the digital world of work and to build new processes it is important for the organisation to adequately invest and prepare the workforce for the incoming changes. As governments all over the world begin to reduce COVID-19 restrictions and people must now start living with the virus, organisations had to also start thinking about what models of work would work best for their business and their employees.

Published
2024

4. How Compliant Are Physiotherapists Affiliated to Occupational Health Services with Clinical Audit Practice?

Author

Laran Chetty

Abstract

The purpose of this project was to determine the extent to which physiotherapists affiliated to occupational health services undertake the full clinical audit cycle. A sample of convenience was used to select physiotherapists that were in attendance over two conference days. Those who verbally consented to take part were asked to complete a survey and place it in an envelope which was immediately sealed to maintain confidentiality of responses. The majority of physiotherapists undertook clinical audit practice (94%). The common frequency of clinical audits was annually (77%). Most physiotherapists did not contribute their clinical audit outcomes into any quality improvement initiatives (42%). Occupational health services should encourage physiotherapists affiliated to them to identify and link their outcomes into broader quality improvement initiatives so that the full audit cycle can be completed.

Published
2024

5. Fetal Brain MRI Findings in Myotonic Dystrophy and Considerations for Prenatal Genetic Testing.

Author

Shear, Matthew, Penon-Portmann, Monica, Shieh, Joseph, Glenn, Orit, Thiet, Mari-Paule, Chetty, Shilpa, Sparks, Teresa, and Gano, Dawn

Abstract

BACKGROUND: Congenital myotonic dystrophy type 1 (DM1) is a rare congenital neuromuscular disorder associated with high morbidity and potential early mortality requiring lifelong symptomatic management. Prenatal presentations of DM1 have been associated with nonspecific ultrasound findings such as clubbed foot, polyhydramnios, ventriculomegaly, and decreased fetal movement, but many cases of DM1 have no ultrasound anomalies. METHODS: We sought to compare the clinical course and prenatal imaging findings in two cases of DM1 using retrospective chart review. RESULTS: This report demonstrates potential expansion of the prenatal phenotype of DM1 including fetal SVT and frontal bossing. Both cases shared unique prenatal imaging features of lateral ventricle dilation involving the anterior bodies and frontal horns on fetal MRI. DISCUSSION: Because congenital DM1 is most often maternally inherited, attention to maternal symptoms, physical examination, and family history can be helpful in recognizing cases. Molecular diagnosis of DM1 requires specialized testing of the 3 untranslated region of the DMPK gene, and DM1 will not be detected by current standard prenatal genetic testing with microarray, karyotype, or exome sequencing.

Published
2024

6. Energy Saving in 6G O-RAN Using DQN-based xApp

Author

Wang, Qiao, Chetty, Swarna, Al-Tahmeesschi, Ahmed, Liang, Xuanyu, Chu, Yi, and Ahmadi, Hamed

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized framework in academia and industry. In the context of Open RAN, the importance of Energy Saving (ES) is heightened, especially with the current direction of network densification in sixth generation of mobile networks (6G). Traditional energy-saving methods in RAN struggle with the increasing dynamics of the network. This paper proposes using Reinforcement Learning (RL), a subset of Machine Learning (ML), to improve ES. We present a novel deep RL method for ES in 6G O-RAN, implemented as xApp (ES-xApp). We developed two Deep Q-Network (DQN)-based ES-xApps. ES-xApp-1 uses RSS and User Equipment (UE) geolocations, while ES-xApp-2 uses only RSS. The proposed models significantly outperformed heuristic and baseline xApps, especially with over 20 UEs. With 50 UEs, 50% of Radio Cards (RCs) were switched off, compared to 17% with the heuristic algorithm. We have observed that more informative inputs may lead to more stable training and results. This paper highlights the necessity of energy conservation in wireless networks and offers practical strategies and evidence for future research and industry practices., Comment: accepted in CAMAD 2024

Published
2024

7. Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA)

Author

Tran, Van Hong, Mehrotra, Aarushi, Sharma, Ranya, Chetty, Marshini, Feamster, Nick, Frankenreiter, Jens, and Strahilevitz, Lior

Subjects
Computer Science - Human-Computer Interaction
Abstract

To protect consumer privacy, the California Consumer Privacy Act (CCPA) mandates that businesses provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy over the opt-out process allows them to impose hurdles on consumers aiming to opt out, including by employing dark patterns. Motivated by the enactment of the California Privacy Rights Act (CPRA), which strengthens the CCPA and explicitly forbids certain dark patterns in the opt-out process, we investigate how dark patterns are used in opt-out processes and assess their compliance with CCPA regulations. Our research reveals that websites employ a variety of dark patterns. Some of these patterns are explicitly prohibited under the CCPA; others evidently take advantage of legal loopholes. Despite the initial efforts to restrict dark patterns by policymakers, there is more work to be done.

Published
2024

8. Diffusion Model-based Contrastive Learning for Human Activity Recognition

Author

Xiao, Chunjing, Han, Yanhui, Yang, Wei, Hou, Yane, Shi, Fangzhan, and Chetty, Kevin

Subjects
Computer Science - Networking and Internet Architecture
Abstract

WiFi Channel State Information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in practical applications, general CSI-based recognition models may face challenges related to the limited generalization capability, since individuals with different behavior habits will cause various fluctuations in CSI data and it is difficult to gather enough training data to cover all kinds of motion habits. To tackle this problem, we design a diffusion model-based Contrastive Learning framework for human Activity Recognition (CLAR) using WiFi CSI. On the basis of the contrastive learning framework, we primarily introduce two components for CLAR to enhance CSI-based activity recognition. To generate diverse augmented data and complement limited training data, we propose a diffusion model-based time series-specific augmentation model. In contrast to typical diffusion models that directly apply conditions to the generative process, potentially resulting in distorted CSI data, our tailored model dissects these condition into the high-frequency and low-frequency components, and then applies these conditions to the generative process with varying weights. This can alleviate data distortion and yield high-quality augmented data. To efficiently capture the difference of the sample importance, we present an adaptive weight algorithm. Different from typical contrastive learning methods which equally consider all the training samples, this algorithm adaptively adjusts the weights of positive sample pairs for learning better data representations. The experiments suggest that CLAR achieves significant gains compared to state-of-the-art methods., Comment: The paper has been accepted by IEEE Internet of Things Journal

Published
2024
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9. Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction

Author

Khanmohammadi, Reza, Ghanem, Ahmed I., Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Luo, Bing, Chetty, Indrin J., Alhanai, Tuka, Thind, Kundan, and Ghassemi, Mohammad M.

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computational limitations, and operational costs. This study investigates the optimization of compact LLMs for cancer toxicity symptom extraction using a novel iterative refinement approach. We employ a student-teacher architecture, utilizing Zephyr-7b-beta and Phi3-mini-128 as student models and GPT-4o as the teacher, to dynamically select between prompt refinement, Retrieval-Augmented Generation (RAG), and fine-tuning strategies. Our experiments on 294 clinical notes covering 12 post-radiotherapy toxicity symptoms demonstrate the effectiveness of this approach. The RAG method proved most efficient, improving average accuracy scores from 0.32 to 0.73 for Zephyr-7b-beta and from 0.40 to 0.87 for Phi3-mini-128 during refinement. In the test set, both models showed an approximate 0.20 increase in accuracy across symptoms. Notably, this improvement was achieved at a cost 45 times lower than GPT-4o for Zephyr and 79 times lower for Phi-3. These results highlight the potential of iterative refinement techniques in enhancing the capabilities of compact LLMs for clinical applications, offering a balance between performance, cost-effectiveness, and privacy preservation in healthcare settings.

Published
2024

10. PANDORA: Deep graph learning based COVID-19 infection risk level forecasting

Author

Yu, Shuo, Xia, Feng, Wang, Yueru, Li, Shihao, Febrinanto, Falih, and Chetty, Madhu

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Physics - Physics and Society
Abstract

COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measurable actions based on the pandemic's severity to minimize the detrimental impact of COVID-19. A proper forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relations and transportation frequency as higher-order structural properties formulated by higher-order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline method with higher accuracy and faster convergence speed, no matter which aggregator is chosen. We believe that PANDORA using deep graph learning provides a promising approach to get superior performance in infection risk level forecasting and help humans battle the COVID-19 crisis.

Published
2024

11. Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment

Author

Liang, Xuanyu, Al-Tahmeesschi, Ahmed, Wang, Qiao, Chetty, Swarna, Sun, Chenrui, and Ahmadi, Hamed

Subjects
Electrical Engineering and Systems Science - Systems and Control, Electrical Engineering and Systems Science - Signal Processing
Abstract

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions., Comment: 6 pages, 4 figures

Published
2024

12. Continuous Transfer Learning for UAV Communication-aware Trajectory Design

Author

Sun, Chenrui, Fontanesi, Gianluca, Chetty, Swarna Bindu, Liang, Xuanyu, Canberk, Berk, and Ahmadi, Hamed

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires significant retraining when the UAV is confronted with a new environment, resulting in wasted resources and time. Therefore, it is essential to develop techniques that can reduce the overhead of retraining DRL models, enabling them to adapt to constantly changing environments. This paper presents a novel method to reduce the need for extensive retraining using a double deep Q network (DDQN) model as a pretrained base, which is subsequently adapted to different urban environments through Continuous Transfer Learning (CTL). Our method involves transferring the learned model weights and adapting the learning parameters, including the learning and exploration rates, to suit each new environment specific characteristics. The effectiveness of our approach is validated in three scenarios, each with different levels of similarity. CTL significantly improves learning speed and success rates compared to DDQN models initiated from scratch. For similar environments, Transfer Learning (TL) improved stability, accelerated convergence by 65%, and facilitated 35% faster adaptation in dissimilar settings., Comment: 6 pages

Published
2024

13. 'Community Guidelines Make this the Best Party on the Internet': An In-Depth Study of Online Platforms' Content Moderation Policies

Author

Schaffner, Brennan, Bhagoji, Arjun Nitin, Cheng, Siyuan, Mei, Jacqueline, Shen, Jay L., Wang, Grace, Chetty, Marshini, Feamster, Nick, Lakier, Genevieve, and Tan, Chenhao

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Social and Information Networks
Abstract

Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. Each platform has thus developed bespoke content moderation policies, but there is little work towards a comparative understanding of these policies across platforms and topics. This paper presents the first systematic study of these policies from the 43 largest online platforms hosting user-generated content, focusing on policies around copyright infringement, harmful speech, and misleading content. We build a custom web-scraper to obtain policy text and develop a unified annotation scheme to analyze the text for the presence of critical components. We find significant structural and compositional variation in policies across topics and platforms, with some variation attributable to disparate legal groundings. We lay the groundwork for future studies of ever-evolving content moderation policies and their impact on users.

Published
2024
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14. Digital Twin Generators for Disease Modeling

Author

Alam, Nameyeh, Basilico, Jake, Bertolini, Daniele, Chetty, Satish Casie, D'Angelo, Heather, Douglas, Ryan, Fisher, Charles K., Fuller, Franklin, Gomes, Melissa, Gupta, Rishabh, Lang, Alex, Loukianov, Anton, Mak-McCully, Rachel, Murray, Cary, Pham, Hanalei, Qiao, Susanna, Ryapolova-Webb, Elena, Smith, Aaron, Theoharatos, Dimitri, Tolwani, Anil, Tramel, Eric W., Vidovszky, Anna, Viduya, Judy, and Walsh, Jonathan R.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world.

Published
2024

19. Measuring Compliance with the California Consumer Privacy Act Over Space and Time

Author

Tran, Van, Mehrotra, Aarushi, Chetty, Marshini, Feamster, Nick, Frankenreiter, Jens, and Strahilevitz, Lior

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

The widespread sharing of consumers personal information with third parties raises significant privacy concerns. The California Consumer Privacy Act (CCPA) mandates that online businesses offer consumers the option to opt out of the sale and sharing of personal information. Our study automatically tracks the presence of the opt-out link longitudinally across multiple states after the California Privacy Rights Act (CPRA) went into effect. We categorize websites based on whether they are subject to CCPA and investigate cases of potential non-compliance. We find a number of websites that implement the opt-out link early and across all examined states but also find a significant number of CCPA-subject websites that fail to offer any opt-out methods even when CCPA is in effect. Our findings can shed light on how websites are reacting to the CCPA and identify potential gaps in compliance and opt-out method designs that hinder consumers from exercising CCPA opt-out rights.

Published
2024
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20. Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models

Author

Khanmohammadi, Reza, Ghanem, Ahmed I, Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Chetty, Indrin, Ghassemi, Mohammad M., and Thind, Kundan

Subjects
Computer Science - Computation and Language
Abstract

This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mixtral, the student model, initially extracts symptoms, followed by GPT-4, the teacher model, which refines prompts based on Mixtral's performance. This iterative process involved 294 single symptom clinical notes across 12 symptoms, with up to 16 rounds of refinement per epoch. Results showed significant improvements in extracting symptoms from both single and multi-symptom notes. For 59 single symptom notes, accuracy increased from 0.51 to 0.71, precision from 0.52 to 0.82, recall from 0.52 to 0.72, and F1 score from 0.49 to 0.73. In 375 multi-symptom notes, accuracy rose from 0.24 to 0.43, precision from 0.6 to 0.76, recall from 0.24 to 0.43, and F1 score from 0.20 to 0.44. These results demonstrate the effectiveness of advanced prompt engineering in LLMs for radiation oncology use.

Published
2024

21. Designing and Testing a Mobile Application for Collecting WhatsApp Chat Data While Preserving Privacy

Author

Schaffner, Brennan, Brohn, Archie, Chee, Jason, Feng, K. J., and Chetty, Marshini

Subjects
Computer Science - Human-Computer Interaction
Abstract

It is common practice for researchers to join public WhatsApp chats and scrape their contents for analysis. However, research shows collecting data this way contradicts user expectations and preferences, even if the data is effectively public. To overcome these issues, we outline design considerations for collecting WhatsApp chat data with improved user privacy by heightening user control and oversight of data collection and taking care to minimize the data researchers collect and process off a user's device. We refer to these design principles as User-Centered Data Sharing (UCDS). To evaluate our UCDS principles, we implemented a mobile application representing one possible instance of these improved data collection techniques and evaluated the viability of using the app to collect WhatsApp chat data. Second, we surveyed WhatsApp users to gather user perceptions on common existing WhatsApp data collection methods as well as UCDS methods. Our results show that we were able to glean similar informative insights into WhatsApp chats using UCDS principles in our prototype app to common, less privacy-preserving methods. Our survey showed that methods following the UCDS principles are preferred by users because they offered users more control over the data collection process. Future user studies could further expand upon UCDS principles to overcome complications of researcher-to-group communication in research on WhatsApp chats and evaluate these principles in other data sharing contexts.

Published
2024

30. Safety and pharmacokinetics of subcutaneous administration of broadly neutralizing anti-HIV-1 monoclonal antibodies (bNAbs), given to HIV-1 exposed, uninfected neonates and infants: study protocol for a phase I trial

Author

Goga, Ameena, Ramraj, Trisha, Naidoo, Logashvari, Daniels, Brodie, Matlou, Masefetsane, Chetty, Terusha, Dassaye, Reshmi, Ngandu, Nobubelo K., Galli, Laura, Reddy, Tarylee, Seocharan, Ishen, Ndlangamandla, Qondeni, September, Qholokazi, Ngcobo, Nokwanda, Reddy, Mayuri, Cafun-Naidoo, Tamon, Woeber, Kubashni, Jeenarain, Nitesha, Imamdin, Rabia, Maharajh, Keshnee, Ramjeth, Ashmintha, Bhengu, Thobile, Clarence, Emma, Van de Perre, Philippe, Tylleskär, Thorkild, Nagot, Nicolas, Moles, Jean-Pierre, Moore, Penny L., Mkhize, Nonhlanhla N., Gama, Lucio, Dispinseri, Stefania, Biswas, Priscilla, and Scarlatti, Gabriella

Published
2024
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34. A hybrid digital parenting programme to prevent abuse of adolescents in Tanzania: study protocol for a pragmatic cluster-randomised controlled trial

Author

Baerecke, Lauren, Ornellas, Abigail, Wamoyi, Joyce, Wambura, Mwita, Klapwijk, Jonathan, Chetty, Angelique N., Simpson, Ashlin, Janowski, Roselinde, de Graaf, Kristen, Stern, David, Clements, Lily, te Winkel, Esmee, Christine, Laetitia, Mbosoli, Gervas, Nyalali, Kija, Onduru, Onduru Gervas, Booij, Anna, Mjwara, Sussie N., Tsoanyane, Sibongile, Mshana, Gerry, Mwakitalu, Mbutolwe Esther, Melendez-Torres, G. J., Calderon, Francisco, Awah, Isang, Green, Ohad, Vallance, Inge, Somefun, Oluwaseyi, Gardner, Frances, Sherr, Lorraine, Martin, Mackenzie, Lachman, Jamie M., and Cluver, Lucie D.

Published
2024
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36. Environmental impact assessment of alternative technologies for production of biofuels from spent coffee grounds

Author

Wilberforce Kisiga, Manimagalay Chetty, and Sudesh Rathilal

Subjects
biofuels, environmental impact, LCA, life‐cycle analysis, Technology, Science
Abstract

Abstract In the strategy to combat climate change that has been caused by the world's overdependence on fossil fuels, current research is focusing on the decarbonisation of the energy sector through the production of renewable cleaner energy, such as biofuels. Spent coffee grounds (SCGs), the waste stream of the coffee brewing industry, are a potential feedstock for the production of valuable products, including biofuels. However, the environmental implications for the valorisation of this valuable waste need to be investigated. This study assesses the environmental impacts of six biomass‐to‐fuel processing technologies using SCGs as a feedstock, with the aim of identifying the most environmentally friendly technology. A cradle‐to‐gate life‐cycle assessment (LCA) was conducted on fast pyrolysis, fermentation, anaerobic digestion (AD), hydrothermal liquefaction (HTL), gasification, and biodiesel production. The mass and energy balances obtained from Aspen Plus simulations served as the life‐cycle inventory data. Using the ReCiPe 2016 midpoint (H) and Eco‐Indicator 99 as the assessment methods, potential environmental impacts were calculated in OpenLCA software. Electricity generation and carbon dioxide emissions were the biggest contributors of environmental impacts. For each category, the maximum result was set to 100% and the results of the other variants were displayed in relation to this result. AD, with the smallest total weighted score (160), was the most environmentally friendly biomass‐to‐fuel processing route, while HTL, with the biggest total weighted score (893), was the worst. A sensitivity analysis indicated that the environmental performance of biofuel production from SCGs was highly influenced by energy input flows and the source of energy generation.

Published
2024
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37. Evaluating the therapeutic potential of different sources of mesenchymal stem cells in acute respiratory distress syndrome

Author

S. Regmi, A. Ganguly, S. Pathak, R. Primavera, S. Chetty, J. Wang, Shaini Patel, and A. S. Thakor

Subjects
Mesenchymal stem cells, Acute respiratory distress syndrome, Umbilical cord, Inflammation, Immune responses, Medicine (General), R5-920, Biochemistry, QD415-436
Abstract

Abstract Background Mesenchymal stem/stromal cells (MSCs) have attracted interest as a potential therapy given their anti-inflammatory and immunomodulatory properties. However, clinical trials using MSCs for acute respiratory distress syndrome (ARDS) have produced mixed and inconclusive data. In previous work, we performed a “head-to-head” comparison between different sources of MSCs and showed that each source had a unique genomic and proteomic “signature”. Method This study investigated which sources of MSC: bone marrow derived-MSCs (BM-MSCs), adipose tissue derived-MSCs (AD-MSCs) and umbilical cord derived-MSCs (UC-MSCs) would be the optimal candidate to be used as a therapy in an LPS-induced mouse model of ARDS. Immune cells assessment, tissue transcriptomics, animal survival, and endothelial-epithelial barrier assessment were used to evaluate their effects. Results When comparing the three most commonly used MSC sources, we found that UC-MSCs exhibited greater efficacy compared to other MSCs in improving animal survival, mitigating epithelial/endothelial damage, decreasing lung inflammation via reducing neutrophil infiltration, T cell proliferation, and M1 polarization. Bulk RNA sequencing of lung tissue also showed that UC-MSCs have the capability to downregulate extracellular trap formation, by the downregulation of key genes like Elane and Padi4. Notably, treatment with UC-MSCs demonstrated a significant reduction in Fc-γ R mediated phagocytosis, which has been associated with monocyte pyroptosis and intense inflammation in the context of COVID-19. Conclusion Our findings suggest that UC-MSCs are an optimal source of MSC to treat acute inflammatory conditions in the lungs, such as ARDS.

Published
2024
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38. EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features

Author

Chowtapalle Anuraag Chetty, Harsha Bhardwaj, G. Pradeep Kumar, T. Devanand, C. S. Aswin Sekhar, Tuba Aktürk, Ilayda Kiyi, Görsev Yener, Bahar Güntekin, Justin Joseph, and Chinnakkaruppan Adaikkan

Subjects
EEG-based biomarker, Theta-alpha power ratio, Gamma, Eyes closed EEG, Slowing of oscillations, Pairwise phase consistency, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Biomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals. Methods We collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity. Results Spectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features. Conclusions Our findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Published
2024
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39. Development of intelligent suite for malaria pathogen detection in microscopy images

Author

Anand Koirala, Meena Jha, Girija Chetty, Srinivas Bodapati, Animesh Mishra, Praveen Kishore Sahu, Sanjib Mohanty, Timir Kanta Padhan, Ajat Hukkoo, and Jyoti Mattoo

Subjects
Medical, Software, Automation, Medicine, Science
Abstract

Abstract The identification of malaria infection using microscope images of blood smears is considered as a ‘gold standard’. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using ‘cvui’ library to interact with the OpenVINO’s inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported.

Published
2024
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41. Women's Social Capital and Social Cohesion in Kerala: The Case of Kudumbashree

Author

Bhoola, Sheetal, Chetty, Dasarath, Chathukulam, Jos, Ngcobo, Nolwazi, Moolakkattu, and John

Subjects
women, kerala, kudumbashree, social capital, social cohesion, Business, HF5001-6182
Abstract

This paper examines the claim that the Kudumbashree women of Kerala, built around microfinance but now a key actor in Kerala’s economic, social, and welfare landscape, constitute a form of social capital and contributes to social cohesion. The study is based on desk research based on the existing studies supplemented by interviews with key informants and focus group discussions. An overview of Kerala’s society and politics, the rise of inclusive neighbourhood movements as precursors of the Kudumbashree and the functions that the poverty-focused and state-initiated Kudumbashree women’s program is discussed. It then discusses the communal situation in Kerala and how stiff competition between the dominant communities have operated without violence and how the sporadic communal conflicts are contained through accommodation and public disapproval. It then concludes with a critical assessment of the role of Kudumbashree as a form of social capital primarily and as a force for social cohesion.

Published
2024

42. Disease Burden Associated with All Infants in Their First RSV Season in the UK: A Static Model of Universal Immunization with Nirsevimab Against RSV-Related Outcomes

Author

Alexia Kieffer, Matthieu Beuvelet, Gerald Moncayo, Mersha Chetty, Aditya Sardesai, Robert Musci, and Richard Hudson

Subjects
Economics, Cost-effectiveness, Model, Lower respiratory tract disease, Nirsevimab, RSV, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Introduction Respiratory syncytial virus (RSV) leads to significant morbidity in newborn infants in the United Kingdom (UK). Nirsevimab, a long-acting monoclonal antibody, received approval from the European Medicines Agency and has been licensed by the Medicines and Healthcare products Regulatory Agency for preventing RSV lower respiratory tract disease (LRTD) in neonates and infants during their first RSV season. The objective of this study was to assess the potential impact of nirsevimab on RSV-associated LRTDs, related costs, and loss of quality-adjusted life years (QALYs) in infants experiencing their first RSV season. Methods The impact of administering nirsevimab across all infant populations compared to palivizumab in the high-risk palivizumab-eligible population was assessed via a static decision-analytic model specified for a UK birth cohort experiencing their first RSV season. The RSV-related health events of interest included primary care (PC), accident and emergency (A&E) visits, hospitalizations [including hospitalizations alone and those resulting in intensive care unit (ICU) admissions], recurrent wheezing in infants who were previously hospitalized, and all-cause LRTD hospitalizations. Results Under the current standard of practice (SoP), RSV was estimated to result in 329,425 RSV LRTDs annually, including 24,381 hospitalizations and ICU admissions, representing £117.8 million (2024 GBP) in costs. Comparatively, universal immunization of all infants with nirsevimab could avoid 198,886 RSV LRTDs, including 16,657 hospitalizations and ICU admissions, resulting in savings of £77.2 million in RSV treatment costs. Considering the impact on all-cause LRTD of a universal immunization strategy, nirsevimab could be valued between £243 and £274, assuming willingness-to-pay (WTP) thresholds of £20,000 and £30,000 per QALY saved, respectively. Conclusions This analysis demonstrated that the health and economic burden of RSV would be substantially reduced in all infants experiencing their first RSV season in the UK (including term, preterm, and palivizumab-eligible infants) as a result of a universal immunization strategy with nirsevimab.

Published
2024
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43. Real-world treatment outcomes for Hodgkin lymphoma in South Africa: a prospective observational study

Author

Samantha L. Vogt, Garrick Laudin, Marianna Zahurak, Jenifer Vaughan, Atul Lakha, Sugeshnee Pather, Ziyaad Waja, Deshan Chetty, Tanvier Omar, Wendy Stevens, Philippa Ashmore, Kennedy Otwombe, Khuthadzo Hlongwane, Ravi Varadhan, Moosa Patel, Richard F. Ambinder, Neil A. Martinson, Rena R. Xian, and Vinitha Philip

Subjects
HIV, Hodgkin lymphoma, Tuberculosis, Bone marrow involvement, Overall survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Prospective data from sub-Saharan Africa suggests that treatment outcomes for people living with HIV (PWH) with Hodgkin lymphoma (HL) are similar to those without HIV. However, real-world data from high-resource settings and retrospective studies from sub-Saharan Africa, suggest inferior outcomes. We set out to evaluate the real-world treatment outcomes for HL in South Africa to better understand the disparate outcomes. Methods We established a prospective, observational cohort of newly diagnosed, adult (≥ 18 years) HL cases recruited from Chris Hani Baragwanath Academic and Netcare Olivedale Hospitals in Johannesburg, South Africa between March 2021 and March 2023. Participants were followed for up to 18 months after enrollment with data censored on December 23rd, 2023. The primary endpoint was 1-year overall survival. Results We enrolled 47 participants with HL including 31 PWH and 16 HIV-negative. Advanced stage disease and B symptoms were common at time of diagnosis irrespective of HIV status. Bone marrow biopsy, performed during the work-up and evaluation of cytopenias, provided the initial diagnosis of HL in 16/31 (52%) PWH. HIV status and bone marrow involvement were associated with early mortality (within 3 months of diagnosis) and a poorer 1-year overall survival from diagnosis (HIV: 55% vs. 88%; p = 0.03; bone marrow involvement: 50% vs. 80%; p = 0.02). Among evaluable participants, those that received at least 6 cycles of chemotherapy and underwent response assessment, there was no difference between those with and without HIV. Conclusion Traditional laboratory markers of poor prognosis including anemia, lymphopenia and hypoalbuminemia were more common among PWH and those with bone marrow involvement and suggest high risk disease. A better understanding of the drivers of these aggressive presentations is warranted to ensure more PWH are able to tolerate chemotherapy.

Published
2024
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44. Innovate! Accelerate! Evaluate! Harnessing the RE-AIM framework to examine the global dissemination of parenting resources during COVID-19 to more than 210 million people

Author

Jamie M. Lachman, Nisso Nurova, Angelique Nicole Chetty, Zuyi Fang, Alison Swartz, Lorraine Sherr, Helen Mebrahtu, Kasonde Mwaba, Ohad Green, Isang Awah, Yuanling Chen, Inge Vallance, and Lucie Cluver

Subjects
RE-AIM framework, Implementation science, Parenting, COVID-19, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Parents were at the forefront of responding to the needs of children during the COVID-19 pandemic. This study used the RE-AIM framework to examine the Reach, Effectiveness, Adoption, Implementation, and Maintenance of a global inter-agency initiative that adapted evidence-based parenting programs to provide immediate support to parents. Methods Data were collected via short surveys sent via email, online surveys, and analysis of social media metrics and Google Analytics. Retrospective surveys with 1,303 parents and caregivers in 11 countries examined impacts of the resources on child maltreatment, positive relationship building, parenting efficacy, and parenting stress. Results The parenting resources were translated into over 135 languages and dialects; reached an estimated minimum 212.4 million people by June 2022; were adopted by 697 agencies, organizations, and individuals; and were included in 43 national government COVID-19 responses. Dissemination via social media had the highest reach (n = 144,202,170, 67.9%), followed by radio broadcasts (n = 32,298,525, 15.2%), text messages (n = 13,565,780, 6.4%), and caseworker phone calls or visits (n = 8,074,787, 3.8%). Retrospective surveys showed increased parental engagement and play, parenting self-efficacy, confidence in protecting children from sexual abuse, and capacity to cope with stress, as well as decreased physical and emotional abuse. Forty-four organizations who responded to follow-up surveys in April 2021 reported sustained use of the resources as part of existing services and other crisis responses. Conclusion This study highlights the importance of a) establishing an international collaboration to rapidly adapt and disseminate evidence-based content into easily accessible resources that are relevant to the needs of parents; b) creating open-source and agile delivery models that are responsive to local contexts and receptive to further adaptation; and c) using the best methods available to evaluate a rapidly deployed global emergency response in real-time. Further research is recommended to empirically establish the evidence of effectiveness and maintenance of these parenting innovations.

Published
2024
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45. Experimental Investigation into Deploying a Wi-Fi6 Mesh System for Underground Gold and Platinum Mine Stopes

Author

Brenton Lloyd Chetty, Tom Mmbasu Walingo, Carel Phillip Kruger, and Sherrin John Isaac

Subjects
B.A.T.M.A.N., HWMP, jitter, latency, multipath, OpenThread, Mining engineering. Metallurgy, TN1-997
Abstract

Stopes suffer from unreliable wireless communication due to their harsh environment. There is a lack of confidence within industry regarding the effectiveness of existing solutions in providing reliable high-bandwidth performance in hard rock stopes. This work proposes that Wi-Fi6 is a good candidate for reliable high-bandwidth communications in underground hard rock stopes. Experiments in a tunnel and mine stope were conducted to evaluate the performance of Wi-Fi6 in terms of latency, jitter, and throughput. Different criteria, such as multi-hop systems, varying multipath, mesh routing protocols, and frequencies at different bandwidths, were used to evaluate performance. The results show that Wi-Fi6 performance is greater in stopes compared to tunnels. Signal quality evaluations were conducted using the Asus RT-AX53U running OpenWrt, and an additional experiment was conducted on the nrf7002dk running Zephyr OS to evaluate the power consumption of Wi-Fi6 against the industry standard for low-powered wireless communications, IEEE 802.15.4. Wi-Fi6 was found to be more power-efficient than IEEE 802.15.4 for Mbps communications. These experiments highlight the signal robustness of Wi-Fi6 in stope environments and also highlights its low-powered nature. This work also highlights the performance of the two most widely used open-source mesh routing protocols for Wi-Fi.

Published
2024
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47. Time-course analysis of cisplatin induced AKI in preclinical models: implications for testing different sources of MSCs

Author

Abantika Ganguly, Shashank Chetty, Rosita Primavera, Steven Levitte, Shobha Regmi, Benjamin William Dulken, Scott M. Sutherland, Wendy Angeles, Jing Wang, and Avnesh S. Thakor

Subjects
Acute kidney injury, Cisplatin, Mesenchymal Stem Cells, Cell therapy, Transcriptomics, Temporal profile, Medicine
Abstract

Abstract Background Kidneys are at risk from drug-induced toxicity, with a significant proportion of acute kidney injury (AKI) linked to medications, particularly cisplatin. Existing cytoprotective drugs for cisplatin-AKI carry side effects, prompting a search for better biological therapies. Mesenchymal Stem Cells (MSCs) are under consideration given their regenerative properties, yet their clinical application has not achieved their full potential, mainly due to variability in the source of MSC tested. In addition, translating treatments from rodent models to humans remains challenging due to a lack of standardized dosing and understanding potential differential responses to cisplatin between animal strains. Method In the current study, we performed a time-course analysis of the effect of cisplatin across different mouse strains and evaluated gender related differences to create a robust preclinical model that could then be used to explore the therapeutic efficacy of different sources of MSCs for their ability to reverse AKI. Result Our data indicated that different mouse strains produce differential responses to the same cisplatin dosing regimen. Despite this, we did not observe any gender-related bias towards cisplatin nephrotoxicity. Furthermore, our time-course analysis identified that cisplatin-induced inflammation was driven by a strong CXCL1 response, which was used as a putative biomarker to evaluate the comparative therapeutic efficacy of different MSC sources in reversing AKI. Our data indicates that UC-MSCs have a stronger anti-inflammatory effect compared to BM-MSCs and AD-MSCs, which helped to ameliorate cisplatin-AKI. Conclusion Overall, our data underscores the importance of using an optimized preclinical model of cisplatin-AKI to test different therapies. We identified CXCL1 as a potential biomarker of cisplatin-AKI and identified the superior efficacy of UC-MSCs in mitigating cisplatin-AKI. Graphical abstract

Published
2024
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48. Safety and pharmacokinetics of subcutaneous administration of broadly neutralizing anti-HIV-1 monoclonal antibodies (bNAbs), given to HIV-1 exposed, uninfected neonates and infants: study protocol for a phase I trial

Author

Ameena Goga, Trisha Ramraj, Logashvari Naidoo, Brodie Daniels, Masefetsane Matlou, Terusha Chetty, Reshmi Dassaye, Nobubelo K. Ngandu, Laura Galli, Tarylee Reddy, Ishen Seocharan, Qondeni Ndlangamandla, Qholokazi September, Nokwanda Ngcobo, Mayuri Reddy, Tamon Cafun-Naidoo, Kubashni Woeber, Nitesha Jeenarain, Rabia Imamdin, Keshnee Maharajh, Ashmintha Ramjeth, Thobile Bhengu, Emma Clarence, Philippe Van de Perre, Thorkild Tylleskär, Nicolas Nagot, Jean-Pierre Moles, Penny L. Moore, Nonhlanhla N. Mkhize, Lucio Gama, Stefania Dispinseri, Priscilla Biswas, Gabriella Scarlatti, and the PedMAb1 clinical trial team

Subjects
HIV, Broadly neutralizing antibody, Vertical transmission of HIV-1, Vertical transmission, Breastfeeding, Pre-exposure prophylaxis, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The ambitious goal to eliminate new pediatric HIV infections by 2030 requires accelerated prevention strategies in high-risk settings such as South Africa. One approach could be pre-exposure prophylaxis (PrEP) with broadly neutralizing anti-HIV-1 monoclonal antibodies (bNAbs). The aim of our study is to define the optimal dose(s), the ideal combination(s) of bNAbs in terms of potency and breadth, and timing of subcutaneous (SC) administration(s) to prevent breast milk transmission of HIV. Methods Two bNAbs, CAP256V2LS and VRC07-523LS, will be assessed in a sequential and randomized phase I, single-site, single-blind, dose-finding trial. We aim to investigate the 28-day safety and pharmacokinetics (PK) profile of incrementally higher doses of these bNAbs in breastfeeding HIV-1 exposed born without HIV neonates alongside standard of care antiretroviral (ARV) medication to prevent (infants) or treat (mothers) HIV infection. The trial design includes 3 steps and 7 arms (1, 2, 3, 4, 5, 6 and 6b) with 8 infants in each arm. The first step will evaluate the safety and PK profile of the bNAbs when given alone as a single subcutaneous (SC) administration at increasing mg/kg body weight doses within 96 h of birth: arms 1, 2 and 3 at doses of 5, 10, and 20 mg/kg of CAP256V2LS, respectively; arms 4 and 5 at doses of 20 and 30 mg/kg of VRC07-523LS, respectively. Step two will evaluate the safety and PK profile of a combination of the two bNAbs administered SC at fixed doses within 96 h of birth. Step three will evaluate the safety and PK profile of the two bNAbs administered SC in combination at fixed doses, after 3 months. Arms 1 and 6 will follow sequential recruitment, whereas randomization will occur sequentially between arms (a) 2 & 4 and (b) 3 & 5. Before each randomization, a safety pause will allow review of safety data of the preceding arms. Discussion The results of this trial will guide further studies on bNAbs to prevent breast milk transmission of HIV. Protocol version Version 4.0 dated 15 March 2024. Trial registration Pan African Clinical Trial Registry (PACTR): PACTR202205715278722, 21 April 2022; South African National Clinical Trial Registry (SANCTR): DOH-27–062022-6058.

Published
2024
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49. A hybrid digital parenting programme to prevent abuse of adolescents in Tanzania: statistical analysis plan for a pragmatic cluster randomised controlled trial

Author

Jonathan Klapwijk, G. J. Melendez-Torres, Abigail Ornellas, Mwita Wambura, Angelique N. Chetty, Lauren Baerecke, Joyce Wamoyi, and Lucie D. Cluver

Subjects
Child abuse, Parenting, Adolescents, Digital, Low- and middle-income countries, Violence against children, Medicine (General), R5-920
Abstract

Abstract Background Globally, violence against children poses substantial health and economic challenges, with estimated costs nearing USD 7 trillion. This prompts the urgent call for effective evidence-based interventions in preventing and mitigating violence against children. ParentApp is a mobile, open-source application designed to offer a remote version of the Parenting for Lifelong Health (PLH) programme. ParentApp is the first digital parenting intervention for caregivers of adolescents aged 10–17 years to be tested in low- and middle-income settings. Methods This study is a pragmatic, two-arm, cluster-randomised trial in Mwanza, Tanzania’s urban and peri-urban areas. Assessments are set for baseline, 1 month post-intervention, and 12 months post-intervention. We randomised 80 clusters, each with about 30 caregiver-adolescent dyads, with a 1:1 ratio stratified by urban or peri-urban location. Both arms receive an entry-level smartphone preloaded with Kiswahili apps—ParentApp for intervention and WashApp control. The primary method of analysis will be generalised linear mixed-effects models with adjustment for person-level characteristics and multiple imputation. In three-level models, measurement waves are nested within a person, nested within a sub-ward. Regressions will constrain groups to be equal at baseline and include covariates for stratification, percentage of male caregivers, and individual-level characteristics. Discussions Preparations for the trial began in December 2022, including community mobilisation and sensitisation. Rolling recruitment, baseline data collection, and implementation onboarding took place between April and September 2023. One-month post-test data collection began in August 2023 and thus far achieved 97% and 94% retention rates for caregivers and adolescents respectively. Final post-test data collection will begin in September 2024, anticipated to run until April 2025. This SAP was submitted to the journal before the interim analysis to preserve scientific integrity under a superiority hypothesis testing framework. Trial registration The trial was registered on the Open Science Framework on 14 March 2023: https://doi.org/10.17605/OSF.IO/T9FXZ . The trial protocol was published in Trials 25, 119 (2024): Baerecke, L., Ornellas, A., Wamoyi, J. et al. A hybrid digital parenting programme to prevent abuse of adolescents in Tanzania: study protocol for a pragmatic cluster-randomised controlled trial. Trials 25, 119 (2024). https://doi.org/10.1186/s13063-023-07893-x .

Published
2024
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50. Multi-omic approaches for host-microbiome data integration

Author

Ashwin Chetty and Ran Blekhman

Subjects
Multiomics, microbiome, network, disease, analysis, inference, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

ABSTRACTThe gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Published
2024
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