Online Networks Research Articles

Engaging chemistry educators through virtual roundtables: how the COVID-19 pandemic led to a community-wide initiative

Members of the Interactive Online Network of Inorganic Chemists (IONiC) report the development and sustained efforts to provide virtual professional development sessions: Supporting Learning with Interactive Teaching: a Hosted, Engaging Roundtable (SLiThEr). SLiThEr’s emerged from a community need at the start of the COVID-19 pandemic; since then, SLiThEr’s have become a standard event for the IONiC community. SLiThEr’s are real- time virtual community events, after which a recording of the SLiThEr is posted on the IONiC YouTube channel. In this paper, an overview of the structure, format, and emergence of SLiThEr’s is reported, followed by evaluation of SLiThEr offerings, a plan for future SLiThEr offerings, and a discussion of how SLiThEr’s impact teaching practice.Graphical

Contextual Collapse Perspectives on the Socialisation Practices of Text-Pleasing Syndrome in Online Media Environment

The development of media technology has allowed different social contexts to be integrated into online networking platforms, and users face the breakdown and risk of uncontrolled social management in multiple contexts, which has given rise to a variety of new types of social interaction that are different from traditional offline face-to-face communication. The masses use softened language to maintain pluralistic online social relationships, and use ingratiating texts to achieve their performative purposes. The group power structure and the use of symbolic violence regulate the innovative development of the Internet context, and the self-alienation and the lack of media construct a new round of contextual collapse. Starting from the symbolic representation of text-pleasing syndrome, we explore its metaphorical multi-contextual collapse and the way to alleviate the social dilemma so as to construct a new context, and deeply consider the human-technology-society relationship triggered by media availability.

Affective polarization and dynamics of information spread in online networks

Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide—called affective polarization by political scientists—there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotions and toxicity of reply interactions. We demonstrate that, as predicted by affective polarization, interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks. Specifically, we show that emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to anger, disgust, and toxicity. Finally, we show that similar information exhibits different dynamics when spreading in emotionally polarized groups. These findings are consistent across diverse datasets spanning discussions on topics such as the COVID-19 pandemic and abortion in the US. Our research provides insights into the complex social dynamics of affective polarization in the digital age and its implications for political discourse.

Providing Evidence of Online Networking's Role in Political Education: A Case Study of INEC X Handle

Providing Evidence of Online Networking's Role in Political Education: A Case Study of INEC X Handle

Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis

The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.

Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension.

Patients with pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD) have an increased risk of disease exacerbation and decreased survival. We aimed to develop and validate a non-invasive nomogram for predicting COPD associated with severe PH and a prognostic nomogram for patients with COPD and concurrent PH (COPD-PH). This study included 535 patients with COPD-PH from six hospitals. A multivariate logistic regression analysis was used to analyse the risk factors for severe PH in patients with COPD and a multivariate Cox regression was used for the prognostic factors of COPD-PH. Performance was assessed using calibration, the area under the receiver operating characteristic curve and decision analysis curves. Kaplan-Meier curves were used for a survival analysis. The nomograms were developed as online network software. Tricuspid regurgitation velocity, right ventricular diameter, N-terminal pro-brain natriuretic peptide (NT-proBNP), the red blood cell count, New York Heart Association functional class and sex were non-invasive independent variables of severe PH in patients with COPD. These variables were used to construct a risk assessment nomogram with good discrimination. NT-proBNP, mean pulmonary arterial pressure, partial pressure of arterial oxygen, the platelet count and albumin were independent prognostic factors for COPD-PH and were used to create a predictive nomogram of overall survival rates. The proposed nomograms based on a large sample size of patients with COPD-PH could be used as non-invasive clinical tools to enhance the risk assessment of severe PH in patients with COPD and for the prognosis of COPD-PH. Additionally, the online network has the potential to provide artificial intelligence-assisted diagnosis and treatment. A multicentre study with a large sample of chronic obstructive pulmonary disease (COPD) patients diagnosed with PH through right heart catheterisation. A non-invasive online clinical tool for assessing severe pulmonary hypertension (PH) in COPD. The first risk assessment tool was established for Chinese patients with COPD-PH.

ELECTRONIC TEST SYSTEM

Abstract : The electronic test system (ETS) will assist in speeding up the process of conducting assessment. Examiner will be able to conduct exams by integrating a set of questions. The questions can be multiple choice questions or text solution to the questions. The methodology will have the potential to do automated processing of the results of assessment based on the question which admin has put into database. The methodology will have brainy capabilities to pick the writing based solutions when not only there is an exact match, but it will also find out same solutions by verifying/scanning the synonyms of the words used in the solution. The ETS methodology engine will also avail us with the manual override specification where the examiner can manually pick up or update the result for an exam. The methodology will present an effortless to use interface for examiner, Candidates and Super Admin. Any person can act as an examiner or candidate. Once logged in, candidate will be able to see the assessments due and can take the assessment.The main aim of online network based Electronic Test System is to effectively form an idea to assess the applicant exhaustively through a fully automatic methodology that not only spares you with a lot of time but also gives speeder and exact conclusions. For Candidates, they give exam according to their comfort from any location by using net and there is no essentiality of using add-ons like pen, papers, etc. Keywords: Examination, Examiner, Candidate, Super Admin. Manual Override, Automated System

Online learning deep neural network fuzzy control of structures under earthquake motions: numerical and experimental test

Online learning deep neural network fuzzy control of structures under earthquake motions: numerical and experimental test

Unauthentic Information About Celiac Disease on Social Networking Pages: Is It a Matter of Concern in Celiac Disease Management?

Facebook (FB) is the most popular online networking platform. Many celiac disease Facebook (CD-FB) pages spread awareness about celiac disease (CD). To get the latest information, patients with CD frequently follow such pages. However, little is known about whether such pages provide authentic and reliable information. This study aims to investigate whether CD-FB pages spread misleading information to patients with CD. On the Facebook social networking platform, CD-FB pages created in three celiac-prevalent countries (Italy, the USA, and India) were explored using different combinations of keywords. The type/category of the CD-FB page, country of origin, purpose, page web link, and number of followers/members were documented in a Microsoft spreadsheet. All posts distributed on selected CD-FB pages in the last 3 years were thoroughly screened. From August 2022 to March 2023, a total of 200 CD-FB pages from Italy, the USA, and India were explored. Out of these 200 pages, 155 CD-FB (Italy 70; the USA 46; India 39) were found eligible. Of them, 20 (13%) CD-FB pages (Italy 4; the USA 5; India 11) shared misleading information about CD. Surprisingly, 11 (8%) of these 20 pages (Italy 0; the USA 2; India 9) supported alternative treatment options for CD. CD-FB pages are useful for disseminating celiac-disease-related information. While most such pages provide useful information, 13% of CD-FB pages allow misleading information. Patients with CD should consult their treating unit before following any uncertain information posted on CD-FB pages.

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis.

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD). This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD. A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures. Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases. The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

Augmenting TikTok shop adoption: The mediating role of KOCs in live streaming, video content, and AI recommendations

There is a growing trend of social media networks transitioning into channels primarily focused on providing entertainment content. This research used the Uses and Gratifications (U&G) theory to examine the manner in which individuals transition from other platforms to TikTok with regards to their engagement in entertainment and self-publishing of videos. Key Opinion Consumers (KOCs) are individuals who provide evaluations from the consumer’s perspective. This emerging trend involves the creation of online review videos, which aligns with the evolving nature of e-commerce platforms. An online survey was administered to enlist a total of 251 individuals who are active users of the social media platform TikTok. The recruitment process included an online network sampling approach. The results of our study indicate that factors such as video lengths, live streaming, and Artificial Intelligence (AI) algorithms for recommendation have a substantial impact on the use of TikTok. The findings indicate that KOCs play a crucial role in mediating TikTok use patterns and uptake. This research enhances the comprehension of media use within the framework of uses and gratification, both in terms of theoretical and empirical aspects.

#837 Information needs and emotional impact when receiving a diagnosis of chronic kidney disease: insights from DISCOVER CKD

Abstract Background and Aims Chronic kidney disease (CKD) is often a silent condition, with symptoms typically not appearing until a large proportion of kidney function has been lost. Therapeutic interventions, e.g., disease-modifying medications, blood pressure and diabetes control, and lifestyle changes, can delay CKD progression, particularly if instigated early. However, psychological distress can reduce adherence to interventions. It is important patients are adequately informed about their diagnosis to improve adherence and reduce the impact of distress. Multinational, real-world data on patient experiences with CKD, particularly about clinical management, treatment patterns and quality of life, are limited. This DISCOVER CKD sub-study aimed to capture the emotions elicited by patients at CKD diagnosis and their perceptions of the information received. Method DISCOVER CKD (NCT04034992) is a multinational cohort study that aims tocharacterise the epidemiology of CKD and describe patient characteristics, CKD progression, clinical outcomes, the patient journey, practice patterns, and clinical management. The current analysis includes a subset of patients from the USA, UK, Japan, and Spain, who participated in 1-2-1 telephone interviews about their CKD experience, including their disease journey and interactions with the healthcare system. Interview topics in DISCOVER CKD were informed by a pilot study that utilised data from PatientsLikeMe, an online network for patients to share personal stories and health data. Qualitative interviews, conducted in the local language by trained interviewers, January–June 2023, lasted ∼60–90 minutes. Transcribed interviews were translated into English for coding (MAXQDA Plus 2022 v22.3.0) and analysis. The study received research ethics board approval and all participants provided written informed consent. Results 103 patients (mean age 63.1 years; 42.7% female; 51.5% with type 2 diabetes) were interviewed. Most had CKD stage 3A (22.3%) or 3B (28.2%); 26.2% had stage 4 or 5 no dialysis and 9.7% were stage 4 (n = 1) or 5 on dialysis. Average time since diagnosis was 9.5 years. Over one-third (38.8%; n = 40/103) of participants did not recall any specific symptoms or signs of CKD prior to diagnosis. Of the pre-diagnosis symptoms reported, the most common was fatigue (21.4%; n = 22/103), then high blood pressure (15.5%; n = 16/103) and swelling (11.7%; n = 12/103). With respect to patient experiences, 55.2% (n = 32/58) said they did not feel sufficient information about CKD was provided at diagnosis and 59.2% (n = 45/76) said no information about their CKD stage was shared. Participants felt overwhelmed and confused about the diagnostic process and struggled to understand the information provided to them, including information about kidney function and symptoms. To supplement their understanding, participants reported using additional sources of information, including the internet (52.9%; n = 36/68), other healthcare professionals (HCPs) (19.1%; n = 13/68), books/printed information (11.8%; n = 8/68) and patient support groups (8.8%; n = 6/68). The most common emotions experienced at the time of diagnosis were worry and fear (28.2%; n = 29/103), then shock/surprise (25.2%; n = 26/103) (Figure). For those reporting no reaction (27.2%; n = 28/103), many stated the condition had no discernible effect on their daily lives. Reasons for fear or worry were related to the potential for disease progression. Conclusion Many patients expressed dissatisfaction with the information they received at their CKD diagnosis. This suggests that HCPs could dedicate more time to explaining the diagnosis, the characteristics and severity of CKD, likelihood of progression, and the benefits of self-management and adherence to intervention. This could be particularly important for those without symptoms at the time of diagnosis, and patients who rely on HCPs as their primary source of information. Patients should be asked what information they want to receive and be directed to trusted sources. The diagnostic process was a highly emotional experience for many patients and counselling or psychological therapy should be considered.

Fandom in Action: Online Mobilisation of Thai Youth in the 2020-2021 Anti-Government Protests

The convergence of social media with political communication and the emergence of celebrity politics have significantly altered the landscape of political participation, particularly among the younger generation. Yet, comprehensive research on online political fandom remains limited despite its critical intersection with politics and digital media. This study addresses this gap, focusing on the in-depth case study of the 2020-2021 Thai anti-government protest. It scrutinises how tech-savvy Thai youth, propelled by political fandom surrounding Thanathorn Juangroongruangkit, a Future Forward Party opposition leader, harnessed social media and pre-existing online networks to organise these anti-government protests. Despite lacking a formal hierarchy, their organisational prowess, especially in information dissemination, highlights the intricate interplay of political fandom, social media, and youth mobilisation. By employing a trace interview method and conducting semi-structured interviews, the study explores how political fans transitioned from online engagement to active protest participation. The evidence contributes to a nuanced understanding of how fandom culture shapes their political judgment and behaviour. Participants in this study are political fans who publicly engage with politics on social media and participate in large-scale protests offline. They were recruited through a two-step process: first, the Twitter API identified users with high engagement in political fandom and anti-government hashtags. Then, a snowball sampling method extended the participant pool based on referrals from initial interviewees. A thematic analysis was conducted on the interview transcripts. The initial findings indicate that the influence of charismatic political figures, the empowerment of the fan community, and the belief that online political expression is ineffective draw participants toward offline participation. The resemblance between the political fandom of Thanathorn Juangroongruangkit and the more mainstream popular fandoms highlights the importance of considering the role of popular culture influences in shaping people’s political behaviours, particularly in contemporary digital communication technologies. In conclusion, this study aims to uncover the intricate relationship between political fandoms, social media, and offline activism and to emphasise the pervasive influence of popular culture on political behaviours in the digital age.

Design and Implementation Accurate Three Distance Sensors Device Using Neural Network

This paper suggested and created a proposed device for sensing an accurate distance from 5 to 400 cm with three sensors using the proposed on-line Feedforward Backpropagation Neural Network (FFBPN). Firstly, preparing a dataset (distances) with two distances utilizing Arduino and measured distance calculated from three sensors (2 ultrasonic and 1 IR). Secondly, target distances are calculated from the ground manually using a distance tape tool. Thirdly, interfacing Arduino and MATLAB using USB easily saved datasets from Arduino directly to MATLAB, then training, and testing data. The proposed device divides reading distances into three distances, getting from three sensors to get accurate distances. The first reading was 5-10 cm from down ultrasonic sensor 3. The second reading was 10-80 cm from middle IR sensor 2. The third reading was 80-400 cm from up ultrasonic sensor 1. The dataset was created and improved using the suggested FFBPN to create a very accurate distance gadget with three sensors. A suggested FFBPN consisted of 3 layers: The first input measured distance layer (from 3 sensors), the second hidden layer with ten neurons, and the third output layer distance. The numerical results showed that the proposed distance device was significantly accurate due to regression result R=1 using the proposed Neural Network (NN), which meanings that the device had 100% fitting. It had the best validation performance in 464 epochs, i.e., is 0.0036122. Furthermore, the proposed on-line FFBPN was fast because the training time equals three seconds. This distance device was implemented for use in robotic and radar applications to detect objects accurately because this device successfully detects near and far objects.

The Macrophage migration inhibitory factor is a vital player in Pan-Cancer by functioning as a M0 Macrophage biomarker

BackgroundThe role of the macrophage migration inhibitory factor (MIF) has recently attracted considerable attention in cancer research; nonetheless, the insights provided by current investigations remain constrained. Our main objective was to investigate its role and the latent mechanisms within the pan-cancer realm. MethodsWe used comprehensive pan-cancer bulk sequencing data and online network tools to investigate the association between MIF expression and patient prognosis, genomic instability, cancer cell stemness, DNA damage repair, and immune infiltration. Furthermore, we validated the relationship between MIF expression and M0 macrophages using single-cell datasets, the SpatialDB database, and fluorescence staining. Additionally, we assessed the therapeutic response using the ROC plotter tool. ResultsWe observed the upregulation of MIF expression across numerous cancer types. Notably, elevated MIF levels were associated with a decline in genomic stability. We found a significant correlation between increased MIF expression and increased expression of mismatch repair genes, stemness features, and homologous recombination genes across diverse malignancies. Subsequently, through an analysis using ESTIMATE and cytokine results, we revealed the involvement of MIF in immune suppression. Then, we validated MIF as a hallmark of the M0 macrophages involved in tumor immunity. Our study suggests an association with other immune-inhibitory cellular populations and restraint of CD8 + T cells. In addition, we conducted a comparative analysis of MIF expression before and after treatment in three distinct sets of therapy responders and non-responders. Intriguingly, we identified notable disparities in MIF expression patterns in bladder urothelial carcinoma and ovarian cancer following particular therapeutic interventions. ConclusionComprehensive pan-cancer analysis revealed notable enrichment of MIF within M0 macrophages, exerting a profound influence on tumor-associated immunosuppression and the intricate machinery of DNA repair.

Online identification and feed-forward compensation of nonlinear friction in servo system based on RBF neural network model

In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.

It’s not just a game: social networks, isolation and mental health in online gamers

PurposeOnline gaming has emerged as a popular activity providing a social outlet for millions. However, implications of online game networks for mental health remain disputed. Concepts of bridging social capital and bonding social capital may help characterize protective factors within social networks. This study aims to examine the associations between social capital derived from online versus in-person networks and mental health indicators among gamers.Design/methodology/approachOnline gamers (n = 301) completed an online survey assessing their social networks (both in-person and through online gaming) and mental health indicators (depressive symptoms, anxiety, social isolation, perceived social support). Social network analysis was used to analyze bridging (network size, effective size, heterogeneity, weak ties) and bonding (closeness, frequent contact, confiding, connection quality) social capital. Separate linear regression models evaluated associations between bridging and bonding social capital for both online and in-person networks and depressive symptoms, anxiety, social support and social isolation.FindingsIn-person network characteristics showed the strongest associations with mental health outcomes. Greater average closeness and frequent confiding in the in-person network predicted lower isolation and fewer depressive symptoms. More diverse relationship types also correlated with lower depression. For online networks, closeness and confiding ties associated only with less isolation and greater support, not depressive symptoms, or anxiety.Originality/valueWhile online gaming networks provide some degree of social support, in-person social capital exhibited stronger associations with mental health. This reinforces the importance of face-to-face relationships for emotional well-being. Findings suggest helping gamers cultivate close bonds offline. However, online connections still matter and should not be discounted.

The efficacy of digital platforms in the development and relevance of fashion business: Aftermath of COVID-19

The purpose of the study was to assess how well fashion companies were able to sustain themselves throughout the COVID-19 pandemic by leveraging internet platforms. In spite of the numerous disruptions the fashion industry has experienced as a result of the COVID-19 pandemic's effects on the workplace and the shift in consumer behaviour towards digital platforms, industry stakeholders think that digital platforms offer potential for the industry's revival because many fashion businesses were forced to close during the pandemic. The paper examines and summarises the extent to which digital platforms have been effective in fostering the long-term expansion of fashion companies and their influence on livelihoods in the COVID-19 pandemic, using a narrative literature approach. Based on productive interactions and transactions, digital platforms provide a 21st-century, cost-effective way for businesses to promote themselves. Prior to the pandemic, fashion companies were able to market their goods and services, track live events, keep an eye on trends and projections, and boost their online presence, all of which contributed to increased revenue. Notwithstanding the fact that they continue to face major obstacles that negatively impact the livelihoods of their employees, the study shows how digital platforms have been incredibly effective in helping fashion businesses, especially those in the United States, the United Kingdom, Europe, and Asia, to endure and flourish during COVID-19. Although African customers have become accustomed to online shopping and e-commerce, digital platforms have not been especially popular throughout the continent, as fashion designers and businesses have not properly utilised them. In order to promote fashion businesses and designers, governments and policymakers should support the development of digital infrastructure and digital literacy initiatives. Additionally, fashion brands and businesses in the supply chain should improve their knowledge and understanding of how consumers and potential clients communicate and transact in their online networks. Finally, fashion businesses and designers should invest in social media sentiment analysis tools to facilitate effective online visibility, seamless interactions, and transactions with consumers.

A sparse quantized hopfield network for online-continual memory

An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local learning algorithms and are trained in an offline, non-noisy, independent, identically distributed setting. Understanding how neural networks learn under the same constraints as the brain is an open problem for neuroscience and neuromorphic computing. A standard approach to this problem has yet to be established. In this paper, we propose that discrete graphical models that learn via an online maximum a posteriori learning algorithm could provide such an approach. We implement this kind of model in a neural network called the Sparse Quantized Hopfield Network. We show our model outperforms state-of-the-art neural networks on associative memory tasks, outperforms these networks in online, continual settings, learns efficiently with noisy inputs, and is better than baselines on an episodic memory task.

Abstract PO1-12-07: Denosumab vs. Pamidronate in the Management of Osteolytic Bone Metastases Secondary to Breast Cancer, a Multi-Institutional Analysis

Abstract Breast cancer is one of the most common cancers in women and is associated with poor outcomes due to the metastatic potential of high-grade tumors. One common site of breast cancer metastasis is to the bone, leading to serious adverse effects such as bone pain, bone fracture, and spinal cord compression. Denosumab, a monoclonal antibody against the receptor activator of nuclear factor kappa-b (RANK) ligand (RANKL), and Pamidronate, a bisphosphonate, are two pharmacologic agents offered for the treatment of these adverse complications. The goal of this study was to conduct a retrospective cohort analysis across multiple institutions to compare the efficacy of Denosumab vs. Pamidronate in the management of osteolytic bone metastases secondary to breast cancer by evaluating the risk of adverse events. TriNetX, an online network that provides access to anonymized medical records for more than 108 million patients in 72 large healthcare organizations, was used in this study. Two cohorts, consisting of 847 patients with a diagnosis of bone metastases and breast cancer, were created and subsequently matched for age, race, gender, and ethnicity. One cohort was given Denosumab while the other was given Pamidronate, and each cohort was excluded from receiving the other medication. The occurrence of pathologic fracture in each arm, spinal cord compression, and overall five-year survival rate was measured and analyzed. The results of this study showed that Denosumab had a statistically insignificant risk reduction in both pathological fracture (2.7% vs. 2.8%, p = 0.88) and spinal cord compression (2.6% vs. 2.7%, p = 0.88) and a statistically insignificant difference in the overall five-year survival rate (45.5% vs. 52.4%, p = 0.78) when compared to Pamidronate. As a result, additional factors such as financial cost, route of administration, dosing schedule, and potential adverse side effects should be discussed with each individual who requires pharmacological therapy for bone metastases secondary to breast cancer. Citation Format: Megan Bradley, Mabry Nichols, Brittany Miles. Denosumab vs. Pamidronate in the Management of Osteolytic Bone Metastases Secondary to Breast Cancer, a Multi-Institutional Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-12-07.