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Abstract Therapeutic resistance presents a significant hurdle in combating inflammatory breast cancer (IBC), adding to the complexity of its management. To investigate these mechanisms, we conducted a comprehensive analysis using transcriptomic and proteomic profiling in a preclinical model alone with correlates of treatment response in IBC patients. This included SUM149 cell lines derived from treatment-naïve patients, along with acquired drug resistance (rSUM149) and others in a state of resistance reversal (rrSUM149), aiming to uncover drug resistance networks. We identified specific ribosomal proteins associated with acquiring resistance. These correlated with elevated levels of molecular markers such as pERK, CDK1, XIAP, and SOD2. While resistance reversal in rrSUM149 cells largely normalized the expression profile, VIPER analysis revealed persistent alterations in ribosomal process-related proteins (AGO2, Exportin 1, RPL5), suggesting their continued involvement in drug resistance. Moreover, genes linked to ribosomal processes were significantly enriched (P

Abstract The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

Objective: To assess the expression pattern of X-linked inhibitor of apoptosis protein (XIAP), a cellular stress sensor, and delineate the associated changes in the tumor immune microenvironment (TiME) for prognostic value and new therapeutic targets in inflammatory breast cancer (IBC). Methods: Immunohistochemistry was conducted to assess the spatial localization of immune subsets, XIAP, and PDL1 expression in IBC and non-inflammatory breast cancer (nIBC) pretreatment tumors (n = 142). Validation and further exploration were performed by gene expression analysis of patient tumors along with signaling studies in a co-culture model. Results: High XIAP in 37/81 IBC patients correlated significantly with high PD-L1, increased infiltration of FOXP3+ Tregs, CD163+ tumor-associated macrophages (TAMs), low CD8/CD163 ratio in both tumor stroma (TS) and invasive margins (IM), and higher CD8+ T cells and CD79α+ B cells in the IM. Gene set enrichment analysis identified cellular stress response- and inflammation-related genes along with tumor necrosis factor receptor 1 (TNFR1) expression in high-XIAP IBC tumors. Induction of TNFR1 and XIAP was observed when patient-derived SUM149 IBC cells were co-cultured with human macrophage-conditioned media simulating TAMs, further demonstrating that the TNF-α signaling pathway is a likely candidate governing TAM-induced XIAP overexpression in IBC cells. Finally, addition of Birinapant, a pan IAP antagonist, induced cell death in the pro-survival cytokine-enriched conditions. Conclusion: Using immunophenotyping and gene expression analysis in patient biospecimens along with in silico modeling and a preclinical model with a pan-IAP antagonist, this study revealed an interplay between increased TAMs, TNF-α signaling, and XIAP activation during (immune) stress in IBC. These data demonstrate the potential of IAP antagonists as immunomodulators for improving IBC therapeutic regimens.

Abstract Individual environmental contaminants have been associated with breast cancer; however, evaluations of multiple exposures simultaneously are limited. Herein, we evaluated associations between breast cancer summary stages and the Environmental Quality Index (EQI), which includes a range of environmental factors across five domains. The EQI (2000–2005) was linked to county-level age-standardized incidence rates (SIRs) obtained from the North Carolina Central Cancer Registry (2010–2014). Incidence rates and SIRs of total, in situ, localized, regional, and distant breast cancers were evaluated stratified by rural–urban status. In counties with poor environmental quality compared to those with good environmental quality, total breast cancer incidence was higher by 10.82 cases per 100,000 persons (95% CI 2.04, 19.60, p = 0.02). This association was most pronounced for localized breast cancer (β = 5.59, 95% CI 0.59, 10.58, p = 0.03). Higher incidence of early-stage disease (carcinoma in situ β = 5.25, 95% CI 2.34, 8.16, p = 0.00 and localized breast cancer β = 6.98, 95% CI 2.24, 11.73, p = 0.00) and total breast cancer (β = 11.44, 95% CI 3.01, 19.87, p = 0.01) occurred in counties with poor land quality, especially urban counties. Our analyses indicate significant associations between environmental quality and breast cancer incidence, which differ by breast cancer stage and urbanicity, identifying a critical need to assess cumulative environmental exposures in the context of cancer stage.