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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.

The purpose of this study, based in the United States, was to evaluate knowledge gaps and barriers related to diagnosis and care of inflammatory breast cancer (IBC), a rare but lethal breast cancer subtype, amongst Primary Care Providers (PCP) as they are often the first point of contact when patients notice initial symptoms. PCP participants in the Duke University Health System, federally qualified health center, corporate employee health and community practices, nearby academic medical center, Duke physician assistant and advanced practice nurse leadership program alumni were first selected in a convenience sample and for semi-structured interviews (n = 11). Based on these data, an online survey tool was developed and disseminated (n = 78) to assess salient measures of IBC diagnosis, health disparity factors, referral and care coordination practices, COVID-19 impact, and continuing medical education (CME). PCP reported access to care and knowledge gaps in symptom recognition (mean = 3.3, range 1–7) as major barriers. Only 31 % reported ever suspecting IBC in a patient. PCP (n = 49) responded being challenged with referral delays in diagnostic imaging. Additionally, since the COVID-19 pandemic started, 63 % reported breast cancer referral delays, and 33 % reported diagnosing less breast cancer. PCP stated interest in CME in their practice for improved diagnosis and patient care, which included online (53 %), lunch time or other in-service training (33 %), patient and provider-facing websites (32 %). Challenges communicating rare cancer information, gaps in confidence in diagnosing IBC, and timely follow-up with patients and specialists underscores the need for developing PCP educational modules to improve guideline-concordant care.

Abstract Aggressive breast cancer variants, like triple negative and inflammatory breast cancer, contribute to disparities in survival and clinical outcomes among African American (AA) patients compared to White (W) patients. We previously identified the dominant role of anti-apoptotic protein XIAP in regulating tumor cell adaptive stress response (ASR) that promotes a hyperproliferative, drug resistant phenotype. Using The Cancer Genome Atlas (TCGA), we identified 46–88 ASR genes that are differentially expressed (2-fold-change and adjusted p-value

Abstract Inflammatory breast cancer (IBC) is an aggressive disease for which the spectrum of preclinical models was rather limited in the past. More recently, novel cell lines and xenografts have been developed. This study evaluates the transcriptome of an extended series of IBC preclinical models and performed a comparative analysis with patient samples to determine the extent to which the current models recapitulate the molecular characteristics of IBC observed clinically. We demonstrate that the IBC preclinical models are exclusively estrogen receptor (ER)-negative and of the basal-like subtype, which reflects to some extent the predominance of these subtypes in patient samples. The IBC-specific 79-signature we previously reported was retrained and discriminated between IBC and non-IBC preclinical models, but with a relatively high rate of false positive predictions. Further analyses of gene expression profiles revealed important roles for cell proliferation, MYC transcriptional activity, and TNFɑ/NFκB in the biology of IBC. Patterns of MYC expression and transcriptional activity were further explored in patient samples, which revealed interactions with ESR1 expression that are contrasting in IBC and nIBC and notable given the comparatively poor outcomes of ER+ IBC. Our analyses also suggest important roles for NMYC, MXD3, MAX, and MLX in shaping MYC signaling in IBC. Overall, we demonstrate that the IBC preclinical models can be used to unravel cancer cell intrinsic molecular features, and thus constitute valuable research tools. Nevertheless, the current lack of ER-positive IBC models remains a major hurdle, particularly since interactions with the ER pathway appear to be relevant for IBC.

North Carolina Central University (NCCU) and Duke Cancer Institute implemented an NCI-funded Translational Cancer Disparities Research Partnership to enhance translational cancer research, increase the pool of underrepresented racial and ethnic group (UREG) researchers in the translational and clinical research workforce, and equip UREG trainees with skills to increase diversity in clinical trials. The Cancer Research Education Program (C-REP) provided training for UREG graduate students and postdoctoral fellows at Duke and NCCU. An innovative component of C-REP is the Translational Immersion Experience (TIE), which enabled Scholars to gain knowledge across eight domains of clinical and translational research (clinical trials operations, data monitoring, regulatory affairs, UREG accrual, biobanking, community engagement, community outreach, and high-throughput drug screening). Program-specific evaluative metrics were created for three broad domains (clinical operations, basic science/lab research, and population-based science) and eight TIE domains. Two cohorts (n = 13) completed pre- and post-surveys to determine program impact and identify recommendations for program improvement. Scholars reported statistically significant gains in knowledge across three broad domains of biomedical research and seven distinct areas within TIE. Training in translational research incorporating immersions in clinical trials operation, biobanking, drug development, and community engagement adds value to career development of UREG researchers.

IntroductionThe Duke Multidisciplinary Education and Research in Translational Sciences Program provides educational resources for faculty and trainees in translational research.MethodsTo aid in program development, we assessed perceptions of translational science through focus groups targeting different career stages.ResultsIn total, 3 essential themes emerged: collaboration, movement toward application, and public health impact. Facilitators and barriers varied among groups.ConclusionTraining programs must provide specific strategies for collaboration and selectively accelerating discoveries to therapies.

The ability to predict the behaviour of polymeric nanomedicines can often be obfuscated by subtle modifications to the corona structure, such as incorporation of fluorophores or other entities. However, these interactions provide an intriguing insight into how selection of molecular components in multifunctional nanomedicines contributes to the overall biological fate of such materials. Here, we detail the internalisation behaviours of polymeric nanomedicines across a suite of cell types and extrapolate data for distinguishing the underlying mechanics of cyanine-5-driven interactions as they pertain to uptake and endosomal escape. By correlating the variance of rate kinetics with endosomal escape efficiency and endogenous lipid polarity, we identify that observed cell-type dependencies correspond with an underlying susceptibility to dye-mediated effects and nanomedicine accumulation within polar vesicles. Further, our results infer that the ability to translocate endosomal membranes may be improved in certain cell types, suggesting a potential role for diagnostic moieties in trafficking of drug-loaded nanocarriers.

Introduction: Acinetobacter causes deadly hospital acquired infections. This is study was planned to identify the clinical and resistance profile, outcome and mortality predictors of acinetobacter bacteremia in a tertiary care centre in north Kerala. Methods: retrospective observational study. Data obtained from case records, microbiology culture reports and by interviewing primary care team of each patient Result: A total of 90 cases positive blood culture reports were collected and required data obtained from 50. 27 cases were analyzed. Resistance was found to be respectively 92% to cephalosporin, 77% to ciprofloxacin and 41% to amikacin. 38% isolates were multidrug resistant. Acinetobacter bacteremia was mostly due to VAP, CLABSI, severe skin infections and neutropenic sepsis. Conclusion: Acinetobacter bacteremia has an overall mortality rate of 44%, with 75% mortality in VAP presentation. 38% of acinetobacter isolates were multidrug resistant. Most of the patients had malignancies, Diabetes Mellitus or cirrhosis as risk factors.

A Givens rotation based scalable QRD core which utilizes an efficient pipelined and unfolded 2D multiply and accumulate (MAC) based systolic array architecture with dynamic partial reconfiguration (DPR) capability is proposed. The square root and inverse square root operations in the Givens rotation algorithm are handled using a modified look-up table (LUT) based Newton-Raphson method, thereby reducing the area by 71% and latency by 50% while operating at a frequency 49% higher than the existing boundary cell architectures. The proposed architecture is implemented on Xilinx Virtex-6 FPGA for any real matrices of size m×n, where 4≤n≤8 and m≥n by dynamically inserting or removing the partial modules. The evaluation results demonstrate a significant reduction in latency, area, and power as compared to other existing architectures. The functionality of the proposed core is evaluated for a variable length adaptive equalizer.

The forkhead transcription factor FOXP3 is necessary for induction of regulatory T lymphocytes (Tregs) and their immunosuppressive function. We have previously demonstrated that targeting Tregs by vaccination of mice with murine FOXP3 mRNA-transfected dendritic cells (DCs) elicits FOXP3-specific T cell responses and enhances tumor immunity. It is clear that FOXP3 expression is not restricted to T-cell lineage and herein, using RT-PCR, flow cytometry, and western immunoblot we demonstrate for the first time that FOXP3 is expressed in inflammatory breast cancer (IBC) cells, SUM149 (triple negative, ErbB1-activated) and SUM190 (ErbB2-overexpressing). Importantly, FOXP3-specific T cells generated in vitro using human FOXP3 RNA-transfected DCs as stimulators efficiently lyse SUM149 cells. Interestingly, an isogenic model (rSUM149) derived from SUM149 with an enhanced anti-apoptotic phenotype was resistant to FOXP3-specific T cell mediated lysis. The MHC class I cellular processing mechanism was intact in both cell lines at the protein and transcription levels suggesting that the resistance to cytolysis by rSUM149 cells was not related to MHC class I expression or to the MHC class I antigen processing machinery in these cells. Our data suggest that FOXP3 may be an effective tumor target in IBC cells however increased anti-apoptotic signaling can lead to immune evasion.