Overexpressing S100A9 ameliorates NK cell dysfunction in estrogen receptor-positive breast cancer.

BACKGROUND: Estrogen receptor (ER) positive human epidermal growth factor receptor 2 (HER2) negative breast cancer (ER+/HER2-BC) and triple-negative breast cancer (TNBC) are two distinct breast cancer molecular subtypes, especially in tumor immune microenvironment (TIME). The TIME of TNBC is considered to be more inflammatory than that of ER+/HER2-BC. Natural killer (NK) cells are innate lymphocytes that play an important role of tumor eradication in TME. However, studies focusing on the different cell states of NK cells in breast cancer subtypes are still inadequate. METHODS: In this study, single-cell mRNA sequencing (scRNA-seq) and bulk mRNA sequencing data from ER+/HER2-BC and TNBC were analyzed. Key regulator of NK cell suppression in ER+/HER2-BC, S100A9, was quantified by qPCR and ELISA in MCF-7, T47D, MDA-MB-468 and MDA-MB-231 cell lines. The prognosis predictability of S100A9 and NK activation markers was evaluated by Kaplan-Meier analyses using TCGA-BRAC data. The phenotype changes of NK cells in ER+/HER2-BC after overexpressing S100A9 in cancer cells were evaluated by the production levels of IFN-gamma, perforin and granzyme B and cytotoxicity assay. RESULTS: By analyzing scRNA-seq data, we found that multiple genes involved in cellular stress response were upregulated in ER+/HER2-BC compared with TNBC. Moreover, TLR regulation pathway was significantly enriched using differentially expressed genes (DEGs) from comparing the transcriptome data of ER+/HER2-BC and TNBC cancer cells, and NK cell infiltration high/low groups. Among the DEGs, S100A9 was identified as a key regulator. Patients with higher expression levels of S100A9 and NK cell activation markers had better overall survival. Furthermore, we proved that overexpression of S100A9 in ER+/HER2-cells could improve cocultured NK cell function. CONCLUSION: In conclusion, the study we presented demonstrated that NK cells in ER+/HER2-BC were hypofunctional, and S100A9 was an important regulator of NK cell function in ER+BC. Our work contributes to elucidate the regulatory networks between cancer cells and NK cells and may provide theoretical basis for novel drug development.

BPIFB1 promotes metastasis of hormone receptor-positive breast cancer via inducing macrophage M2-like polarization.

Metastasis is an important factor affecting the prognosis of hormone receptor-positive breast cancer (BC). However, the molecular basis for migration and invasion of tumor cells remains poorly understood. Here, we identify that bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1), which plays an important role in innate immunity, is significantly elevated in breast cancer and associated with lymph node metastasis. High expression of BPIFB1 and its coding mRNA are significantly associated with poor prognosis of hormone receptor-positive BC. Using enrichment analysis and constructing immune infiltration evaluation, we predict the potential ability of BPIFB1 to promote macrophage M2 polarization. Finally, we demonstrate that BPIFB1 promotes the metastasis of hormone receptor-positive BC by stimulating the M2-like polarization of macrophages via the establishment of BC tumor cells/THP1 co-culture system, qPCR, Transwell assay, and animal experiments. To our knowledge, this is the first report on the role of BPIFB1 as a tumor promoter by activating the macrophage M2 polarization in hormone receptor-positive breast carcinoma. Together, these results provide novel insights into the mechanism of BPIFB1 in BC.

Survival Outcomes of Breast-Conserving Therapy versus Mastectomy in Early-Stage Breast Cancer, Including Centrally Located Breast Cancer: A SEER-Based Study.

Purpose: This study aims to analyze the survival outcomes of breast cancer (BC) patients, especially centrally located breast cancer (CLBC) patients undergoing breast-conserving therapy (BCT) or mastectomy. Methods: Surveillance, epidemiology, and end results (SEER) data of patients with T1-T2 invasive ductal or lobular breast cancer receiving BCT or mastectomy were reviewed. We used X-tile software to convert continuous variables to categorical variables. Chi-square tests were utilized to compare baseline information. The multivariate logistic regression model was performed to evaluate the relationship between predictive variables and treatment choice. Survival outcomes were visualized by Kaplan-Meier curves and cumulative incidence function curves and compared using multivariate analyses, including the Cox proportional hazards model and competing risks model. Propensity score matching was performed to alleviate the effects of baseline differences on survival outcomes. Result: A total of 180,495 patients were enrolled in this study. The breast preservation rates fluctuated around 60% from 2000 to 2015. Clinical features including invasive ductal carcinoma (IDC), lower histologic grade, smaller tumor size, fewer lymph node metastases, positive ER and PR status, and chemotherapy use were independently correlated with BCT in both BC and CLBC cohorts. In all the classic Cox models and competing risks models, BCT was an independent favorable prognostic factor for BC, including CLBC patients in most subgroups. In addition, despite the low breast-conserving rate compared with tumors located in the other areas, CLBC did not impair the prognosis of BCT patients. Conclusion: BCT is optional and preferable for most early-stage BC, including CLBC patients.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

Morphological dissection and cellular and transcriptome characterizations of bamboo pith cavity formation reveal a pivotal role of genes related to programmed cell death.

Pith cavity formation is critical for bamboo to overcome the bending force during its fast growth; however, the underlying molecular mechanisms remain largely unknown. Multiple approaches, including anatomical dissection, mathematical modelling and transcriptome profiling, were employed in this study to investigate the biology of pith cavity formation in bamboo Pseudosasa japonica. We found that the corruption of pith tissue occurred sequentially and asymmetrically from the top-centre of the internode down to the bottom, which might be caused by the combined effects of asymmetrical radial and axial tensile forces during shoot-wall cell elongation and spiral growth of bamboo internodes. Programmed cell death (PCD) in pitch manifested by TUNEL positive nuclei, DNA cleavage and degraded organelles, and potentially regulated by ethylene and calcium signalling pathway, ROS burst, cell wall modification, proteolysis and nutrient recycle genes, might be responsible for pith tissue corruption of Ps. japonica. Although similar physiological changes and transcriptome profiles were found in different bamboo species, different formation rates of pith cavity were observed, which might be caused by different pith cells across the internode that were negatively correlated with the culm diameter. These findings provided a systematical view on the formation of bamboo pith cavity and revealed that PCD plays an important role in the bamboo pith cavity formation.

Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.

A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.

Multi-level Supervision and Modification of Artificial Pancreas Control System.

Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

Exploring key cellular processes and candidate genes regulating the primary thickening growth of Moso underground shoots.

The primary thickening growth of Moso (Phyllostachys edulis) underground shoots largely determines the culm circumference. However, its developmental mechanisms remain largely unknown. Using an integrated anatomy, mathematics and genomics approach, we systematically studied cellular and molecular mechanisms underlying the growth of Moso underground shoots. We discovered that the growth displayed a spiral pattern and pith played an important role in promoting the primary thickening process of Moso underground shoots and driving the evolution of culms with different sizes among different bamboo species. Different with model plants, the shoot apical meristem (SAM) of Moso is composed of six layers of cells. Comparative transcriptome analysis identified a large number of genes related to the vascular tissue formation that were significantly upregulated in a thick wall variant with narrow pith cavity, mildly spiral growth, and flat and enlarged SAM, including those related to plant hormones and those involved in cell wall development. These results provide a systematic perspective on the primary thickening growth of Moso underground shoots, and support a plausible mechanism resulting in the narrow pith cavity, weak spiral growth but increased vascular bundle of the thick wall Moso.

Optimal Base Station Density of Dense Network: From the Viewpoint of Interference and Load.

Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

Identification of a PANoptosis-related Gene Signature for Predicting the Prognosis, Tumor Microenvironment and Therapy Response in Breast Cancer.

Breast cancer (BC) is the most prevalent malignancy among women worldwide. Mounting evidence suggests that PANoptosis participates in cancer development and therapy. However, the role of PANoptosis in BC remains unclear. In this study, we identified ten PANoptosis-related genes using Cox regression analysis, random forest (RF) algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. A PANoptosis-related score (PRS) was calculated based on the coefficient of LASSO. Notably, we divided the patients into high- and low-risk groups according to the PRS and revealed a negative correlation between PRS and overall survival. Next, a nomogram model was constructed and validated to improve the clinical application of PRS. Functional enrichment analyses and the Bayesian network demonstrated that differentially expressed genes between high- and low-risk groups were mainly enriched in immune-related pathways. Besides, we found significant differences in tumor mutation burden and tumor immune microenvironment between patients in these two groups using bulk-RNA and single-cell RNA sequencing data. Furthermore, charged multivesicular body protein 2B (CHMP2B) was identified as the hub gene by combining LASSO, weighted gene co-expression network analysis, RF and eXtreme Gradient Boosting. Importantly, using immunohistochemistry analysis based on our tissue microarray, we found that CHMP2B was highly expressed in tumor tissue, and CD4 and CD8 were more likely to be positive in the CHMP2B-negative group. Survival analyses revealed that CHMP2B adversely impacted the survival of BC patients. In conclusion, we not only constructed a highly accurate predictive model based on PRS, but also revealed the importance of PANoptosis-related gene signature in the modulation of the tumor microenvironment and drug sensitivity in BC.

Comparing survival outcomes between neoadjuvant and adjuvant chemotherapy within T2N1M0 stage hormone receptor-positive, HER2-negative breast cancer: a retrospective cohort study based on SEER database.

BACKGROUND: Guideline recommendations for the application of neoadjuvant chemotherapy (NACT) in T2N1M0 stage hormone receptor-positive, HER2-negative (HR + /HER2-) breast cancer are ambiguous. The debate continues regarding whether NACT or adjuvant chemotherapy (ACT) offers superior survival outcomes for these patients. MATERIALS AND METHODS: Female patients diagnosed with HR + /HER2- breast cancer at T2N1M0 stage between 2010 and 2020, were identified from the Surveillance, Epidemiology, and End Results database and divided into two groups, the NACT group and the ACT group. Propensity score matching (PSM) was utilized to establish balanced cohorts between groups, considering baseline features. Kaplan-Meier (K-M) analysis and the Cox proportional hazards model were executed to assess the efficacy of both NACT and ACT in terms of overall survival (OS) and breast cancer-specific survival (BCSS). A logistic regression model was employed to examine the association between predictive variables and response to NACT. RESULTS: After PSM, 4,682 patients were finally included. K-M curves showed that patients receiving NACT exhibited significantly worse OS and BCSS when compared with patients undergoing ACT. Multivariable Cox analysis indicated that not achieving pathologic complete response (non-pCR) after NACT (versus ACT), was identified as an adverse prognostic factor for OS (HR 1.58, 95% CI 1.36-1.83) and BCSS (HR 1.70, 95% CI 1.44-2. 02). The logistic regression model revealed that low tumor grade independently predicted non-pCR. CONCLUSION: Among T2N1M0 stage HR + /HER2- patients, OS and BCSS of NACT were inferior to ACT. Patients who attained non-pCR after NACT demonstrated significantly worse survival outcomes compared with those who received ACT.

Inhibition of lncRNA PCAT19 promotes breast cancer proliferation.

BACKGROUND: Breast cancer (BC) is the most common malignancy affecting women. It is vital to explore sensitive biological markers to diagnose and treat BC patients. Recent studies have proved that long noncoding RNAs (lncRNAs) were involved in breast tumor progression. Nonetheless, whether lncRNA prostate cancer-associated transcript 19 (PCAT19) impacts BC development remains unknown. METHODS: We performed various bioinformatic analyses, including machine learning models to identify critical regulatory lncRNAs affecting prognosis in BC. The in situ hybridization (ISH) assay was carried out to confirm the expression levels of lncRNA PCAT19 in tissue specimens. MTT assay, wound healing assay, and transwell assay were performed to investigate PCAT19's impact on proliferation, migration, and invasion of BC cells. Mouse xenografts were used to examine the proliferation-inhibiting function of PCAT19 in vivo. RESULTS: Among the prognosis-associated lncRNAs, PCAT19 predicted a favorable prognosis in BC. Patients with high expression levels of PCAT19 had a lower clinical stage and less lymph node metastasis. The PCAT19-related genes were enriched in signaling pathways involved in tumor development, indicating PCAT19 was an essential regulator of BC. Using the ISH assay, we confirmed the expression level of lncRNA PCAT19 in human BC tissues was lower than normal breast tissues. Moreover, the knockdown of PCAT19 further confirmed its inhibiting ability in BC cell proliferation. Correspondingly, overexpressing PCAT19 reduced tumor size in mouse xenografts. CONCLUSIONS: Our study demonstrated that lncRNA PCAT19 suppressed the development of BC. PCAT19 might be a promising prognostic biomarker, which provides new insights into risk stratification for BC patients.

Leaf Coloration in Acer palmatum Is Associated with a Positive Regulator ApMYB1 with Potential for Breeding Color-Leafed Plants.

Anthocyanin biosynthesis and accumulation is closely associated with tissue/organ coloring in plants. To gain insight into the physiological and molecular mechanisms of leaf coloring in Acer palmatum, a deciduous tree during autumnal senescence, we first investigated concentration dynamics of pigments (i.e., chlorophyll, carotenoid and anthocyanin) in leaves with differential coloring. It was found that compared to green leaves (GN), anthocyanins were accumulated actively in semi-red (SR) and total-red (TR) leaves, accompanied with chlorophyll and carotenoid degradation. Then transcriptional profiling on GN and SR leaves identified thousands of transcripts with differential expression in SR compared to GN leaves. An annotation search showed that the entire flavonoid/anthocyanin biosynthesis pathway from the production of naringenin chalcone to modification of flavonoid backbone was extensively activated at the transcriptional level in SR leaves. Phylogenetic analysis of putative MYB proteins identified ApMYB1 as a putative regulator promoting anthocyanin biosynthesis. Expression of ApMYB1 in leaves was induced by exogenous hormones including abscisic acid. Stable overexpression of ApMYB1 in tobacco resulted in leaves with higher accumulation of anthocyanins. Collectively, our results identified ApMYB1 as a positive regulator associated with leaf coloring in Acer palmatum during autumnal senescence, which may be regarded a potential target for breeding color-leafed plants.

LncRNA MBNL1-AS1 Represses Proliferation and Cancer Stem-Like Properties of Breast Cancer through MBNL1-AS1/ZFP36/CENPA Axis.

Background: Emerging studies have revealed long noncoding RNAs (lncRNAs) were key regulators of cancer progression. In this research, the expression and roles of MBNL1-AS1 were explored in breast cancer (BC). Methods: In this study, the MBNL1-AS1 expression in breast cancer tissue, as well as in cell line, was studied by qRT-PCR assays. The effects of MBNL1-AS1 on proliferation and stemness were evaluated by MTT assays, colony formation assays, orthotopic breast tumor mice models, extreme limiting dilution analysis (ELDA), fluorescence in situ hybridization (FISH), flow cytometry assays, and sphere formation assays. Flexmap 3D assays were performed to show that MBNL1-AS1 downregulated the centromere protein A (CENPA) secretion in BC cells. Western blot, RNA pull-down assays, RNA immunoprecipitation (RIP) assays, and FISH were conducted to detect the mechanism. Results: The results showed that the expression levels of MBNL1-AS1 were downregulated in breast cancer tissues and cell lines. In vitro and in vivo studies demonstrated that overexpression of MBNL1-AS1 markedly inhibited BC cells proliferation and stemness. RNA pull-down assay, RIP assay, western blot assay, and qRT-PCR assay showed that MBNL1-AS1 downregulated CENPA mRNA via directly interacting with Zinc Finger Protein 36 (ZFP36) and subsequently decreased the stability of CENPA mRNA. Restoration assays also confirmed that MBNL1-AS1 suppressed the CENPA-mediated proliferation and stemness in breast cancer cells. Conclusions: The new mechanism of how MBNL1-AS1 regulates BC phenotype is elucidated, and the MBNL1-AS1/ZFP36/CENPA axis may be served as a therapeutic target for BC patients.

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer.

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC).

Multi-Model Sensor Fault Detection and Data Reconciliation: A Case Study with Glucose Concentration Sensors for Diabetes.

Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor-based subspace identification, and approximate linear dependency-based kernel recursive least squares is proposed. The residuals are further analyzed by artificial neural networks and a voting algorithm. The performance of the SFD&DR algorithm is illustrated by clinical data from artificial pancreas experiments with people with diabetes. The glucose-insulin metabolism has time-varying parameters and nonlinearities, providing a challenging system for fault detection and data reconciliation. Data from 17 clinical experiments collected over 896 hours were analyzed; the results indicate that the proposed SFD&DR algorithm is capable of detecting and diagnosing sensor faults and reconciling the erroneous sensor signals with better model-estimated values.

Multivariable Artificial Pancreas for Various Exercise Types and Intensities.

BACKGROUND: Exercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden. METHODS: The MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 18 60-h closed-loop experiments with 96 exercise sessions with three different protocols were completed. Each day, the subjects completed one resistance and one treadmill exercise (moderate continuous training [MCT] or high-intensity interval training [HIIT]). The primary outcome is time spent in each glycemic range during the exercise + recovery period. Secondary measures include average GC and average change in GC during each exercise modality. RESULTS: The GC during exercise + recovery periods were within the euglycemic range (70-180 mg/dL) for 69.9% of the time and within a safe glycemic range for exercise (70-250 mg/dL) for 93.0% of the time. The exercise sessions are defined to begin 30 min before the start of exercise and end 2 h after start of exercise. The GC were within the severe hypoglycemia (<55 mg/dL), moderate hypoglycemia (55-70 mg/dL), moderate hyperglycemia (180-250 mg/dL), and severe hyperglycemia (>250 mg/dL) for 0.9%, 1.3%, 23.1%, and 4.8% of the time, respectively. The average GC decline during exercise differed with exercise type (P = 0.0097) with a significant difference between the MCT and resistance (P = 0.0075). To prevent large GC decreases leading to hypoglycemia, MAAP recommended carbohydrates in 59% of MCT, 50% of HIIT, and 39% of resistance sessions. CONCLUSIONS: A consistent GC decline occurred in exercise and recovery periods, which differed with exercise type. The average GC at the start of exercise was above target (185.5 ± 56.6 mg/dL for MCT, 166.9 ± 61.9 mg/dL for resistance training, and 171.7 ± 41.4 mg/dL HIIT), making a small decrease desirable. Hypoglycemic events occurred in 14.6% of exercise sessions and represented only 2.22% of the exercise and recovery period.

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.

BACKGROUND: Automatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake. METHODS: The meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings. The CHO content of the meals/snacks is estimated by a fuzzy system using CGM and subcutaneous insulin delivery data. The meal bolus amount is computed according to the patient's insulin to CHO ratio. Integration of the meal module into a multivariable AP system allows revision of estimated CHO based on knowledge about physical activity, sleep, and the risk of hypoglycemia before the final decision for a meal bolus is made. RESULTS: The algorithm is evaluated by using 117 meals/snacks in retrospective data from 11 subjects with type 1 diabetes. Sensitivity, defined as the percentage of correctly detected meals and snacks, is 93.5% for meals and 68.0% for snacks. The percentage of false positives, defined as the proportion of false detections relative to the total number of detected meals and snacks, is 20.8%. CONCLUSIONS: Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.