Breast Cancer Diagnosis Using a Microfluidic Multiplexed Immunohistochemistry Platform

BACKGROUND: Biomarkers play a key role in risk assessment, assessing treatment response, and detecting recurrence and the investigation of multiple biomarkers may also prove useful in accurate prediction and prognosis of cancers. Immunohistochemistry (IHC) has been a major diagnostic tool to identify therapeutic biomarkers and to subclassify breast cancer patients. However, there is no suitable IHC platform for multiplex assay toward personalized cancer therapy. Here, we report a microfluidics-based multiplexed IHC (MMIHC) platform that significantly improves IHC performance in reduction of time and tissue consumption, quantification, consistency, sensitivity, specificity and cost-effectiveness. METHODOLOGY/PRINCIPAL FINDINGS: By creating a simple and robust interface between the device and human breast tissue samples, we not only applied conventional thin-section tissues into on-chip without any additional modification process, but also attained perfect fluid control for various solutions, without any leakage, bubble formation, or cross-contamination. Four biomarkers, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR) and Ki-67, were examined simultaneously on breast cancer cells and human breast cancer tissues. The MMIHC method improved immunoreaction, reducing time and reagent consumption. Moreover, it showed the availability of semi-quantitative analysis by comparing Western blot. Concordance study proved strong consensus between conventional whole-section analysis and MMIHC (n = 105, lowest Kendall's coefficient of concordance, 0.90). To demonstrate the suitability of MMIHC for scarce samples, it was also applied successfully to tissues from needle biopsies. CONCLUSIONS/SIGNIFICANCE: The microfluidic system, for the first time, was successfully applied to human clinical tissue samples and histopathological diagnosis was realized for breast cancers. Our results showing substantial agreement indicate that several cancer-related proteins can be simultaneously investigated on a single tumor section, giving clear advantages and technical advances over standard immunohistochemical method. This novel concept will enable histopathological diagnosis using numerous specific biomarkers at a time even for small-sized specimens, thus facilitating the individualization of cancer therapy

Intracoronary versus intravenous glycoprotein IIb/IIIa inhibitors during primary percutaneous coronary intervention in patients with STEMI: a systematic review and meta-analysis

Background Intracoronary (IC) administration of glycoprotein IIb/IIIa inhibitors (GPIs) has been studied as an adjunctive therapy to improve outcomes in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention. In this systematic review and meta-analysis, we aimed to evaluate the efficacy and safety of IC administration of GPIs compared with those of intravenous (IV) administration in patients with STEMI. Methods We searched the MEDLINE, Embase, and Cochrane CENTRAL databases for relevant studies published before September 21, 2022. In total, 22 randomized controlled trials involving 7,699 patients were included. Results The proportions of patients achieving thrombolysis in myocardial infarction grade 3 flow, myocardial blush grade 2/3, and complete ST-segment resolution were significantly higher in the IC group than in the IV group. Major adverse cardiac events (MACE) (RR: 0.54, 95% CI: 0.37–0.80) and heart failure (RR: 0.48, 95% CI: 0.25–0.91) within 1 month were significantly lower in the IC group than in the IV group; however, after 6 months, no difference was observed in MACE risk. Additionally, the risks of death and bleeding did not differ between the two routes of administration. Conclusions When considering adjunctive GPI administration for patients with STEMI, the IC route may offer greater benefits than the IV route in terms of myocardial reperfusion and reduced occurrence of MACE and heart failure within 1 month. Nonetheless, when making decisions for IC administration of GPIs, the absence of a benefit for bleeding risk and difficulty accessing the administration route should be considere

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning

Implementation of Institute for Safe Medication Practices (ISMP) Initiatives to Reduce Medication Overrides in the Surgical Intensive Care Unit at Veterans Affairs Academic Medical Center (I-REMO Study)

Background: The automated dispensing cabinets (ADCs) now allow for more rapid access to medications for both providers and pharmacists. However, automation may generate its own challenges with patient care. Medication overrides from ADCs circumvent pharmacist verification and creates an opportunity for medication errors. Methods: A 60-week quasi-experimental study has been conducted at large Veterans Affairs (VA) academic medical center from January 1, 2019 to February 29, 2020 to assess the efficacy of the ISMP-endorsed interventions in reducing medication overrides. Three interventions were implemented for this study: 1:1 nursing education, medication override list, and ADC medication override privilege modification. The interrupted-time series with multiple regression analysis was conducted to assess the efficacy of each intervention. The primary endpoint was the rate of medication overrides (primarily controlled substances and antibiotics) from the unit ADC at each intervention time periods. The secondary endpoints included medication override rates for controlled substances and fentanyl intravenous piggyback (IVPB), the most common overridden item, at each study intervention time periods. The other secondary endpoint was the comprehensive medication override rates for all medications in the unit ADC after November 1, 2019. Results: Total of 1,783 medication overrides from January 1, 2019 to February 29, 2020 were included in the final analysis from 616 patients. The interrupted time series with multiple logistic regression showed that the 1:1 nursing education significantly reduced the medication overrides (t = -6.10 [95% CI: -15.34 to -7.75]; P < 0.0001) and the decreased trend was maintained afterwards. No significance was found from the medication override list (t = -0.91 [95% CI: -5.17 to 1.94]; P=0.366) and the nursing ADC access privilege restriction (t = -0.82 [95% CI: -4.75 to 1.98; P=0.414]). Secondary endpoints have seen similar results. The 1:1 nursing education significantly reduced controlled substance (t = -6.34 [95% CI: -17.79 to -9.25]; P < 0.0001) and fentanyl IVPB override rates (t = -3.08 [95% CI: -43.69 to -9.28]; P = 0.003). The medication override list did not statistically reduce the controlled substances and fentanyl IVPB; whereas, ADC medication override privilege modification made a significant impact on fentanyl IVPB (t = -2.47 [95% CI: -34.08 to -3.56]; P=0.017). All medication overrides after November 1, 2019 have also significantly decreased monthly medication override rate from 7.63% to 2.90%. Conclusion: An interdisciplinary approach to ISMP-endorsed interventions significantly reduced the overall medication overrides rates in Veterans Affairs intensive care unit

Shared resource management for efficient heterogeneous computing

The demand for heterogeneous computing, because of its performance and energy efficiency, has made on-chip heterogeneous chip multi-processors (HCMP) become the mainstream computing platform, as the recent trend shows in a wide spectrum of platforms from smartphone application processors to desktop and low-end server processors. The performance of on-chip GPUs is not yet comparable to that of discrete GPU cards, but vendors have integrated more powerful GPUs and this trend will continue in upcoming processors. In this architecture, several system resources are shared between CPUs and GPUs. The sharing of system resources enables easier and cheaper data transfer between CPUs and GPUs, but it also causes resource contention problems between cores. The resource sharing problem has existed since the homogeneous (CPU-only) chip-multi processor (CMP) was introduced. However, resource sharing in HCMPs shows different aspects because of the different nature of CPU and GPU cores. In order to solve the resource sharing problem in HCMPs, we consider efficient shared resource management schemes, in particular tackling the problem in shared last-level cache and interconnection network. In the thesis, we propose four resource sharing mechanisms: First, we propose an efficient cache sharing mechanism that exploits the different characteristics of CPU and GPU cores to effectively share cache space between them. Second, adaptive virtual channel partitioning for on-chip interconnection network is proposed to isolate inter-application interference. By partitioning virtual channels to CPUs and GPUs, we can prevent the interference problem while guaranteeing quality-of-service (QoS) for both cores. Third, we propose a dynamic frequency controlling mechanism to efficiently share system resources. When both cores are active, the degree of resource contention as well as the system throughput will be affected by the operating frequency of CPUs and GPUs. The proposed mechanism tries to find optimal operating frequencies for both cores, which reduces the resource contention while improving system throughput. Finally, we propose a second cache sharing mechanism that exploits GPU-semantic information. The programming and execution models of GPUs are more strict and easier than those of CPUs. Also, programmers are asked to provide more information to the hardware. By exploiting these characteristics, GPUs can energy-efficiently exercise the cache and simpler, but more efficient cache partitioning can be enabled for HCMPs.Ph.D

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning

TAP: ATLP-Aware Cache Management Policy for a CPU-GPU Heterogeneous Architecture

Combining CPUs and GPUs on the same chip has become a popular architectural trend. However, these heterogeneous architectures put more pressure on shared resource management. In particular, managing the lastlevel cache (LLC) is very critical to performance. Lately, manyresearchershaveproposedseveralsharedcachemanagement mechanisms, including dynamic cache partitioning and promotion-based cache management, but no cache management work has been done on CPU-GPU heterogeneousarchitectures. Sharing the LLC between CPUs and GPUs brings new challenges due to the different characteristics of CPU and GPGPU applications. Unlike most memory-intensive CPU benchmarks that hide memory latency with caching, many GPGPU applications hide memory latency by combining thread-level parallelism (TLP)andcaching. In this paper, we propose a TLP-aware cache managementpolicyforCPU-GPUheterogeneousarchitectures. We introduce a core-sampling mechanism to detect how caching affects the performance of a GPGPU application. Inspired by previous cache management schemes, UtilitybasedCachePartitioning(UCP)andRe-ReferenceInterval Prediction(RRIP), we propose two newmechanisms: TAP-UCP and TAP-RRIP. TAP-UCP improves performance by 5 % over UCP and 11 % over LRU on 152 heterogeneous workloads, and TAP-RRIP improves performance by 9% overRRIP and12 % overLRU. 1

Blockchain-Based One-Off Address System to Guarantee Transparency and Privacy for a Sustainable Donation Environment

The problem of transparency in donation systems has long been a topic for discussion. However, the emphasis on transparency raises privacy concerns for donors and recipients, with some people attempting to hide donations or the receipt of money. Therefore, a donation system that guarantees transparency and privacy is required to avoid negative side effects. In this study, we developed a system that protects personal information by using a one-time account address system based on a blockchain while emphasizing transparency. The developed system could contribute to the creation of a sustainable and safe donation environment and culture