BlobCUT: A contrastive learning method to support small blob detection in medical imaging

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency

20(S)-Protopanaxadiol Inhibits Angiotensin II-Induced Epithelial- Mesenchymal Transition by Downregulating SIRT1

20(S)-Protopanaxadiol (PPD) is one of the major active metabolites in ginseng saponin. Our previous studies revealed a broad spectrum of antitumor effects of PPD. Angiotensin II (Ang II), the biologically active peptide of the renin-angiotensin system (RAS), plays a critical role in the metastasis of various cancers. However, its role in the anti-metastatic effects of PPD is not clearly understood. In this study, we investigated the inhibitory effect of PPD on Ang II-induced epithelial-mesenchymal transition (EMT) in non-small cell lung cancer (NSCLC) cells, and the potential molecular mechanisms of suppression of NSCLC migration and metastasis by PPD. Treatment of A549 cells with Ang II increased metastases in an experimental model of cancer metastasis in vivo. PPD effectively prevented Ang II-induced EMT, as indicated by upregulation of E-cadherin and downregulation of vimentin. Additionally, Ang II upregulated the class III deacetylase sirtuin 1 (SIRT1) expression in EMT progression, while downregulation of SIRT1 was involved in suppression of Ang II-induced EMT by PPD. Moreover, the inhibitory effect of PPD was reversed by SIRT1 upregulation, and PPD demonstrated synergy with an SIRT1 inhibitor on Ang II-induced EMT. Taken together, our data reveal the mechanism of the anti-metastatic effects of PPD on Ang II-induced EMT and indicate that PPD can be used as an effective anti-tumor treatment

Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy

Over 55,000 people in the United States are diagnosed with pancreatic ductal adenocarcinoma (PDAC) yearly, and fewer than 20% of these patients survive a year beyond diagnosis. Chemotherapies are considered or used in nearly every PDAC case, but there is limited understanding of the complex signaling responses underlying resistance to these common treatments. Here, we take an unbiased approach to study protein kinase network changes following chemotherapies in patient-derived xenograft (PDX) models of PDAC to facilitate design of rational drug combinations. Proteomics profiling following chemotherapy regimens reveals that activation of JNK-JUN signaling occurs after 5-fluorouracil plus leucovorin (5-FU + LEU) and FOLFOX (5-FU + LEU plus oxaliplatin [OX]), but not after OX alone or gemcitabine. Cell and tumor growth assays with the irreversible inhibitor JNK-IN-8 and genetic manipulations demonstrate that JNK and JUN each contribute to chemoresistance and cancer cell survival after FOLFOX. Active JNK1 and JUN are specifically implicated in these effects, and synergy with JNK-IN-8 is linked to FOLFOX-mediated JUN activation, cell cycle dysregulation, and DNA damage response. This study highlights the potential for JNK-IN-8 as a biological tool and potential combination therapy with FOLFOX in PDAC and reinforces the need to tailor treatment to functional characteristics of individual tumors

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Research progress in hemostatic techniques during partial hepatectomy

Due to the continuous development of surgical bleeding control technology, partial hepatectomy has been a routine surgery currently. This article reviews the published literature over the past 20 years with particular attention to the progress in bleeding control techniques and applications in partial hepatectomy. It is concluded that adequate preparation before operation is the primary measure for minimizing blood loss during hepatectomy. Furthermore, hepatic inflow occlusion, anatomical hepatectomy, section and suture method, reducing the central venous pressure, and timely and appropriate external application of topical hemostatic agents and materials are the key skills to control bleeding during liver surgery

Microbiological Quality and Safety of Pizza Held Out of Temperature Control in University Dining Halls

Pizza is a popular food consumed around the world every day. Hot food temperatures were obtained from 19,754 nonpizza samples and 1,336 pizza temperatures were taken from dining facilities operated by Rutgers University between 2001 and 2020. These data showed that pizza was more frequently out of temperature control than many other foods. A total of 57 pizza samples that were out of temperature control were collected for further study. Pizza was tested for total aerobic plate count (TPC), Staphylococcus aureus, Bacillus cereus, Lactic acid bacteria, coliforms, and Escherichia coli. Water activity of pizza and surface pH of each individual pizza component (topping, cheese, bread) were measured. Predictions for the growth of four relevant pathogens were made for select pH and water activity values using ComBase. Rutgers University dining hall data show only about 60% of all foods that are pizza are held at the appropriate temperature. When pizza contained detectable microorganisms (∼70% of samples), average TPC ranged from 2.72 log CFU/g to 3.34 log CFU/g. Two pizza samples contained detectable S. aureus (∼50 CFU/g). Two other samples contained B. cereus (∼50 and 100 CFU/g). Five pizza samples contained coliforms (4–9 MPN/g), and no E. coli were detected. Correlation coefficients (R2 values) for TPC and pickup temperature are quite low (<0.06). Based on the pH and water activity measurements, most (but not all) of the pizza samples would be considered to potentially require time temperature control for safety. The modeling analysis shows that the organism most likely to pose a risk would be S. aureus, and the largest magnitude increase predicted is 0.89 log CFU at 30°C, pH 5.52, and water activity 0.963. The overall conclusion from this study is that while pizza represents a theoretical risk, the actual risk would likely only manifest for pizza samples that are held out of temperature control for time periods of more than eight hours

SatEdge: Platform of Edge Cloud at Satellite and Scheduling Mechanism for Microservice Modules

Edge cloud at satellite (ECS) is a newly developed edge computing (EC) technology that uses EC services offered by satellites to support high reliability and seamless global coverage. Satellites assume the role of computing and storage nodes for edge clouds, while terrestrial control centers function as cloud centers. In this paper, we propose a novel system and software architecture for the ECS to improve the cloud management of satellite networks and increase the flexibility of satellite service provision at the edge. Then, we propose a platform for the ECS based on KubeEdge called SatEdge. SatEdge has many function modules to meet the needs of the satellite-terrestrial network (STN) such as high reliability, high flexibility, and low latency. On this platform, we designed a microservice scheduling algorithm called optimal microservice scheduling with adaptivity and mobility (OMS-AM). OMS-AM can schedule a globally optimal workflow for microservice modules on the satellites to minimize task processing latency, failed task rate, and energy consumption. Compared with our last work, OMS-AM reduces the task processing latency by 14&#x0025; at most. Additionally, OMS-AM improves the mobility of the current scheduling method put forth in our previous study, which may help lower the task failure rate. Energy usage and the total normalized costs are additional indicators of the efficiency of the microservice architecture