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Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) of secondary sludge is a rate-limiting step due to the bacterial cell wall. In this study, experiments were performed to characterize secondary sludges from three wastewater treatment plants (WWTPs), and to investigate the feasibility of using bacteriophage lysozymes to speed up AD by accelerating the degradation of bacterial cell walls. Protein was the main organic material (67.7% of volatile solids in the sludge). The bacteriophage T4 lysozyme (T4L) was tested for hydrolysis and biochemical methane potential. Variations in the volatile suspended solid (VSS) concentration and biogas production were monitored. The VSS reduction efficiencies by hydrolysis using T4L for 72 h increased and ranged from 17.8% to 26.4%. Biogas production using T4L treated sludges increased and biogas production was increased by as much as 82.4%. Biogas production rate also increased, and the average reaction rate coefficient of first-order kinetics was 0.56 ± 0.02/d, which was up to 47.5% higher compared to the untreated samples at the maximum. Alphaproteobacteria, Betaproteobacteria, Flavobacteriia, Gammaproteobacteria, and Sphingobacteriia were major microbial classes in all sludges. The interpretation of the microbial community structure indicated that T4L treatment is likely to increase the rate of cell wall digestion. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the association between initial fibrinogen levels and massive transfusion (MT) in emergency department (ED) patients with primary postpartum hemorrhage (PPH). Methods: This retrospective study was conducted in the ED of a university-affiliated, tertiary referral center from January 2004 to August 2023. Patients were divided into two groups: the MT group, which included those who received a transfusion of 10 or more units of packed red blood cells within the first 24 h, and the Non-MT group. Results: Out of the 364 patients included in the study, 97 (26.6%) required MT. Fibrinogen, shock index, and lactate were independently associated with MT (odds ratio [OR] 0.987; 95% confidence interval [CI] 0.983–0.991; p < 0.001, OR 7.277; 95% CI 1.856–28.535; p = 0.004, and OR 1.261; 95% CI 1.021–1.557; p = 0.031, respectively). The area under the receiver operating characteristic curve for fibrinogen, shock index, and lactate in predicting MT was 0.871 (95% CI 0.832–0.904; p < 0.001), 0.821 (95% CI 0.778–0.859; p < 0.001), and 0.784 (95% CI 0.738–0.825; p < 0.001), respectively. When the cutoff value of fibrinogen was 400 mg/dL, both the sensitivity and negative predictive values for predicting MT were 100.0%. When the cutoff value of fibrinogen was 100 mg/dL, the specificity and positive predictive values were 91.8% and 70.7%, respectively. Conclusion: The initial fibrinogen levels were independently associated with the need for MT in ED patients with primary PPH. [ABSTRACT FROM AUTHOR]

Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second. [ABSTRACT FROM AUTHOR]

Basal-like breast cancer (BLBC) has a clinically aggressive nature. It is prevalent in young women and is known to often relapse rapidly. To date, the molecular mechanisms regarding the aggressiveness of BLBC have not been fully understood. In the present study, mechanisms of aggressiveness of BLBC involving EGFR and/or HER2 expression and interactions between tumor and tumor-associated macrophages (TAMs) were explored. The prognosis of breast cancer patients who underwent surgery at Samsung Medical Center was analyzed. It was found that the co-expression of EGFR and HER2 was associated with a worse prognosis. Therefore, we generated EGFR-positive BLBC cells with stable HER2 overexpression and analyzed the profile of secretory cytokines. Chemokine (C-C motif) ligand 2 (CCL2) expression was increased in HER2-overexpressed BLBC cells. Recombinant human CCL2 treatment augmented the motility of TAMs. In addition, the conditioned culture media of HER2-overexpressed BLBC cells increased the motility of TAMs. Furthermore, activation of TAMs by CCL2 or the conditioned culture media of HER2-overexpressed cells resulted in the production of pro-inflammatory cytokines, such as IL-8 and IL-1β. These observations reveal that CCL2 derived from EGFR and HER2 co-expressed BLBC cells can lead to increased TAM recruitment and the induction of IL-8 and IL-1β from recruited TAMs, triggering the tumorigenesis of breast cancer with the expression of both EGFR and HER2. Our findings demonstrate that EGFR+ and HER2+ BLBC aggressiveness is partially mediated through the interaction between BLBC and TAMs recruited by CCL2. [ABSTRACT FROM AUTHOR]

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation. [ABSTRACT FROM AUTHOR]

Interleukin-1 (IL1) is a proinflammatory cytokine and promotes cancer cell proliferation and invasiveness in a diversity of cancers, such as breast and colon cancer. Here, we focused on the pharmacological effect of Entelon® (ETL) on the tumorigenesis of triple-negative breast cancer (TNBC) cells by IL1-alpha (IL1A). IL1A enhanced the cell growth and invasiveness of TNBC cells. We observed that abnormal IL1A induction is related with the poor prognosis of TNBC patients. IL1A also increased a variety of chemokines such as CCL2 and IL8. Interestingly, IL1A expression was reduced by the ETL treatment. Here, we found that ETL significantly decreased the MEK/ERK signaling pathway in TNBC cells. IL1A expression was reduced by UO126. Lastly, we studied the effect of ETL on the metastatic potential of TNBC cells. Our results showed that ETL significantly reduced the lung metastasis of TNBC cells. Our results showed that IL1A expression was regulated by the MEK/ERK- and PI3K/AKT-dependent pathway. Taken together, ETL inhibited the MEK/ERK and PI3K/AKT signaling pathway and suppressing the lung metastasis of TNBC cells through downregulation of IL1A. Therefore, we propose the possibility of ETL as an effective adjuvant for treating TNBC. [ABSTRACT FROM AUTHOR]