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Growing tumors are dynamic and nonlinear ecosystems, wherein cancer cells adapt to their local microenvironment, and these adaptations further modify the environment, inducing more changes. From nascent intraductal neoplasms to disseminated metastatic disease, several levels of evolutionary adaptations and selections occur. Here, we focus on one example of such an adaptation mechanism, namely, "niche construction" promoted by adaptation to acidosis, which is a metabolic adaptation to the early harsh environment in intraductal neoplasms. The avascular characteristics of ductal carcinoma in situ (DCIS) make the periluminal volume profoundly acidic, and cancer cells must adapt to this to survive. Based on discovery proteomics, we hypothesized that a component of acid adaptation involves production of collagen by pre-cancer cells that remodels the extracellular matrix (ECM) and stabilizes cells under acid stress. The proteomic data were surprising as collagen production and deposition are commonly believed to be the responsibility of mesenchymally derived fibroblasts, and not cells of epithelial origin. Subsequent experiments in 3D culture, spinning disk and second harmonic generation microscopy of DCIS lesions in patients' samples are concordant. Collagen production assay by acid-adapted cells in vitro demonstrated that the mechanism of induction involves the RAS and SMAD pathways. Secretome analyses show upregulation of ECM remodeling enzymes such as TGM2 and LOXL2 that are collagen crosslinkers. These data strongly indicate that acidosis in incipient cancers induces collagen production by cancer cells and support the hypothesis that this adaptation initiates a tumor-permissive microenvironment promoting survival and growth of nascent cancers.

Background: The impact of and countermeasures for Ureaplasma spp. in neonates remain controversial. The aim of this study was to evaluate the associated perinatal factors that can predict the likelihood of respiratory tract Ureaplasma spp. colonization and analyze the subsequent clinical course of affected infants, thereby providing the rationale for their diagnosis, treatment, and future study. Methods: This was a retrospective observational study of infants born at a gestational age (GA) of less than 32 weeks. Results: The prevalence of respiratory tract Ureaplasma spp. colonization was 25.8% (75/291), and it increased with a decrease in GA and birth weight (BW). Maternal vaginal Ureaplasma spp. colonization increased the risk of neonatal Ureaplasma spp. colonization, with an OR of 7.8 (95% CI: 3.1, 20.0). Infants with Ureaplasma spp. colonization had a higher white blood cell (WBC) count, normal C-reactive protein (CRP) level, and higher failure rate of weaning from mechanical ventilation (30.7% vs. 17.1%, p = 0.014); they also suffered more from interstitial pneumonia (20.0% vs. 5.6%, p < 0.001) and bronchopulmonary dysplasia (36.0% vs. 13.4%, p < 0.001). Infants receiving anti-Ureaplasma spp. treatment had a lower GA, lower BW, and more severe respiratory syndromes. However, the difference in respiratory manifestation became insignificant after adjusting for GA. Conclusions: GA and maternal vaginal Ureaplasma spp. colonization could be used to predict neonatal respiratory tract Ureaplasma spp. colonization. An elevated WBC count combined with normal CRP is a good marker of Ureaplasma spp. colonization/infection. It is conventional practice to start anti-Ureaplasma spp. treatment when infants present with a deteriorated respiratory condition. This practice warrants further investigation considering GA as a predominant intermediate variable. [ABSTRACT FROM AUTHOR]

Objectives: Network pharmacology is essential for understanding the multi-target and multi-pathway therapeutic mechanisms of traditional Chinese medicine. This study aims to evaluate the influence of database quality on target identification and to explore the therapeutic potential of rhynchophylline (Rhy) in treating overactive bladder (OAB). Methods: An OAB dataset was constructed through extensive literature screening. Using this dataset, we applied network pharmacology to predict potential targets for Rhy, which is known for its therapeutic effects but lacks a well-defined target profile. Predicted targets were validated through in vitro experiments, including DARTS and CETSA. Results: Our analysis identified Rhy as a potential modulator of the M3 receptor and TRPM8 channel in the treatment of OAB. Validation experiments confirmed the interaction between Rhy and these targets. Additionally, the GeneCards database predicted other targets that are not directly linked to OAB, corroborated by the literature. Conclusions: We established a more accurate and comprehensive dataset of OAB targets, enhancing the reliability of target identification for drug treatments. This study underscores the importance of database quality in network pharmacology and contributes to the potential therapeutic strategies for OAB. [ABSTRACT FROM AUTHOR]

As a departure from previous research that takes social ventures as a distinct organizational type, this study investigates the relationship between the pro‐market institutions in a country and the degree of hybridity in social ventures, focusing on the dimension of organizational goals. Additionally, it examines the contingent effects of organizational activities, such as the novelty of market offering and the implementation of social performance measurement. Combining Global Entrepreneurship Monitor data in 2009 and 2015 with other cross‐national databases, we test our theoretical predictions using 3648 startup social ventures across 42 countries. We find that pro‐market institutions have an inverted U‐shaped effect on the degree of hybridity in social ventures. Furthermore, the novelty of market offering and social performance measurement attenuate the impact of pro‐market institutions on organizational hybridity by shifting the inverted U‐shape to relatively negative and linear. Through this cross‐level empirical exploration, we develop a richer explanation of organizational hybridity and contribute to the growing literature on social ventures and hybrid organizations. [ABSTRACT FROM AUTHOR]

PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model. [ABSTRACT FROM AUTHOR]

The pricing and hedging of financial derivatives have become one of the hot research issues in mathematical finance today. In the case of non-risk neutrality, this article uses the martingale method and probability measurement method to study the pricing method and hedging strategy of financial derivatives. This paper also further studies the hedging strategy of financial derivatives in the incomplete market based on the BSM model and converts the solution of this problem into solving a vector on the Hilbert space to its closure. The problem of space projection is to use projection theory to decompose financial derivatives under a given martingale measure. In the imperfect market, the vertical projection theory is used to obtain the approximate pricing method and hedging strategy of financial derivatives in which the underlying asset follows the martingale process; the projection theory is further expanded, and the pricing problem of financial derivatives under the mixed-asset portfolio is obtained. Approximate pricing of financial derivatives; in the discrete state, the hedging investment strategy of financial derivatives H in the imperfect market is found through the method of variance approximation.