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Cancer-associated fibroblasts (CAFs) and their extracellular matrix are active participants in cancer progression. While it is known that functionally different subpopulations of CAFs co-exist in ovarian cancer, it is unclear whether certain CAF subsets are enriched during metastatic progression and/or chemotherapy. Using computational image analyses of patient-matched primary high-grade serous ovarian carcinomas, synchronous pre-chemotherapy metastases, and metachronous post-chemotherapy metastases from 42 patients, we documented the dynamic spatiotemporal changes in the extracellular matrix, fibroblasts, epithelial cells, immune cells, and CAF subsets expressing different extracellular matrix components. Among the different CAF subsets, COL11A1+ CAFs were associated with linearized collagen fibers and exhibited the greatest enrichment in pre- and post-chemotherapy metastases compared to matched primary tumors. Although pre- and post-chemotherapy metastases were associated with increased CD8+ T cell infiltration, the infiltrate was not always evenly distributed between the stroma and cancer cells, leading to an increased frequency of the immune-excluded phenotype where the majority of CD8+ T cells are present in the tumor stroma but absent from the tumor parenchyma. Overall, most of the differences in the tumor microenvironment were observed between primary tumors and metastases, while fewer differences were observed between pre- and post-treatment metastases. These data suggest that the tumor microenvironment is largely determined by the primary vs. metastatic location of the tumor while chemotherapy does not have a significant impact on the host microenvironment.

Iodine (I) abundance in marine carbonates (measured as an elemental ratio with calcium, I / Ca) is of broad interest as a proxy for local/regional ocean redox. This connection arises because the speciation of iodine in seawater, the balance between iodate (IO 3-) and iodide (I−), is sensitive to the prevalence of oxic vs. anoxic conditions. However, although I / Ca ratios are increasingly commonly being measured in ancient carbonate samples, a fully quantitative interpretation of this proxy requires the availability of a mechanistic interpretative framework for the marine iodine cycle that can account for the extent and intensity of ocean deoxygenation in the past. Here we present and evaluate a representation of marine iodine cycling embedded in an Earth system model ("cGENIE") against both modern and paleo-observations. In this framework, we account for IO 3- uptake and release of I− through the biological pump, the reduction in ambient IO 3- to I− in the water column, and the re-oxidation of I− to IO 3-. We develop and test a variety of different plausible mechanisms for iodine reduction and oxidation transformation and contrast model projections against an updated compilation of observed dissolved IO 3- and I− concentrations in the present-day ocean. By optimizing the parameters controlling previously proposed mechanisms involved in marine iodine cycling, we find that we can obtain broad matches to observed iodine speciation gradients in zonal surface distribution, depth profiles, and oxygen-deficient zones (ODZs). However, we also identify alternative, equally well performing mechanisms which assume a more explicit mechanistic link between iodine transformation and environment – an ambiguity that highlights the need for more process-based studies on modern marine iodine cycling. Finally, to help distinguish between competing representations of the marine iodine cycle and because our ultimate motivation is to further our ability to reconstruct ocean oxygenation in the geological past, we conducted "plausibility tests" of different model schemes against available I / Ca measurements made on Cretaceous carbonates – a time of substantially depleted ocean oxygen availability compared to modern and hence a strong test of our model. Overall, the simultaneous broad match we can achieve between modeled iodine speciation and modern observations, and between forward proxy modeled I / Ca and geological elemental ratios, supports the application of our Earth system modeling in simulating the marine iodine cycle to help interpret and constrain the redox evolution of past oceans. [ABSTRACT FROM AUTHOR]

Previous research on respiratory system mortality primarily focused on understanding their combined effects and have neglected the fact that air pollution mixtures are interrelated. This study used Bayesian kernel machine regression (BKMR) to analyze the relationship between air pollutant mixtures and respiratory mortality in Hangzhou, China from 2014 to 2018. The results showed a significant association between pollutant mixtures and respiratory system mortality primarily driven by PM2.5 and SO2. The joint exposure of air pollutants was positively correlated with respiratory system mortality at lag 01 and lag 02 days. The estimated joint effects of log-transformed mixture air pollution exposure on log-transformed respiratory system mortality increased from −0.02 (95% CI: −0.08–0.02) and −0.01 (95% CI: −0.05–0.04) at the 25th percentile to 0.06 (95% CI: 0.01–0.12) and 0.04 (95% CI: −0.001, 0.09) at the 75th percentile. Additionally, there was evidence of an interaction between O3 and PM10. This study confirms that exposure to multiple pollutants is a significant public health problem facing the Hangzhou population given the compounded effect proven with regression analysis, while furthermore, the control of PM2.5 and SO2 also represents a serious concern. Implications: Evidence indicates interactions between O3 and PM10. This study demonstrates that exposure to multiple pollutants exerts combined effects on the public health of the Hangzhou population, highlighting the importance of controlling PM2.5 and SO2. [ABSTRACT FROM AUTHOR]

Evidence regarding the cognitive effects of air pollution is inconsistent. This study aimed to quantitatively analyze the relationship between air pollutants and cognitive abilities using the Mini-Mental State Examination (MMSE). We systematically searched for studies published until July 1, 2023, and assessed heterogeneity with Cochran’s Q test and I² statistics, while publication bias was evaluated using Funnel plots and Egger’s test. Out of 380 studies, 17 were included in the meta-analysis. Results demonstrated significant relationship among long-term exposure to PM2.5, O3, and cognitive decline. The pooled RR of cognitive impairment per 10μg/m3 increase were 1.17 for PM2.5 and 1.07 for PM10. Furthermore, there was a significant association between cognitive decline and long-term exposure to PM2.5 (β = -0.30; I2 = 95.30%) and PM10 (β = -0.15; I2 = 87.50%). Our research suggests that long-term exposure to air pollution, especially PM2.5, is a risk factor for cognitive impairment. [ABSTRACT FROM AUTHOR]

Objective: To explore the accuracy of a dietary recording tool based on the mobile phone WeChat applet-"Zhishi AI Dietitian" applied to dietary records., Methods: The research subjects were 109 full-time undergraduates from Zhejiang University. Respondents completed one round of dietary records of "Zhishi AI Dietitian" for three non-consecutive days and one round of non-consecutive three-day 24-hour dietary review method records. The two method must overlap for one day. The energy, nutrients and various food intake data obtained from the Zhishi AI nutritionist survey were sorted and compared with the corresponding survey result of the 24-hour dietary review method. Pearson correlation coefficient or Spearman correlation coefficient was used for correlation analysis, intra-group correlation coefficient was used for reliability analysis, and Bland-Atlman scatter plot was used for consistency analysis., Results: In terms of reliability, the two method had certain reliability in assessing intake of various foods, energy and nutrients. After energy correction, the reliability of nutrient intake was enhanced. In terms of correlation, the correlation coefficients of food groups ranged from 0.34 to 0.79(mean 0.60), and the energy and nutrient correlation coefficients ranged from 0.34 to 0.72(mean 0.55). In terms of consistency, the proportion of research subjects outside the 95% consistency interval is less than 10%, indicating that the two have good consistency., Conclusion: Zhishi AI Dietitian applied to college students' dietary records has good accuracy.