Causal link between hypothyroidism and gastric cancer risk: insights gained through multivariable Mendelian randomization and mediation analysis.

Background: Gastric cancer (GC) is the third leading cause of cancer death worldwide, and hypothyroidism has been identified as a potential influencing factor. Despite known associations between hypothyroidism and various cancers, the causal link between hypothyroidism and GC and potential mediators of this relationship remains unclear. This study aimed to clarify these relationships using Mendelian randomization (MR). Methods: Utilizing genetic variant information from the FinnGen and MRC Integrative Epidemiology Unit open genome-wide association studies (GWAS) databases, we conducted univariable and multivariable MR analyses to explore the causal relationship between hypothyroidism and GC risk. The analysis was adjusted for confounders such as BMI, smoking status, and alcohol intake, and included mediator MR analysis to examine the role of high cholesterol. Results: We identified a significant inverse association between hypothyroidism and GC risk (OR = 0.93, 95% CI= 0.89-0.98, P = 0.003), with no evidence of reverse causation or pleiotropy. Adjustments for Helicobacter pylori infection weakened this association. Mediator analysis highlighted high cholesterol levels, chronic hepatitis B infection, and diabetes/endocrine disease status as significant mediators of the protective effect of hypothyroidism on GC risk. Conclusion: Our findings suggest that hypothyroidism may confer a protective effect against GC, mediated in part by high cholesterol and other factors. These results underscore the importance of thyroid function and metabolic health in GC risk, offering new insights for preventive strategies and highlighting the need for further research into these complex associations.

Mechanisms of microbial coexistence in a patchy ecosystem: Differences in ecological niche overlap and species fitness between rhythmic and non-rhythmic species.

Resource patchiness caused by external events breaks the continuity and homogeneity of resource distribution in the original ecosystem. For local organisms, this leads to drastic changes in the availability of resources, breaks down the co-existence of species, and reshuffles the local ecosystem. West Lake is a freshwater lake with resource patchiness caused by multiple exogenous disturbances that has strong environmental heterogeneity that prevents clear observation of seasonal changes in the microbial communities. Despite this, the emergence of rhythmic species in response to irregular changes in the environment has been helpful for observing microbial communities dynamics in patchy ecosystems. We investigated the ecological mechanisms of seasonal changes in microbial communities in West Lake by screening rhythmic species based on the ecological niche and modern coexistence theories. The results showed that rhythmic species were the dominant factors in microbial community changes and the effects of most environmental factors on the microbial community were indirectly realised through the rhythmic species. Random forest analyses showed that seasonal changes in the microbial community were similarly predicted by the rhythmic species. In addition, we incorporated species interactions and community phylogenetic patterns into stepwise multiple regression analyses, the results of which indicate that ecological niches and species fitness may drive the coexistence of these subcommunities. Thus, this study extends our understanding of seasonal changes in microbial communities and provides new ways for observing seasonal changes in microbial communities, especially in ecosystems with resource patches. Our study also show that combining community phylogenies with co-occurrence networks based on ecological niches and modern coexistence theory can further help us understand the ecological mechanisms of interspecies coexistence.

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

The application of 3D-printed oral stents in intensity-modulated radiotherapy for oropharyngeal cancer and their dosimetric effect on organs at risk.

BACKGROUND: This study investigates the accuracy of 3D-printed dental stents in intensity-modulated radiotherapy (IMRT) for oropharyngeal cancer (OPC) and their dosimetric effects on normal tissues. METHODS: We selected 60 patients with OPC who underwent IMRT in the Department of Oncology, Special Medical Center of Army Medical University. These patients were randomly assigned into 3D-printed oral stent, simple glass bottle, and nonstent groups (20 patients/group). The positioning error was analyzed with the onboard imaging system once a week after 5 fractions of IMRT. The conformity index (CI), homogeneity index (HI), radiation dose of organs at risk (OARs), and oral mucosal reaction were compared among the three groups. RESULTS: No significant difference was observed in the conformity and uniformity of the target dose and the dose received by the spinal cord, larynx, and bilateral parotid glands among the three groups (P > 0.05). Meanwhile, the dose received by the upper cheek, hard palate, and soft palate of patients was significantly lower in the 3D-printed oral stent group than in the nonstent group (P < 0.05) but insignificantly different between the 3D-printed oral stent and simple glass bottle groups (P > 0.05). When compared with the nonstent group, the simple glass bottle group showed a markedly lower dose received by the upper cheek (P < 0.05) and an insignificantly different dose received by the hard palate and soft palate (P > 0.05). According to Common Terminology Criteria for Adverse Events v.5.0, the adverse response rate of the hard palate mucosa was lower in the 3D-printed oral stent group than in the simple glass bottle and nonstent groups (P < 0.05). CONCLUSIONS: For OPC patients undergoing IMRT, the application of 3D-printed oral stents can significantly reduce the exposure dose of the upper cheek and hard palate and decrease the occurrence of adverse events such as oral mucositis although it cannot affect the positioning error.

Long-term effects of rewetting and drought on GPP in a temperate peatland based on satellite remote sensing data.

Rewetting previously drained peatlands restores the critical function of peatlands as long-term carbon storages and sinks currently threatened by climate change and additional human-induced disturbances. Understanding and projecting the restoration process by rewetting, however, currently face a pressing challenge, the lack of consistent and gap-free records of important carbon cycling indicators of peatlands such as the gross primary production (GPP) over long term. In this study, we reconstructed the GPP in a rewetted peatland called Zarnekow (Fluxnet-ID: DE-Zrk) in Germany from 2000 to 2020 by combining long-term satellite observations and limited-term tower-based eddy covariance (EC) measurements based on Random Forest regression models. The R2 between the reconstructed data and EC data was 0.6. The reasonable reconstruction of long-term GPP enabled trend analysis that identified two distinct periods of decreasing/increasing in GPP due to rewetting and droughts. Rewetting in the winter of 2004 and 2005 stabilized GPP after a decreasing period. A drought in 2018 significantly increased GPP, and GPP remained high over the following two years. Furthermore, the month-specific trends show significant seasonality at this site, specifically, an increasing trend over the 21 years in the growing-season months of June to August and a decreasing trend in the other months. The most important variables for satellite-based estimates of GPP at this site include total evapotranspiration, land surface temperature, enhanced vegetation index and near-infrared reflectance vegetation index. Long-term analyses of carbon fluxes through the combination of satellite observations and EC measurements provide crucial insights into the restoration of carbon sequestration functions in rewetted peatlands.

Short-term health impacts related to ozone in China before and after implementation of policy measures: A systematic review and meta-analysis.

This paper presents a meta-analysis of the impacts of short-term exposure to ozone (O3) on three health endpoints: all-cause, cardiovascular, and respiratory mortality in China. All relevant studies from January 1990 to December 2021 were searched from four databases. After screening, 30 studies were included for the meta-analysis. The results showed that a significant rise of 0.41 % (95 % confidence interval (CI): 0.35 %-0.48 %) in all-cause, 0.60 % (95 % CI: 0.51 %-0.68 %) in cardiovascular and 0.45 % (95 % CI: 0.28 %-0.62 %) in respiratory mortality for each 10 µg m-3 increase in the maximum daily 8 h average O3 concentration (MDA8 O3). Moreover, results stratified by heterogeneous time periods before and after implementing a policy measure in 2013, showed that the pooled effects for all-cause and respiratory mortality before were greater than those after, while the pooled effects for cardiovascular mortality before 2013 were slightly smaller than those after. The finding that short-term exposure to O3 was positively related to the three health endpoints was validated by means of a sensitivity analysis. Furthermore, we did not observe any publication bias. Our results present an updated and better understanding of the relationship between short-term exposure to O3 and the three health endpoints, while providing a reference for further assessment of the impact of short-term O3 exposure on human health.