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This paper utilizes the digital economy, regional spatial distribution digital mining technology, establishes the indicator system, assigns values to the indicators, and applies the indicator system to evaluate the development of industrial structure upgrading and transformation of the regional economy. According to the research results of the basic structure and algorithm of the self-organized neural network model, combined with the classification layer smoothing algorithm, the regional spatial clustering model is constructed. Using the constructed model, the regional economic spatial dynamic data is mined, and the path of industrial transformation and upgrading in the regional spatial area is dynamically studied. Furthermore, using the CA algorithm, the evolution law of land use structure in the region is predicted, and the development of the regional economy is inferred. At the same time, through the spatial autocorrelation regional prediction model, the relevant regional spatial expansion prediction method is proposed, the three research hypotheses of this paper are made, and the constructed model is utilized to innovate the exploration of industrial transformation and upgrading paths from the spatial dimension. Using empirical research methods, descriptive statistics, and mediating effect heterogeneity analysis are carried out to analyze the factors that influence regional economic variables in place A. The empirical analysis shows that the fluctuation range interval of industrial structure is between [0.811,10.2805], which brings a large positive impact on the regional economy.2017 national digital economic growth rate changed from -0.17446 to 0.17143 to realize a positive growth rate, and at the same time, there is a heterogeneity in the development of the east-west and central regions of A. The hypotheses presented in this paper are all valid.

In order to assess the characteristics of polybrominated diphenyl ether (PBDEs) in the atmosphere of urban Jinan, eastern China, gas phase and PM2.5 were hereby sampled in July 2020. The concentration and composition characteristics of 11 PBDEs were obtained. The Human exposure was evaluated by the daily exposure calculation method. During the observation period, the average concentration of ∑11PBDEs in PM2.5 and gas phase was 214.8±31.3 pg/m3, that of low-PBDEs was 119.1±2.1 pg/m3, and that of BDE209 was 95.8±15.4 pg/m3. Among the 10 low-PBDEs, the highest single content was BDE99, followed by BDE47, accounting for 43%. PCA showed the main sources of PBDEs in the atmosphere of urban Jinan were Penta-BDEs and Deca-BDEs. The daily respiratory exposure of children were higher than that of adults, and the potential risk should be taken seriously.

Inflammation of adipose tissue induces metabolic derangements associated with obesity. Thus, determining ways to control or inhibit inflammation in adipose tissue is of clinical interest. The present study tested the hypothesis that in mouse adipose tissue, endogenous prostaglandin E2 (PGE2) negatively regulates inflammation via activation of prostaglandin E receptor 4 (EP4). PGE2 (5–500nM) attenuated lipopolysaccharide-induced mRNA and protein expression of chemokines, including interferon-γ-inducible protein 10 and macrophage-inflammatory protein-1α in mouse adipose tissue. A selective EP4 antagonist (L161,982) reversed, and two structurally different selective EP4 agonists [CAY10580 and CAY10598] mimicked these actions of PGE2. Adipose tissue derived from EP4-deficient mice did not display this response. These findings establish the involvement of EP4 receptors in this anti-inflammatory response. Experiments performed on adipose tissue from high-fat-fed mice demonstrated EP4-dependent attenuation of chemokine production during diet-induced obesity. The anti-inflammatory actions of EP4 became more important on a high-fat diet, in that EP4 activation suppressed a greater variety of chemokines. Furthermore, adipose tissue and systemic inflammation was enhanced in high-fat-fed EP4-deficient mice compared with wild-type littermates, and in high-fat-fed untreated C57BL/6 mice compared with mice treated with EP4 agonist. These findings provide in vivo evidence that PGE2-EP4 signaling limits inflammation. In conclusion, PGE2, via activation of EP4 receptors, functions as an endogenous anti-inflammatory mediator in mouse adipose tissue, and targeting EP4 may mitigate adipose tissue inflammation.

BackgroundHyperglycemia is recognized as a common adverse event for patients receiving alpelisib but has been little studied outside of clinical trials. We report the frequency of alpelisib-associated hyperglycemia in a real-world setting and evaluate proposed risk factors.Patients and methodsWe retrospectively identified patients with PIK3CA-mutated, hormone receptor-positive, metastatic breast cancer who initiated treatment with alpelisib plus fulvestrant between August 2019 and December 2021. Ordinal logistic regression evaluated 5 characteristics (diabetes, prediabetes, body mass index [BMI], age, and Asian ancestry) as independent risk factors for ALP-associated hyperglycemia grades 2-4. Risk of error from multiple hypothesis testing was controlled using the false discovery rate method.ResultsThe study included n = 92 subjects, all but 1 female, mean age 59.9 (+11.9) years with 50% non-Hispanic White, 15% Hispanic/Latino, 13% Asian, 9% African/Black, and 13% other/unknown. In total 34% of patients had diabetes, 10% had pre-diabetes, and 56% had normoglycemia. Thirty-six percent were obese, 32% were overweight, 25% were normal weight, and 7% were lean. Frequency of grades 1-4 hyperglycemia in current subjects (64.1%) was similar to hyperglycemia reported in the SOLAR-1 trial (63.7%). Our subjects' risk of grades 2-4 hyperglycemia was independently increased by pre-existing diabetes (Odds ratio 3.75, 95% CI, 1.40-10.01), pre-diabetes (6.22, 1.12-34.47), Asian ancestry (7.10, 1.75-28.84), and each unit of BMI above 20 (1.17, 1.07-1.28).ConclusionWhile receiving alpelisib, patients of Asian ancestry, as well as patients with pre-existing hyperglycemia and/or BMI above 20, should be closely monitored for hyperglycemia. The mechanism underlying the current association of alpelisib-associated hyperglycemia with Asian ancestry is independent of BMI and merits further study. The high incidence of hyperglycemia resulted in a change in practice to include consultation with a diabetes nurse educator or endocrinologist at the start of alpelisib.

Simple Summary: Despite major advances in treating non-small-cell lung cancer (NSCLC), the five-year survival rates for patients without specific genetic mutations remain low. This study aims to find better ways to treat these patients. We focused on a protein called Aurora kinase A (AURKA), which our prior study suggested may play a role in modulating resistance to the common cancer drug cisplatin. We looked at how AURKA affects the sensitivity of NSCLC cells to platinum-based chemotherapy and radiation. In lab tests, AURKA levels increased after cisplatin treatment. Blocking AURKA with a drug called alisertib or using a genetic method made cisplatin and radiation more effective, causing tumors to shrink in mouse models. The treated cancer cells showed more DNA damage and underwent cell death. Blocking AURKA also increased markers that help the immune system recognize cancer. Our study suggests that combining AURKA inhibitors with other treatments could improve outcomes for NSCLC patients. Despite major advances in non-small-cell lung cancer (NSCLC) treatment, the five-year survival rates for patients with non-oncogene-driven tumors remain low, necessitating combinatory approaches to improve outcomes. Our prior high-throughput RNAi screening identified Aurora kinase A (AURKA) as a potential key player in cisplatin resistance. In this study, we investigated AURKA's role in platinum and radiation sensitivity in multiple NSCLC cell lines and xenograft mouse models, as well as its effect on immune checkpoints, including PD-L1, B7x, B7-H3, and HHLA2. Of 94 NSCLC patient tumor specimens, 91.5% tested positive for AURKA expression, with 34% showing moderate-to-high levels. AURKA expression was upregulated following cisplatin treatment in NSCLC cell lines PC9 and A549. Both AURKA inhibition by alisertib and inducible AURKA knockdown potentiated the cytotoxic effects of cisplatin and radiation, leading to tumor regression in doxycycline-inducible xenograft mice. Co-treated cells exhibited increased DNA double-strand breaks, apoptosis, and senescence. Additionally, AURKA inhibition alone by alisertib increased PD-L1 and B7-H3 expression. In conclusion, our study demonstrates that AURKA inhibition enhances the efficacy of platinum-based chemotherapy in NSCLC cells and modulates the expression of multiple immune checkpoints. Therefore, combinatory regimens with AURKA inhibitors should be strategically designed and further studied within the evolving landscape of chemo-immunotherapy. [ABSTRACT FROM AUTHOR]

Testing drugs for anti-aging effects has historically been conducted in mouse life-span studies, but are costly and time consuming, and more importantly, difficult to recapitulate in humans. In addition, life-span studies in mice are not well suited to testing drug combinations that target multiple factors involved in aging. Additional paradigms for testing therapeutics aimed at slowing aging are needed. A new paradigm, designated as the Geropathology Grading Platform (GGP), is based on a standardized set of guidelines developed to detect the presence or absence of low-impact histopathological lesions and to determine the level of severity of high-impact lesions in organs from aged mice. The GGP generates a numerical score for each age-related lesion in an organ, summed for total lesions, and averaged over multiple mice to obtain a composite lesion score (CLS). Preliminary studies show that the platform generates CLSs that increase with the age of mice in an organ-dependent manner. The CLSs are sensitive enough to detect changes elicited by interventions that extend mouse life span, and thus help validate the GGP as a novel tool to measure biological aging. While currently optimized for mice, the GGP could be adapted to any preclinical animal model.