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In recent years, China’s regional economic differences have become more and more apparent, and the problem of unbalanced allocation of financial resources has become more and more prominent. In this context, it is of great practical significance to study the construction strategy of regional investment and financial management systems using the spatial effect model. This study adopts the spatial econometric method to establish the spatial effect model. Considering the limitations of the spatial panel data model, this paper also uses the general dynamic spatial panel model with fixed effects to analyze the relationship between regional investment and the financial management system. Finally, through the Granger causality test on the spatial effect analysis of regional investment and financial management, this paper verifies the study of the interaction between the financial management system and regional investment. The research data show that in the spatial panel model, the regression coefficient of the explanatory variable F financial management system is 0.2431, which is significant at the 1% level, indicating that with a little increase in the degree of financial management system, the regional economy grows by 0.2431. The fixed effect general dynamic spatial panel model’s superiority is confirmed by the higher goodness of fit of the general dynamic spatial panel model compared to the spatial panel model.

Prostate cancer (PCa) is one of the most common male genitourinary system malignancies. Despite the significant benefits of anti-PD-L1 immune checkpoint inhibitor therapy in other cancers, the reasons for its poor therapeutic efficacy in prostate cancer (PCa) remain unclear.NDR1 plays an important role in innate immunity, but its role in tumor immunity and immunotherapy has not been investigated. The role of NDR1 in the immune microenvironment of PCa and the related mechanisms are unknown. Here, we found a positive correlation between NDR1 and PD-L1 expression in PCa. NDR1 significantly inhibits CD8 + T cell infiltration and function, thereby promoting immune escape in prostate cancer.More importantly, NDR1 inhibition significantly enhanced CD8 + T cell activation, which enhanced the therapeutic effect of anti-PD-L1. Mechanistic studies revealed that NDR1 inhibits ubiquitination-mediated PD-L1 degradation via the deubiquitinase USP10, upregulates PD-L1, and promotes PCa immune escape. Thus, our study suggests a unique PD-L1 regulatory mechanism underlying PCa immunotherapy failure. The significance of NDR1 in PCa immune escape and its mechanism of action were clarified, and combined NDR1/PD-L1 inhibition was suggested as an approach to boost PCa immunotherapy effectiveness. [ABSTRACT FROM AUTHOR]

Renal cancer is a highlyheterogeneous malignancy characterized by rising global incidence and mortalityrates. The complex interplay and dysregulation of multiple signaling pathways,including von Hippel–Lindau (VHL)/hypoxia‐inducible factor (HIF), phosphoinositide 3‐kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR), Hippo–yes‐associated protein (YAP), Wnt/ß‐catenin, cyclic adenosine monophosphate (cAMP), and hepatocyte growth factor (HGF)/c‐Met, contribute to theinitiation and progression of renal cancer. Although surgical resection is thestandard treatment for localized renal cancer, recurrence and metastasiscontinue to pose significant challenges. Advanced renal cancer is associatedwith a poor prognosis, and current therapies, such as targeted agents andimmunotherapies, have limitations. This review presents a comprehensiveoverview of the molecular mechanisms underlying aberrant signaling pathways inrenal cancer, emphasizing their intricate crosstalk and synergisticinteractions. We discuss recent advancements in targeted therapies, includingtyrosine kinase inhibitors, and immunotherapies, such as checkpoint inhibitors.Moreover, we underscore the importance of multiomics approaches and networkanalysis in elucidating the complex regulatory networks governing renal cancerpathogenesis. By integrating cutting‐edge research and clinical insights, this review contributesto the development of innovative diagnostic and therapeutic strategies, whichhave the potential to improve risk stratification, precision medicine, andultimately, patient outcomes in renal cancer. [ABSTRACT FROM AUTHOR]

To evaluate the surgical outcomes in patients with Posner-Schlossman syndrome (PSS) at one year. A prospective interventional study in PSS patients with penetrating canaloplasty. Main outcome measure was success rate (6 mmHg≤ intraocular pressure (IOP) ≤ 21 mmHg) with or without medications. Thirteen eyes in 13 patients with PSS underwent complete catheterization. The mean IOP and medications (Meds) were reduced to 16.1 ± 4.8 mmHg on 0.5 ± 1.0 Meds at 12 months. Complete and qualified success rates were 61.5% and 84.6% at 12 months. The postoperative recurrent rate of PSS was 69.2%, the mean peak IOP during attacks and episodes were decreased to 26.7 ± 8.3 mmHg and 1.7 ± 2.0 times respectively. Transient IOP spike (61.5%) and hyphema (38.5%) were the two most common postoperative complications. Penetrating canaloplasty achieves a high success rate in PSS without serious complications. [ABSTRACT FROM AUTHOR]

AbstractAdvances in artificial intelligence have led to breakthroughs in large language models (LLMs), like ChatGPT, opening up exciting possibilities for conceptual design. However, it’s essential to gain an in-depth understanding of how LLMs impact conceptual design output, process, and human designers’ perception. To this end, we chose ChatGPT as an example and conducted the investigation with 30 participants divided into Human-LLMs groups and human-human groups. The results indicated that there was no significant difference between their outputs, but the incorporation of LLMs shortened the completion time with fewer design steps and less time allocated to the late stages of design. Despite being perceived as less efficient and trusted, LLMs can still be viewed as potential collaborators, with humans holding the leadership. These findings offer the HCI community a thorough comprehension of how LLMs influence creativity-related practices, providing valuable insights for designing future interactions with LLMs. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of tribological self‐lubricating over a wide temperature range by doping Si and Sn solid solution components on the A‐sites of Ti3AlC2 to adapt to the severe circumstances. The specimens were sintered at 1450°C for 120 min under a pressure of 30 MPa in a vacuum environment, and their dry sliding tribological capabilities against Al2O3 at temperatures ranging from ambient temperature to 800°C were investigated using a rotational ball‐on‐disk tribo‐tester. The findings demonstrated a constantly decreased friction coefficient of Ti3Al0.8Si0.2Sn0.2C2 throughout the temperature range and admirable antifriction at higher temperatures where wear rates were 1–2 orders of magnitude lower than those at room temperature, which was superior to earlier reported Ti3AlC2. The predominant friction surface self‐adaptability mechanisms after solid solution treatment were determined to be tribo‐oxidation wear and tribo‐chemical reactions that played a vital role by examining the microstructural evolutions and tribo‐chemical phase composition on friction surfaces at various temperatures. The tribo‐oxidation‐induced generated lubricant film consisting of TiO2, Al2O3, SnO2 mixture oxide phases, and aluminosilicates was responsible for the decreased friction coefficient and wear rate. [ABSTRACT FROM AUTHOR]

Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC., (© 2024. The Author(s).)

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

Leukemia is a kind of hematological malignancy originating from bone marrow, which provides essential signals for initiation, progression, and recurrence of leukemia. However, how to specifically deliver drugs to the bone marrow remains elusive. Here, we develop biomimetic vesicles by infusing hematopoietic stem and progenitor cell (HSPC) membrane with liposomes (HSPC liposomes), which migrate to the bone marrow of leukemic mice via hyaluronic acid-CD44 axis. Moreover, the biomimetic vesicles exhibit superior binding affinity to leukemia cells through intercellular cell adhesion molecule-1 (ICAM-1)/integrin β2 (ITGB2) interaction. Further experiments validate that the vesicles carrying chemotherapy drug cytarabine (Ara-C@HSPC-Lipo) markedly inhibit proliferation, induce apoptosis and differentiation of leukemia cells, and decrease number of leukemia stem cells. Mechanically, RNA-seq reveals that Ara-C@HSPC-Lipo treatment induces apoptosis and differentiation and inhibits the oncogenic pathways. Finally, we verify that HSPC liposomes are safe in mice. This study provides a method for targeting bone marrow and treating leukemia., (© 2024. The Author(s).)

Pinellia ternata (Thunb.) Breit is a traditional Chinese medicine with important pharmacological effects. However, its cultivation is challenged by soil degradation following excessive use of chemical fertilizer. We conducted an experiment exploring the effects of replacing chemical fertilizers with organic fertilizers (OF) on the growth and yield of P. ternata, as well as on the soil physicochemical properties and microbial community composition using containerized plants. Six fertilization treatments were evaluated, including control (CK), chemical fertilizer (CF), different proportions of replacing chemical fertilizer with organic fertilizer (OM 1-4 ). Containerized P. ternata plants in each OF treatment had greater growth and yield than the CK and CF treatments while maintaining alkaloid content. The OM 3 treatment had the greatest yield among all treatments, with an increase of 42.35% and 44.93% compared to the CK and CF treatments, respectively. OF treatments improved soil quality and fertility by enhancing the activities of soil urease (S-UE) and sucrase (S-SC) enzymes while increasing soil organic matter and trace mineral elements. OF treatments increased bacterial abundance and changed soil community structure. In comparison to the CK microbial groups enriched in OM 3 were OLB13, Vicinamibacteraceae, and Blrii41. There were also changes in the abundance of gene transcripts among treatments. The abundance of genes involved in the nitrogen cycle in the OM 3 has increased, specifically promoting the transformation of N-NO 3 - into N-NH 4 + , a type of nitrogen more easily absorbed by P. ternata. Also, genes involved in "starch and sucrose metabolism" and "plant hormone signal transduction" pathways were positively correlated to P. ternata yield and were upregulated in the OM 3 treatment. Overall, OF in P. ternata cultivation is a feasible practice in advancing sustainable agriculture and is potentially profitable in commercial production., (© 2024. The Author(s).)