Explore the vast range of titles available.

Asymmetric information and economic uncertainty lead to more serious financial constraints on Chinese listed companies, thereby prompting them to hold more cash to guard against uncertainty and market competition. Using a sample of 45,303 observations of Chinese listed companies between 2010 and 2022, this study analyzed the relationship between corporate growth opportunity, product market competition (PMC), and cash holdings (CH). A quantity analysis model is used to examine the correlation between corporate growth opportunity and corporate CH while considering external factors such as PMC. Corporate growth opportunity leads to enterprises increasing their CH. However, fierce PMC reduces the enterprise CH level, and this effect is even more pronounced for high-growth opportunity enterprises, thus indicating that market competition efficiently mitigates such enterprises holding excess cash. Therefore, to improve enterprise cash-use efficiency, reducing information asymmetry under fierce PMC is essential. In summary, this study not only presents valuable insights into enhancing cash-use efficiency but also helps enterprises to implement more favorable market competition strategies and maintain a dominant position under fierce market competition. JEL Classification: G30 Plain Language Summary Corporate Growth Opportunity and Product Market Competition on Free Cash Flow This study is based on Chinese-listed companies as the sample to analyze the relationship between corporate growth opportunity, product market competition and cash holdings. This study is based on the quantity analysis model to examine the correlation between corporate growth opportunity and corporate cash holdings and analyze the result while considering external factors such as product market competition. The study reveals that corporate growth opportunity leads to enterprises increasing their cash holdings. However, fierce product market competition reduces enterprise cash holding level, and this effect is even more pronounced for high-growth opportunity enterprises, indicating that market competition efficiently mitigates high corporate growth opportunity enterprises hold excess cash. In summary, this research not only shows valuable insights into enhancing cash use efficiency but also helps enterprises to implement more favorable market competition strategies and maintain a dominant position in the fierce market competition.

Background Concurrent chemoradiotherapy (CCRT) is effective in the treatment of locally advanced cervical squamous cell carcinoma (SCC). However, whether treatment outcomes of cervical adenocarcinoma are equivalent to SCC after CCRT has been a topic of debate. Methods Medical records of cervical cancer patients treated with definitive radiotherapy or CCRT in our institute from January 2011 to December 2014 were reviewed. Patients were treated with intensity modulated radiation therapy combined with intracavitary brachytherapy. Weekly cisplatin was the first line regimen of concurrent chemotherapy. The treatment outcomes of patients with SCC and adenocarcinoma were compared with a multivariate Cox regression model, and log-rank method before and after propensity score matching (1:1). Results A total of 815 patients with stage IB-IVA cervical cancer were included, with 744 patients in the SCC group and 71 patients in adenocarcinoma group. The median follow-up period was 36.2 months (range, 1.0–76.2 months). The 3-year overall survival (OS), disease-free survival (DFS), pelvic control and distant control rates of patients in the SCC group and adenocarcinoma group were 85.2 and 75.4% ( p  = 0.005), 77.5 and 57.3% ( p  < 0.001), 89.0 and 74.0% ( p  = 0.001) and 86.0 and 74.4% ( p  = 0.011), respectively. After multivariate analysis, histology was an independent factor of OS ( p  = 0.003), DFS ( p  < 0.001), pelvic control ( p  = 0.002) and distant control ( p  = 0.003). With propensity score matching, 71 pairs of patients were selected. After matching, the OS ( p  = 0.017), DFS ( p  = 0.001), pelvic control ( p  = 0.015) and distant control ( p  = 0.009) of patients with adenocarcinoma were poorer than those of patients with SCC. In subgroup analysis, patients with adenocarcinoma had significantly worse OS and DFS compared with patients with SCC, regardless of treatment with radiotherapy alone or CCRT. Conclusion The present study demonstrated that patients with adenocarcinoma of the cervix had poorer OS and DFS than patients with SCC, regardless of treatment with radiotherapy alone or CCRT. New treatment approaches should be considered for cervical adenocarcinoma.

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical staining image features using machine learning methods. Initially, CRC disease-specific genes were identified through bioinformatics analysis, SVM-RFE and Random Forest algorithm utilizing RNA-seq data from both GEO and TCGA databases. Subsequently, verification of these genes was performed using proteomics data from CPTAC and HPA database, resulting in identification of target proteins (AKR1B10, CA2, DHRS9, and ZG16) for further investigation. SVM and CNN were then employed to analyze and integrate the characteristics of immunohistochemical images to construct a reliable CRC diagnostic model. During the training and validation process of this model, cross-validation along with external validation methods were implemented to ensure accuracy and reliability. The results demonstrate that the established diagnostic model exhibits excellent performance in distinguishing between CRC and normal controls (accuracy rate: 0.999), thereby presenting potential prospects for clinical application. These findings are expected to provide innovative perspectives as well as methodologies for personalized diagnosis of CRC while offering more precise references for promising treatment.

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

As the security of cloud storage cannot be effectively guaranteed, many users are reluctant to upload their key data to the cloud for storage, which seriously hinders the development of cloud storage. Since ensuring the confidentiality of user data and avoiding unauthorized access is the key to solving the security problems of cloud storage, there has been much cryptographic research proposing the use of the combination of cryptography technologies and access control model to guarantee the data security on untrusted cloud providers. However, the vast majority of existing access control schemes for ciphertext in cloud storage do not support the dynamic update of access control policies, and the computational overhead is also very large. This is contrary to the needs of most practical applications, which leverage dynamic data and need low computation cost. To solve this problem, combined with identity-based cryptosystem (IBC) and role-based access control (RBAC) model, we propose an RBAC (In this paper we use RBAC1 model which is richer access control model)) scheme for ciphertext in cloud storage. We also give the formal definitions of our scheme, a detailed description of four tuple used to represent access control strategy, the hybrid encryption strategy and write-time re-encryption strategy, which are designed for improving the system efficiency. The detailed construction processes of our scheme which. Include system initialization, add and delete users, add and delete permissions, add and delete roles, add and delete role inheritance, assign and remove user, assign and remove permission, read and write file algorithm are also given. Finally, we analyze the scheme and prove that it is correct,access control preserving (AC- preserving) and secure.

This study aimed to investigate the clinicopathological characteristics, survival outcomes, and to construct and validate a prognostic nomogram for mucinous gastric carcinoma (MGC) using a population-based cohort. Data were obtained from the SEER database (2000–2021) for patients diagnosed with MGC. Clinicopathological characteristics, including age, gender, tumor location, size, grade, treatment modalities, and survival outcomes, were analyzed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors, and a nomogram was developed. Model performance was evaluated using C-index, calibration plots, ROC curve analysis, and decision curve analysis. A total of 719 patients diagnosed with MGC were included from the SEER database. The median OS was 20 months, and the median CSS was 27 months. Eligible patients were randomly divided into a training cohort and a validation cohort in a 7:3 ratio. Univariate analysis revealed that multiple clinicopathological factors were significantly associated with OS. The final independent prognostic factors for OS included age, income, T stage, N stage, M stage, tumor size, diagnosis-to-treatment interval, surgery, and chemotherapy. A prognostic nomogram was constructed based on these variables. The concordance index for OS prediction was 0.721 in the training cohort and 0.717 in the validation cohort. The area under the curve values for 1-, 3- and 5-year OS predictions were 0.808, 0.784 and 0.782 in the training cohort, and 0.760, 0.797 and 0.787 in the validation cohort, respectively. Calibration curves demonstrated good agreement between predicted and observed outcomes. DCA and clinical impact curves indicated that the nomogram provided clinical benefit across a range of risk thresholds. The developed nomogram provides an individualized tool for predicting survival in MGC patients, offering a more accurate prognostic method than traditional staging systems. Incorporate with additional genetic markers and clinical factors might enhance its prognostic value in the future.

Microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) colorectal cancer (CRC) is characterized by high tumor mutational burden and strong immunogenicity, making it responsive to immune checkpoint inhibitors. Hepatoid adenocarcinoma (HAC) of the colon is an exceptionally rare and aggressive subtype, often resistant to conventional chemotherapy. We report a 77-year-old woman who presented with progressive anemia and a right-sided colonic mass. She underwent laparoscopic radical right hemicolectomy, and pathology revealed hepatoid features with vascular and neural invasion. Immunohistochemistry showed loss of MLH1, PMS2, and MSH6, confirming dMMR status, and MSI testing indicated MSI-H. BRAF V600E mutation was identified, and germline testing excluded Lynch syndrome. Given her age and potential chemotherapy toxicity, she received eight cycles of adjuvant envafolimab (200 mg every 3 weeks). Over 38 months of follow-up, she remained disease-free without experiencing any grade ≥2 immune-related adverse events. This case illustrates that adjuvant PD-1 blockade can be effective and well-tolerated in elderly patients with rare MSI-H CRC subtypes, including BRAF-mutated HAC. Comprehensive molecular profiling can help guide personalized immunotherapy decisions. Further studies are needed to confirm long-term benefits, optimize treatment duration and dosing, and identify predictive biomarkers for high-risk CRC.

This study is aimed at identifying risk factors of Low Anterior Resection Syndrome following ileostomy reversal in rectal cancer patients who had undergone preventive ileostomy. This retrospective analysis was conducted on a cohort of 605 patients treated at the Fourth Hospital of Hebei Medical University between January 2018 and December 2021. These patients were grouped based on LARS score, and Clinical and follow-up data were collected to conduct univariate analyses of potential factors influencing LARS occurrence based on variable type. Variables with statistical significance were included in a logistic regression model to analyze potential influences on the occurrence of LARS. Univariate and Multivariate logistic regression analysis showed that N2 stage (OR = 2.290 95%CI: 1.076–4.873, P  = 0.031), chemoradiotherapy (OR = 2.271, 95%CI: 1.246–4.138, P  = 0.007), and anastomosis height (OR = 0.836, 95%CI: 0.717–0.975, P  = 0.022) were independent influences on the occurrence of LARS. In model 3 (adjusting for all covariates), the relationship between anastomotic height and patient LARS status showed a negative correlation. In subgroup analyses, there were significant differences in the effect of anastomotic height on LARS in subgroups with different hemoglobin concentrations. A high occurrence rate of LARS is observed in rectal cancer patients with preventive ileostomy reversal. N2 stage, history of chemoradiotherapy, and anastomotic height are independent influence factors for the occurrence of major LARS after ileostomy reversal.

A freeform off-axis three-mirror anastigmat (TMA) optical system with a large field of view (FOV) can obtain target image information with a larger spatial range and more spatial details, which is a development trend within the realm of space optics. The optical aberration increases exponentially with the FOV, resulting in a significant increase in error sensitivity for large-FOV optical systems. To address this issue, a method for designing optical systems with a large FOV and low error sensitivity is proposed. The FOV is gradually expanded from a small initial value in equal-length increments until it reaches the full FOV. At each step, the error sensitivity is recalculated and controlled to a lesser extent than in the previous step. In this design process, the freeform surface is used to correct the aberration and obtain low error sensitivity. An optical system with a focal length of 1000 mm and an F-number of 10 is used as an example, and the FOV is enlarged from 5° × 1° to 20° × 4°. The design results show that the modulation transfer function (MTF) of the optical system can reach 0.45@50 lp/mm, and the average wavefront aberration is 0.029λ. After four rounds of FOV expansion and error sensitivity optimization, the error sensitivity is reduced by 37.27% compared to the initial system, which verifies the correctness and practicality of the method.

To construct nomograms predicting survival and patterns of failure in patients with cervical cancer treated with concurrent chemoradiotherapy (CCRT). A total of 833 patients with cervical cancer treated with definitive radiotherapy or CCRT in our institute from January 2011 to December 2014 were included. Cox proportional hazard regression models were used in univariate and multivariate analysis. The following variables were included in the univariate analysis: histology, FIGO stage, lymph node metastases (para-aortic, pelvic, common iliac, binary pelvic, and binary common iliac LNMs), the number of pelvic metastatic lymph nodes (MLNs), and the diameter of pelvic MLNs. Nomograms predicting the 3- and 5-year overall survival (OS), disease-free survival (DFS), local control (LC) and distant metastasis-free (DMF) were constructed. The nomograms were internally validated with respect to discrimination and calibration. The median follow-up period was 36.4 months (range,1.0 to 76.2 months). After univariate and multivariate analysis, histology, FIGO stage, para-aortic LNM, pelvic LNM, number of MLNs and diameter of pelvic MLNs significantly predicted OS, DFS, LC or DMF. Nomograms predicting the 3- and 5-year OS, DFS, LC and DMF were constructed incorporating these significant variables. These nomograms showed good discrimination and calibration, with a concordance index of 0.73 for predicting OS, 0.71 for DFS, 0.73 for LC and 0.67 for DMF. We constructed nomograms predicting survival and patterns of failure with a special focus on regional LNM in patients with cervical cancer treated with concurrent chemoradiotherapy.