Targeted Inhibition of Lymphovascular Invasion Formation with CREKA Peptide-Modified Silicasomes to Boost Chemotherapy in Bladder Cancer.

Despite its significant clinical efficacy as a first-line treatment for advanced bladder cancer, cisplatin-based chemotherapy provides a limited benefit for patients with lymphovascular invasion (LVI), which is characterized by the presence of tumor emboli within blood vessels and associated with enhanced cisplatin resistance and metastatic potential. Notably, platelets, a critical component of LVI, hinder the delivery of chemotherapeutic agents to tumors and facilitate metastasis. Consequently, platelet function inhibition holds the potential to disrupt LVI formation, as well as augment the antitumor activity of cisplatin. Herein, we developed a tumor microenvironment-targeted nanodrug with lipid-coated mesoporous silica nanoparticles (silicasomes) that synergistically combines cisplatin with an antiplatelet agent, tirofiban, for bladder cancer treatment. The customized nanodrug can concurrently prevent LVI formation and enhance the chemotherapeutic efficacy without significant adverse effects. This study supports the integration of chemotherapy and antiplatelet therapy via a silicasome-based nanosystem as a highly promising strategy for bladder cancer management.

CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma.

Objectives: This study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models' performance. Methods: This clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation. Results: For tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103-0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213-2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000-1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023-1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756-0.877) in the training set and 0.789 (95% CI: 0.689-0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731-0.891) in the training set and 0.833 (95% CI: 0.733-0.932) in the test set. Conclusion: For tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

Associations of evening-type and insomnia symptoms with depressive symptoms among youths.

BACKGROUND: Evening-type and insomnia symptoms are significantly related to each other and independently associated with depressive symptoms, yet few studies have examined the potential interaction between these two conditions. Therefore, we aimed to examine the associations of evening-type and insomnia symptoms with depressive symptoms among Chinese youths, with a specific focus on the joint effects of the two conditions on depressive symptoms. METHODS: Participants aged between 12 and 25 were invited to participate in an online survey from December 15, 2022, to May 26, 2023. Multivariate logistic regression models and additive interaction models were used to examine the independent and joint effects of chronotypes and insomnia symptoms on depressive symptoms, respectively. RESULTS: Of the 6145 eligible youths, the prevalence of evening-type and insomnia symptoms were 24.9 % and 29.6 %, respectively. Both evening-type (adjusted OR, [AdjOR]: 3.21, 95 % CI: 2.80-3.67) and insomnia symptoms (AdjOR: 10.53, 95 % CI: 9.14-12.12) were associated with an increased risk of depressive symptoms. In addition, the additive interaction models showed that there is an enhanced risk of depression related to interaction between evening-type and insomnia symptoms (relative excess risk due to interaction, [RERI]: 11.66, 95 % CI: 7.21-16.11). CONCLUSIONS: The present study provided additional evidence demonstrating the presence of interaction between evening-type and insomnia symptoms, which can lead to a higher risk of depressive symptoms. Our findings argue the need for addressing both sleep and circadian factors in the management of depressive symptoms in young people.

Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.