Latent profiles of academic resilience in undergraduate nursing students and their association with resilience and self-efficacy.

AIM: This study aimed to investigate the heterogeneity of academic resilience among nursing students using latent profile analysis and its associated influencing factors. BACKGROUND: Nursing students experience higher levels of stress compared to their peers in other professions, and the cultivation of academic resilience plays a pivotal role in their ability to effectively cope with this stress. Academic resilience not only facilitates success in the face of academic adversity but also contributes to the promotion of mental well-being among nursing students. However, the current research on the academic resilience of nursing students has predominantly focused on a scale-centered total score approach, disregarding individual variability, and hindering the development to inform personalized interventions for enhancing academic resilience. DESIGN: A cross-sectional study. METHODS: A convenience sampling method was used to collect a total of 644 nursing students from two medical schools in Guangzhou City. The participants were recruited through an online survey conducted from January to March 2023. The questionnaires consisted of a general information form, the Chinese version of the Academic Resilience Scale-30 (C-ARS-30), the 10-item Connor Davidson Resilience Scale (CD-RISC-10), and the General Self-Efficacy Scale (GSES). Latent profile analysis was used to identify distinct categories of academic resilience among nursing students, and influencing factors were examined through ordinal logistic regression analysis. RESULTS: The academic resilience levels of nursing students can be divided into three potential categories: 'low academic resilience' (13.0%), 'moderate academic resilience' (70.0%), and 'high academic resilience' (17.0%). Level of grade, GPA, self-reported physical health level, resilience and self-efficacy were significantly influenced the different categories of academic resilience of nursing students (P<0.05). CONCLUSIONS: The majority of undergraduate nursing students were placed in the moderate academic resilience group, however, educational institutions should pay special attention to nursing students demonstrating low levels. Regular assessments of academic resilience are recommended, and personalized interventions should be tailored to address specific academic resilience characteristics across different grades of nursing students. Strategies aimed at enhancing academic resilience among nursing students may include improvements in GPA performance, attention to physical health, and the reinforcement of resilience and self-efficacy.

Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.

Capsule endoscopy (CE) is non-invasive and painless during gastrointestinal examination. However, capsule endoscopy can increase the workload of image reviewing for clinicians, making it prone to missed and misdiagnosed diagnoses. Current researches primarily concentrated on binary classifiers, multiple classifiers targeting fewer than four abnormality types and detectors within a specific segment of the digestive tract, and segmenters for a single type of anomaly. Due to intra-class variations, the task of creating a unified scheme for detecting multiple gastrointestinal diseases is particularly challenging. A cascade neural network designed in this study, Cascade-EC, can automatically identify and localize four types of gastrointestinal lesions in CE images: angiectasis, bleeding, erosion, and polyp. Cascade-EC consists of EfficientNet for image classification and CA_stm_Retinanet for lesion detection and location. As the first layer of Cascade-EC, the EfficientNet network classifies CE images. CA_stm_Retinanet, as the second layer, performs the target detection and location task on the classified image. CA_stm_Retinanet adopts the general architecture of Retinanet. Its feature extraction module is the CA_stm_Backbone from the stack of CA_stm Block. CA_stm Block adopts the split-transform-merge strategy and introduces the coordinate attention. The dataset in this study is from Shanghai East Hospital, collected by PillCam SB3 and AnKon capsule endoscopes, which contains a total of 7936 images of 317 patients from the years 2017 to 2021. In the testing set, the average precision of Cascade-EC in the multi-lesions classification task was 94.55%, the average recall was 90.60%, and the average F1 score was 92.26%. The mean mAP@ 0.5 of Cascade-EC for detecting the four types of diseases is 85.88%. The experimental results show that compared with a single target detection network, Cascade-EC has better performance and can effectively assist clinicians to classify and detect multiple lesions in CE images.

A carboxylate linker strategy mediated densely accessible Fe-N4 sites for enhancing oxygen electroreduction in Zn-air batteries.

Iron-nitrogen-carbon single-atom catalysts derived from zeolitic-imidazolate-framework-8 (ZIF-8) have presented its great potential for the oxygen reduction reaction (ORR) in Zn-air batteries (ZABs). However, due to insufficient active Fe-N sites, its ORR activity is inferior to Pt-based catalysts. Herein, a carboxylate (OAc) linker strategy is proposed to design a ZIF-8-derived FeNCOAc catalyst with abundant accessible Fe-N4 single-atom sites. Except that imidazole groups can coordinate with Fe ions, the OAc linker on the unsaturated coordination Zn nodes can anchor and coordinate with more Fe ions, resulting in a significant increase in Fe-N4 site density. Meanwhile, the corrosion of carbon skeleton by OAc oxidation during heat-treatment leads to improved porosity of catalyst. Benefitting from the highly dense Fe-N4 sites and hierarchical pores, the FeNCOAc endows superior performance in alkaline medium (E1/2 = 0.906 V), which is confirmed by density functional theory calculation results. Meanwhile, the assembled liquid ZAB delivers a favorable peak power density of 173.9 mW cm-2, and a high specific capacity of 770.9 mAh g-1 as well as outstanding durability. Besides, the solid-state ZAB also shows outstanding discharge performance.

STK3 kinase activation inhibits tumor proliferation through FOXO1-TP53INP1/P21 pathway in esophageal squamous cell carcinoma.

PURPOSE: Esophageal squamous cell carcinoma (ESCC) is an aggressive disease with a poor prognosis, caused by the inactivation of critical cell growth regulators that lead to uncontrolled proliferation and increased malignancy. Although Serine/Threonine Kinase 3 (STK3), also known as Mammalian STE20-like protein kinase 2 (MST2), is a highly conserved kinase of the Hippo pathway, plays a critical role in immunomodulation, organ development, cellular differentiation, and cancer suppression, its phenotype and function in ESCC require further investigation. In this study, we report for the first time on the role of STK3 kinase and its activation condition in ESCC, as well as the mechanism and mediators of kinase activation. METHODS: In this study, we investigated the expression and clinical significance of STK3 in ESCC. We first used bioinformatics databases and immunohistochemistry to analyze STK3 expression in the ESCC patient cohort and conducted survival analysis. In vivo, we conducted a tumorigenicity assay using nude mouse models to demonstrate the phenotypes of STK3 kinase. In vitro, we conducted Western blot analysis, qPCR analysis, CO-IP, and immunofluorescence (IF) staining analysis to detect molecule expression, interaction, and distribution. We measured proliferation, migration, and apoptosis abilities in ESCC cells in the experimental groups using CCK-8 and transwell assays, flow cytometry, and EdU staining. We used RNA-seq to identify genes that were differentially expressed in ESCC cells with silenced STK3 or FOXO1. We demonstrated the regulatory relationship of the TP53INP1/P21 gene medicated by the STK3-FOXO1 axis using Western blotting and ChIP in vitro. RESULTS: We demonstrate high STK3 expression in ESCC tissue and cell lines compared to esophageal epithelium. Cellular ROS induces STK3 autophosphorylation in ESCC cells, resulting in upregulated p-STK3/4. STK3 activation inhibits ESCC cell proliferation and migration by triggering apoptosis and suppressing the cell cycle. STK3 kinase activation phosphorylates FOXO1Ser212, promoting nuclear translocation, enhancing transcriptional activity, and upregulating TP53INP1 and P21. We also investigated TP53INP1 and P21's phenotypic effects in ESCC, finding that their knockdown significantly increases tumor proliferation, highlighting their crucial role in ESCC tumorigenesis. CONCLUSION: STK3 kinase has a high expression level in ESCC and can be activated by cellular ROS, inhibiting cell proliferation and migration. Additionally, STK3 activation-mediated FOXO1 regulates ESCC cell apoptosis and cell cycle arrest by targeting TP53INP1/P21. Our research underscores the anti-tumor function of STK3 in ESCC and elucidates the mechanism underlying its anti-tumor effect on ESCC.

Morphological control and performance engineering of Co-based materials for supercapacitors.

As one of the most promising energy storage devices, supercapacitors exhibit a higher power density than batteries. However, its low energy density usually requires high-performance electrode materials. Although the RuO2 material shows desirable properties, its high cost and toxicity significantly limit its application in supercapacitors. Recent developments demonstrated that Co-based materials have emerged as a promising alternative to RuO2 for supercapacitors due to their low cost, favorable redox reversibility and environmental friendliness. In this paper, the morphological control and performance engineering of Co-based materials are systematically reviewed. Firstly, the principle of supercapacitors is briefly introduced, and the characteristics and advantages of pseudocapacitors are emphasized. The special forms of cobalt-based materials are introduced, including 1D, 2D and 3D nanomaterials. After that, the ways to enhance the properties of cobalt-based materials are discussed, including adding conductive materials, constructing heterostructures and doping heteroatoms. Particularly, the influence of morphological control and modification methods on the electrochemical performances of materials is highlighted. Finally, the application prospect and development direction of Co-based materials are proposed.

Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021.

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

IGF-1-mediated FOXC1 overexpression induces stem-like properties through upregulating CBX7 and IGF-1R in esophageal squamous cell carcinoma.

Substantial evidence attests to the pivotal role of cancer stem cells (CSC) in both tumorigenesis and drug resistance. A member of the forkhead box (FOX) family, FOXC1, assumes significance in embryonic development and organogenesis. Furthermore, FOXC1 functions as an overexpressed transcription factor in various tumors, fostering proliferation, enhancing migratory capabilities, and promoting drug resistance, while maintaining stem-cell-like properties. Despite these implications, scant attention has been devoted to its role in esophageal squamous cell carcinoma. Our investigation revealed a pronounced upregulation of FOXC1 expression in ESCC, correlating with a poor prognosis. The downregulation of FOXC1 demonstrated inhibitory effects on ESCC tumorigenesis, proliferation, and tolerance to chemotherapeutic agents, concurrently reducing the levels of stemness-related markers CD133 and CD44. Further studies validated that FOXC1 induces ESCC stemness by transactivating CBX7 and IGF-1R. Additionally, IGF-1 activated the PI3K/AKT/NF-κB and MEK/ERK/NF-κB pathways through its binding to IGF-1R, thereby augmenting FOXC1 expression. Conversely, suppressing FOXC1 impeded ESCC stemness induced by IGF-1. The presence of a positive feedback loop, denoted by IGF-1-FOXC1-IGF-1R, suggests the potential of FOXC1 as a prognostic biomarker for ESCC. Taken together, targeting the IGF-1-FOXC1-IGF-1R axis emerges as a promising approach for anti-CSC therapy in ESCC.

Design of HL7 FHIR Profiles for Pathology Reports Integrated with Pathology Images.

This paper describes the development of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) profiles for pathology reports integrated with whole slide images and clinical data to create a pathology research database. A report template was designed to collect structured reports, enabling pathologists to select structured terms based on a checklist, allowing for the standardization of terms used to describe tumor features. We gathered and analyzed 190 non-small-cell lung cancer pathology reports in free text format, which were then structured by mapping the itemized vocabulary to FHIR observation resources, using international standard terminologies, such as the International Classification of Diseases, LOINC, and SNOMED CT. The resulting FHIR profiles were published as an implementation guide, which includes 25 profiles for essential data elements, value sets, and structured definitions for integrating clinical data and pathology images associated with the pathology report. These profiles enable the exchange of structured data between systems and facilitate the integration of pathology data into electronic health records, which can improve the quality of care for patients with cancer.

Effective control of refractory pulmonary hypertension with iloprost inhalation in an infant with congenital absence of the right pulmonary artery: a case report.

Unilateral absence of the pulmonary artery is a rare congenital cardiovascular anomaly that can lead to pulmonary hypertension and poor outcomes. We report the case of a 1-month-old infant with isolated unilateral absence of the pulmonary artery and severe pulmonary hypertension on the right and left sides, respectively. The patient was unresponsive to multiple medications for pulmonary hypertension, and surgical revascularisation was unfeasible. However, iloprost inhalation was effective.

ELF4 contributes to esophageal squamous cell carcinoma growth and metastasis by augmenting cancer stemness via FUT9.

Esophageal squamous cell carcinoma (ESCC) commonly has aggressive properties and a poor prognosis. Investigating the molecular mechanisms underlying the progression of ESCC is crucial for developing effective therapeutic strategies. Here, by performing transcriptome sequencing in ESCC and adjacent normal tissues, we find that E74-like transcription factor 4 (ELF4) is the main upregulated transcription factor in ESCC. The results of the immunohistochemistry show that ELF4 is overexpressed in ESCC tissues and is significantly correlated with cancer staging and prognosis. Furthermore, we demonstrate that ELF4 could promote cancer cell proliferation, migration, invasion, and stemness by in vivo assays. Through RNA-seq and ChIP assays, we find that the stemness-related gene fucosyltransferase 9 ( FUT9) is transcriptionally activated by ELF4. Meanwhile, ELF4 is verified to affect ESCC cancer stemness by regulating FUT9 expression. Overall, we first discover that the transcription factor ELF4 is overexpressed in ESCC and can promote ESCC progression by transcriptionally upregulating the stemness-related gene FUT9.

Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S.

OBJECTIVES: The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. METHODS: We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model-which had the greatest AUC of 0.83 (95% CI 0.81-0.85)-was then only used on the holdout testing data. RESULTS: On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79-0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques-specifically, the LGBM classifier-with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. CONCLUSIONS: Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.

Synergistic Interfacial Engineering of Heterostructured Cobalt Phosphide Spheres/Cobalt Hydroxide Nanosheets for Overall Water Splitting.

Recently, transition metal phosphides (TMPs) have been widely explored for the hydrogen evolution reaction (HER) due to their advantaged activity. Nevertheless, the OER performance of TMPs in an alkaline medium is still unsatisfactory. Therefore, interfacial engineering of TMPs to enhance the OER performance is highly desirable. Herein, a Co(OH)2 nanosheet coupled with a CoP sphere supported on nickel foam (NF) is developed by a simple two-step electrodeposition. The large surface area derived from stacked nanosheets and the electronic regulation induced by heterostructure can significantly enhance charge/mass transfer and expose more active sites, thus accelerating the kinetics of the reaction. In addition, the strong electronic interaction between CoP and Co(OH)2 is conducive to the generation of a high valence cobalt center; thus, the electrocatalytic performances toward HER and OER are remarkably improved. Impressively, the optimized CoP/Co(OH)2@NF heterostructure obtains an excellent HER and OER performance with low overpotentials of 76 and 266 mV at 10 mA cm-2, respectively, superior to the commercial Pt/C and RuO2. Moreover, the optimized CoP/Co(OH)2@NF can afford the lowest cell voltage of 1.58 V to achieve 10 mA cm-2 for alkaline overall water splitting and shows outstanding long-term stability.

Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.

Purpose: To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan. Methods: This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established. Results: The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers. Conclusion: A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00248-5.

Survival benefit of a third dose of the COVID-19 vaccine among hemodialysis patients: A prospective cohort study.

BACKGROUND: Hemodialysis (HD) patients are particularly vulnerable to severe coronavirus disease 2019 (COVID-19) due to their immunocompromised state and comorbid conditions. Timely vaccination could be the most effective strategy to reduce morbidity and mortality. However, data on the survival benefit of the COVID-19 vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and death among HD patients are limited, especially during the Omicron-dominant period. METHODS: In this prospective hospital-based cohort study, we identified HD patients from July 1, 2021, to April 29, 2022. The patients were divided into fully vaccinated and partially vaccinated groups. We compared the humoral response, risk of developing SARS-CoV-2 infection, and all-cause mortality between the two groups. RESULTS: Among the 440 HD patients included, 152 patients were fully vaccinated, and 288 patients were partially vaccinated. Patients in the fully vaccinated group exhibited higher anti-spike protein receptor-binding domain (S protein RBD) antibody levels and lower risks of all-cause mortality (adjusted hazard ratio, 0.35; 95% confidence interval, 0.17-0.73; p = 0.005) than the partially vaccinated group. However, the risk for SARS-CoV-2 infection did not significantly differ between the two groups. Irrespective of the number of vaccinations, the risk of all-cause mortality was lower in patients with anti-S protein RBD antibody levels in the higher tertile. CONCLUSION: A third dose of the COVID-19 vaccine was associated with a decreased risk of all-cause mortality among HD patients during the Omicron-dominant period. A higher post-vaccination anti-S protein RBD antibody level was also associated with a lower risk of mortality.

Interplay of Chemokines Receptors, Toll-like Receptors, and Host Immunological Pathways.

A comprehensive framework has been established for understanding immunological pathways, which can be categorized into eradicated and tolerable immune responses. Toll-like receptors (TLRs) are associated with specific immune responses. TH1 immunity is related to TLR7, TLR8, and TLR9, while TH2 immunity is associated with TLR1, TLR2, and TLR6. TH22 immunity is linked to TLR2, TLR4, and TLR5, and THαß (Tr1) immunity is related to TLR3, TLR7, and TLR9. The chemokine receptor CXCR5 is a marker of follicular helper T cells, and other chemokine receptors can also be classified within a framework based on host immunological pathways. On the basis of a literature review on chemokines and immunological pathways, the following associations were identified: CCR5 with TH1 responses, CCR1 with TH1-like responses, CCR4 (basophils) and CCR3 (eosinophils) with TH2 and TH9 responses, CCR10 with TH22 responses, CCR6 with TH17 responses, CXCR3 with THαß responses, CCR8 with regulatory T cells (Treg), and CCR2 with TH3 responses. These findings contribute to the identification of biomarkers for immune cells and provide insights into host immunological pathways. Understanding the chemokine and Toll-like receptor system is crucial for comprehending the function of the innate immune system, as well as adaptive immune responses.

Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS: A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS: Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION: This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.

TAOK3 Facilitates Esophageal Squamous Cell Carcinoma Progression and Cisplatin Resistance Through Augmenting Autophagy Mediated by IRGM.

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancers because of its robust aggressive phenotype and chemoresistance. TAO kinase belongs to mitogen-activated protein kinases, which mediate drug resistance in multiple cancers. However, the role of TAO kinase in ESCC progression and chemoresistance has never been explored. Here, it is reported that TAOK3 augments cell autophagy and further promotes ESCC progression and chemoresistance. Mechanistically, TAOK3 phosphorylates KMT2C at S4588 and strengthens the interaction between KMT2C and ETV5. Consequently, the nuclear translocation of KMT2C is increased, and the transcription of autophagy-relevant gene IRGM is further upregulated. Additionally, the inhibitor SBI-581 can significantly suppress cell autophagy mediated by TAOK3 and synergizes with cisplatin to treat ESCC in vitro and in vivo.

Body composition as a predictor of oncological outcome in patients with non-muscle-invasive bladder cancer receiving intravesical instillation after transurethral resection of bladder tumor.

Introduction: Body status, categorized as sarcopenia or obesity and assessed using body mass index and body composition, affects the outcome of bladder cancer patients. However, studies comparing disease progression, recurrence, or overall survival in patients with non-muscle-invasive bladder cancer (NMIBC) with different body compositions are lacking. Therefore, we conducted a retrospective study to identify the impact of body composition, sarcopenia, and obesity on the oncological prognosis of patients with NMIBC who underwent transurethral resection of bladder tumor (TURBT) with Bacillus Calmette-Guerin (BCG) intravesical instillation (IVI). Methods: Patients with NMIBC who had undergone TURBT with adjuvant IVI with BCG from March 2005 to April 2021 were included. Body composition parameters were evaluated using computed tomography images of the third lumbar vertebrae and further categorized by sarcopenia and obesity. Oncological outcomes including recurrence-free survival (RFS), progression-free survival, and overall survival (OS) after treatment were analyzed. Results: A total of 269 patients were enrolled. Subcutaneous adipose tissue (SAT) density was a significant predictor of RFS, whereas psoas muscle density was a significant predictor of OS in the multivariate analysis. Patients with sarcopenia but without obesity tolerated significantly fewer BCG IVIs than patients without sarcopenia or obesity. Patients with sarcopenia had poorer RFS and OS than those without sarcopenia. In contrast, patients with obesity had better OS than those without obesity. Discussion: Body composition parameters, including SAT density and psoas muscle density, emerged as significant predictors of OS and RFS, respectively. Hence, our findings indicate that body composition is a helpful measurement to assess the oncological outcomes of patients with NMIBC.

Fortifying Health Care Intellectual Property Transactions With Blockchain.

BACKGROUND: Intellectual property (IP) is a substantial competitive advantage in the health care industry. However, the COVID-19 pandemic highlighted the need for open innovation and collaboration for the greater good. Despite this, the industry faces challenges with innovation owing to organizational and departmental barriers. A secure platform is necessary to facilitate IP sharing without compromising the rights of IP owners. OBJECTIVE: This study proposes a blockchain-based framework to secure IP transactions in health care and bring social impact. METHODS: This study reviews existing researches, publications, practical cases, firm and organization websites, and conferences related to blockchain technology, blockchain in health care, blockchain in IP management, IP pledge research, and practice of IP management blockchain. The platform architecture has 7 components: pledgers, advanced research technology (ART), IP pledge platforms, IP databases, health care research, seeking ART, and transaction condition setting. These components work together seamlessly to support the sharing and pledging of ART and knowledge, while ensuring the platform's transparency, security, and trust. RESULTS: The open IP pledge framework can promote technology dissemination and use, reduce research and development costs, foster collaboration, and serve the public interest. Medical organizations' leadership and support and active participation from stakeholders are necessary for success. By leveraging blockchain technology, the platform ensures tamper-proof and transparent transactions and protects the rights of IP owners. In addition, the platform offers incentive mechanisms through pledge tokens that encourage stakeholders to share their ART and contribute to the platform. CONCLUSIONS: Overall, the proposed framework can facilitate technological innovation, tackle various challenges, and secure IP transactions. It provides a secure platform for stakeholders to share their IP without compromising their rights, promoting collaboration and progress in the health care industry. The implementation of the framework has the potential to revolutionize the industry's approach to innovation, allowing a more open and collaborative environment driven by the greater good.