Naples Prognostic Score: A Novel Predictor of Survival in Patients with Triple-Negative Breast Cancer.

Purpose: This study investigated the correlation between the Naples prognostic score (NPS), clinicopathological traits, and the postoperative prognoses of patients with triple-negative breast cancer (TNBC). Based on NPS, a predictive nomogram was developed to estimate the long-term survival probabilities of patients with TNBC post-surgery. Patients and Methods: We retrospectively examined the clinical records of 223 women with TNBC treated at Ningbo Medical Center, Lihuili Hospital between January 1, 2016 and December 31, 2020. Blood tests and biochemical analyses were conducted before surgery. The prognostic nutritional index (PNI), controlling nutritional status (CONUT), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and NPS were determined based on blood-related markers. A Kaplan-Meier survival analysis assessed the association between NPS, PNI, CONUT score, overall survival (OS), and breast cancer-specific survival (BCSS). Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and C index. The patients were randomly divided into the training and the validation group (6:4 ratio). A nomogram prediction model was developed and evaluated using the R Software for Statistical Computing (RMS) package. Results: NPS outperformed other scores in predicting inflammation outcomes. Patients with an elevated NPS had a poorer prognosis (P<0.001). Lymph node ratio (LNR), surgical method, postoperative chemotherapy, and NPS independently predicted OS, whereas M stage, LNR, and NPS independently predicted BCSS outcome. The OS and BCSS predicted by the nomogram model aligned well with the actual OS and BCSS. The decision curve analysis showed significant clinical utility for the nomogram model. Conclusion: In this study, NPS was an important prognostic indicator for patients with TNBC. The nomogram prognostic model based on NPS outperformed other prognostic scores for predicting patient prognosis. The model demonstrated a clear stratification ability for patient prognosis, which emphasized the potential benefits of early intervention for high-risk patients.

In this study, we aimed to understand how the Naples prognostic score (NPS) scoring system could predict the prognosis for patients with triple-negative breast cancer (TNBC). TNBC is a type of breast cancer that can be difficult to treat. Medical records of 223 women with TNBC were retrospectively analyzed. These women had their blood tested before surgery to check for certain markers related to nutrition and inflammation. NPS was used along with other scores to determine their accuracy in predicting survival. NPS was better at predicting outcomes than the other scores. The patients with higher NPS scores tended to have poorer outcomes. We also created a visual tool called a nomogram to help doctors predict patient outcomes based on the NPS scores. NPS can be a valuable tool for doctors treating patients with TNBC because it can help them predict how well a patient might do after surgery. This information could be used to tailor treatment plans for these patients. The nomogram provides a user-friendly way for doctors to use NPS in their practice. Overall, this study showed that NPS is a powerful tool for predicting outcomes for patients with TNBC, which could lead to better treatment decisions and improved outcomes for these patients.

Triple-negative breast cancer survival prediction: population-based research using the SEER database and an external validation cohort.

Introduction: Triple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis. Methods: We included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis. Results: In the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately. Conclusion: These nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

Carbon Dot-Based Photo-Cross-Linked Gelatin Methacryloyl Hydrogel Enables Dental Pulp Regeneration: A Preliminary Study.

An essential factor in tooth nutritional deficits and aberrant root growth is pulp necrosis. Removing inflammatory or necrotic pulp tissue and replacing it with an inert material are the most widely used therapeutic concepts of endodontic treatment. However, pulp loss can lead to discoloration, increased fracture risk, and the reinfection of the damaged tooth. It is now anticipated that the pulp-dentin complex will regenerate through a variety of application methods based on human dental pulp stem cells (hDPSC). In order to create a photo-cross-linked gelatinized methacrylate hydrogel, GelMA/EUO-CDs-E (ECE), that is biodegradable and injectable for application, we created a novel nanoassembly of ECE based on eucommia carbon dots (EUO-CDs) and epigallocatechin gallate (EGCG). We then loaded it onto gelatin methacryloyl (GelMA) hydrogel. We have evaluated the material and examined its in vivo and in vitro angiogenesis-promoting potential as well as its dentin differentiation-enabling characteristics. The outcomes of the experiment demonstrated that GelMA/ECE was favorable to cell proliferation and enhanced hDPSC's capacity for angiogenesis and dentin differentiation. The regeneration of vascular-rich pulp-like tissues was found to occur in vivo when hDPSC-containing GelMA/ECE was injected into cleaned human root segments (RS) for subcutaneous implantation in nude mice. This suggests that the injectable bioscaffold is appropriate for clinical use in pulp regenerative medicine.

Recent Advances in Whiskers: Properties and Clinical Applications in Dentistry.

Whiskers are nanoscale, high-strength fibrous crystals with a wide range of potential applications in dentistry owing to their unique mechanical, thermal, electrical, and biological properties. They possess high strength, a high modulus of elasticity and good biocompatibility. Hence, adding these crystals to dental composites as reinforcement can considerably improve the mechanical properties and durability of restorations. Additionally, whiskers are involved in inducing the value-added differentiation of osteoblasts, odontogenic osteocytes, and pulp stem cells, and promoting the regeneration of alveolar bone, periodontal tissue, and pulp tissue. They can also enhance the mucosal barrier function, inhibit the proliferation of tumor cells, control inflammation, and aid in cancer prevention. This review comprehensively summarizes the classification, properties, growth mechanisms and preparation methods of whiskers and focuses on their application in dentistry. Due to their unique physicochemical properties, excellent biological properties, and nanoscale characteristics, whiskers show great potential for application in bone, periodontal, and pulp tissue regeneration. Additionally, they can be used to prevent and treat oral cancer and improve medical devices, thus making them a promising new material in dentistry.

Joint analysis of proteome, transcriptome, and multi-trait analysis to identify novel Parkinson's disease risk genes.

Genome-wide association studies (GWAS) have identified multiple risk variants for Parkinson's disease (PD). Nevertheless, how the risk variants confer the risk of PD remains largely unknown. We conducted a proteome-wide association study (PWAS) and summary-data-based mendelian randomization (SMR) analysis by integrating PD GWAS with proteome and protein quantitative trait loci (pQTL) data from human brain, plasma and CSF. We also performed a large transcriptome-wide association study (TWAS) and Fine-mapping of causal gene sets (FOCUS), leveraging joint-tissue imputation (JTI) prediction models of 22 tissues to identify and prioritize putatively causal genes. We further conducted PWAS, SMR, TWAS, and FOCUS using a multi-trait analysis of GWAS (MTAG) to identify additional PD risk genes to boost statistical power. In this large-scale study, we identified 16 genes whose genetically regulated protein abundance levels were associated with Parkinson's disease risk. We undertook a large-scale analysis of PD and correlated traits, through TWAS and FOCUS studies, and discovered 26 casual genes related to PD that had not been reported in previous TWAS. 5 genes (CD38, GPNMB, RAB29, TMEM175, TTC19) showed significant associations with PD at both the proteome-wide and transcriptome-wide levels. Our study provides new insights into the etiology and underlying genetic architecture of PD.