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Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization.Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets.Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses.Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.

To improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an "old" melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (Dm) at the bottom of the melting layer and produces more reasonable melting layer signatures for all three double‐moment microphysics schemes that more closely match the corresponding radar observations. This suggests that the new melting model has more reasonable implicit estimates of mixing ratios and number concentrations of melting hydrometeor species than the "old" melting model. Plain Language Summary: Weather radars can provide information about observed precipitation that can be used to improve weather forecast models. Making use of this information requires linking the radar observations with the variables these models predict in an efficient and accurate way. One of the most pronounced signatures in weather radar observations occurs as precipitation melts from ice into rain. However, current operational forecast models do not explicitly predict melting ice as a separate category of precipitation. Therefore, existing methods for comparing radar observations to forecast models use an empirical melting model to estimate the mass of melting ice and assume melting ice has the same concentration as the model‐predicted dry ice. These assumptions result in a simulated radar melting layer signature that is unrealistically thick and low. In this study, a new melting model is proposed for estimating both the mass and concentration of melting ice, and its effects on simulated radar variables are evaluated for a severe thunderstorm case. For all of the microphysics schemes investigated, the newly proposed melting model results in improved simulations of the size of melting ice particles and simulated radar variables that are closer to those observed. Key Points: A new melting model is proposed and introduced into forward operators for simulation of polarimetric radar dataMore reasonable polarimetric signatures of the melting layer are simulated using different double‐moment microphysics schemesThese polarimetric forward operators have potential to be used in data assimilation for improving high‐resolution severe weather forecasts [ABSTRACT FROM AUTHOR]

In the vast expanse of restorative surgical procedures, the Deep Inferior Epigastric Perforator (DIEP) flap, originating from the inferior epigastric artery, has emerged as the preferred method of breast reconstruction, attributable to its myriad advantages. The technique provides reliable vascular supply, robust tissue volume for excision, minimal invasiveness to the donor site, with direct closure and concealment of the said site. This paper embarks on an elaborate elucidation of the DIEP surgical procedure, pivoting on the analytical exploration of a particular instance where necrosis of the skin flap occurred following immediate DIEP breast reconstruction in a patient diagnosed with early-stage breast cancer. This patient had previously undergone Nipple Areola Complex Sparing Mastectomy (NSM). We endeavor to extrapolate insights from this singular case of post-NSM DIEP breast reconstruction failure and correlate our findings with current literature dedicated to similar instances of surgical failure in DIEP breast reconstruction. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) constitutes one of the most pervasive malignancies affecting the female population. Despite progressive improvements in diagnostic and therapeutic technologies, leading to an increased detection of early stage BCs, locally advanced breast cancer (LABC) persists as a significant clinical challenge. Owing to its poor overall survival (OS) rate, elevated recurrence rate, and high potential for distant metastasis, LABC prominently impacts the comprehensive efficacy of BC treatments. Radiotherapy, encompassing preoperative, intraoperative, and postoperative modalities, is acknowledged as an effective strategy for mitigating BC metastasis and enhancing survival rates among patients. Nevertheless, the domain of preoperative neoadjuvant radiotherapy (NART) remains conspicuously underexplored in clinical studies. Available research suggests that NART can induce tumor volume reduction, provoke fibrotic changes in tumor and adjacent normal tissues, thereby mitigating intraoperative cancer propagation and enhancing the quality of life for LABC patients. This manuscript seeks to provide a review of contemporary research pertaining to LABC and its preoperative radiotherapy. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that there exists a significant correlation between oral microbiome and the occurrence of malignancies. However, the prognostic significance of oral microbiome for cancer patients remains unclear. The purpose of this meta-analysis is to evaluate the impact of oral microbiome on the survival of patients with malignant neoplasms. Methods: We conducted a thorough literature search of PubMed, Embase, and Cochrane Library databases until September 2022. The hazard ratio (HR) with a corresponding 95% confidence interval (CI) was analyzed using Review Manager 5.4 software for survival outcomes, including overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and disease-free survival (DFS). Results: A total of 15 studies, covering 5191 samples with various types of cancers, were selected based on specified inclusion and exclusion criteria. In both univariate and multivariate analysis, patients with low diversity of the oral microbiome, or those with Fusobacterium-high/positive, or P. gingivalis positive in cancer tissue displayed poorer OS (univariate HR = 1.74; 95% CI 1.15–2.62; P = 0.009; multivariate HR = 1.56; 95% CI 1.07–2.27; P = 0.02), DSS (univariate HR = 2.06; 95% CI 1.50–2.84; P < 0.00001; multivariate HR = 1.80; 95% CI 1.48–2.20; P < 0.00001), and PFS/DFS (univariate HR = 2.00; 95% CI 1.12–3.58; P = 0.002; multivariate HR = 1.78; 95% CI 1.05–3.02; P = 0.003). Subgroup analysis revealed that Fusobacterium positive or high abundance in cancer tissues was associated with poor OS in multivariate analysis but had no statistical differences in PFS or DFS in univariate and multivariate analysis. Additionally, P. gingivalis positive in cancer tissue was also associated with worse OS. Conclusions: Our meta-analysis suggests that the composition of the oral microbiome may play a significant role in predicting survival outcomes for cancer patients. [ABSTRACT FROM AUTHOR]

Introduction To date, the role of deficient mismatch repair (dMMR) remains to be proven in gastric cancer, and it is difficult to judge its value in clinical application. Our study aimed to investigate how MMR status affected the prognosis in patients with gastrectomy, as well as the efficacy of neoadjuvant chemotherapy and adjuvant chemotherapy in patients with dMMR with gastric cancer. Materials and Methods Patients with gastric cancer with certain pathologic diagnosis of dMMR or proficient MMR (pMMR) using immunohistochemistry from 4 high-volume hospitals in China were included. Propensity score matching was used to match patients with dMMR or pMMR in 1:2 ratios. Overall survival (OS) and progression-free survival (PFS) curves were plotted using the Kaplan-Meier method and compared statistically using the log-rank test. Univariate and multivariate Cox proportional hazards models based on hazard ratios (HRs) and 95% confidence intervals (CIs) were used to determine the risk factors for survival. Results In total, data from 6176 patients with gastric cancer were ultimately analyzed, and loss of expression of one or more MMR proteins was observed in 293 patients (293/6176, 4.74%). Compared to patients with pMMR, patients with dMMR are more likely to be older (≥66, 45.70% vs. 27.94%, P  < .001), distal location (83.51% vs. 64.19%, P  < .001), intestinal type (42.21% vs. 34.46%, P  < .001), and in the earlier pTNM stage (pTNM I, 32.79% vs. 29.09%, P  = .009). Patients with gastric cancer with dMMR showed better OS than those with pMMR before PSM (P  = .002); however, this survival advantage was not observed for patients with dMMR after PSM (P  = .467). As for perioperative chemotherapy, results of multivariable Cox regression analysis showed that perioperative chemotherapy was not an independent prognostic factor for PFS and OS in patients with dMMR with gastric cancer (HR = 0.558, 95% CI, 0.270-1.152, P  = .186 and HR = 0.912, 95% CI, 0.464-1.793, P  = .822, respectively). Conclusion In conclusion, perioperative chemotherapy could not prolong the OS and PFS of patients with dMMR with gastric cancer. [ABSTRACT FROM AUTHOR]