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Aim No or unlimited dispersal capacities of species are commonly assumed in species distribution models (SDMs) used to assess potential impacts of climate change on biodiversity. However, these assumptions may lead to overly pessimistic or optimistic predictions. We aim to assess how outputs of broad‐scale SDMs of plants based on species‐specific dispersal estimates compare to outputs based on no and unlimited dispersal assumptions under climate change. Location Europe. Method We developed SDMs using occurrence records of 1318 vascular plant species in Europe and predictor variables representative of climate, soil and topographic conditions. We used the SDMs to project species to 2071–2100 for two climate scenarios (mild and severe climate change) combined with three dispersal assumptions: species‐specific, no dispersal, and unlimited dispersal. We then calculated the range sizes, species richness, and changes in both for each combination. Results Range size changes and species richness changes based on species‐specific dispersal estimates generally approximated those under the no dispersal assumption in both climate scenarios. This reflects that the majority of the species in our sample have a limited capacity to disperse. Only for the minority of species dispersed by wind or animals did the incorporation of species‐specific dispersal estimates result in clearly smaller projected range losses compared to the no dispersal assumption. However, also for these species the assumption of unlimited dispersal was generally too optimistic. Main Conclusions We found that many plant species considered in our study have a limited capacity to disperse beyond their current range under climate change. We recommend incorporating species‐specific dispersal estimates in broad‐scale SDMs of plants, to reduce uncertainties in species distributions projected under climate change. In the absence of this information, we recommend assuming no dispersal capacity for species with low dispersal rates, because the assumption of unlimited dispersal leads to considerably overestimated future range sizes and species richness.

Peripherally inserted central catheter (PICC)-related bloodstream infections (BSIs) are serious complications in breast cancer patients. Reliable early risk assessment remains limited. Female breast cancer patients (n = 384) receiving PICCs were retrospectively analyzed. Serum CGRP levels were measured by ELISA, while PD-1 and PD-L1 expression was quantified via qPCR. A 3-point BioScore was calculated by assigning one point per abnormal biomarker (CGRP < 42.817 pg/mL, PD-1 > 2.301, PD-L1 < 1.613). Patients were stratified into low (0), intermediate (1–2), or high (3) risk groups. The cohort was split into training (n = 269) and validation (n = 115) sets. BSIs occurred in 78 patients (20.3%). The BioScore demonstrated excellent discrimination (AUC 0.96 in training and 0.93 in validation) and good calibration (Hosmer–Lemeshow P  = 0.797 and 0.875). BSI rates rose with BioScore category. While the BioScore was derived from biomarker values measured at the time of clinical suspicion for BSI, its strong discriminatory performance suggests potential for earlier application, such as at routine follow-ups, pending prospective validation. The BioScore demonstrated excellent internal discrimination and calibration in a retrospective, single-center cohort. Further external and prospective validation is necessary before clinical use.

Risk stratification in sepsis remains a major clinical challenge in hospital settings, where timely recognition of disease progression can critically influence outcomes. Traditional scoring systems, such as SOFA and APACHE II, are frequently applied but are limited by their complexity and inconsistent predictive accuracy. Integrating biological markers with clinical scores may enhance the early identification of patients with an unfavorable prognosis. The objective of this investigation was to determine the prognostic performance of two composite scoring systems, BIO-S and BIO-SC, in predicting 28-day mortality among patients with sepsis or septic shock. We conducted a retrospective single-center study including 125 adult surgical patients with sepsis or septic shock. BIO-S was calculated using procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR), INR, and SOFA score, whereas BIO-SC extended this model by incorporating the Charlson Comorbidity Index (CCI). Both bioscores were calculated at admission and analyzed in relation to 28-day mortality and discharge status. Among the 125 patients included, 28-day all-cause mortality was 36% (n = 45). The BIO-SC score achieved the highest predictive accuracy for 28-day mortality (AUC = 0.942), surpassing BIO-S (AUC = 0.930), SOFA (AUC = 0.928), and APACHE II (AUC = 0.918). Both bioscores correlated strongly with discharge outcomes and were independent predictors of 28-day mortality (p < 0.001). Integrating inflammatory biomarkers, organ dysfunction, and comorbidity burden into composite prognostic models such as BIO-S and BIO-SC significantly improves early mortality risk assessment and outcome prediction in sepsis, although external validation remains necessary.

Drainage, agricultural conversion, and climate change threaten wetlands and their unique biodiversity. Species distribution models (SDMs) can help to identify effective conservation measures. However, existing SDMs for wetland plants are often geographically limited, miss variables representing hydrological conditions, and neglect moss species, essential to many wetlands. Here, we developed and validated SDMs for 265 vascular plant and moss species characteristic of European wetlands, using environmental variables representing climate, soil, hydrology, and anthropogenic pressures. We validated the spatial predictions of the SDMs through cross‐validation and against independent data from the Global Biodiversity Information Facility (GBIF). Further, we validated the niche optima of the species, as obtained from the modelled species response curves, with empirical niche optima. The spatial validation revealed good predictive power of the SDMs, especially for diagnostic mosses, for which we obtained median cross‐validated values of the area under the curve (AUC) and true skill statistic (TSS) of 0.93 and 0.73, respectively, and a median true positive rate (TPR) based on GBIF records of 0.77. SDMs of diagnostic vascular plants performed well, too, with median AUC, TSS, and TPR of 0.91, 0.69, and 0.67, respectively. SDMs of non‐diagnostic plants had the lowest performance, with median AUC, TSS, and TPR values of 0.84, 0.53, and 0.62, respectively. Correlations between modelled and empirical niche optima were typically in the expected direction. Climate variables, particularly the mean temperature of the coldest month, were the strongest predictors of species occurrence. At the same time, groundwater table depth was a significant predictor for diagnostic vascular plants but not for mosses. We concluded that our SDMs are suitable for predicting broad‐scale patterns of wetland plant species distributions as governed by climatic conditions. Alternative or additional variables or a different modelling approach might be needed to represent better the local heterogeneity in the hydrological conditions of wetlands. We fitted species distribution models (SDMs) for 265 wetland plant species in Europe. We validated predicted occurrences using independent data. The SDMs showed a high predictive performance, such that our SDMs can be used to predict broad‐scale distributions of wetland plants.

Patients with triple-negative breast cancer (TNBC) who have not achieved pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) were considered for adjuvant capecitabine. This study was to explore the utility of the Neo-Bioscore in guiding post-surgical therapy in TNBC. The Neo-Bioscore was calculated for patients with non-metastatic primary breast cancer who received NAC at National Cancer Center Hospital East, Japan. A total of 329 patients were evaluated. The Neo-Bioscore stratified prognosis after NAC better than clinical or pathological stage. The Neo-Bioscore performed well in the selection of patients with TNBC with excellent prognoses despite non-pCR; no death was observed in patients who had a Neo-Bioscore of 2, the lowest score in those with TNBC. The Neo-Bioscore can improve the prognostic stratification of patients after NAC for breast cancer over clinical and pathological staging and may enable the identification of patients with non-pCR TNBC who can avoid additional adjuvant chemotherapy.

PurposeDifferent tumor-related factors have been proposed to assess the risk of disease progression and death in women undergoing neoadjuvant breast cancer chemotherapy. Recently, besides the classical pre-treatment clinical stage (CS) and post-treatment pathologic stage (PS), estrogen receptor status and histologic grade (CPS + EG score) and HER2 results (Neo-Bioscore) have also been added to this suite of staging systems, generating new scores. The present study aims to compare the performance of these four staging systems, namely CS, PS, CPS + EG and Neo-Bioscore, in the prognosis of breast cancer in women undergoing neoadjuvant chemotherapy.MethodsThis study comprises a retrospective cohort study of female breast cancer patients diagnosed at the Brazilian National Cancer Institute, Brazil from January 2013 to December 2015. A descriptive analysis of patient characteristics was conducted, and Kaplan–Meier curves, a Cox proportional hazard analysis and Receiver Operating Characteristic (ROC) curves were developed according to the assessed staging system scores.ResultsA total of 803 patients were eligible for this study. Most were under 65 years old (88.0%), presented advanced tumors (clinical stage ≥ IIB 77.1%), with positive estrogen receptor (71.2%) and negative HER2 (75.7%) results. During the follow-up, 172 patients (21.4%) evolved to death. A statistical difference (p < 0.001) was observed between 5 year disease-free survival and 5 year overall survival rates according to the PS, CPS + EG and Neo-Bioscore staging systems.ConclusionThe PS, CPS + EG and Neo-Bioscore staging systems were proven to be equivalent to predict the prognosis of patients undergoing neoadjuvant chemotherapy.

Background: Recently, the classification of HER2 status evolves from binary to ternary, and HER2-low expression may exhibit prognostic significance. We aimed to investigate whether HER2-low tumor is distinct from HER2-zero or HER2-positive tumors, and then to develop a modified staging system (mNeo-Bioscore) that incorporates HER2-low status into Neo-Bioscore. Patients and Methods: This cohort study was conducted using data from the prospective database on breast cancer patients between January 2014 and February 2019. Results: Among 259 patients enrolled in the study, the HER2-low tumor exhibited significantly lower histological grade, pathological staging and Ki-67 level than the other two groups. HER2-low patients and HER2-positive patients receiving concurrent HER2-directed therapy may have similar LRFS (p = 0.531) and OS (p = 0.853), while HER2-zero peers may have significantly worse LRFS (p = 0.006) and OS (p = 0.017). In particular, a similar trend was also found in the patients without pathological complete response after surgery. Incorporation of HER2-low status made improvement in fit: 5-year OS rate estimates ranged from 33.33% to 100% for mNeo-Bioscore vs 61.36% to 100% for Neo-Bioscore. Conclusions: This study demonstrated that HER2-low tumor may exhibit prognostic significance. The innovative mNeo-Bioscore, based on a new classification of HER2 status, may serve as a prognostic staging system superior to Neo-Bioscore.

Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE. Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE. Department of Rheumatology of the Second Hospital of Shanxi Medical University. SLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study. The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established. Both PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794-0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8-10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE. The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.

Background Neoadjuvant chemotherapy (NAC) is widely used in the treatment of primary breast cancer. Different staging systems have been developed to evaluate the residual tumor after NAC and classify patients into different prognostic groups. Ki67, a proliferation marker, has been shown to be useful in predicting treatment response and prognosis. We aimed to investigate the prognostic importance Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels in breast cancer patients who received NAC and correlations between Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels. Methods A total of 176 invasive breast carcinoma patients who underwent NAC were included in the study. Ki67 levels were evaluated by immunohistochemical methods in Trucut biopsy and surgical excision specimens. Patients were classified into prognostic groups using the Neo-Bioscore staging system. Results Patients with high pretreatment Ki67 score were more likely to be in the higher Neo-Bioscore risk group (p < 0.001). Patients with a high posttreatment Ki67 score were more likely to be in the higher Neo-Bioscore prognostic risk group (p < 0.001). Overall survival (OS) and disease-free survival (DFS) were shorter in patients with high posttreatment Ki67 scores and in patients in the higher Neo-Bioscore risk group. We also determined a cutoff 37% for pathological complete response. Conclusion Neo-Bioscore staging system is found to be important in predicting survival. The posttreatment Ki67 level is more important than pretreatment Ki67 level in predicting survival.

Prognostic assessment after preoperative systemic therapy (PST) is critical to develop a therapeutic strategy for breast cancer management. Currently, a clinical–pathologic staging system that incorporates ER status and nuclear grading (CPS + EG), and the Neo‐Bioscore system that includes HER2 status into CPS + EG, are used to predict outcomes in patients with breast cancer after PST. While HER2‐positive is recognized as a favorable factor in the Neo‐Bioscore system based on results in patients administered one year of trastuzumab as anti‐HER2 therapy, most HER2‐positive cases have difficulty accessing anti‐HER2 treatment in China. Therefore, it is crucial that a modified Neo‐Bioscore staging system is developed that incorporates an additional factor of poor prognosis, HER2‐positive status without trastuzumab treatment, to determine accurate prognosis. We propose a retrospective multicenter cohort study in China to validate CPS + EG, Neo‐Bioscore, and the modified Neo‐Bioscore system and determine the accuracy of prediction. Primary breast cancer patients without metastasis treated with PST and surgery in academic institutions or hospitals of provincial level in China will be included. Disease‐free, disease specific, and overall survival will be calculated using the Kaplan–Meier Method, stratified by CPS + EG, Neo‐Bioscore, and the modified Neo‐Bioscore staging system. Areas under the curve of each staging system will be calculated. Multivariate analysis using Wald testing and maximum likelihood estimates in a Cox proportional hazards model will be conducted.