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Abstract Background Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer. Methods We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images. Results Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P

Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology.

Background Twice-weekly hemodialysis (HD) could be regarded as an important part of incremental hemodialysis, volume status of this treatment model remains to be elucidated. Methods Patients undergoing regular twice-weekly or thrice-weekly hemodialysis in our unit on June 2015 were enrolled into the cohort study with an average of 2.02 years follow-up. Volume status of the subjects was evaluated by clinical characteristics, plasma B-type natriuretic peptide (BNP) levels and bioimpedance assessments with body composition monitor (BCM). Cox proportional hazards models and Kaplan–Meier analysis were used to compare patient survival between the two groups. Results Compared with patients on thrice-weekly HD, twice-weekly HD patients had significantly higher log-transformed BNP levels (2.54 ± 0.41 vs. 2.33 ± 0.49 pg/ml, p = 0.010). Overhydration (OH) and the ratio of overhydration to extracellular water (OH/ECW) in twice-weekly HD group were significantly higher than that of thrice-weekly HD (OH, 2.54 ± 1.42 vs. 1.88 ± 1.46, p = 0.033; OH/ECW, 0.17 ± 0.07 vs. 0.12 ± 0.08, p = 0.015). However, subgroup analysis of patients within 6 years HD vintage indicated that the two groups had similar hydration status. Multivariate Cox regression analysis showed that log-transformed BNP levels, serum albumin and diabetes status were predictors of mortality in hemodialysis patients. Kaplan–Meier survival analysis indicated that patients with BNP levels higher than 500 pg/ml had significantly worse survival compared with those with lower BNP levels (p = 0.014). Conclusions Twice-weekly hemodialysis patients had worse volume status than that of thrice-weekly HD patients especially for those with long-term dialysis vintage, BNP level was a powerful predictor of mortality in HD patients.

Introduction: Automated processing of electronic health data to classify complications of ischemic stroke serves numerous purposes, including improved electronic phenotyping for clinical research. Here, we present a natural language processing (NLP) approach to identify critical findings in acute ischemic stroke from unstructured radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). Methods: Text reports of CT and MRI scans taken from 2292 patients admitted for large (>1/2 middle cerebral artery territory), acute anterior circulation ischemic stroke were gathered from a single‐institution retrospective cohort. Reports were reviewed and labelled for the presence of hemorrhagic conversion, intracerebral edema, midline shift, intraventricular hemorrhage and parenchymal hematoma as defined by European Cooperative Acute Stroke Study PH1 and PH2 categories. For binary classifications, we quantified co‐occurrence of individual words within reports using two separate NLP methods: Bag‐of‐Words (BOW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). We then trained Lasso regression, random forest, and neural network classifiers to predict all complications based on word co‐occurrence. Classifier performance was measured by area under receiver operating characteristic curves (AUC) using five separate folds of an internal test dataset. To predict midline shift as a continuous outcome, we developed a semantic rule‐based system (RBS) based on regular radiographic report expressions. This system was tested using an external validation dataset of 1472 acute large anterior circulation stroke reports from a separate hospital. Results: 2292 reports were fully labelled for the presence of all stroke complications. Lasso regression consistently displayed the best discrimination among all models. For BOW and TF‐IDF, Lasso yielded respective AUCs of 0.894 and 0.919 (hemorrhagic conversion), 0.935 and 0.950 (intracerebral edema), 0.968 and 0.963 (midline shift), 0.933 and 0.904 (intraventricular hemorrhage), and 0.873 and 0.879 (parenchymal hematoma). All models were well‐calibrated to underlying complication rates. The RBS also achieved strong performance in quantifying midline shift, achieving a mean absolute error (MAE) of 0.103 mm, sensitivity of 99.1% and specificity of 97.5% in the original cohort. In the external validation set of 1472 additional stroke reports, this same system achieved a MAE of 0.126 mm, sensitivity of 99.5% and specificity of 97.5% for midline shift. Wilcoxon rank sum testing on bootstrapped samples confirmed no statistically‐significant differences in RBS performance between institutions when comparing MAE (p = 0.918), sensitivity (p = 0.152), and specificity (p = 0.929). Conclusions: A machine learning pipeline based on Lasso regression successfully identified critical complications of large anterior circulation ischemic stroke from unstructured radiology reports, while our RBS quantified midline shift with a high degree of generalized accuracy between different institutions. We propose that these systems may warrant prospective validation in care settings and data mining for stroke research.

The aim of the present study was to investigate the expression and clinical significance of hypoxia-inducible factor 1a (HIF-1a), Snail and E-cadherin in ovarian cancer. The expression levels were assessed in a number of ovarian cancer cell lines and ovarian cancer tissues, and correlations between the expression of the three proteins and clinical pathological factors were analyzed. Transwell assays showed that the invasive ability of the ovarian cancer cell lines SKOV3 and ES-2 were significantly higher than those of TYK and 3AO (P<0.01). Furthermore, the expression levels of HIF-1a and Snail in SKOV3 and ES-2 were significantly higher than those in TYK and 3AO, whereas the expression levels of E-cadherin in SKOV3 and ES-2 were significantly lower than those in TYK and 3AO (P<0.05). In ovarian cancer tissues, the expression levels of HIF-1a, Snail and E-cadherin were correlated with clinical pathological factors (P<0.01); furthermore, there was a positive correlation between the expression levels of HIF-1a and Snail (r=0.231; P=0.021), and a negative correlation between the expression levels of Snail and that of E-cadherin (r=-0.225; P=0.028). HIF-1a was suggested to be able to suppress the expression of E-cadherin by upregulating Snail, thus serving an important role in invasion and metastasis of ovarian cancer. [ABSTRACT FROM AUTHOR]

Globally, chronic nitrogen (N) deposition into terrestrial ecosystems has resulted in changes in community composition, depending on the responses of co-existing species to increasing soil N availability. The levels of plant non-structural carbohydrates (NSCs) produced by photosynthesis are associated with leaf N content, and together they represent the capital of a plant for its life including competitive ability. However, the specific ways that concentrations of plant NSCs, and also N, influence the growth of plants, and thus their productivity, are still abscue. Here, we explored the effect of variations in plant leaf NSCs (starch and soluble sugars) and N concentrations on the growth of plants with different soil N uptake capacities, i.e. two grasses (Leymus chinensis and Stipa baicalensis), two forbs (Potentilla tanacetifolia and Galium verum), and one legume (Thermopsis lanceolate) in Inner Mongolian grasslands subjected to three N addition rates (i.e., 0, 10, or 20 g N m−2 yr−1) and three N forms [i.e., NH4NO3, (NH4)2SO4, or CO(NH2)2] for four years. Irrespective of N addition rate and N form, N addition significantly increased aboveground biomass of the grasses, but not that of the forbs or the legume. The grasses had higher increase in N concentrations than the forbs and the legume. N addition increased the starch concentrations of grasses, but decreased that of forbs and the legume. Changes in the aboveground biomass of all tested plant species were significantly positively correlated with changes in concentrations of both NSCs and N. Our results indicate that, irrespective of the N addition rates and N forms, greater increase in NSCs and N concentrations contributed to higher aboveground productivity in grasses, but not in forbs and legumes. These results imply that future nitrogen deposition will benefit grasses over other plant forms, and thus the former may become more dominant in grasslands in northern China.

An amendment to this paper has been published and can be accessed via the original article.