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Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
In this comprehensive review, we will dissect the impact of research on proteoglycans focusing on recent developments involved in their synthesis, degradation, and interactions, while critically assessing their usefulness in various biological processes. The emerging roles of proteoglycans in global infections, specifically the SARS-CoV-2 pandemic, and their rising functions in regenerative medicine and biomaterial science have significantly affected our current view of proteoglycans and related compounds. The roles of proteoglycans in cancer biology and their potential use as a next-generation protein-based adjuvant therapy to combat cancer is also emerging as a constructive and potentially beneficial therapeutic strategy. We will discuss the role of proteoglycans in selected and emerging areas of proteoglycan science, such as neurodegenerative diseases, autophagy, angiogenesis, cancer, infections and their impact on mammalian diseases.
Long non-coding RNAs (lncRNAs) orchestrate various biological processes and regulate the development of cardiovascular diseases. Their potential therapeutic benefit to tackle disease progression has recently been extensively explored. Our study investigates the role of lncRNA Nudix Hydrolase 6 (NUDT6) and its antisense target fibroblast growth factor 2 (FGF2) in two vascular pathologies: abdominal aortic aneurysms (AAA) and carotid artery disease. Using tissue samples from both diseases, we detected a substantial increase of NUDT6, whereas FGF2 was downregulated. Targeting Nudt6 in vivo with antisense oligonucleotides in three murine and one porcine animal model of carotid artery disease and AAA limited disease progression. Restoration of FGF2 upon Nudt6 knockdown improved vessel wall morphology and fibrous cap stability. Overexpression of NUDT6 in vitro impaired smooth muscle cell (SMC) migration, while limiting their proliferation and augmenting apoptosis. By employing RNA pulldown followed by mass spectrometry as well as RNA immunoprecipitation, we identified Cysteine and Glycine Rich Protein 1 (CSRP1) as another direct NUDT6 interaction partner, regulating cell motility and SMC differentiation. Overall, the present study identifies NUDT6 as a well-conserved antisense transcript of FGF2. NUDT6 silencing triggers SMC survival and migration and could serve as a novel RNA-based therapeutic strategy in vascular diseases.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Background: Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, with few treatment options. NAPOLI 3 aimed to compare the efficacy and safety of NALIRIFOX versus nab-paclitaxel and gemcitabine as first-line therapy for metastatic pancreatic ductal adenocarcinoma (mPDAC).
Methods: NAPOLI 3 was a randomised, open-label, phase 3 study conducted at 187 community and academic sites in 18 countries worldwide across Europe, North America, South America, Asia, and Australia. Patients with mPDAC and Eastern Cooperative Oncology Group performance status score 0 or 1 were randomly assigned (1:1) to receive NALIRIFOX (liposomal irinotecan 50 mg/m2, oxaliplatin 60 mg/m2, leucovorin 400 mg/m2, and fluorouracil 2400 mg/m2, administered sequentially as a continuous intravenous infusion over 46 h) on days 1 and 15 of a 28-day cycle or nab-paclitaxel 125 mg/m2 and gemcitabine 1000 mg/m2, administered intravenously, on days 1, 8, and 15 of a 28-day cycle. Balanced block randomisation was stratified by geographical region, performance status, and liver metastases, managed through an interactive web response system. The primary endpoint was overall survival in the intention-to-treat population, evaluated when at least 543 events were observed across the two treatment groups. Safety was evaluated in all patients who received at least one dose of study treatment. This completed trial is registered with ClinicalTrials.gov, NCT04083235.
Findings: Between Feb 19, 2020 and Aug 17, 2021, 770 patients were randomly assigned (NALIRIFOX, 383; nab-paclitaxel–gemcitabine, 387; median follow-up 16·1 months [IQR 13·4–19·1]). Median overall survival was 11·1 months (95% CI 10·0–12·1) with NALIRIFOX versus 9·2 months (8·3–10·6) with nab-paclitaxel–gemcitabine (hazard ratio 0·83; 95% CI 0·70–0·99; p=0·036). Grade 3 or higher treatment-emergent adverse events occurred in 322 (87%) of 370 patients receiving NALIRIFOX and 326 (86%) of 379 patients receiving nab-paclitaxel–gemcitabine; treatment-related deaths occurred in six (2%) patients in the NALIRIFOX group and eight (2%) patients in the nab-paclitaxel–gemcitabine group.
Interpretation: Our findings support use of the NALIRIFOX regimen as a possible reference regimen for first-line treatment of mPDAC.
Purpose: The purpose of the study is to retrospectively evaluate the development and technological progress in local oncological treatments of hepatocellular carcinoma (HCC) by means of ablation techniques like laser interstitial thermal therapy (LITT), microwave ablation (MWA) and transarterial chemoembolization (TACE) in a multimodal application.
Method: This retrospective single-center study uses data generated between 1993 and 2020 (1,045 patients). Therapy results are evaluated using survival rates of Kaplan-Meier estimator, Cox proportional hazard regression and log-rank test.
Results: Median survival times in group LITT (25 patients) are 1.6 years, and, 2.6 years for LITT + TACE (67 patients). For LITT only treatments 1-/3-/5-year survival rates scored 64%, 24% and 20%. Results for combined LITT + TACE treatments were 84%, 37% and 14%. Median survival time in group MWA (227 patients) is 4.5 years. Estimated median survival time for MWA + TACE (108 patients) leads to a median survival time of 2.7 years. In group MWA the 1-/3-/5-year survival rates are 85%, 54%, 45%. Group MWA + TACE shows values of 79%, 41% and 25%. A separate group of 618 patients has been analyzed with TACE as monotherapy. Median survival time of 1 year was estimated in this group. 1-/3-/5-year survival rates are 48%, 15% and 8%. - Cox regression analysis showed that the different treatment methods are statistically significant predictors for survival of patients.
Conclusions: Treatments with MWA resulted in best median survival rates, followed by MWA + TACE in combination. Survival rates of MWA only are significantly higher vs. LITT, vs. LITT + TACE and vs. TACE monotherapy.
Adhesion of human pathogenic bacteria to endothelial cells is facilitated by fibronectin interaction
(2023)
Human pathogenic bacteria circulating in the bloodstream need to find a way to interact with endothelial cells (ECs) lining the blood vessels to infect and colonise the host. The extracellular matrix (ECM) of ECs might represent an attractive initial target for bacterial interaction, as many bacterial adhesins have reported affinities to ECM proteins, in particular to fibronectin (Fn). Here, we analysed the general role of EC-expressed Fn for bacterial adhesion. For this, we evaluated the expression levels of ECM coding genes in different ECs, revealing that Fn is the highest expressed gene and thereby, it is highly abundant in the ECM environment of ECs. The role of Fn as a mediator in bacterial cell-host adhesion was evaluated in adhesion assays of Acinetobacter baumannii, Bartonella henselae, Borrelia burgdorferi, and Staphylococcus aureus to ECs. The assays demonstrated that bacteria colocalised with Fn fibres, as observed by confocal laser scanning microscopy. Fn removal from the ECM environment (FN1 knockout ECs) diminished bacterial adherence to ECs in both static and dynamic adhesion assays to varying extents, as evaluated via absolute quantification using qPCR. Interactions between adhesins and Fn might represent the crucial step for the adhesion of human-pathogenic Gram-negative and Gram-positive bacteria targeting the ECs as a niche of infection.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Background: Spondylodiscitis is a potentially life-threatening infection of the intervertebral disk and adjacent vertebral bodies, with a mortality rate of 2–20%. Given the aging population, the increase in immunosuppression, and intravenous drug use in England, the incidence of spondylodiscitis is postulated to be increasing; however, the exact epidemiological trend in England remains unknown.
Objective: The Hospital Episode Statistics (HES) database contains details of all secondary care admissions across NHS hospitals in England. This study aimed to use HES data to characterise the annual activity and longitudinal change of spondylodiscitis in England.
Methods: The HES database was interrogated for all cases of spondylodiscitis between 2012 and 2019. Data for the length of stay, waiting time, age-stratified admissions, and ‘Finished Consultant Episodes’ (FCEs), which correspond to a patient's hospital care under a lead clinician, were analysed.
Results: In total, 43135 FCEs for spondylodiscitis were identified between 2012 and 2022, of which 97.1% were adults. Overall admissions for spondylodiscitis have risen from 3 per 100,000 population in 2012/13 to 4.4 per 100,000 population in 2020/21. Similarly, FCEs have increased from 5.8 to 10.3 per 100,000 population, in 2012–2013 and 2020/21 respectively. The highest increase in admissions from 2012 to 2021 was recorded for those aged 70–74 (117% increase) and aged 75-59 (133% increase), among those of working age for those aged 60–64 years (91% increase).
Conclusion: Population-adjusted admissions for spondylodiscitis in England have risen by 44% between 2012 and 2021. Healthcare policymakers and providers must acknowledge the increasing burden of spondylodiscitis and make spondylodiscitis a research priority.