The impact of an integrated heart failure service in a medium-sized district general hospital

Objectives: Assessing the impact of a new integrated heart failure service (IHFS) in a medium-sized district general hospital (DGH) on heart failure (HF) mortality, readmission rates, and provision of HF care. Methods: A retrospective, observational study encompassing all patients admitted with a diagnosis of HF over two 12-month periods before (2012/2013), and after (2015/2016) IHFS establishment. Results: Total admissions for HF increased by 40% (385 vs 540), with a greater number admitted to the cardiology ward (231 vs 121). After IHFS implementation, patients were more likely to see a cardiologist (35.1% vs 43.7%, p=0.009), undergo echocardiography (70.1% vs 81.5%, p<0.001), be initiated on all three disease modifying HF medications (angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB) and mineralocorticoid receptor antagonists (MRA)) in the heart failure with reduced ejection fraction (HFrEF) group (42% vs 99%, p<0.001) and receive specialist HF input (81.6% vs 85.4%, p=0.2). Both 30-day post-discharge mortality and HF related readmissions were significantly lower in patients with heart failure with preserved ejection fraction (HFpEF) (8.9% vs 3.1%, p=0.032, 58% reduction, p=0.043 respectively) with no-significant reductions in all other HF groups. In-patient mortality was similar. Length of stay in Cardiology wards increased from 8.4 to 12.7 days (p<0.001). Conclusion: Establishment of an IHFS within a DGH with limited resources and only a modest service re-design has resulted in significantly improved provision of specialist in-patient care, use of HFrEF medications, early heart failure nurse follow-up, and is associated with a reduction in early mortality, particularly in the HFpEF cohort, and HF related readmissions.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.published version, accepted version, submitted versio

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery

HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative

AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast

Clinical implication of PD-L2 in the prognosis assessment of HNSCC immunotherapy

Background and purpose: Programmed death-1 (PD-1) monoclonal antibody therapy plays an increasingly important role in the treatment of head and neck squamous cell carcinoma (HNSCC). However, low response rate and lack of predictive biomarkers are still the challenging problems. This study aimed to confirm that programmed death ligand-2 (PD-L2) is a predictive biomarker for the outcome of HNSCC anti-PD-1 immunotherapy. Methods: The samples and clinical data of 50 HNSCC patients undergoing PD-1 monoclonal antibody immunotherapy were collected. Immunohistochemical staining was used to analyze the level of programmed death ligand-1 (PD-L1) and PD-L2. Kaplan-Meier overall survivals were analyzed using SPSS 26.0 software, grouped by the basic clinical characteristics and the PD-L1 and PD-L2 levels. Survival curves were plotted using GraphPad Prism. Results: HNSCC had a relatively high expression rate of PD-L2 with more than 80% of cases detected as PD-L2 positive. The expression of PD-L2 significantly correlated with the clinical outcome of immunotherapy, with a mean survival of 18.8 (16.0-21.7) months for patients with high PD-L2 expression and 11.0 (9.1-12.8) months for patients with low PD-L2 expression, this difference being statistically significant. Conclusion: PD-L2 has the potential to be used as a predictive biomarker for HNSCC anti-PD-1 immunotherapy

G9a Is Essential for EMT-Mediated Metastasis and Maintenance of Cancer Stem Cell-Like Characters in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a particularly aggressive cancer with poor prognosis, largely due to lymph node metastasis and local recurrence. Emerging evidence suggests that epithelial-to-mesenchymal transition (EMT) is important for cancer metastasis, and correlated with increased cancer stem cells (CSCs) characteristics. However, the mechanisms underlying metastasis to lymph nodes in HNSCC is poorly defined. In this study, we show that E-cadherin repression correlates with cancer metastasis and poor prognosis in HNSCC. We found that G9a, a histone methyltransferase, interacts with Snail and mediates Snail-induced transcriptional repression of E-cadherin and EMT, through methylation of histone H3 lysine-9 (H3K9). Moreover, G9a is required for both lymph node-related metastasis and TGF-β-induced EMT in HNSCC cells since knockdown of G9a reversed EMT, inhibited cell migration and tumorsphere formation, and suppressed the expression of CSC markers. Our study demonstrates that the G9a protein is essential for the induction of EMT and CSC-like properties in HNSCC. Thus, targeting the G9a-Snail axis may represent a novel strategy for treatment of metastatic HNSCC

Autocrine Epiregulin Activates EGFR Pathway for Lung Metastasis Via EMT in Salivary Adenoid Cystic Carcinoma

Salivary adenoid cystic carcinoma (SACC) is characterized by invasive local growth and a high incidence of lung metastasis. Patients with lung metastasis have a poor prognosis. Treatment of metastatic SACC has been unsuccessful, largely due to a lack of specific targets for the metastatic cells. In this study, we showed that epidermal growth factor receptors (EGFR) were constitutively activated in metastatic lung subtypes of SACC cells, and that this activation was induced by autocrine expression of epiregulin (EREG), a ligand of EGFR. Autocrine EREG expression was increased in metastatic SACC-LM cells compared to that in non-metastatic parental SACC cells. Importantly, EREG-neutralizing antibody, but not normal IgG, blocked the autocrine EREG-induced EGFR phosphorylation and the migration of SACC cells, suggesting that EREG-induced EGFR activation is essential for induction of cell migration and invasion by SACC cells. Moreover, EREG-activated EGFR stabilized Snail and Slug, which promoted EMT and metastatic features in SACC cells. Of note, targeting EGFR with inhibitors significantly suppressed both the motility of SACC cells in vitro and lung metastasis in vivo. Finally, elevated EREG expression showed a strong correlation with poor prognosis in head and neck cancer. Thus, targeting the EREG-EGFR-Snail/Slug axis represents a novel strategy for the treatment of metastatic SACC even no genetic EGFR mutation

A functional variant in promoter region of platelet-derived growth factor-D is probably associated with intracerebral hemorrhage

<p>Abstract</p> <p>Background</p> <p>Platelet-derived growth factor D (PDGF-D) plays an important role in angiogenesis, vessel remodeling, inflammation and repair in response to injury. We hypothesized that genetic variation in <it>PDGFD </it>gene might alter the susceptibility to stroke.</p> <p>Findings</p> <p>We determined the genotypes of a single nucleotide polymorphism (SNP) (-858A/C, rs3809021) in 1484 patients with stroke (654 cerebral thrombosis, 419 lacunar infarction, 411 intracerebral hemorrhage [ICH]) and 1528 control subjects from an unrelated Chinese Han population and followed the stroke patients up for a median of 4.5 years.</p> <p>The -858AA genotype showed significantly increased risk of ICH (dominant model: odds ratio [OR] 1.29, 95% confidence interval [CI] 1.00-1.68, <it>P </it>= 0.05; additive model: OR 1.24, 95% CI 1.01-1.52, <it>P </it>= 0.04) than wild-type genotype. Further analyses showed that -858AA genotype conferred about 2-fold increase in risk of non-hypertensive ICH (dominant model: OR 2.1, 95%CI 1.34-3.29, <it>P </it>= 0.001; additive model: OR 1.75, 95% CI 1.24-2.46, <it>P </it>= 0.001). After a median follow-up of 4.5 years, -858AA genotype was associated with a reduced risk of ICH recurrence (dominant model: adjusted hazard ratio [HR] 0.09, 95%CI 0.01-0.74, P = 0.025; additive model: HR 0.21, 95% CI 0.04-1.16, <it>P </it>= 0.073) in non-hypertensive patients.</p> <p>Conclusions</p> <p>The -858AA genotype is probably associated with risk for non-hypertensive ICH. Further studies should be conducted to reveal the role of PDGF-D at various stages of ICH development--beneficial, or deleterious.</p

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)