MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

The role of microstructure of extracellular proteins in dewaterability of alkaline pretreatment sludge during bioleaching

Alkaline pre-treatment is known to enhance the acid production efficiency of sludge but adversely affects its dewatering performance. In this study, the improvement of sludge dewaterability by a novel bioleaching system with inoculating domesticated acidified sludge (AS) and its underlying mechanism were investigated. The results showed that although the addition of Fe2+ and the reduction of pH improved the dewatering performance of sludge, their effects were inferior to that of AS + Fe. The addition of AS and Fe2+ significantly reduced the specific resistance to filtration and capillary suction time of the sludge by 98.6 % and 95.5 %, respectively. This improvement in dewatering performance was achieved through the combined actions of bio-acidification, bio-oxidation, and bio-flocculation. Remarkably, under alkaline pH, microorganisms in AS remained active, leading to the formation of iron-based bioflocculants, along with a rapid pH decrease. These bioflocculants, in combination with protein (PN) in tightly bound extracellular polymeric substances (TB-EPS) through amide bonding, transformed TB-EPS from extractable to non-extractable form, reducing PN content from 12.1 mg g-1DS to 5.09 mg g-1DS and altering the protein's secondary structure. Consequently, the gel-like TB-EPS matrix effectively broke down, releasing cellular water and significantly enhancing sludge dewaterability.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding

MOTIVATION

Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.

RESULTS

In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development.

AVAILABILITY

The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information. To overcome this limitation, we propose DeepMPSF, a phosphorylation site prediction model based on multiple protein sequence features. There are two types of features: sequence semantic features, which comprise protein residue type information and relative position information within protein sequence, and protein background biophysical features, which include global semantic information containing more comprehensive protein background information obtained from pretrained models. To extract these features, DeepMPSF employs two separate subnetworks: the S71SFE module and the BBFE module, which automatically extract high-level semantic features. Our model incorporates a learning strategy for handling imbalanced datasets through ensemble learning during training and prediction. DeepMPSF is trained and evaluated on a well-established dataset of human proteins. Comparing the analysis with other benchmark methods reveals that DeepMPSF outperforms in predicting both S/T residues and Y residues. In particular, DeepMPSF showed excellent generalization performance in cross-species blind test performance, with an average improvement of 5.63%/5.72%, 22.28%/25.94%, 20.11%/17.49%, and 26.40%/28.33% for Mus musculus/Rattus norvegicus test sets in area under curves (AUCs) of ROC curve, AUC of the PR curve, F1-score, and MCC metrics, respectively. Furthermore, it also shows excellent performance in the latest updated case of natural proteins with functional phosphorylation sites. Through an ablation study and visual analysis, we uncover that the design of different feature modules significantly contributes to the accurate classification of DeepMPSF, which provides valuable insights for predicting phosphorylation sites and offers effective support for future downstream research.

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer

Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurate prediction of RNA modifications. However, those methods generally predicted only a single type of RNA modification. In addition, such methods suffered from the scarcity of the interpretability for their predicted results. In this work, a new Transformer-based deep learning method was proposed to predict multiple RNA modifications simultaneously, referred to as TransRNAm. More specifically, TransRNAm employs Transformer to extract contextual feature and convolutional neural networks to further learn high-latent feature representations of RNA sequences relevant for RNA modifications. Importantly, by integrating the self-attention mechanism in Transformer with convolutional neural network, TransRNAm is capable of not only capturing the critical nucleotide sites that contribute significantly to RNA modification prediction, but also revealing the underlying association among different types of RNA modifications. Consequently, this work provided an accurate and interpretable predictor for multiple RNA modification prediction, which may contribute to uncovering the sequence-based forming mechanism of RNA modification sites.

Enhancement of sludge dewaterability by repeated inoculation of acidified sludge: Extracellular polymeric substances molecular structure and microbial community succession

Improving the dewatering performance of sewage sludge is of great scientific and engineering significance in the context of accelerated urbanization and increasingly strict environmental regulations. Acidified sludge (AS) can improve sludge dewatering performance, but the dewatering effect of repeated inoculation is unclear. The effects of long-term repeated inoculation of AS on the sludge dewaterability were investigated. The molecular structure and microbial community succession of extracellular polymeric substances (EPS) are emphasized. The results revealed that increasing the inoculation ratio of AS reduced the pH, absolute value of sludge zeta potential, and sludge particle size, and the decreasing trend was more evident with prolonging treatment time. Under the conditions of 30% and 50% AS inoculation, the dewatering performance of the sludge was significantly improved (p < 0.05). Compared with the raw sludge, the specific resistance of filtration (SRF) and capillary suction time of 30% inoculation were reduced by 64.3% and 50.1% after 30 cycles, respectively. Excluding loosely bound (LB)-EPS, soluble (S)-EPS and tightly bound (TB)-EPS exhibited a visible decrease, the protein in TB-EPS was significantly related to sludge dewaterability (p < 0.05). The fluorescent components of aromatic protein and fulvic acid-like substances in TB-EPS were significantly associated with SRF, with a correlation coefficient 0.99 (p < 0.05). Both the increase in the percentages of random coil and decrease in α-helix in TB-EPS contributed to improving dewaterability. Increasing Firmicutes and decreasing Chloroflexi levels improved the sludge dewatering capacity. Repeated inoculation did not disrupt the dewatering effect of AS rather increased the feasibility of the engineering application of AS. Considering the dewatering performance and cost synthetically, 30% AS inoculated ratio is feasible for practical applications.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

The impact of sitagliptin in palmitic acid-induced insulin resistance in human HepG2 cells through the suppressor of cytokine signaling 3/phosphoinositide 3-kinase/protein kinase B pathway

Patients with type 2 diabetes respond differently to sitagliptin, an oral anti-hyperglycemic medication. Patients whose blood sugar levels were effectively managed while using sitagliptin had significantly lower levels of a protein called suppressor of cytokine signaling 3 (SOCS3), according to our earlier research. In this study, we established an in vitro insulin resistance cell model for human HepG2 cells to investigate the possible mechanism of the effect of sitagliptin on glucose metabolism via the SOCS3/phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. Since insulin resistance first develops in the liver, palmitic acid was used to generate an insulin resistance cell model in human HepG2 cells, after which small interfering ribonucleic acid (siRNA)-SOCS3 and sitagliptin were used to intervene. We then examined the changes in cell viability and biochemical indices in the insulin resistance cell model. SOCS3, Akt, and glycogen synthase kinase 3beta (GSK-3β) gene expression levels were quantified using reverse transcription-polymerase chain reaction, and the protein expression levels of SOCS3, Akt, phosphorylated Akt (p-Akt), GSK-3β, and phosphorylated GSK-3β (p-GSK-3β) were quantified using Western blot. In results: the expression of the SOCS3 gene was considerably raised in both the insulin resistance model group and the insulin resistance model + siRNA-negative control group, but decreased following treatment with sitagliptin. After sitagliptin intervention, the protein expression of Akt, p-Akt, and p-GSK-3β were dramatically decreased in the model group, while SOCS3 was significantly decreased. We conclude that sitagliptin can reduce insulin resistance by downregulating SOCS3 and regulating glucose metabolism in a hypoglycemic manner.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets

The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism

MOTIVATION

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

RESULTS

In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.

AVAILABILITY AND IMPLEMENTATION

The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP²ISP to improve the prediction of protein-protein interaction sites. ctP²ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP²ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP²ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events

Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence

MOTIVATION

Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology.

RESULTS

This article proposes a deep learning-based multi-objective computational approach, iHMnBS, to identify which of the seven typical histone modifications a DNA sequence may choose to bind, and which parts of the DNA sequence bind to them. iHMnBS employs a customized dataset that allows the marking of modifications contained in histones that may bind to any position in the DNA sequence. iHMnBS tries to mine the information implicit in this richer data by means of deep neural networks. In comprehensive comparisons, iHMnBS outperforms a baseline method, and the probability of binding to modified histones assigned to a representative nucleotide of a DNA sequence can serve as a reference for biological experiments. Since the interaction between transcription factors and histone modifications has an important role in gene expression, we extracted a number of sequence patterns that may bind to transcription factors, and explored their possible impact on disease.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/lennylv/iHMnBS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TransPPMP: predicting pathogenicity of frameshift and non-sense mutations by a Transformer based on protein features

MOTIVATION

Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen out the potentially pathogenic sites from a large number of mutated genes and then use these sites as drug targets to speed up diagnosis and improve access to treatment. The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched.

RESULTS

We built a Transformer-based neural network model to predict the pathogenicity of frameshift and non-sense mutations on protein features and named it TransPPMP. The feature matrix of contextual sequences computed by the ESM pre-training model, type of mutation residue and the auxiliary features, including structure and function information, are combined as input features, and the focal loss function is designed to solve the sample imbalance problem during the training. In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four other advanced methods, namely, ENTPRISE-X, VEST-indel, DDIG-in and CADD. In addition, we demonstrate the usefulness of the multi-head attention mechanism in Transformer to predict the pathogenicity of mutations-not only can multiple self-attention heads learn local and global interactions but also functional sites with a large influence on the mutated residue can be captured by attention focus. These could offer useful clues to study the pathogenicity mechanism of human complex diseases for which traditional machine learning methods fall short.

AVAILABILITY AND IMPLEMENTATION

TransPPMP is available at https://github.com/lennylv/TransPPMP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq)to predict TF binding specificities. We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell)to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor. The online server for SemanticCS is freely available at http://qianglab.scst.suda.edu.cn/semanticCS/.

Quantifying Intensities of Transcription Factor-DNA Binding by Learning From an Ensemble of Protein Binding Microarrays

The control of the coordinated expression of genes is primarily regulated by the interactions between transcription factors (TFs) and their DNA binding sites, which are an integral part of transcriptional regulatory networks. There are many computational tools focused on determining TF binding or unbinding to a DNA sequence. However, other tools focused on further determining the relative preference of such binding are needed. Here, we propose a regression model with deep learning, called SemanticBI, to predict intensities of TF-DNA binding. SemanticBI is a convolutional neural network (CNN)-recurrent neural network (RNN) architecture model that was trained on an ensemble of protein binding microarray data sets that covered multiple TFs. Using this approach, SemanticBI exhibited superior accuracy in predicting binding intensities compared to other popular methods. Moreover, SemanticBI uncovered vectorized sequence-oriented features using its CNN-RNN architecture, which is an abstract representation of the original DNA sequences. Additionally, the use of SemanticBI raises the question of whether motifs are necessary for computational models of TF binding. The online SemanticBI service can be accessed at http://qianglab.scst.suda.edu.cn/semantic/.

Identifying dust as the dominant source of exposure to heavy metals for residents around battery factories in the Battery Industrial Capital of China

Heavy metals (HMs) are constantly released into the environment during the production and use of batteries. Battery manufacturing has been ongoing for over six decades in the "Battery Industrial Capital" (located in Xinxiang City) of China, but the potential exposure pathways of residents in this region to HMs remain unclear. To clarify the exposure pathways and health risk of human exposure to HMs, hand wipe samples (n=82) and fingernail samples (n=36) were collected from residents (including young children (0-6 years old), children (7-12 years old) and adults (30-60 years old)) living around battery factories. The total concentrations of the target HMs (Zn, Mn, Cu, Pb, Ni, Cr, Cd, Co) in hand wipes ranged from 133 to 8040 μg/m², and those in fingernails ranged from 9.7-566 μg/g. HM levels in the hand wipe and fingernail samples both decreased with age, and higher HM levels were observed for males than females. The HM composition profiles in these two matrices represented a high degree of similarity, with Zn as the predominant element, and thus, oral ingestion and dermal exposure via dust were expected to be the most important HM exposure pathways for residents in this region. The non-carcinogenic risks (HQs) from dermal and oral ingestion exposure to Cd, Cr, and Pb were higher than those of the other five elements for all three populations, and the HQ_derm of Cd for young children was 2.1 (HQ_oral=0.6). Moreover, the hazard index (HI) values of ∑₈HMs for young children (HI_total=5.2, HI_oral=2.0, HI_dermal=3.2) and children (HI_total=1.6, HI_oral=1.3, HI_dermal=0.3) exceeded the safe threshold (1.0). Therefore, young children and children should be prioritized for protection from HM pollution, and more attention should be paid to young children's dermal exposure to Cd in this region.

Forensic Analysis of 43 Medical Disputes Caused by Death after Cardiac Surgery

Abstract

Objective To explore the causes and characteristics of medical disputes caused by death after cardiac surgery and to analyze the pathological changes after cardiac surgery and the key points of forensic anatomy, thus to provide pathological evidence for clinical diagnosis and treatment of cardiac surgery and judicial appraisal as well as reference for the prevention of medical disputes in such cases. Methods Forensic pathological cases of medical disputes caused by death after cardiac surgery which were accepted by the Center for Medicolegal Expertise of Sun Yat-Sen University from 2013 to 2018 were analyzed retrospectively from aspects such as causes of death, pathological diagnosis, surgery condition, medical misconduct, and so on. Results The causes of death after cardiac surgery of 43 patients were abnormal operation, low cardiac output syndrome, postoperative infection, postoperative thrombosis, and other diseases. Among the 43 cases, there were 18 cases without medical fault while 25 cases had medical fault. Conclusion The medical disputes caused by death after cardiac surgery are closely related to the operative technique and postoperative complications. The causes of medical faults include defects in diagnosis and treatment technique, as well as unfulfillment of duty of care.

Characteristics and outcomes of AED use in pediatric cardiac arrest in public settings: The influence of neighborhood characteristics

BACKGROUND

Automated external defibrillators (AEDs) are critical in the chain of survival following out-of-hospital cardiac arrest (OHCA), yet few studies have reported on AED use and outcomes among pediatric OHCA. This study describes the association between bystander AED use, neighborhood characteristics and survival outcomes following public pediatric OHCA.

METHODS

Non-traumatic OHCAs among children less than18 years of age in a public setting between from January 1, 2013 through December 31, 2017 were identified in the CARES database. A neighborhood characteristic index was created from the addition of dichotomous values of 4 American Community Survey neighborhood characteristics at the Census tract level: median household income, percent high school graduates, percent unemployment, and percent African American. Multivariable logistic regression models assessed the association of OHCA characteristics, the neighborhood characteristic index and outcomes.

RESULTS

Of 971 pediatric OHCA, AEDs were used by bystanders in 10.3% of OHCAs. AEDs were used on 2.3% of children ≤1 year (infants), 8.3% of 2-5 year-olds, 12.4% of 6-11 year-olds, and 18.2% of 12-18 year-olds (p < 0.001). AED use was more common in neighborhoods with a median household income of >$50,000 per year (12.3%; p = 0.016), <10% unemployment (12.1%; p = 0.002), and >80% high school education (11.8%; p = 0.002). Greater survival to hospital discharge and neurologically favorable survival were among arrests with bystander AED use, varying by neighborhood characteristics.

CONCLUSIONS

Bystander AED use is uncommon in pediatric OHCA, particularly in high-risk neighborhoods, but improves survival. Further study is needed to understand disparities in AED use and outcomes.

Co0.8Zn0.2MoO4/C Nanosheet Composite: Rational Construction via a One-Stone-Three-Birds Strategy and Superior Lithium Storage Performances for Lithium-Ion Batteries

CoMoO₄ has gained great attention as an anode material for lithium-ion batteries owing to its high theoretical capacity of 980 mAh g^-1 and relatively high electrochemical activity. Unfortunately, CoMoO₄ anode also has some drawbacks such as low electronic/ionic conductivity, inferior cyclic stability, and relative severe volumetric expansion during the lithiation/delithiation process, greatly inhibiting its further development and application. Herein, we report Co_0.8Zn_0.2MoO₄/C nanosheet composite constructed via a novel and facile one-stone-three-birds strategy. The preparation of the Co_0.8Zn_0.2MoO₄/C nanosheet is based on the following two-step process: the formation of Co/Zn nanosheet precursors derived from Co/Zn-ZIF rhombic dodecahedra via solvothermal pretreatment, followed by a calcination treatment with molybdic acid (H₂MoO₄) in air. The as-prepared Co_0.8Zn_0.2MoO₄/C is monoclinic crystal structured composite with the in situ formed active carbon, which is well-defined nanosheet with a rough surface and mean thickness of 60-70 nm for a single sheet. This Co_0.8Zn_0.2MoO₄/C nanosheet composite possesses a larger surface area of 37.60 m² g^-1, showing a mesoporous structure. When used as anode materials, the as-obtained Co_0.8Zn_0.2MoO₄/C composite can deliver as high as a discharge capacity of 1337 mAh g^-1 after 300 cycles at 0.2C and still retain the capacity of 827 mAh g^-1 even after 600 cycles at 1C, exhibiting outstanding lithium storage performances. The higher capacity and superior cyclic stability of the Co_0.8Zn_0.2MoO₄/C composite should be ascribed to the synergistic effect of the substitution of Zn²⁺, in situ composited active carbon and the as-constructed unique microstructure for the Co_0.8Zn_0.2MoO₄/C composite. Our present work provides a facile one-stone-three-birds strategy to effectively construct the architectures and significantly enhance electrochemical performances for other transition metal electrode materials.

Three novel microRNAs based on microRNA signatures for gastric mucosa-associated lymphoid tissue lymphoma

This study aimed to identify novel microRNAs (miRNAs) that play crucial regulatory roles in the pathogenesis of mucosa-associated lymphoid tissue (MALT) lymphoma by retrieving and analyzing the miRNA expression profile GSE23877. Differentially expressed miRNAs between gastric MALT lymphoma samples and human tonsil tissue samples as well as their target genes were identified. The transcriptional regulatory relationships between miRNAs and target genes were analyzed, and the regulatory network between them was constructed. Target genes annotated as transcription factors (TFs) were screened, and an miRNA-target gene regulatory network was established. Moreover, the expression levels of miRNAs and target genes as well as the correlation between them were verified. In total, 53 upregulated and 25 downregulated miRNAs were obtained, for which 35 and 25 experimentally validated miRNA-target interactions, respectively, were screened. Some miRNAs were significantly enriched in certain pathways; for example, miR-320a was enriched in systemic lupus erythematosus and ribosome, miR-622 in the p53 signaling pathway and chronic myeloid leukemia, and miR-429 in cancer-related pathways. In addition, upregulated miRNAs, including miR-320a, miR-940, and miR-622, and downregulated miRNAs, including miR-331-3p and miR-429, were hub nodes in the miRNA-target gene regulatory network, and the TF MYC was a co-target of miR-320a, miR-622, and miR-429. The expression trends of miR-320a and miR-429 as well as of some of their target genes were consistent with those in the results of microarray analysis. In conclusion, miR-320a, miR-622, and miR-429 are possibly novel miRNAs participating in the pathomechanism of gastric MALT lymphoma.

Strategy of the Causes of Death of Dependents

Abstract

Objective To discuss the methods and strategies to identify the causes of dependents' deaths, as well as provide the experiences that can be used for reference and scientific basis for the forensic identification of the potentially growing deaths of the same kind in the future. Methods The 13 cases concerning death of dependents accepted by Sun Yat-sen University Forensic Center were collected, and the basic information of the dependents were statistically described. The nutritional status, environmental condition and medical care condition were evaluated according to dietary energy, living space, environment and medical treatment condition. Results Among the 13 dependents, there were 11 males and 2 females, with the oldest 74 and the youngest 9 and dwelling time was from 0.4 to 5.6 years. Forensic pathological examination showed that 13 dependents had infectious diseases and 11 were severely dystrophic. There were no fatal mechanical injuries or poisoning in dependents. Molecular pathological screening of 4 cases revealed no pathogenic variants of sudden death susceptible genes. The poor status of the diet, nutrition, living environment and medical care of these dependents were discovered. The direct cause of death of all 13 dependents was identified to be disease. The lack of nutrition, poor living environment and lack of medical care were thought to play a dominant role in causing the deaths of 12 dependants. Conclusion The death identification should follow the judicial procedure. In identification of the causes of death and analysis of the proportion of the affecting factors resulting in death, all factors, including nutrition,environment, medical care, injury and diseases, need to be considered.

DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions

Nearly one-third of non-synonymous single-nucleotide polymorphism (nsSNPs) are deleterious to human health, but recognition of the disease-associated mutations remains a significant unsolved problem. We proposed a new algorithm, DAMpred, to identify disease-causing nsSNPs through the coupling of evolutionary profiles with structure predictions of proteins and protein-protein interactions. The pipeline was trained by a novel Bayes-guided artificial neural network algorithm that incorporates posterior probabilities of distinct feature classifiers with the network training process. DAMpred was tested on a large-scale data set involving 10,635 nsSNPs from 2154 ORFs in the human genome and recognized disease-associated nsSNPs with an accuracy 0.80 and a Matthews correlation coefficient of 0.601, which is 9.1% higher than the best of other state-of-the-art methods. In the blind test on the TP53 gene, DAMpred correctly recognized the mutations causative of Li-Fraumeni-like syndrome with a Matthews correlation coefficient that is 27% higher than the control methods. The study demonstrates an efficient avenue to quantitatively model the association of nsSNPs with human diseases from low-resolution protein structure prediction, which should find important usefulness in diagnosis and treatment of genetic diseases.

Magnetic graphene enabled tunable microwave absorber via thermal control

By synthesizing nitrogen-doped graphene (NG) via a facile thermal annealing method, a fine control of the amount and location of doped nitrogen as well as the oxygen-containing functional groups is achieved with varying annealing temperature. The favorable magnetic properties have been achieved for N-doped rGO samples obtained at two temperatures of all NG samples, i.e., 500 °C and 900 °C with saturation magnetization of 0.63 emu g^-1 and 0.67 emu g^-1 at 2 K, respectively. This is attributed to the optimized competition of the N-doping and reduction process at 500 °C and the dominated reduction process at 900 °C. NG obtained at 300 °C affords the best overall absorbing performance: when the absorber thickness is 3.0 mm, the maximum absorption was -24.6 dB at 8.51 GHz, and the absorption bandwidth was 4.89 GHz (7.55-12.44 GHz) below -10 dB. It owes its large absorbing intensity to the good impedance match and significant dielectric loss. The broad absorption bandwidth benefits from local fluctuations of dielectric responses contributed by competing mechanisms. Despite the significant contribution from materials loss to the absorption, the one quarter-wavelength model is found to be responsible for the reflection loss peak positions. Of particular significance is that an appropriate set of electromagnetic parameters associated with reasonable reduction is readily accessible by convenient control of annealing temperature to modulate the microwave absorbing features of graphene. Thus, NG prepared by thermal annealing promises to be a highly efficient microwave absorbent.

seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.

[Correlation between Genetic Variants and Polymorphism of Caveolin and Sudden Unexplained Death]

OBJECTIVES

To explore the genetic variation sites of caveolin （CAV） and their correlation with sudden unexplained death （SUD）.

METHODS

The blood samples were collected from SUD group （71 cases）, coronary artery disease （CAD） group （62 cases） and control group （60 cases）, respectively. The genome DNA were extracted and sequencing was performed directly by amplifying gene coding region and exon-intron splicing region of CAV1 and CAV3 using PCR. The type of heritable variation of CVA was confirmed and statistical analysis was performed.

RESULTS

A total of 4 variation sites that maybe significative were identified in SUD group, and two were newfound which were CAV1： c.45C>T （T15T） and CAV1：c.512G>A （R171H）, and two were SNP loci which were CAV1：c.246C>T （rs35242077） and CAV3：c.99C>T （rs1008642） and had significant difference （P<0.05） in allele and genotype frequencies between SUD and control groups. Forementioned variation sites were not found in CAD group.

CONCLUSIONS

The variants of CAV1 and CAV3 may be correlated with a part of SUD group.

Evolutionary genotypes of influenza A (H7N9) viruses over five epidemic waves in China

Since the first human case of influenza A (H7N9) infection was identified in March 2013, five epidemics have emerged in China. Diverse H7N9 virus genotypes created through reassortments were already detected in the first epidemic wave, but how the H7N9 virus genetic diversities have evolved during the subsequent epidemics remained unclear. Here, to assess the ongoing genetic evolution of H7N9 viruses, we performed in-depth investigations of the dynamic H7N9 genotypes in these waves. We found that the H7N9 genotypes in the second and third epidemic waves are more diverse than those in the first wave, due to new reassortments that occurred during the second wave. However, the number of different H7N9 genotypes identified in the fourth and fifth waves decreased significantly. Furthermore, we found that different dominant genotypes existed in each of the five epidemic waves, and these wave-specific genotypes possess unique mutations that are enriched in the PB2 protein.

[Research Progress of the Correlation between Caveolin and Unexpected Sudden Cardiac Death]

Due to the negative autopsy and without cardiac structural abnormalities, unexpected sudden cardiac death （USCD） is always a tough issue for forensic pathological expertise. USCD may be associated with parts of fatal arrhythmic diseases. These arrhythmic diseases may be caused by disorders of cardiac ion channels or channel-related proteins. Caveolin can combine with multiple myocardial ion channel proteins through its scaffolding regions and plays an important role in maintaining the depolarization and repolarization of cardiac action potential. When the structure and function of caveolin are affected by gene mutations or abnormal protein expression, the functions of the regulated ion channels are correspondingly impaired, which leads to the occurrence of multiple channelopathies, arrhythmia or even sudden cardiac death. It is important to study the effects of caveolin on the functions of ion channels for exploring the mechanisms of malignant arrhythmia and sudden cardiac death.

Relative 3D Reconstruction Using Multiple Uncalibrated Images

In this article we show how relative 3D reconstruction from point correspondences of multiple uncalibrated images can be achieved through reference points. The original contributions with respect to related works in the field are mainly a direct global method for relative 3D reconstruction and a geometric method to select a correct set of reference points among all im age points. Experimental results from both simulated and real image sequences are presented, and robustness of the method and reconstruction precision of the results are discussed.

STRUM: structure-based prediction of protein stability changes upon single-point mutation

MOTIVATION

Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling.

RESULTS

We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations.

AVAILABILITY AND IMPLEMENTATION

http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of potential therapeutic targets for melanoma using gene expression analysis

Metastatic melanoma represents a significant cause of death in patients with melanoma and the frequency is increasing. The aim of this study was to identify potential therapeutic targets for metastatic melanoma. Gene expression profile GSE44660 was downloaded from Gene Expression Omnibus database. A total of 22 samples were analyzed in our study, including 3 specimens of normal melanocytes, 12 specimens of melanoma LNM (lymph node metastasis) and 7 specimens of MBM (melanoma brain metastasis). DEGs (differentially expressed genes) in LNM and MBM were identified respectively using Limma package. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways analyses of common DEGs between two comparison groups were performed using DAVID, followed by cancer-related genes and transcription factor analysis. PPI (protein-protein interaction) network was constructed by STRING, and significant key genes were selected. Totally, 401 common DEGs were identified. Disease analysis showed that ICAM1 (intercellular adhesion molecule 1) and NBN (nibrin) were related to melanoma. In the PPI network, BIRC5 (baculoviral IAP repeat containing 5), BUB1 (BUB1 mitotic checkpoint serine/threonine kinase), GMNN (geminin, DNA replication inhibitor), AURKA (aurora kinase A), TOP2A (topoisomerase (DNA) II alpha) and BUB1B (BUB1 mitotic checkpoint serine/threonine kinase B) were with higher degree more than 50. ICAM1, NBN, BIRC5, BUB1, BUB1B, GMNN, AURKA and TOP2A may play key roles in the progression and development of melanoma. They may be used as specific therapeutic targets in the treatment of metastatic melanoma. However, further experiments are still needed to confirm our results.

Improved packing of protein side chains with parallel ant colonies

INTRODUCTION

The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains.

METHODS

We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library.

RESULTS

We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains.

CONCLUSIONS

This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

PaFlexPepDock: parallel ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta

Structural information related to protein–peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein–peptide interactions explored by experimental ways. Protein–peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein–peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein–peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions.

Association between wearing a personal floatation device and death by drowning among recreational boaters: a matched cohort analysis of United States Coast Guard data

OBJECTIVE

To estimate the association between wearing a personal floatation device (PFD) and death by drowning among recreational boaters.

DESIGN

Matched cohort study analysis of Coast Guard data.

SETTING

United States.

SUBJECTS

Recreational boaters during 2000-2006.

MAIN OUTCOME MEASURES

Risk ratio (RR) for drowning death comparing boaters wearing a PFD with boaters not wearing a PFD.

RESULTS

Approximately 4915 boater records from 1809 vessels may have been eligible for our study, but because of missing records and other problems, the analysis was restricted to 1597 boaters in 625 vessels with 878 drowning deaths. The adjusted RR was 0.51 (95% CI 0.35 to 0.74).

CONCLUSIONS

If the estimated association is causal, wearing a PFD may potentially prevent one in two drowning deaths among recreational boaters. However, this estimate may be biased because many vessels had to be excluded from the analysis.

Treatment of chromoblastomycosis with terbinafine: A report of four cases

This report is about four cases of chromoblastomycosis confirmed by direct examination, histopathology and cultures. The duration of disease infection varied from 5 to 12 years. By culture, Cladosporium carrionii was isolated in two cases and Fonsecaea pedrosoi in the other two cases. Terbinafine 0.25 g twice daily for 1 month and 0.25 g once daily for maintenance therapy were given to three patients. Terbinafine 0.25 g once daily was given to one patient. After 4-8 months of therapy, all cases were cured without relapse when followed up for 6 months. The total dosage of terbinafine was 37.5-60 g. No relevant side effects showed during treatment.

Beliefs and practices to prevent drowning among Vietnamese-American adolescents and parents

OBJECTIVE

To determine the beliefs, attitudes and practices regarding water safety among Vietnamese-Americans through focus group interviews.

PARTICIPANTS

15 teenagers (aged 15-19 years) and 20 parents participated, and reported similar attitudes, beliefs and practices regarding water activities. Participants identified a lack of familiarity with water activities and few swimming skills, noting that these activities are not perceived as recreational sports among the Vietnamese. They reported recreating at open water sites because they are free and available, and attributed drowning to fate. Vietnamese youth swim unsupervised, responding to peer pressure despite lack of skills. Participants had negative attitudes toward life jackets using, swimming pools and lessons, because of the costs, but would attend lessons in Vietnamese. They identified schools and Vietnamese media as means of delivering injury-prevention messages.

CONCLUSIONS

Decreasing drowning among Vietnamese-Americans requires changing the knowledge, attitudes and safety practices with programs and messages in Vietnamese, as well as targeting the dominant culture.

Comparison of three instructional methods for teaching cardiopulmonary resuscitation and use of an automatic external defibrillator to high school students

OBJECTIVE

To evaluate new instructional methods for teaching high school students cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) knowledge, actions and skills.

METHODS

We conducted a cluster-controlled trial of 3 instructional interventions among Seattle area high school students, with random allocation based on classrooms, during 2003-04. We examined two new instructional methods: interactive-computer training and interactive-computer training plus instructor-led (hands-on) practice, and compared them with traditional classroom instruction that included video, teacher demonstration and instructor-led (hands-on) practice, and with a control group. We assessed CPR and AED knowledge, performance of key AED and CPR actions, and essential CPR ventilation and compressions skills 2 days and 2 months after training. All outcomes were transformed to a scale of 0-100%.

RESULTS

For all outcome measures mean scores were higher in the instructional groups than in the control group. Two days after training all instructional groups had mean CPR and AED knowledge scores above 75%, with use of the computer program scores were above 80%. Mean scores for key AED actions were above 80% for all groups with training, with hands-on practice enhancing students' positive outcomes for AED pad placement. Students who received hands-on practice more successfully performed CPR actions than those in the computer program only group. In the 2 hands-on practice groups the scores for 3 of the outcomes ranged from 57 to 74%; they were 32 to 54% in the computer only group. For the outcome of continuing CPR until the AED was available scores were high, 89 to 100% in all 3 training groups. Mean CPR skill scores were low in all groups. The highest mean score for successful ventilations was 15% and for compressions, 29%. The pattern of results was similar after 2 months.

CONCLUSIONS

We found evidence that interactive computer based self instruction alone was sufficient to teach CPR and AED knowledge and AED actions to high school students. All forms of instruction were highly effective in teaching AED use. In contrast to AED skills, CPR remains a set of difficult psychomotor skills that is challenging to teach to high school students as well as other members of the lay public.

Tissue microarray analysis of human FRAT1 expression and its correlation with the subcellular localisation of beta-catenin in ovarian tumours

The mechanisms involved in the pathogenesis of ovarian cancer are poorly understood, but evidence suggests that aberrant activation of Wnt/beta-catenin signalling pathway plays a significant role in this malignancy. However, the molecular defects that contribute to the activation of this pathway have not been elucidated. Frequently rearranged in advanced T-cell lymphomas-1 (FRAT1) is a candidate for the regulation of cytoplasmic beta-catenin. In this study, we developed in situ hybridisation probes to evaluate the presence of FRAT1 and used an anti-beta-catenin antibody to evaluate by immunohistochemistry the expression levels and subcellular localisation of beta-catenin in ovarian cancer tissue microarrays. Expression of FRAT1 was found in some human normal tissues and 47% of ovarian adenocarcinomas. A total of 46% of ovarian serous adenocarcinomas were positive for FRAT1 expression. Accumulation of beta-catenin in the nucleus and/or cytoplasm was observed in 55% ovarian adenocarcinomas and in 59% of serous adenocarcinomas. A significant association was observed in ovarian serous adenocarcinomas between FRAT1 and beta-catenin expression (P<0.01). These findings support that Wnt/beta-catenin signalling may be aberrantly activated through FRAT1 overexpression in ovarian serous adenocarcinomas. The mechanism behind the overexpression of FRAT1 in ovarian serous adenocarcinomas and its significance is yet to be investigated.

The PPARgamma ligand, rosiglitazone, reduces airways hyperresponsiveness in a murine model of allergen-induced inflammation

There is considerable interest in the role of peroxisome proliferator activated receptors (PPARs) as ligand-activated transcription factors in the airways. This study examines the effects of a potent synthetic PPARgamma ligand, rosiglitazone (RG), in a murine model of allergen-induced inflammation, to explore its potential regulation of airways inflammation, structure and function. C57BL/6 mice were sensitised with ovalbumin (OVA, 50 microg i.p., days 0, 12) and challenged with aerosolized OVA (1% w v(-1), 30 min day(-1)) for 7 days (days 20-26). Mice were treated with RG (5 mg kg(-1) i.p.) or vehicle during the challenge period. The OVA challenge induced increases in leukocyte number and MMP-2 activity in bronchoalveolar lavage fluid and in goblet cell number in lung tissue obtained on Day 27. RG failed to inhibit inflammatory cell infiltration, MMP-2 activity or goblet cell hyperplasia. Respiratory resistance in response to methacholine (MCh i.v.) was greater in OVA-challenged mice than saline-challenged mice and this airways hyperresponsiveness (AHR) was reduced by RG. However, RG did not affect MCh-induced contraction in isolated guinea-pig tracheal rings, nor did it influence the airway obstruction induced by MCh in saline-challenged mice, so a direct effect on airway obstruction is unlikely. These data suggest that RG modulates AHR in this model, by a mechanism that is also potentially independent of an anti-inflammatory action.