FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers

The design of compounds that target specific biological functions with relevant selectivity is critical in the context of drug discovery, especially due to the polypharmacological nature of most existing drug molecules. In recent years, in silico-based methods combined with deep learning have shown promising results in the de novo drug design challenge, leading to potential leads for biologically interesting targets. However, several of these methods overlook the importance of certain properties, such as validity rate and target selectivity, or simplify the generative process by neglecting the multi-objective nature of the pharmacological space. In this study, we propose a multi-objective Transformer-based architecture to generate drug candidates with desired molecular properties and increased selectivity toward a specific biological target. The framework consists of a Transformer-Decoder Generator that generates novel and valid compounds in the SMILES format notation, a Transformer-Encoder Predictor that estimates the binding affinity toward the biological target, and a feedback loop combined with a multi-objective optimization strategy to rank the generated molecules and condition the generating distribution around the targeted properties. The results demonstrate that the proposed architecture can generate novel and synthesizable small compounds with desired pharmacological properties toward a biologically relevant target. The unbiased Transformer-based Generator achieved superior performance in the novelty rate (97.38%) and comparable performance in terms of internal diversity, uniqueness, and validity against state-of-the-art baselines. The optimization of the unbiased Transformer-based Generator resulted in the generation of molecules exhibiting high binding affinity toward the Adenosine A2A Receptor (AA2AR) and possessing desirable physicochemical properties, where 99.36% of the generated molecules follow Lipinski's rule of five. Furthermore, the implementation of a feedback strategy, in conjunction with a multi-objective algorithm, effectively shifted the distribution of the generated molecules toward optimal values of molecular weight, molecular lipophilicity, topological polar surface area, synthetic accessibility score, and quantitative estimate of drug-likeness, without the necessity of prior training sets comprising molecules endowed with pharmacological properties of interest. Overall, this research study validates the applicability of a Transformer-based architecture in the context of drug design, capable of exploring the vast chemical representation space to generate novel molecules with improved pharmacological properties and target selectivity. The data and source code used in this study are available at: https://github.com/larngroup/FSM-DDTR.

Incidence of interstitial lung disease and cardiotoxicity with trastuzumab deruxtecan in breast cancer patients: a systematic review and single-arm meta-analysis

BACKGROUND

Trastuzumab deruxtecan (T-DXd) has been shown to benefit progression-free survival and overall survival in patients with metastatic breast cancer (mBC) after progression on ≥1 human epidermal growth factor receptor 2 (HER2)-targeted therapies. However, interstitial lung disease (ILD) and cardiotoxicity are the most significant toxicities associated with T-DXd. Therefore, we conducted a systematic review and meta-analysis to assess the incidence and severity of these toxicities in mBC patients treated with T-DXd.

MATERIALS AND METHODS

We searched PubMed, Cochrane, and Scopus databases, and conferences websites for randomized clinical trials and nonrandomized studies of intervention including HER2-low or HER2-positive mBC patients who received at least one dose of T-DXd. Statistical analysis was carried out using R software.

RESULTS

We included 15 studies comprising 1970 patients with a mean follow-up of 13.3 months. Median age ranged from 53 to 59 years, 61.9% were non-Asian, and 67.4% had hormone receptor-positive mBC. In a pooled analysis, the incidence of ILD was 11.7% [222 patients; 95% confidence interval (CI) 9.1% to 15.0%]. Patients receiving T-DXd dose of 6.4 mg/kg developed a significantly higher rate of ILD (22.7%) compared to those receiving a dose of 5.4 mg/kg (9.3%) (P < 0.01). Most cases of ILD (80.2%; 174/217 patients) were mild (grade 1 or 2). Grade 3 or 4 ILD was reported in 29 patients (13.4%), and grade 5 in 14 patients (6.4%). The incidence of decreased left ventricular ejection fraction (LVEF) was 1.95% (95% CI 0.65% to 3.73%), and the QT interval (QTi) prolongation was 7.77% (95% CI 2.74% to 20.11%). Most patients were asymptomatic, but four had LV dysfunction and heart failure (0.26%).

CONCLUSIONS

In this meta-analysis of 1970 patients with mBC, treatment with T-DXd was associated with a 11.7% incidence of ILD, 7.7% incidence of prolonged QTi, and 1.9% incidence of reduced LVEF. Early detection and management of T-DXd-related toxicity by a multidisciplinary team may ultimately improve patient outcomes.

A Reliable and Secure Method for Sharing Genomic Data

Genomics has significantly impacted the field of medicine, with advances in DNA sequencing leading to personalized medicine and a deeper understanding of the genomic basis of various diseases. The ability to share genomic data is crucial for advancing this field and developing new approaches to understanding the genome. However, the sensitive nature of this data requires secure methods for protecting it during storage and transfer. In this paper, we present a new tool for the secure encryption and decryption of FASTA files without sharing a common secret and with a reduced number of shared keys between the pairs. Our proposal combines symmetric and asymmetric encryption techniques, including the AES (Advanced Encryption Standard) cypher and RSA (Rivest-Shamir-Adleman). The tool is fast, reliable, and secure, outperforming existing tools in terms of security and ease of use. This makes it a valuable solution for the secure sharing and use of sensitive genomic data, representing a significant advancement in the field of genomics.

Use of 2D and co-culture cell models to assess the toxicity of zein nanoparticles loading insect repellents icaridin and geraniol

After the latest dengue and Zika outbreaks, the fight against mosquito vectors has become an emerging area of research. One tool for this combat is repellents; however, these products are composed of different toxic agents. Botanical compounds with repellent potential are an alternative; however these compounds are highly volatile. Thus, the present study aimed to synthesize zein-based polymeric nanoparticles as an efficient carrier system for the sustained release of the repellents icaridin and geraniol and evaluate the toxicity of these nanorepellents comparing two different cell models. In vitro tests were carried out due to current Brazilian legislation prohibiting animal testing for cosmetics (current classification of repellents). The cytotoxicity and genotoxicity of the nanoparticles were evaluated in 2D and co-culture cell models (A549/lung epithelium, HaCaT/keratinocytes, HT-29/intestinal epithelium, and THP-1/peripheral blood monocytes). Cell viability by mitochondrial activity, cell membrane integrity, damage to genetic material, and expression of genes involved in the allergic/inflammatory system were evaluated. The results of cytotoxicity evaluation showed cell viability above 70% in both cell models. No differences were observed in genotoxicity assessment between cells exposed to nanorepellents and controls. In contrast, gene expression analysis showed increased cytokine expression for the emulsion compounds in 2D cell cultures compared to co-cultures. These findings open perspectives that zein-based nanorepellents have potential applications due to the reduced toxicity observed when the compounds are encapsulated and emerge as an alternative for arbovirus control. In addition, the study demonstrated that depending on the analysis, different results might be observed when comparing 2D and co-culture cell models to evaluate the toxicity of new nanosystems.

Designing optimized drug candidates with Generative Adversarial Network

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Explainable deep drug-target representations for binding affinity prediction

Background

Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model’s decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data.

Results

The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug–target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches.

Conclusions

This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04767-y.

Melatonin improves development, mitochondrial function and promotes the meiotic resumption of sheep oocytes from in vitro grown secondary follicles

The aim of this study was to evaluate follicular survival and development of ovine isolated secondary follicles cultured in medium containing fixed or sequential concentrations of melatonin and further oocyte maturation. Isolated secondary follicles were cultured for 18 days in α-MEM⁺ alone (control) or with different concentrations of melatonin (100, 500 or 1000 pg/mL) or sequential concentrations of melatonin (Mel Seq: Day 6 = 100; Day 12 = 500; Day 18 = 1000 pg/mL). The percentages of morphologically normal follicles and antral cavity formation increased significantly in 1000 pg/mL melatonin compared to the other treatments. After 18 days, 1000 pg/mL melatonin (Mel 100) showed a greater (P < 0.05) follicular diameter than α-MEM⁺, 100 and 500 pg/mL melatonin. In addition, the concentration of 500 pg/mL melatonin showed a higher (P < 0.05) percentage of fully grown oocytes than α-MEM⁺, Mel 100 and Mel Seq treatments. After oocyte maturation, the levels of ROS were lower (P < 0.05) in 1000 pg/mL melatonin (Mel 1000) than in other treatments. Both Mel 1000 and Mel Seq treatments showed significantly higher levels of mitochondrial activity than other treatments. There were no significant differences between 500 and 1000 pg/mL melatonin regarding meiotic stages. In conclusion, the concentration of 1000 pg/mL melatonin maintains survival, promotes follicular development and increases the levels of active mitochondria after in vitro culture of sheep secondary follicles. Moreover, this concentration promotes the meiotic competence of oocytes and decreases the production of ROS during oocyte maturation.

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Phenotypic heterogeneity of genetically identical cells can generate nonheritable variation in a population. Is this heterogeneity favorable for microbes? In a changing environment, the answer is a definite yes. While scholars have argued that stochastically generated variation precedes genetic changes and thereby facilitate the evolution of complex traits, this idea has remained disputed, not least because of the shortage of experimental studies. We address this long-standing and controversial issue by integrating synthetic biology, laboratory experimental evolution, and genomic analyses. We explicitly tested the mechanisms whereby phenotypic heterogeneity may promote evolvability. Our work demonstrates that phenotypic heterogeneity facilitates evolutionary rescue from deteriorating environmental stress by generating individuals with exceptionally high fitness. Remarkably, elevated phenotypic heterogeneity evolves as a direct response to stress and thereby it promotes evolution of rare combinations of mutations. These results indicate that phenotypic heterogeneity might have an important role in the evolution of key innovations.

A Cloud Architecture for Teleradiology-as-a-Service

BACKGROUND

Telemedicine has been promoted by healthcare professionals as an efficient way to obtain remote assistance from specialised centres, to get a second opinion about complex diagnosis or even to share knowledge among practitioners. The current economic restrictions in many countries are increasing the demand for these solutions even more, in order to optimize processes and reduce costs. However, despite some technological solutions already in place, their adoption has been hindered by the lack of usability, especially in the set-up process.

OBJECTIVES

In this article we propose a telemedicine platform that relies on a cloud computing infrastructure and social media principles to simplify the creation of dynamic user-based groups, opening up opportunities for the establishment of teleradiology trust domains.

METHODS

The collaborative platform is provided as a Software-as-a-Service solution, supporting real time and asynchronous collaboration between users. To evaluate the solution, we have deployed the platform in a private cloud infrastructure. The system is made up of three main components - the collaborative framework, the Medical Management Information System (MMIS) and the HTML5 (Hyper Text Markup Language) Web client application - connected by a message-oriented middleware.

RESULTS

The solution allows physicians to create easily dynamic network groups for synchronous or asynchronous cooperation. The network created improves dataflow between colleagues and also knowledge sharing and cooperation through social media tools. The platform was implemented and it has already been used in two distinct scenarios: teaching of radiology and tele-reporting.

CONCLUSIONS

Collaborative systems can simplify the establishment of telemedicine expert groups with tools that enable physicians to improve their clinical practice. Streamlining the usage of this kind of systems through the adoption of Web technologies that are common in social media will increase the quality of current solutions, facilitating the sharing of clinical information, medical imaging studies and patient diagnostics among collaborators.

A document processing pipeline for annotating chemical entities in scientific documents

Background

The recognition of drugs and chemical entities in text is a very important task within the field of biomedical information extraction, given the rapid growth in the amount of published texts (scientific papers, patents, patient records) and the relevance of these and other related concepts. If done effectively, this could allow exploiting such textual resources to automatically extract or infer relevant information, such as drug profiles, relations and similarities between drugs, or associations between drugs and potential drug targets. The objective of this work was to develop and validate a document processing and information extraction pipeline for the identification of chemical entity mentions in text.

Results

We used the BioCreative IV CHEMDNER task data to train and evaluate a machine-learning based entity recognition system. Using a combination of two conditional random field models, a selected set of features, and a post-processing stage, we achieved F-measure results of 87.48% in the chemical entity mention recognition task and 87.75% in the chemical document indexing task.

Conclusions

We present a machine learning-based solution for automatic recognition of chemical and drug names in scientific documents. The proposed approach applies a rich feature set, including linguistic, orthographic, morphological, dictionary matching and local context features. Post-processing modules are also integrated, performing parentheses correction, abbreviation resolution and filtering erroneous mentions using an exclusion list derived from the training data. The developed methods were implemented as a document annotation tool and web service, freely available at http://bioinformatics.ua.pt/becas-chemicals/.

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.

Species-specific codon context rules unveil non-neutrality effects of synonymous mutations

Background

Codon pair usage (codon context) is a species specific gene primary structure feature whose evolutionary and functional roles are poorly understood. The data available show that codon-context has direct impact on both translation accuracy and efficiency, but one does not yet understand how it affects these two translation variables or whether context biases shape gene evolution.

Methodologies/Principal Findings

Here we study codon-context biases using a set of 72 orthologous highly conserved genes from bacteria, archaea, fungi and high eukaryotes to identify 7 distinct groups of codon context rules. We show that synonymous mutations, i.e., neutral mutations that occur in synonymous codons of codon-pairs, are selected to maintain context biases and that non-synonymous mutations, i.e., non-neutral mutations that alter protein amino acid sequences, are also under selective pressure to preserve codon-context biases.

Conclusions

Since in vivo studies provide evidence for a role of codon context on decoding fidelity in E. coli and for decoding efficiency in mammalian cells, our data support the hypothesis that, like codon usage, codon context modulates the evolution of gene primary structure and fine tunes the structure of open reading frames for high genome translational fidelity and efficiency in the 3 domains of life.

Can tricuspid annuloplasty of the donor heart reduce valve insufficiency following cardiac transplantation with bicaval anastomosis?

BACKGROUND

The aim of this study was to evaluate the degree of tricuspid valve insufficiency after orthotopic cardiac transplantation with bicaval anastomosis and prophylactic donor heart annuloplasty.

METHODS

At present, our cardiac transplantation experience includes 478 cases. After January 2002, we included 30 consecutive patients in this study who had undergone orthotopic cardiac transplantation and survived >6 months. The patients were divided into 2 groups: group I, 15 patients who underwent transplantation with prophylactic tricuspid annuloplasty on the donor heart with the De Vega technique; and group II, 15 patients who underwent transplantation without this procedure. Their preoperative clinical characteristics were the same. During the late postoperative follow-up, the degree of tricuspid insufficiency was evaluated by transthoracic Doppler echocardiography and assessed according to the Simpson scale: 0, absent; 1, mild; 2, moderate; and 3, severe. Hemodynamic parameters were evaluated invasively by means of a Swan-Ganz catheter during routine endomyocardial biopsies.

RESULTS

The mean follow-up time was 26.9 +/- 5.4 months (range, 12-36 months). In group I, 1 patient (6.6%) died from infection in the 18th month after the operation; the death was not related to the annuloplasty. In group II, 1 death (6.6%) occurred after 10 months because of rejection (P > .05). After the 24-month follow-up, the mean degree of tricuspid insufficiency was 0.4 +/- 0.5 in group I and 1.7 +/- 0.9 in group II (P < .05). Similarly, the 2 groups were significantly different with respect to the right atrium pressure, which was higher in group II.

CONCLUSIONS

Prophylactic tricuspid annuloplasty on the donor heart was able to reduce significantly the degree of valvular insufficiency, even in cardiac transplantation with bicaval anastomosis; however, it did not modify significantly the hemodynamic performance of the allograft during the investigation period. It is very important to extend the observation period and casuistics to verify other benefits that this technique may offer.

Heart transplantation in 107 cases of Chagas' disease

INTRODUCTION

Chagas' disease is endemic in South America.

OBJECTIVE

This research reviewed the experience with cardiac transplantation in Chagas' disease, emphasizing reactivation, immunosuppression, and mortality.

METHODS

Over 25 years from March 1985 to March 2010, 107/409 (26.2%) patients with Chagas' disease underwent heart transplantation, patients including 74 (71.1%) men and 72 (67.2%), in functional class IV with 33 (30.8%) on vasopressors and 17 (10.7%) on mechanical circulatory support.

RESULTS

The diagnosis of disease reactivation was performed by identifying the parasite in the myocardium (n = 23; 71.8%) in the subcutaneous tissue (n = 8; 25.0%), in blood (n = 11; 34.3%), or in central nervous tissue (n = 1; 3.1%). Hospital mortality was 17.7% (n = 19) due to infection (n = 6; 31.5%), graft dysfunction (n = 6; 31.5%), rejection (n = 4; 21.1%), or sudden death (n = 2; 10.5%). Late mortality was 27 (25.2%) cases, which were distributed as: rejection (n = 6; 22.2%), infection (n = 6; 22.2%), (n = lymphoma 4; 14.8%), sarcoma (n = 2; 7.4%), for constrictive pericarditis (n = 2; 7.4%) reactivation of Chagas' disease in the central nervous system (n = 1; 7.1%).

CONCLUSIONS

Transplantation in Chagas' disease has peculiar problems that differ from other etiologies due to the possibility of disease reactivation and the increased possibility of emergence of cancers. However, transplantation is the only treatment able to modify the natural progression of the disease in its terminal phase. Early diagnosis and rapid introduction of benzonidazole reverses the histological patterns. Immunosuppression, especially steroids, predisposes to the development of cancer and disease reactivation.

Long-term pulmonary vascular reactivity after orthotopic heart transplantation by the biatrial versus the bicaval technique

INTRODUCTION

Advantages of the bicaval versus the biatrial technique have been reported, emphasizing atrial electrical stability and less tricuspid regurgitation.

OBJECTIVE

To analyze the impact of the surgical technique on long-term pulmonary pressures, contractility, and graft valvular behavior after heart transplantation.

METHODS

Among 400 orthotopic heart transplantation recipients from 1985 to 2010, we selected 30 consecutive patients who had survived beyond 3 years. The biatrial versus bicaval surgical technique groups included 15 patients each. Their preoperative clinical characteristics were similar. None of the patients displayed a pulmonary vascular resistance or pulmonary artery pressure over 6U Wood or 60 mm Hg, respectively. We evaluated invasive hemodynamic parameters during routine endomyocardial biopsies. Two-dimensional echocardiographic parameters were obtained from routine examinations.

RESULTS

There were no significant differences regarding right atrial pressure, systolic pulmonary artery pressure, pulmonary capillary wedge pressure, pulmonary vascular resistance, cardiac index, systolic blood pressure, left ventricular ejection fraction, and mitral regurgitation (P > .05). Tricuspid regurgitation increased significantly over the 3 years of observation only among the biatrial group (P = .0212). In both groups, the right atrial pressure, pulmonary wedge capillary pressure, transpulmonary gradient, and pulmonary vascular resistance decreased significantly (P < .05) from the pre- to the postoperative examination. In both groups cardiac index and systemic blood pressure increased significantly after transplantation (P < .05). Comparative analysis of the groups only showed significant differences regarding right atrial pressure and degree of tricuspid regurgitation; the bicaval group showing the best performance.

CONCLUSIONS

Both surgical techniques ensure adequate left ventricular function in the long term; however, the bicaval technique provided better trends in hemodynamic performance, as well as a lower incidence and severity of tricuspid valve dysfunction.

Endomyocardial biopsy as risk factor in the development of tricuspid insufficiency after heart transplantation

OBJECTIVE

Endomyocardial biopsy (EMB), which is used to monitor for rejection, may cause tricuspid regurgitation (TR) after orthotopic heart transplantation (OHT). The purpose of this investigation was to examine the occurrence of tricuspid valve tissue in myocardial specimens obtained by routine EMB performed after OHT.

PATIENTS AND METHODS

From January 2000 to July 2008, 125 of the patients who underwent OHT survived more than 1 month. Their follow-up varied from 1 month to 8.5 years (mean, 5.1 +/- 3.7 years). EMB was the gold standard examination and myocardial scintigraphy with gallium served as a screen to routinely monitor rejection.

RESULTS

Each of 428 EMB including 4 to 7 fragments, totaling 1715 fragments, were reviewed for this study. The number of EMB per patient varied from 3 to 8 (mean, 4.6 +/- 3.5). Histopathological analysis of these fragments showed tricuspid tissue in 4 patients (3.2%), among whom only 1 showed aggravation of TR.

CONCLUSIONS

EMB remains the standard method to diagnose rejection after OLT. It can be performed with low risk. Reducing the number of EMB using gallium myocardial scintigraphy or other alternative methods as well as adoption of special care during the biopsy can significantly minimize trauma to the tricuspid valve.

Heart transplantation in arrhythmogenic right ventricular dysplasia: case reports

OBJECTIVE

Arrhythmogenic right ventricular dysplasia (ARVD) is a myocardial disease of familiar, origin where the myocardium is replaced by fibrofatty tissue predominantly in the right ventricle. Herein we have presented the clinical courses of 4 patients with ARVD who underwent orthotopic heart transplantation.

PATIENTS AND METHODS

Among 358 adult patients undergoing heart transplantation, 4 (1.1%) displayed ARVD. The main indication for transplantation was the progression to heart failure associated with arrhythmias. All 4 patients displayed rapid, severe courses leading to heart failure with left ventricular involvement and uncontrolled arrhythmias.

RESULTS

In all cases the transplantation was performed using a bicaval technique with prophylactic tricuspid valve annuloplasty. One patient developed hyperacute rejection and infection, leading to death on the 7th day after surgery. The other 3 cases showed a good evolution with clinical remission of the symptoms. Pathological study of the explanted hearts confirmed the presence of the disease.

CONCLUSIONS

ARVD is a serious cardiomyopathy that can develop malignant arrhythmias, severe ventricular dysfunction with right ventricular predominance, and sudden cardiac death. Orthotopic heart transplantation must always be considered in advanced cases of ARVD with malignant arrhythmias or refractory congestive heart failure with or without uncontrolled arrhythmias, because it is the only way to remit the symptoms and the disease.

Parallel DNA pyrosequencing unveils new zebrafish microRNAs

Background

MicroRNAs (miRNAs) are a new class of small RNAs of approximately 22 nucleotides in length that control eukaryotic gene expression by fine tuning mRNA translation. They regulate a wide variety of biological processes, namely developmental timing, cell differentiation, cell proliferation, immune response and infection. For this reason, their identification is essential to understand eukaryotic biology. Their small size, low abundance and high instability complicated early identification, however cloning/Sanger sequencing and new generation genome sequencing approaches overcame most technical hurdles and are being used for rapid miRNA identification in many eukaryotes.

Results

We have applied 454 DNA pyrosequencing technology to miRNA discovery in zebrafish (Danio rerio). For this, a series of cDNA libraries were prepared from miRNAs isolated at different embryonic time points and from fully developed organs. Each cDNA library was tagged with specific sequences and was sequenced using the Roche FLX genome sequencer. This approach retrieved 90% of the 192 miRNAs previously identified by cloning/Sanger sequencing and bioinformatics. Twenty five novel miRNAs were predicted, 107 miRNA star sequences and also 41 candidate miRNA targets were identified. A miRNA expression profile built on the basis of pyrosequencing read numbers showed high expression of most miRNAs throughout zebrafish development and identified tissue specific miRNAs.

Conclusion

This study increases the number of zebrafish miRNAs from 192 to 217 and demonstrates that a single DNA mini-chip pyrosequencing run is effective in miRNA identification in zebrafish. This methodology also produced sufficient information to elucidate miRNA expression patterns during development and in differentiated organs. Moreover, some zebrafish miRNA star sequences were more abundant than their corresponding miRNAs, suggesting a functional role for the former in gene expression control in this vertebrate model organism.

Design, development, exploitation and assessment of a Cardiology Web PACS

Healthcare institutions are increasingly turning to digital medical imaging systems to promote better diagnosis and treatment of their patients. The implementation of the Picture Archiving and Communication System (PACS) clearly contributes to an increase in the productivity of health professionals. However, despite the amount of research that has been done in the past two decades, there are still several technological hurdles that hinder the wide adoption of PACS in the Web environment. In this paper, we present a Web-enabled PACS that through the inclusion of several DICOM services and compression methods promotes medical image availability and greater accessibility to users.

Indexing and retrieving DICOM data in disperse and unstructured archives

OBJECTIVE

This paper proposes an indexing and retrieval solution to gather information from distributed DICOM documents by allowing searches and access to the virtual data repository using a Google-like process.

METHODS AND MATERIALS

The medical imaging modalities are becoming more powerful and less expensive. The result is the proliferation of equipment acquisition by imaging centers, including the small ones. With this dispersion of data, it is not easy to take advantage of all the information that can be retrieved from these studies. Furthermore, many of these small centers do not have large enough requirements to justify the acquisition of a traditional PACS.

RESULTS

A peer-to-peer PACS platform to index and query DICOM files over a set of distributed repositories that are logically viewed as a single federated unit. The solution is based on a public domain document-indexing engine and extends traditional PACS query and retrieval mechanisms.

CONCLUSION

This proposal deals well with complex searching requirements, from a single desktop environment to distributed scenarios. The solution performance and robustness were demonstrated in trials. The characteristics of presented PACS platform make it particularly important for small institutions, including educational and research groups.

Codon-triplet context unveils unique features of the Candida albicans protein coding genome

Background

The evolutionary forces that determine the arrangement of synonymous codons within open reading frames and fine tune mRNA translation efficiency are not yet understood. In order to tackle this question we have carried out a large scale study of codon-triplet contexts in 11 fungal species to unravel associations or relationships between codons present at the ribosome A-, P- and E-sites during each decoding cycle.

Results

Our analysis unveiled high bias within the context of codon-triplets, in particular strong preference for triplets of identical codons. We have also identified a surprisingly large number of codon-triplet combinations that vanished from fungal ORFeomes. Candida albicans exacerbated these features, showed an unbalanced tRNA population for decoding its pool of codons and used near-cognate decoding for a large set of codons, suggesting that unique evolutionary forces shaped the evolution of its ORFeome.

Conclusion

We have developed bioinformatics tools for large-scale analysis of codon-triplet contexts. These algorithms identified codon-triplets context biases, allowed for large scale comparative codon-triplet analysis, and identified rules governing codon-triplet context. They could also detect alterations to the standard genetic code.

Large scale comparative codon-pair context analysis unveils general rules that fine-tune evolution of mRNA primary structure

Background

Codon usage and codon-pair context are important gene primary structure features that influence mRNA decoding fidelity. In order to identify general rules that shape codon-pair context and minimize mRNA decoding error, we have carried out a large scale comparative codon-pair context analysis of 119 fully sequenced genomes.

Methodologies/Principal Findings

We have developed mathematical and software tools for large scale comparative codon-pair context analysis. These methodologies unveiled general and species specific codon-pair context rules that govern evolution of mRNAs in the 3 domains of life. We show that evolution of bacterial and archeal mRNA primary structure is mainly dependent on constraints imposed by the translational machinery, while in eukaryotes DNA methylation and tri-nucleotide repeats impose strong biases on codon-pair context.

Conclusions

The data highlight fundamental differences between prokaryotic and eukaryotic mRNA decoding rules, which are partially independent of codon usage.

Comparative study of the degradation of real textile effluents by photocatalytic reactions involving UV/TiO2/H2O2 and UV/Fe2+/H2O2 systems

This work investigated the treatability of real textile effluents using several systems involving advanced oxidation processes (AOPs) such as UV/H2O2, UV/TiO2, UV/TiO2/H2O2, and UV/Fe2+/H2O2. The efficiency of each technique was evaluated according to the reduction levels observed in the UV absorbance of the effluents, COD, and organic nitrogen reduction, as well as mineralization as indicated by the formation of ammonium, nitrate, and sulfate ions. The results indicate the association of TiO2 and H2O2 as the most efficient treatment for removing organic pollutants from textile effluents. In spite of their efficiency, Fenton reactions based treatment proved to be slower and exhibited more complicated kinetics than the ones using TiO2, which are pseudo-first-order reactions. Decolorization was fast and effective in all the experiments despite the fact that only H2O2 was used.

Statistical, computational and visualization methodologies to unveil gene primary structure features

OBJECTIVES

Gene sequence features such as codon bias, codon context, and codon expansion (e.g. trinucleotide repeats) can be better understood at the genomic scale level by combining statistical methodologies with advanced computer algorithms and data visualization through sophisticated graphical interfaces. This paper presents the ANACONDA system, a bioinformatics application for gene primary structure analysis.

METHODS

Codon usage tables using absolute metrics and software for multivariate analysis of codon and amino acid usage are available in public databases. However, they do not provide easy computational and statistical tools to carry out detailed gene primary structure analysis on a genomic scale. We propose the usage of several statistical methods--contingency table analysis, residual analysis, multivariate analysis (cluster analysis)--to analyze the codon bias under various aspects (degree of association, contexts and clustering).

RESULTS

The developed solution is a software application that provides a user-guided analysis of codon sequences considering several contexts and codon usage on a genomic scale. The utilization of this tool in our molecular biology laboratory is focused on particular genomes, especially those from Saccharomyces cerevisiae, Candida albicans and Escherichia coli. In order to illustrate the applicability and output layouts of the software these species are herein used as examples.

CONCLUSIONS

The statistical tools incorporated in the system are allowing to obtain global views of important sequence features. It is expected that the results obtained will permit identification of general rules that govern codon context and codon usage in any genome. Additionally, identification of genes containing expanded codons that arise as a consequence of erroneous DNA replication events will permit uncovering new genes associated with human disease.

Comparative context analysis of codon pairs on an ORFeome scale

We have developed a system for comparative codon context analysis of open reading frames in whole genomes, providing insights into the rules that govern the evolution of codon-pair context.

Codon context is an important feature of gene primary structure that modulates mRNA decoding accuracy. We have developed an analytical software package and a graphical interface for comparative codon context analysis of all the open reading frames in a genome (the ORFeome). Using the complete ORFeome sequences of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Escherichia coli, we show that this methodology permits large-scale codon context comparisons and provides new insight on the rules that govern the evolution of codon-pair context.

Grid requirements for the integration of biomedical information resources for health applications

OBJECTIVES

The goal of this paper is to identify how Grid technology can be applied for the development and deployment of integration systems, bringing together distributed and heterogeneous biomedical information sources for medical applications.

METHODS

The integration of new genetic and medical knowledge in clinical workflows requires the development of new paradigms for information management in which the ability to access and relate disparate data sources is essential. We adopt a requirements perspective based on the user needs we have identified in the development of the INFOGENMED system to assess current Grid technology against those requirements.

RESULTS

The gap between Grid features and distributed biomedical information integration needs is characterized. Results from prospective studies are also reported.

CONCLUSIONS

Grid infrastructures offer advanced features for the deployment of collaborative computational environments across virtual organizations. New Grid developments are in line with the problem of multiple site information integration. From the INFOGENMED point of view, Grid infrastructures need to evolve to implement structured data access services and semantic content description and discovery.

[Thrombolysis with intravenous APSAC in patients with acute myocardial infarction]

PURPOSE

Analysis of the first 20 patients with acute myocardial infarction (AMI) who were treated with intravenous APSAC.

METHODS

Twenty patients with AMI less than 6 hours of duration of symptoms were treated with IV APSAC bolus of 30 mg. Seventeen were males, ages ranging between 40 and 73 (mean 54) years. The first angiographic study was performed in 90 minutes and 5-7 days after drug administration.

RESULTS

In the angiographic study performed at 90 minutes the infarct--related artery were left anterior descending (LAD) in 7 patients (35%), right coronary artery (RCA) in 9 (45%) and left circumflex (LCX) in 4 (20%). In 14 (70%) of the patients had patent infarct-related artery and the mean of left ventricular ejection fraction (LVEF) was 0.49 +/- 0.15. In six non recanalized patients the mean LVEF was 0.40 +/- 0.14. No complications were observed, and in the second angiographic study one patient showed reocclusion of the infarct-related artery.

CONCLUSION

Because of easy application (IV bolus), no complication and high rate of early recanalization, IV APSAC seems to be an efficient thrombolytic agent in the treatment of patients with AMI.

[Treatment of pulmonary thromboembolism with extrinsic plasminogen activator. A case report]

A 49 year-old woman with acute pulmonary thromboembolism and severe hemodynamic impairment was successfully treated with tissue-type plasminogen activator (r-TPA). She did not have previous pulmonary or cardiac diseases. Thirty days after immobilization of the right ankle, she had a sudden onset of dyspnea, epigastrial pain and syncope. As heparin therapy was unsuccessful, 90 mg of IV r-TPA was administered. There was rapid clinical and hemodynamic improvement of her condition. Pulmonary scanning one week later was normal and she was discharged without symptoms 12 days after the acute episode.