Decrease in suicide rates in Brazil during the COVID-19 pandemic

Natural disasters such as public health epidemics may potentially affect suicide rates. The global COVID-19 pandemic poses an unprecedented challenge for healthcare systems and general populations worldwide. In this retrospective ecological study, we aimed to examine any changes in the suicide rates during the COVID-19 pandemic and to assess the relationship between COVID-19 death rates and deaths by suicide in Brazil. Data on suicide and COVID-19 case numbers were extracted from the Ministry of Health agencies and grouped weekly. We performed a time series analysis of suicide rates, a comparison of mean suicide rates between the pre-COVID-19 period and the COVID-19 period, and conducted a Poisson regression to examine the relationship between deaths due to COVID-19 and suicide rates. Our results showed decreased suicide rates during the COVID-19 pandemic. We also found that deaths owing to COVID-19 impact those owing to suicide after 10 weeks in the upward direction; however, we did not observe for enough time to see a change in the suicide rate curve. These findings are fundamental to understand suicidal behaviors in epidemic situations. However, the field needs more studies evaluating the impact of significant public health events on suicidality, incorporating extended follow-up periods.

Coronavirus disease 2019 related parosmia: an exploratory survey of demographics and treatment strategies

OBJECTIVE

To investigate the clinical features, therapeutic efficacy and symptom time course of post-coronavirus disease 2019 parosmia.

METHODS

A 22-item online questionnaire was distributed to AbScent research group and Facebook coronavirus disease 2019 anosmia group adult members to assess clinical features, interventions and their subjective efficacy for parosmia.

RESULTS

A total of 209 participants (86 per cent females) reported: smell loss on average 3 days after coronavirus symptoms, recovery 4 weeks later, and first parosmia symptoms 12 weeks post infection. Respondents reported 10 per cent body weight loss, and listed onion and garlic as significant parosmia triggers. Regarding quality of life, depression was the most cited item (54 per cent). Smell training was trialled by 74 per cent of participants, followed by nasal corticosteroid spray (49 per cent). Stellate ganglion block, trialled by 16 per cent of respondents, had the highest reported improvement (45 per cent), with 21 per cent reporting a sustained benefit - the highest rate amongst registered treatment options.

CONCLUSION

Post-coronavirus parosmia has a significant impact and remains challenging to treat. Stellate ganglion block appears to be successful relative to other reported treatments. Further research into the pathophysiology, efficacy and mechanism of stellate ganglion block effect is warranted.

An interpretable machine learning approach to estimate the influence of inflammation biomarkers on cardiovascular risk assessment

BACKGROUND AND OBJECTIVE

Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment.

METHODS

Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpretable rules supported by clinical evidence; (ii) selection based on a machine learning classifier able to identify for a given patient the most suitable subset of rules; (iii) an ensemble scheme combining the previous subset of rules in the estimation of the patient cardiovascular risk. All the results were statistically validated (t-test, Wilcoxon-signed rank test) according to a previous verification of data normality (Shapiro-Wilk).

RESULTS

The proposed methodology was applied to a real acute coronary syndrome patients dataset (N = 1544) from the Cardiology Unit of Coimbra Hospital and Universitary centre. The first assessment was based on the GRACE tool and a Random Forest classifier, the incorporation of inflammation biomarkers achieved SE=0.83; SP=0.84 whereas the original GRACE risk factors reached SE=0.75; SP=0.85. In the second phase, the proposed approach with inflammation biomarkers achieved SE=0.763 and SP=0.778.

CONCLUSIONS

This approach confirms the potential of combining inflammation markers with the GRACE score, increasing SE and SP, when compared with the original GRACE. Additionally, it assures interpretability and personalization, which are critical issues to allow its application in the daily clinical practice.

Horner's Syndrome as a Complication of Ultrasound-Guided Central Cannulation: A Case Report

Cannulation of the internal jugular vein is often necessary for the management of critically ill patients. Despite being a very common procedure and performed more and more safely, several complications still occur. Horner's Syndrome (HS) is one of those complications described before the use of ultrasound as a method of guidance. HS is caused by functional interruption of sympathetic nerve supply to the eye, leading to a classic triad of ipsilateral ptosis, miosis, and anhidrosis. We present the case of a patient, in need of emergent surgery to control the hemorrhagic focus after delivery, with a transient HS secondary to internal jugular vein cannulation under real-time ultrasound guidance.

Horner's Syndrome and Upper Limb Paresthesia During Labor Epidural Analgesia: A Case Report

Horner's syndrome is a condition that results from sympathetic nervous system dysfunction. Labor epidural analgesia is known to be a rare cause of Horner's Syndrome. However, in the obstetric population, the incidence of Horner's Syndrome is higher than in the rest of the population as it is a consequence of high cephalad spread of local anesthetic (LA) probably enhanced by the anatmophysiologic changes of pregnancy. We present a case of unilateral Horner's syndrome as a complication of epidural analgesia with accompanying upper limb paresthesia and motor weakness, a rarely encountered symptom.

Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems

One of the key challenges when developing a predictive model is the capability to describe the domain knowledge and the cause-effect relationships in a simple way. Decision rules are a useful and important methodology in this context, justifying their application in several areas, particularly in clinical practice. Several machine-learning classifiers have exploited the advantageous properties of decision rules to build intelligent prediction models, namely decision trees and ensembles of trees (ETs). However, such methodologies usually suffer from a trade-off between interpretability and predictive performance. Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules. In this paper, we introduce a novel step to those methodologies. We create a new component to predict if a given rule will be correct or not for a particular patient, which introduces personalization into the procedure. Furthermore, the validation results using three public clinical datasets suggest that it also allows to increase the predictive performance of the selected set of rules, improving the mentioned trade-off.

A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario

INTRODUCTION

The risk prediction of the occurrence of a clinical event is often based on conventional statistical procedures, through the implementation of risk score models. Recently, approaches based on more complex machine learning (ML) methods have been developed. Despite the latter usually have a better predictive performance, they obtain little approval from the physicians, as they lack interpretability and, therefore, clinical confidence. One clinical issue where both types of models have received great attention is the mortality risk prediction after acute coronary syndromes (ACS).

OBJECTIVE

We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and ML models. More specifically, we aim to develop a method that, besides having a good performance, offers a personalized model and outcome for each patient, presents high interpretability, and incorporates an estimation of the prediction reliability which is not usually available. By combining these features in the same approach we expect that it can boost the confidence of physicians to use such a tool in their daily activity.

METHODS

In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction. The methodology was applied to a dataset of 1111 patients admitted with any type of ACS (myocardial infarction and unstable angina) in two Portuguese hospitals, to assess the 30-days all-cause mortality risk, being validated through a Monte-Carlo cross-validation technique. The performance was compared with state-of-the-art approaches: logistic regression (LR), artificial neural network (ANN), and clinical risk score model (namely the Global Registry of Acute Coronary Events - GRACE).

RESULTS

For the scenario being analyzed, the performance of the proposed approach and the comparison models was assessed through discrimination and calibration. The ability to rank the patients was evaluated through the area under the ROC curve (AUC), and the ability to stratify the patients into low or high-risk groups was determined using the geometric mean (GM) of specificity and sensitivity, the negative predictive value (NPV) and the positive predictive value (PPV). The validation calibration curves were also inspected. The proposed approach (AUC = 81%, GM = 74%, PPV = 17%, NPV = 99%) achieved testing results identical to the standard LR model (AUC = 83%, GM = 73%, PPV = 16%, NPV=99%), but offers superior interpretability and personalization; it also significantly outperforms the GRACE risk model (AUC = 79%, GM = 47%, PPV = 13%, NPV = 98%) and the standard ANN model (AUC = 78%, GM = 70%, PPV = 13%, NPV = 98%). The calibration curve also suggests a very good generalization ability of the obtained model as it approaches the ideal curve (slope = 0.96). Finally, the reliability estimation of individual predictions presented a great correlation with the misclassifications rate.

CONCLUSION

We developed and described a new tool that showed great potential to guide the clinical staff in the risk assessment and decision-making process, and to obtain their wide acceptance due to its interpretability and reliability estimation properties. The methodology presented a good performance when applied to ACS events, but those properties may have a beneficial application in other clinical scenarios as well.

Assessing cadmium-based quantum dots effect on the gonads of the marine mussel Mytilus galloprovincialis

This study assesses the sex-specific effects induced by CdTe QDs, on the marine mussel Mytilus galloprovincialis in comparison to its dissolved counterpart. A 14 days exposure to CdTe QDs and dissolved Cd was conducted (10 μg Cd L^-1), analysing Cd accumulation, oxidative stress, biotransformation, metallothionein and oxidative damage in the gonads. Both Cd forms caused significant antioxidant alterations, whereby QDs were more pro-oxidant, leading to oxidative damage, being females more affected. Overall, biochemical impairments on gonads of M. galloprovincialis demonstrate that the reproductive toxicity induced by CdTe QDs in mussels are sex-dependent and mediated by oxidative stress and lipid peroxidation. It is crucial to acknowledge how gametes are affected by metal-based nanoparticles, such as Cd-based QDs. As well as understanding the potential changes they may undergo at the cellular level during gametogenesis, embryogenesis and larval development potentially leading to serious impacts on population sustainability and ecosystem health.

Latent states extraction through Kalman Filter for the prediction of heart failure decompensation events

Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions.In this paper we introduce a methodology with the goal of finding onsets of worsening progressions from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the model composed by only two features using a telemonitoring dataset (myHeart) with 41 patients. Results were achieved by applying leave-one-subject-out validation and correspond to a geometric mean of 83.67%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.

Genetic diversity of pathogenic leptospires from wild, domestic and captive host species in Portugal

Leptospirosis is a neglected zoonotic disease of worldwide distribution with a significant veterinary and public health impact. It is caused by pathogenic bacteria of the genus Leptospira. The availability of effective tools to accurately identify and type leptospires is of utmost importance for the diagnosis of the disease and for assessing its epidemiology. Several multi-locus sequence typing (MLST) approaches were described for the typing of worldwide isolates of Leptospira but an extensive agreement towards the adoption of a unique consensus scheme for this agent is still lacking. Most genotyped strains originate from Asian and South American countries, with a minority originating from Europe (being most countries represented only by one or a few isolates). The knowledge of the diversity of circulating leptospires is the key to understanding the disease transmission and its zoonotic implications. In this study, we revisited the taxonomy of several isolates of pathogenic Leptospira obtained from domestic, wild and captive animals in Portugal, between 1990 and 2012. A selection of these isolates was genotyped using two previously published MLST schemes. A total of seven distinct sequence types (STs) were detected among the Portuguese isolates with two STs representing L. borgpetersenii (ST149 and ST152), two STs representing L. kirschneri (ST117 and ST100) and three STs representing L. interrogans (ST17, ST24 and ST140). Global widespread (and maybe more virulent) Leptospira genotypes seem to circulate in Portugal, particularly the L. interrogans ST17 isolates which are associated with several outbreaks of leptospirosis among humans and animals in different regions of the world. This study contributes to the enrichment of the global MLST databases with a new set of allele and sequence type information also providing novel data on circulating Leptospira serovars in Portugal.

Progression of micturition dysfunction associated with the development of heart failure in rats: Model of overactive bladder

Heart failure (HF) has a strong association with the development of lower urinary tract symptoms, especially overactive bladder (OAB); although this condition remains poorly investigated. In this study, we assess the aortocaval fistula (ACF) model as a novel experimental model of micturition dysfunction, associated with HF, focused on the molecular and functional studies to evaluate the autonomic nervous system and urinary bladder remodeling. Male rats were submitted to ACF for HF induction. Echocardiography, cystometric, histomorphometry and molecular analysis, as well as concentration-response curves to carbachol and ATP and frequency-response curves to electrical field stimulation (EFS) were evaluated in Sham and HF (4- and 12-weeksendpoint) groups. Compared to SHAM, HF groups exhibited progressive increases in the left ventricle (LV) mass and fractional shortening which indicates cardiac dysfunction, although HF was characterized only after 12 weeks by the reduced ejection fraction. For micturition function, HF groups presented increased non-voiding contractions (NVC) and decreased bladder capacity; however, when comparing HF groups, these urinary parameters were significantly impaired over the weeks (12-weeks). The contractile responses induced by CCh, ATP and EFS were greater in detrusor muscle (DSM) from HF rats. mRNA expression for muscarinic receptors (M2 and M3) was higher in DSM only after 12 weeks of ACF, in addition to MMP9 and TGF-beta. Histomorphometric revealed increased urothelium thickness in both HF groups, whereas DSM thickness occurred only after 12 weeks. Thus, the ACF model induced cardiac dyfunction with progressive micturition dysfunction over the weeks, characterized by increased DSM contractile mechanisms as well as extracellular matrix remodeling in the urinary bladder, representing a useful tool to evaluate the OAB associated with HF.

Risk Prediction of Heart Failure Decompensation Events in Multiparametric Feature Spaces

Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions. In this paper we introduce a methodology with the goal of finding relevant feature spaces from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the feature space comprising the following features: mean weight, standard deviation of the blood pressure and mean of extra-thoracic impedance in a time window of 20 days. Results were achieved by applying leave-one-out validation and correspond to a geometric mean of 88.32%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.

Measuring improvement in knowledge of drug policy reforms following a police education program in Tijuana, Mexico

Background

Mexico’s 2009 “narcomenudeo reform” decriminalized small amounts of drugs, shifting some drug law enforcement to the states and mandating drug treatment diversion instead of incarceration. Data from Tijuana suggested limited implementation of this harm reduction-oriented policy. We studied whether a police education program (PEP) improved officers’ drug and syringe policy knowledge, and aimed to identify participant characteristics associated with improvement of drug policy knowledge.

Methods

Pre- and post-training surveys were self-administered by municipal police officers to measure legal knowledge. Training impact was assessed through matched paired nominal data using McNemar’s tests. Multivariable logistic regression was used to identify predictors of improved legal knowledge, as measured by officers’ ability to identify conceptual legal provisions related to syringe possession and thresholds of drugs covered under the reform.

Results

Of 1750 respondents comparing pre- versus post training, officers reported significant improvement (p < 0.001) in their technical understanding of syringe possession (56 to 91%) and drug amounts decriminalized, including marijuana (9 to 52%), heroin (8 to 71%), and methamphetamine (7 to 70%). The training was associated with even greater success in improving conceptual legal knowledge for syringe possession (67 to 96%) (p < 0.001), marijuana (16 to 91%), heroin (11 to 91%), and methamphetamine (11 to 89%). In multivariable modeling, those with at least a high school education were more likely to exhibit improvement of conceptual legal knowledge of syringe possession (adjusted odds ratio [aOR] 2.6, 95% CI 1.4–3.2) and decriminalization for heroin (aOR 2.7, 95% CI 1.3–4.3), methamphetamine (aOR 2.2, 95% CI 1.4–3.2), and marijuana (aOR 2.5, 95% CI 1.6–4).

Conclusions

Drug policy reform is often necessary, but not sufficient to achieve public health goals because of gaps in translating formal laws to policing practice. To close such gaps, PEP initiatives bundling occupational safety information with relevant legal content demonstrate clear promise. Our findings underscore additional efforts needed to raise technical knowledge of the law among personnel tasked with its enforcement. Police professionalization, including minimum educational standards, appear critical for aligning policing with harm reduction goals.

An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment

The development of models able to produce an understandable decision by the clinicians is of great importance to support their decision. Therefore, the research of methodologies able to extract useful knowledge from existing datasets, as well as to integrate this knowledge into the current clinical evidence, is a key aspect in the enhancement of the clinical decision. This work focuses on the development of interpretable models to assess the patient's condition based on supervised clustering theories, enabling the discovery of a set of features that best represents that condition. At the same time, the technique is supported on a structure that enables the formulation of simple and interpretable rules. Despite its general applicability, the proposed methodology is applied to coronary artery disease (CAD), particularly, in the risk of death assessment (30 days after the admission) of patients that have been admitted to the emergency unit. The validation is performed using a real dataset with Acute Coronary Syndromes, provided by the Portuguese Society of Cardiology. While the methodology produces simple and interpretable rules, the performance achieves an improvement of 7% in relation to geometric mean, when compared with GRACE model (commonly used in Portugal).

A non-exercise based V02max prediction using FRIEND dataset with a neural network

The main goal of this work is the development of models, based on computational intelligence techniques, in particular neural networks, to predict the maximum oxygen consumption value. While the maximum oxygen consumption is a direct mark of the cardiorespiratory fitness, several studies have also confirmed it also as a powerful predictor of risk for adverse outcomes, such as hypertension, obesity, and diabetes. Therefore, the existence of simpler and accurate models, establishing an alternative to standard cardiopulmonary exercise tests, with the potential to be employed in the stratification of the general population in daily clinical practice, would be of major importance. In the current study, different models were implemented and compared: 1) the traditional Wasserman/Hansen equation; 2) linear regression and; 3) non-linear neural networks. Their performance was evaluated based on the "FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base" [1] being, in the present study, a subset of 12262 individuals employed. The accuracy of the models was performed through the computation of sensitivity and specificity values. The results show the superiority of neural networks in the prediction of maximum oxygen consumption.

Ecotoxicological assessment of the anticancer drug cisplatin in the polychaete Nereis diversicolor

Anticancer drugs are designed to inhibit tumor cell proliferation by interacting with DNA and altering cellular growth factors. When released into the waterbodies of municipal and hospital effluents these pharmaceutical compounds may pose a risk to non-target aquatic organisms, due to their mode of action (cytotoxic, genotoxic, mutagenic and teratogenic). The present study aimed to assess the ecotoxicological potential of the alkylating agent cisplatin (CisPt) to the polychaete Nereis diversicolor, at a range of relevant environmental concentrations (i.e. 0.1, 10 and 100ngPtL^-1). Behavioural impairment (burrowing kinetic impairment), ion pump effects (SR Ca²⁺-ATPase), neurotoxicity (AChE activity), oxidative stress (SOD, CAT and GPXs activities), metal exposure (metallothionein-like proteins - MTLP), biotransformation (GST), oxidative damage (LPO) and genotoxicity (DNA damage), were selected as endpoints to evaluate the sublethal responses of the ragworms after 14-days of exposure in a water-sediment system. Significant burrowing impairment occurred in worms exposed to the highest CisPt concentration (100ngPtL^-1) along with neurotoxic effects. The activity of antioxidant enzymes (SOD, CAT) and second phase biotransformation enzyme (GST) was inhibited but such effects were compensated by MTLP induction. Furthermore, LPO levels also increased. Results showed that the mode of action of cisplatin may pose a risk to this aquatic species even at the range of ngL^-1.

Contagious equine metritis in Portugal: A retrospective report of the first outbreak in the country and recent contagious equine metritis test results

Contagious equine metritis (CEM), a highly contagious bacterial venereal infection of equids, caused by Taylorella equigenitalis, is of major international concern, causing short-term infertility in mares. Portugal has a long tradition of horse breeding and exportation and until recently was considered CEM-free. However, in 2008, T. equigenitalis was isolated at our laboratory from a recently imported stallion and 2 mares from the same stud. Following this first reported outbreak, the Portuguese Veterinary Authority (DGVA) performed mandatory testing on all remaining equines at the stud (n=30), resulting in a further 4 positive animals. All positive animals were treated and subsequently tested negative for T. equigenitalis. Since this outbreak, over 2000 genital swabs from Portuguese horses have been tested at our laboratory, with no further positive animals identified. The available data suggests that this CEM outbreak was an isolated event and we have no further evidence of CEM cases in Portugal, however, an extended and wider epidemiological study would be needed to better evaluate the incidence of the disease in Portuguese horses.

Improving clinical models based on knowledge extracted from current datasets: a new approach

The Cardiovascular Diseases (CVD) are the leading cause of death in the world, being prevention recognized to be a key intervention able to contradict this reality. In this context, although there are several models and scores currently used in clinical practice to assess the risk of a new cardiovascular event, they present some limitations. The goal of this paper is to improve the CVD risk prediction taking into account the current models as well as information extracted from real and recent datasets. This approach is based on a decision tree scheme in order to assure the clinical interpretability of the model. An innovative optimization strategy is developed in order to adjust the decision tree thresholds (rule structure is fixed) based on recent clinical datasets. A real dataset collected in the ambit of the National Registry on Acute Coronary Syndromes, Portuguese Society of Cardiology is applied to validate this work. In order to assess the performance of the new approach, the metrics sensitivity, specificity and accuracy are used. This new approach achieves sensitivity, a specificity and an accuracy values of, 80.52%, 74.19% and 77.27% respectively, which represents an improvement of about 26% in relation to the accuracy of the original score.

Expert knowledge integration in the data mining process with application to cardiovascular risk assessment

The data mining process, when applied to clinical databases, suffers from critical data problems, from noisy acquisitions to missing or incomplete data points. Expert knowledge, in the form of practitioners' experience and clinical guidelines, is already used to manually correct some of these problems, while enhancing expert's confidence in such systems. In this work, we propose the Knowledge-Biased Tree (KB3), a knowledge biased decision tree inducer that is able to exploit IF THEN rules to guide the tree inducing process. The KB3 approach was tested against its unbiased counterpart, the C5.0 algorithm in the cardiovascular risk assessment task. Using a clinical dataset provided by the hospital of Sta Cruz (Lisbon, Portugal) the performance of the proposed algorithm is compared against the unbiased C5.0 and the state of the art risk score used in clinical practice (GRACE risk score).

Assessment of cardiovascular risk based on a data-driven knowledge discovery approach

The cardioRisk project addresses the development of personalized risk assessment tools for patients who have been admitted to the hospital with acute myocardial infarction. Although there are models available that assess the short-term risk of death/new events for such patients, these models were established in circumstances that do not take into account the present clinical interventions and, in some cases, the risk factors used by such models are not easily available in clinical practice. The integration of the existing risk tools (applied in the clinician's daily practice) with data-driven knowledge discovery mechanisms based on data routinely collected during hospitalizations, will be a breakthrough in overcoming some of these difficulties. In this context, the development of simple and interpretable models (based on recent datasets), unquestionably will facilitate and will introduce confidence in this integration process. In this work, a simple and interpretable model based on a real dataset is proposed. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The validation is performed using a recent dataset provided by the Portuguese Society of Cardiology (11113 patients), which originally comprised 77 risk factors. A sensitivity, specificity and accuracy of, respectively, 80.42%, 77.25% and 78.80% were achieved showing the effectiveness of the approach.

Correlation between autonomous function and left ventricular performance after acute myocardial infarction

Reduced ejection fraction (EF), possibly induced/mediated by autonomic abnormal activation, is one of the most powerful predictors of adverse outcome after acute myocardial infarction (MI). A deep understanding of the correlation between the autonomous functionality and the left ventricular performance in these patients is therefore of paramount importance. The autonomous function is reflected in the cardiac activity and, specifically, in the heart rate variability (HRV) signal. Given the cardiac activity nonlinearity, growing interest is being manifested towards nonlinear methods of analysis, which might provide more significant information than the traditional linear approaches. The aim of the present study was to investigate if non-linear HRV metrics change between MI patients with preserved EF (pEF) and MI patients with reduced EF (rEF). Data were acquired in the context of the cardioRisk project. Ten MI patients with rEF and six MI patients with pEF, admitted to Intensive Cardiac Care after a first acute MI episode, were studied. The ECG was acquired during a Holter recording and the tachogram was extracted. Sample entropy (SE) and Lempel-Ziv Complexity (LZC 1 and LZC 2) metrics were computed on five hour long tachogram portions. A significant correlation was found between LZC indices and EF in the whole population; SE, LZC 1 and LZC 2 were significantly higher in patients with pEF. Our results indicate that lower complexity characterizes the HRV of MI patients with rEF. Complexity reduction might be due to a simplification of regulatory mechanisms, which might explain why MI patients with rEF are at higher risk for subsequent non-fatal and fatal events.

Multi-parametric prediction for cardiovascular risk assessment

The employment of personal health systems (pHealth) is a valuable concept in the management of chronic diseases, particularly in the context of cardiovascular diseases. By means of a continuous monitoring of the patient it is possible to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. In practice, it is possible the prompt diagnosis of events, the early prediction of critical events and the implementation of personalized therapies. Furthermore, the information collected during long periods creates new opportunities in the diagnosis of a disease, in its evolution, and in the prediction of possible complications. The focus of this work is the research and implementation of multi-parametric algorithms for data analysis in pHealth context, including data mining techniques as well as physiological signal modelling and processing. In particular, fusion strategies for cardiovascular status evaluation (namely cardiovascular risk assessment and cardiac function estimation) and multi-parametric prediction algorithms for the early detection of cardiovascular events (such as hypertension, syncope and heart failure decompensation) will be addressed.

New approaches for improving cardiovascular risk assessment

INTRODUCTION AND OBJECTIVES

Clinical guidelines recommend the use of cardiovascular risk assessment tools (risk scores) to predict the risk of events such as cardiovascular death, since these scores can aid clinical decision-making and thereby reduce the social and economic costs of cardiovascular disease (CVD). However, despite their importance, risk scores present important weaknesses that can diminish their reliability in clinical contexts. This study presents a new framework, based on current risk assessment tools, that aims to minimize these limitations.

METHODS

Appropriate application and combination of existing knowledge is the main focus of this work. Two different methodologies are applied: (i) a combination scheme that enables data to be extracted and processed from various sources of information, including current risk assessment tools and the contributions of the physician; and (ii) a personalization scheme based on the creation of patient groups with the purpose of identifying the most suitable risk assessment tool to assess the risk of a specific patient.

RESULTS

Validation was performed based on a real patient dataset of 460 patients at Santa Cruz Hospital, Lisbon, Portugal, diagnosed with non-ST-segment elevation acute coronary syndrome. Promising results were obtained with both approaches, which achieved sensitivity, specificity and geometric mean of 78.79%, 73.07% and 75.87%, and 75.69%, 69.79% and 72.71%, respectively.

CONCLUSIONS

The proposed approaches present better performances than current CVD risk scores; however, additional datasets are required to back up these findings.

Integration of Different Risk Assessment Tools to Improve Stratification of Patients with Coronary Artery Disease

Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predict the risk of occurrence of a cardiovascular disease event (e.g. hospitalization or death). However, these tools present some drawbacks. These problems are addressed through two methodologies: (i) combination of risk assessment tools: fusion of naïve Bayes classifiers complemented with a genetic optimization algorithm and (ii) personalization of risk assessment: subtractive clustering applied to a reduced-dimensional space to create groups of patients. Validation was performed based on two ACS-NSTEMI patient data sets. This work improved the performance in relation to current risk assessment tools, achieving maximum values of sensitivity, specificity, and geometric mean of, respectively, 79.8, 83.8, and 80.9 %. Additionally, it assured clinical interpretability, ability to incorporate of new risk factors, higher capability to deal with missing risk factors and avoiding the selection of a standard CVD risk assessment tool to be applied in the clinical practice.

Personalization algorithms applied to cardiovascular disease risk assessment

Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools applied in clinical practice (78.5%, 53.2%, 64.4%).

Direct detection and differentiation of pathogenic Leptospira species using a multi-gene targeted real time PCR approach

Leptospirosis is a growing public and veterinary health concern caused by pathogenic species of Leptospira. Rapid and reliable laboratory tests for the direct detection of leptospiral infections in animals are in high demand not only to improve diagnosis but also for understanding the epidemiology of the disease. In this work we describe a novel and simple TaqMan-based multi-gene targeted real-time PCR approach able to detect and differentiate Leptospira interrogans, L. kirschneri, L. borgpeteresenii and L. noguchii, which constitute the veterinary most relevant pathogenic species of Leptospira. The method uses sets of species-specific probes, and respective flanking primers, designed from ompL1 and secY gene sequences. To monitor the presence of inhibitors, a duplex amplification assay targeting both the mammal β-actin and the leptospiral lipL32 genes was implemented. The analytical sensitivity of all primer and probe sets was estimated to be <10 genome equivalents (GE) in the reaction mixture. Application of the amplification reactions on genomic DNA from a variety of pathogenic and non-pathogenic Leptospira strains and other non-related bacteria revealed a 100% analytical specificity. Additionally, pathogenic leptospires were successfully detected in five out of 29 tissue samples from animals (Mus spp., Rattus spp., Dolichotis patagonum and Sus domesticus). Two samples were infected with L. borgpetersenii, two with L. interrogans and one with L. kirschneri. The possibility to detect and identify these pathogenic agents to the species level in domestic and wildlife animals reinforces the diagnostic information and will enhance our understanding of the epidemiology of leptopirosis.

Prediction of Heart Failure Decompensation Events by Trend Analysis of Telemonitoring Data

This paper aims to assess the predictive value of physiological data daily collected in a telemonitoring study in the early detection of heart failure (HF) decompensation events. The main hypothesis is that physiological time series with similar progression (trends) may have prognostic value in future clinical states (decompensation or normal condition). The strategy is composed of two main steps: a trend similarity analysis and a predictive procedure. The similarity scheme combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal bases, with the Karhunen-Loève transform, that allows the selection of the reduced set of bases that capture the fundamental behavior of the time series. The prediction process assumes that future evolution of current condition can be inferred from the progression of past physiological time series. Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using physiological data resulting from the myHeart telemonitoring study, namely blood pressure, respiration rate, heart rate, and body weight collected from 41 patients (15 decompensation events and 26 normal conditions). The obtained results suggest, in general, that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.

An effective wavelet strategy for the trend prediction of physiological time series with application to pHealth systems

This work proposes a wavelet decomposition based scheme to estimate the evolution trend of physiological time series. The scheme does not involve the explicit development of a model and is essentially supported on the hypothesis that future evolution of a biosignal can be estimated from similar historic patterns. The strategy considers an a-trous wavelet decomposition, where the most representative trends are extracted from the historic similar patterns. Then, a set of distance-based measures able to assess the prediction likelihood of each representative trend, is introduced. From these measures and through an optimization process, a subset of these trends is selected and aggregated to derive the required time series evolution trend. The effectiveness of the methodology is validated in the prediction of blood pressure signals collected in two telemonitoring studies: TEN-HMS and MyHeart. Additionally, Friedman and Nemenyi statistics tests are implemented to rank several methods, confirming the value of the proposed strategy.

Toxic effects of glibenclamide in fetuses of normoglycemic rats: an alternative therapy for gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is defined as glucose intolerance first diagnosed during the second or third trimester of pregnancy. The treatment aims at glycemic control through changes in the patient's diet with or without exercise, but some patients need insulin therapy. An alternative would be to use oral hypoglycemic agents such as glibenclamide (GLIB). The present study aims to analyze the toxic effects of GLIB in fetuses of pregnant rats which received 5 or 20mg/kg doses of GLIB. Glycemic dosage reveals no significant difference between control (deionized water) and treated groups, showing that these concentrations of GLIB were not effective to cause hypoglycemia in rats. The vitality of the fetuses in all groups was 100%. GLIB administration promoted increase in weight and significant changes in measures of external morphological parameters of treated fetuses. Histological analysis revealed that liver lobes, lobules and central lobular veins were well defined for all treatments. However, GLIB animals presented a light brownish precipitate into the center-lobular veins and in the liver parenchyma among the hepatocytes. These results indicated a possible passage of the drug through the blood-placental membrane, without serious changes that impair the development of neither bone tissue, nor the liver of these animals.

Cardiovascular disease risk assessment innovative approaches developed in HeartCycle project

Two innovative CVD event risk assessment strategies were developed in the scope of HeartCycle project: i) combination of individual risk assessment tools; ii) personalization of risk assessment based on grouping of patients. These approaches aimed to defeat some of the major limitations of the tools currently applied in the daily clinical practice, namely to: i) improve the risk prediction performance when comparing it to the one achieved by the individual current risk assessment tools; ii) consider the available knowledge provided by other risk assessment tools; iii) cope with missing risk factors; iv) incorporate additional clinical knowledge. Two different real patients' datasets were applied to validate the developed strategies: i) Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients; ii) Leiria Pombal Hospital Centre, Portugal, N=99 ACS-NSTEMI. Based on the gathered results, we propose a new strategy in order to improve patients' stratification.

Telehealth streams reduction based on pattern recognition techniques for events detection and efficient storage in EHR

This work proposes a framework for telehealth streams analysis, founded on a pattern recognition technique that evaluates the similarity between multi-sensorial biosignals. The strategy combines the Haar wavelet with the Karhunen-Loève transforms to describe biosignals by means of a reduced set of parameters. These, that reflect the dynamic behavior of the biosignals, can support the detection of relevant clinical conditions. Moreover, the simplicity and fast execution of the proposed approach allow its application in real-time operation, as well as provide a practical way to manage historical electronic health records: i) common and uncommon behaviors can be distinguished; ii) the creation of different models, tailored to specific conditions can be efficiently stored. The efficiency of the methodology is assessed through its performance analysis, namely by computing the required number of operations and the compression rate. Its effectiveness is evaluated in the prediction of decompensation episodes using biosignals daily collected in the myHeart study (blood pressure, weight, respiration and heart rates).

Comparative genotypic and antimicrobial susceptibility analysis of zoonotic Campylobacter species isolated from broilers in a nationwide survey, Portugal

Campylobacter is a major cause of human foodborne disease worldwide and has been associated with the consumption of contaminated poultry. The prevalence of Campylobacter species in broiler flocks from more than 200 producers widespread in mainland Portugal was assessed in 2008 in response to Commission Decision 2007/516/EC. Campylobacter isolates were obtained from 83.3% of 424 pooled cecal samples, with a higher prevalence of Campylobacter coli (61.2%) than Campylobacter jejuni (38.8%). Restriction fragment length polymorphism analysis of the flaA gene (flaA-RFLP) of 112 C. coli isolates and 67 C. jejuni isolates yielded 11 flaA-RFLP patterns with HinfI (Hunter Gaston diversity index [HGDI] = 0.62) and 48 flaA-RFLP patterns with DdeI (HGDI = 0.89), indicating a high level of genetic diversity. A wide geographic distribution of the most frequent restriction pattern was observed. MICs of five antimicrobials (ciprofloxacin, gentamicin, streptomycin, erythromycin, and tetracycline) were determined for 215 C. coli isolates and 136 C. jejuni isolates. The occurrence of non-wild-type isolates, exhibiting an acquired resistance phenotype, was higher for C. coli than C. jejuni for all antimicrobials tested. Sixty-three percent of C. jejuni and C. coli isolates were considered non-wild type based on their response to both ciprofloxacin and erythromycin, which are frequently used in the treatment of human infections. The high prevalence of antimicrobial-resistant Campylobacter strains detected supports the need for increased epidemiological surveillance and prevention in a country where large amounts of poultry meat are consumed.

A multibiomarker approach in the clam Ruditapes decussatus to assess the impact of pollution in the Ria Formosa lagoon, South Coast of Portugal

The Ria Formosa lagoon is an ecosystem whose water quality reflects the anthropogenic influence upon the surrounding areas. In this lagoon, the clam Ruditapes decussatus has a great economical importance and has been widely used as a biomonitor. A multibiomarker approach (δ-aminolevulinic acid dehydratase, metallothionein, lipid peroxidation, acetylcholinesterase, alkali-labile phosphates, DNA damage) was applied to assess the environmental quality of this ecosystem and the accumulation of contaminants and their potential adverse effects on clams. Clams were sampled in different shellfish beds in the period between July 2007 and December 2008 and abiotic parameters (temperature, salinity, pH and dissolved oxygen of seawater and organic matter in the sediment), condition index, metals (Cd, Cu, Zn, Ni, Pb), TBTs and PAHs concentrations were measured in clam tissues. Data was integrated using Principal Component Analyses and biomarker indices: IBR (Integrated Biomarker Response) and HSI (Health Status Index). This multibiomarker approach enabled discrimination of a time and space trend between sites with different degrees of anthropogenic contamination, identifying one of them (site 2) as the most stressful and summer months as the most critical period for clams due to an increase of environmental stress (anthropogenic pressure along with extreme environmental conditions, e.g. temperature, dissolved oxygen, organic matter in the sediments, etc). The selected biomarkers provided an integrated response to assess the environmental quality of the system, proving to be a useful approach when complex mixtures of contaminants occur.

Improvement of CVD risk assessment tools' performance through innovative patients' grouping strategies

There are available in the clinical community several practical risk tools to assess the risk of occurrence of a cardiovascular event. Although valuable, these tools typically present some lack of performance (low sensitivity/low specificity) when applied to a general (average) patient. This flaw is addressed in this work through an innovative personalization strategy that is supported on the evidence that current risk assessment tools perform differently among different populations/groups of patients. The proposed methodology is based on two main hypotheses: i) patients are grouped through a proper dimension reduction technique complemented with an unsupervised learning algorithm, ii) for each group the most suitable risk assessment tool can be selected improving the risk prediction performance. As a result, risk personalization is simply achieved by the identification of the group that patients belong to. The validation of the strategy is carried out through the combination of three current risk assessment tools (GRACE, TIMI, PURSUIT) developed to predict the risk of an event in coronary artery disease patients. The combination of these tools is validated with a real patient testing dataset: Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI patients. Considering the obtained results with the available dataset it is possible to state that the main objective of this work was achieved.