A mathematical model for temporal cerebral blood flow response to acetazolamide evaluated in patients with Moyamoya disease.

BACKGROUND: Paired cerebral blood flow (CBF) measurement is usually acquired before and after vasoactive stimulus to estimate cerebrovascular reserve (CVR). However, CVR may be confounded because of variations in time-to-maximum CBF response (tmax) following acetazolamide injection. With a mathematical model, CVR can be calculated insensitive to variations in tmax, and a model offers the possibility to calculate additional model-derived parameters. A model that describes the temporal CBF response following a vasodilating acetazolamide injection is proposed and evaluated. METHODS: A bi-exponential model was adopted and fitted to four CBF measurements acquired using arterial spin labelling before and initialised at 5, 15 and 25 min after acetazolamide injection in a total of fifteen patients with Moyamoya disease. Curve fitting was performed using a non-linear least squares method with a priori constraints based on simulations. RESULTS: Goodness of fit (mean absolute error) varied between 0.30 and 0.62 ml·100 g-1·min-1. Model-derived CVR was significantly higher compared to static CVR measures. Maximum CBF increase occurred earlier in healthy- compared to diseased vascular regions. CONCLUSIONS: The proposed mathematical model offers the possibility to calculate CVR insensitive to variations in time to maximum CBF response which gives a more detailed characterisation of CVR compared to static CVR measures. Although the mathematical model adapts generally well to this dataset of patients with MMD it should be considered as experimental; hence, further studies in healthy populations and other patient cohorts are warranted.

Comparison of quantitative [11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system.

PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.

Citrinin as a potential anti-cancer therapy: A comprehensive review.

Citrinin (CIT) is a polyketide-derived mycotoxin, which is produced by many fungal strains belonging to the gerena Monascus, Aspergillus, and Penicillium. It has been postulated that mycotoxins have several toxic mechanisms and are potentially used as antineoplastic agents. Therefore, the present study carried out a systematic review, including articles from 1978 to 2022, by collecting evidence in experimental studies of CIT antiplorifactive activity in cancer. The Data indicate that CIT intervenes in important mediators and cell signaling pathways, including MAPKs, ERK1/2, JNK, Bcl-2, BAX, caspases 3,6,7 and 9, p53, p21, PARP cleavage, MDA, reactive oxygen species (ROS) and antioxidant defenses (SOD, CAT, GST and GPX). These factors demonstrate the potential antitumor drug CIT in inducing cell death, reducing DNA repair capacity and inducing cytotoxic and genotoxic effects in cancer cells.

MeHg exposure impairs both the catecholaminergic and cholinergic systems resulting in motor and non-motor behavioral changes in Drosophila melanogaster.

Human exposure to the natural environmental contaminant methylmercury (MeHg) has been associated to adverse health effects. Importantly, the mechanisms by which this organomercurial exerts its neurotoxicity have yet to be fully clarified. Therefore, the aim of this study was to evaluate whether exposure to MeHg alters dopamine (DA) and octopamine (OA) levels, acetylcholinesterase (AChE) activity and impacts both motor and non-motor behaviours. We studied the effect of MeHg by feeding 1-2 d old flies (male and females) with 25 and 50 µM MeHg for 4 d and determined effects on survival, motor and non-motor behaviours, oxidative stress, AChE and tyrosine hydroxylase (TH) activities, as well as DA and OA levels. We found that Drosophila melanogaster (D. melanogaster) exposed to MeHg showed a reduction in survival rate, associated with the inhibition of AChE and TH activities in head of flies and decreased DA and OA levels. These changes were accompanied by behavioural alterations, such as locomotor deficit and increased grooming behaviour, in addition to an increase in oxidative stress markers both in head and in body of flies, and an increase in glutathione-S-transferase (GST) activity in head of flies. Collectively, our data support the hypothesis that MeHg neurotoxicity is associated with altered OA and DA levels, AChE inhibition, which may serve, at least in part, as the underpinnings of both motor and non-motor behavioural changes.

Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR.

In positron emission tomography (PET), 68Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple 68Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered 68Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [11C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every 68Ge-AC map, and averaging the resulting 68Ge-AC maps. [11C]PE2I PET images were reconstructed using CTR-AC and a patient-specific 68Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R1) and non-displaceable binding potential (BPND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R1 (-0.1 ± 3.2%), and putamen BPND (3.7 ± 8.1%). SUV and R1 precision (SD of %bias) were modest and lowest in the anterior cortex, with an R1 %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records.

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.

Data-driven curation process for describing the blood glucose management in the intensive care unit.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging.

BACKGROUND: A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain. METHODS: Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain. RESULTS: For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC. CONCLUSIONS: Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

MOTIVATION: Emergency Departments' (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. OBJECTIVES: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. METHODS: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers' title, abstract and key words for the topics "triage", "emergency department"/"emergency room" and concepts within the field of intelligent systems. RESULTS: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients' age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. CONCLUSIONS: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals' decision-making thereby leading to better clinical management and patients' outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

Anti-Cancer Effects of Asiatic Acid, a Triterpene from Centilla asiatica L: A Review.

BACKGROUND: Centilla asiatica L is a medicinal herb that has been widely used in folk medicine to treat various diseases. Asiatic Acid (AA), a triterpene and a known component of this herb, has been shown to display important biological activities, including anti-inflammatory, antibacterial, antidiabetic and antihyperlipidemic, neuroprotective, anxiolytic and antidepressant, hepatoprotective, pancreas protective, and cardio- protective. OBJECTIVE: This review focuses on AA's anti-cancer effects on the basis of published literature found in a number of databases such as PubMed and Science Direct. Emphasis has been given to the mechanisms of action of its anti-cancer effect. METHODS: A literature survey was conducted using known databases such as PubMed and Science Direct using the keywords 'Asiatic acid', pairing with 'cancer', 'tumor', 'anti-cancer effect', 'cytotoxic effect', 'anti-tumor activity', 'cell line', 'animal cancer', and 'human cancer'. RESULTS: Findings suggest that AA exerts anti-cancer effects in several test systems through various pathways, including oxidative/antioxidant, anti-inflammatory, cytotoxicity, apoptotic cell death, necrosis, anti-angiogenesis, inhibition of proliferation and cell migration, and chemoprevention. CONCLUSION: AA may be an effective plant-based cancer chemotherapeutic agent and a promising lead for the development of potent anticancer drugs.

Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging-comparison to head atlas and 68Ge-transmission-based attenuation correction.

BACKGROUND: MRI does not offer a direct method to obtain attenuation correction maps as its predecessors (stand-alone PET and PET/CT), and bone visualisation is particularly challenging. Recently, zero-echo-time (ZTE) was suggested for MR-based attenuation correction (AC). The aim of this work was to evaluate ZTE- and atlas-AC by comparison to 68Ge-transmission scan-based AC. Nine patients underwent brain PET/MR and stand-alone PET scanning using the dopamine transporter ligand 11C-PE2I. For each of them, two AC maps were obtained from the MR images: an atlas-based, obtained from T1-weighted LAVA-FLEX imaging with cortical bone inserted using a CT-based atlas, and an AC map generated from proton-density-weighted ZTE images. Stand-alone PET 68Ge-transmission AC map was used as gold standard. PET images were reconstructed using the three AC methods and standardised uptake value (SUV) values for the striatal, limbic and cortical regions, as well as the cerebellum (VOIs) were compared. SUV ratio (SUVR) values normalised for the cerebellum were also assessed. Bias, precision and agreement were calculated; statistical significance was evaluated using Wilcoxon matched-pairs signed-rank test. RESULTS: Both ZTE- and atlas-AC showed a similar bias of 6-8% in SUV values across the regions. Correlation coefficients with 68Ge-AC were consistently high for ZTE-AC (r 0.99 for all regions), whereas they were lower for atlas-AC, varying from 0.99 in the striatum to 0.88 in the posterior cortical regions. SUVR showed an overall bias of 2.9 and 0.5% for atlas-AC and ZTE-AC, respectively. Correlations with 68Ge-AC were higher for ZTE-AC, varying from 0.99 in the striatum to 0.96 in the limbic regions, compared to atlas-AC (0.99 striatum to 0.77 posterior cortex). CONCLUSIONS: Absolute SUV values showed less variability for ZTE-AC than for atlas-AC when compared to 68Ge-AC, but bias was similar for both methods. This bias is largely caused by higher linear attenuation coefficients in atlas- and ZTE-AC image compared to 68Ge-images. For SUVR, bias was lower when using ZTE-AC than for atlas-AC. ZTE-AC shows to be a more robust technique than atlas-AC in terms of both intra- and inter-patient variability.

Association of Phytol with Toxic and Cytotoxic Activities in an Antitumoral Perspective: A Meta-Analysis and Systemic Review.

BACKGROUND: Phytol have various pharmacological activities such as antimicrobial, cytotoxic, antitumoral, antimutagenic, anti-atherogenic, antidiabetic, lipid-lowering, antispasmodic, antiepileptic, antinociceptive, antioxidant, anti-inflammatory, anxiolytic, antidepressant and immunoadjuvant. Several studies point to an association of phytol with implications for apoptosis and necrosis at cellular levels in cancer, yet no clear conclusions were drawn. METHOD: To clarify this, we conducted a meta-analysis of non-clinical studies of phytol and its associations with toxicity and cytotoxicity emphasizing the mechanisms of apoptosis and necrosis induction and its importance in tumor therapy. Relevant studies were systematically searched in PubMed and Web of Science. The association between phytol and cyto-/toxicity was assessed by odds ratio (ORs) and 95% confidence intervals (CI). Twentythree studies were finally included in the meta-analysis. A significant association between phytol and toxicity (OR: 1.47; 95% CI = 0.86-2.48) was found among in vivo studies and cytotoxicity (OR: 1.81; 95% CI = 1.12- 2.65, p<0.05) in in vitro and ex vivo studies. In in vitro studies, 24% of them indicate that phytol at high doses induces apoptosis by several mechanisms; while about 40% of ex vivo studies indicate that phytol induces reactive oxygen species generation. But, Phytol does not act as a direct oxidant, unlike its metabolite phytanic acid. The 24% of in vivo studies also highlighted the mechanisms for apoptosis-like including expression of Bcl2 protein or mutations in pro-apoptotic protein Bax. Of them, 8% studies show necrosis and hepatotoxicity. However, in 24% of the articles, the mechanisms of toxicity and cytotoxicity are still not well elucidated. CONCLUSION: This study confirms that the association between phytol and cyto-/toxicity depends on the dose/concentration used in the given experimental conditions. Thus, there are still great prospects for new research aimed at the use of phytol and its metabolite as anticancer agents.

Computational intelligence applied to discriminate bee pollen quality and botanical origin.

The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped. The probabilistic NN prediction model obtained 98.4% of correct classification of the predominant plant genus of pollen. To obtain the secondary and tertiary plant genus of pollen, the results present a lower accuracy. To predict the physicochemical characteristic of a mixture of bee pollen, given their botanical origin, fuzzy models proven the best results with small prediction errors, and variability lower than 10%.

Clinical decision support tool for Co-management signalling.

INTRODUCTION: Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. METHODS: Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD-9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD-9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. RESULTS: Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. CONCLUSIONS: The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables.

Assessment of chemotherapy on various biochemical markers in breast cancer patients.

Chemotherapy is a standard treatment method for the patients with locally advanced breast cancer. Lately, cyclophosphamide (CYP) and doxorubicin (DOX) are used as the major chemotherapeutic agents especially for the treatment of breast cancer. Till date, no serum biomarker has been able to provide an early diagnosis of breast cancer. This study aimed to assess inflammatory, cardiac, renal and hematological markers in 56 breast cancer patients (BCP) before, during and after termination of chemotherapy with CYP and DOX. Blood samples were collected from the patients at the each treatment stages mentioned above. These samples were assessed for interleukin 6 (IL-6), interleukin 10 (IL-10), lactate dehydrogenase (LDH), creatine kinase (CK), creatinine, hemoglobin (Hb), leukocyte, platelet and Na+ /K+ -ATPase levels either by ELISA or colorimetric methods. The results suggest a significant increase in IL-6 level at all the stages in BCP as compared to control group. On the other hand, IL-10, CK and Na+ /K+ -ATPase levels were found to be significantly declined during all the stages. Moreover, the majority of hematological parameters remained unchanged throughout the treatment period with the exception of creatinine and Hb which showed slight modulation in their level at different stages. Based on the results, we conclude that breast cancer and co-treatment with CYP and DOX, interfere arious biological markers, thereby, showing the physiological imbalance.

The influence of heavy metals on toxicogenetic damage in a Brazilian tropical river.

Anthropogenic activities in tropical rivers favor the eutrophication process, which causes increased concentration of heavy metals. The presence and bioaccumulation of metals are directly related to the presence of genotoxic damage in aquatic organisms. Thus, we evaluated the presence of heavy metals (Fe, Zn, Cr, Cu and Al) and performed toxicogenetic tests in surface (S) and bottom (B) of water samples of the Poti river (Piaui/Brazil). Cytotoxicity and genotoxicity tests were performed in Allium cepa, and micronucleus (MN) and comet assay were performed in Oreochromis niloticus. The chemical analysis showed concentrations above the limit for Cu, Cr, Fe and Al according to Brazilian laws, characterizing anthropogenic disturbance in this aquatic environment. Toxicogenetic analysis presented significant cytotoxic, mutagenic and genotoxic effects in different exposure times and water layers (S and B), especially alterations in mitotic spindle defects, MN formations, nuclear bud and DNA strand breaks. Correlations between Fe and cytotoxicity, and Al and mutagenicity were statistically significant and point out to the participation of heavy metals in genotoxic damage. Therefore, Poti river water samples presented toxicogenetic effects on all bioindicators analyzed, which are most likely related to heavy metals pollution.

Doppler ultrasound of the placenta and maternal and fetal vessels during normal gestation in captive agoutis (Dasyprocta prymnolopha, Wagler, 1831).

The use of ultrasound for pregnancy monitoring is critical for the evaluation of hemodynamic parameters essential to fetal viability. In the present study, using B-mode and Doppler ultrasound, we characterized the placenta, subplacenta, maternal, and fetal vessels during normal gestation of healthy agoutis raised in captivity. In total, 30 agoutis were obtained from the Center for the Study and Preservation of Wild Animals, Center of Agricultural Sciences, Federal University of Piauí (Núcleo de Estudos e Preservação de Animais Silvestres-NEPAS, Centro de Ciências Agrárias-CCA, Universidade Federal do Piauí-UFPI). These animals were subjected to B-mode and Doppler ultrasound examinations to evaluate their maternal and fetal hemodynamic profiles. The placenta was located in the mesometrial region and had a discoid, ellipsoid, or globular aspect. With spectral Doppler, characteristic systolic and diastolic flow was observed in the umbilical artery. This flow increased during pregnancy. A cross-sectional view revealed a goblet-shaped placenta. The uteroplacental blood flow was characterized by a marked increase in systolic peak velocity during pregnancy, the presence of a rapid deceleration ramp, and a relatively high diastolic speed. The fetal aortic vascular flow was predominantly systolic and diastolic. The caudal vena cava blood flow was characterized by a systolic peak followed by a decreased diastolic wave throughout pregnancy. In the present study, we characterized the morphologic and hemodynamic interactions of the placenta/subplacenta with maternal and fetal vessels in agoutis at 30, 45, 60, 75, and 90 days gestation using B-mode and Doppler ultrasound. We determined the approximation and separation of the blood flow values of the umbilical artery, subplacental flow, uteroplacental artery, fetal aorta, and fetal vena cava. We believe these values may contribute to an understanding of the gestational biology and aid delivery prediction in this species.

Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.

Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and easily acquired variables in the intensive care unit (ICU) to predict severely depressed LVEF following ICU admission. A retrospective study was conducted. We extracted clinical physiological variables derived from ICU monitoring and available within the MIMIC II database and developed a fuzzy model using sequential feature selection and compared it with the conventional logistic regression (LR) model. Maximum predictive performance was observed using easily acquired ICU variables within 6 hours after admission and satisfactory predictive performance was achieved using variables acquired as early as one hour after admission. The fuzzy model is able to predict LVEF ≤ 25% with an AUC of 0.71 ± 0.07, outperforming the LR model, with an AUC of 0.67 ± 0.07. To the best of the authors' knowledge, this is the first study predicting severely impaired LVEF using multivariate analysis of routinely collected data in the ICU. We recommend inclusion of these findings into triaged management plans that balance urgency with resources and clinical status, particularly for reducing the time of echocardiographic examination.

Thoracic and heart biometrics of non-anesthetized agouti (Dasyprocta primnolopha Wagler, 1831) measured on radiographic images / Biometria torácica e cardíaca de cutias não anestesiadas (Dasyprocta primnolopha Wagler, 1831) mensuradas em imagens radiográficas

The agouti is a species intensively hunted throughout the Amazon and the semi-arid regions of northeastern Brazil. Considering the current trend in conservation management of wild species, the aim of this study was to determine the morphometric reference to the heart of agouti raised in captivity, based on thoracic and cardiac measurements in these animals. Thirty adult agoutis, 1 to 3 years of age, without clinical signs of cardiac disease were selected. The animals were physically restrained and radiographies in laterolateral (LL) and ventrodorsal (VD) recumbence were produced. The following measures were taken: the apicobasilar length of the heart (at the most cranial height of the Carina region to the heart apex) (AB), maximum width of the heart perpendicular to AB (CD), heart inclination angle (AIC), trachea inclination angle (AIT), distance from the right heart wall (DPTd), distance from the left heart wall (DPTe) and vertical depth of the thorax, and the ventral face of the vertebral column to the dorsal border of the sternum at the level of the trachea bifurcation (H). The ratios between AB/CD, AB/H and CD/H were also analyzed. To calculate the vertebral heart scale (VHS), the AB and CD measurements were laid over the thoracic vertebra starting at T4. Radiographic evaluation showed values consistent with those reported in small animals and some wild and exotic species. The main biometric values in the chest cavity and heart of agouti are arranged as follows: (1) The ratios between AB/H ratio and CD/H were not sensitive for identifying heart increases (p>0.05), while the ratio AB/CD was more sensitive in this identification (p<0.05); (2) AIC: 21.2±6.4º (mean between male and famale); (3) AIT for males and females: 9.93±3.23° and 8.4±3.94°; (4) DPTd and DPTe for males: 0.97±0.40cm and 0.7±0.30cm; (5) DPTd and DPTe for females: 1.12±0.42cm and 01.02±0.43cm; (6) VHS for males and females: 7.75±0.48v e 7.61±0.34v; (7) The caudal vena cava (CVC) was visualized dorsal-cranially and located right of the midline. The data obtained allowed the acquisition of the first reference values for biometry of the heart of agoutis, contributing to better understanding of cardiac morphology and identification of cardiomyopathy in these animals.

A cutia é uma espécie intensamente caçada em toda a Amazônia e nas regiões semi-áridas do Nordeste do Brasil. Considerando a tendência atual em gestão de conservação das espécies selvagens, o objetivo deste estudo foi determinar referências morfométricas para o coração de cutia criadas em cativeiro, com base em medições torácicas e cardíacas desses animais. Foram selecionados 30 cutias adultas, sem sinais clínicos de doença cardíaca, com idade entre 1 a 3 anos. Os animais foram contidos fisicamente e realizadas projeções radiográficas laterolaterais e (LL) e ventrodorsais (VD). As seguintes medidas foram tomadas: O comprimento apicobasilar do coração (na altura mais cranial da região carina até o ápice do coração) (AB); a largura máxima coração perpendicular a AB (CD); o ângulo de inclinação do cardíaca (AIC); ângulo de inclinação da traquéia (AIT); a distância a partir da parede direita do coração (DPTd); a distância a partir da parede esquerda do coração (DPTe) e profundidade vertical do tórax, da face ventral da coluna vertebral até à borda dorsal do esterno, no nível da bifurcação da traqueia (H). As relações entre AB/CD, AB/H e CD/H também foram analisadas. Para calcular o "vertebral heart scale" (VHS), as medidas AB e CD foram colocadas sob as vértebras torácicas a partir de T4. A avaliação radiográfica demonstrou valores consistentes com aqueles relatados em pequenos animais e algumas espécies selvagens e exóticos. Os principais valores biométricos mensurados na cavidade torácica e cardíaca da cutia, estão dispostas da seguinte forma: (1) As relações entre AB/H e CD/H não foram sensíveis para identificar aumento do coração (p> 0,05), enquanto a proporção AB/CD foi mais sensível nesta identificação (p <0,05); (2) AIC: 21,2 ± 6.4º (média entre machos e fêmeas); (3) AIT para machos e fêmeas: 9.93 ± 3.23° and 8.4±3.94°; (4) DPTd e DPTe para os machos: 0,97 ± 0,40 cm e 0,7 ± 0,30, (5) DPTd e DPTe para as fêmeas: 1,12 ± 0,42 e 01,02 ± 0,43; (6) VHS para machos e fêmeas: 7.75±0.48v e 7.61±0.34v; (7) A veia cava caudal (CVC) foi visualizada dorso-cranialmente e localizada à direita da linha média. Os dados obtidos permitiram a obtenção dos primeiros valores de referência para a biometria do coração de cutias, contribuindo para uma maior compreensão da morfologia cardíaca e identificação de cardiomiopatia nestes animais.

Missing data in medical databases: impute, delete or classify?

BACKGROUND: The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used strategies for managing missing data are to impute or delete values, and the former can cause bias, while the later can cause both bias and loss of statistical power. OBJECTIVES: In this paper we present a new approach for managing missing data in ICU databases in order to improve overall modeling performance. METHODS: We use a statistical classifier followed by fuzzy modeling to more accurately determine which missing data should be imputed and which should not. We firstly develop a simulation test bed to evaluate performance, and then translate that knowledge using exactly the same database as previously published work by [13]. RESULTS: In this work, test beds resulted in datasets with missing data ranging 10-50%. Using this new approach to missing data we are able to significantly improve modeling performance parameters such as accuracy of classifications by an 11%, sensitivity by 13%, and specificity by 10%, including also area under the receiver-operator curve (AUC) improvement of up to 13%. CONCLUSIONS: In this work, we improve modeling performance in a simulated test bed, and then confirm improved performance replicating previously published work by using the proposed approach for missing data classification. We offer this new method to other researchers who wish to improve predictive risk modeling performance in the ICU through advanced missing data management.