Chronic neuropathic pain components in whiplash-associated disorders correlate with metabolite concentrations in the anterior cingulate and dorsolateral prefrontal cortex: a consensus-driven MRS re-examination.

Introduction: Whiplash injury (WHI) is characterised by a forced neck flexion/extension, which frequently occurs after motor vehicle collisions. Previous studies characterising differences in brain metabolite concentrations and correlations with neuropathic pain (NP) components with chronic whiplash-associated disorders (WAD) have been demonstrated in affective pain-processing areas such as the anterior cingulate cortex (ACC). However, the detection of a difference in metabolite concentrations within these cortical areas with chronic WAD pain has been elusive. In this study, single-voxel magnetic resonance spectroscopy (MRS), following the latest MRSinMRS consensus group guidelines, was performed in the anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex (DLPFC), and occipital cortex (OCC) to quantify differences in metabolite concentrations in individuals with chronic WAD with or without neuropathic pain (NP) components. Materials and methods: Healthy individuals (n = 29) and participants with chronic WAD (n = 29) were screened with the Douleur Neuropathique 4 Questionnaire (DN4) and divided into groups without (WAD-noNP, n = 15) or with NP components (WAD-NP, n = 14). Metabolites were quantified with LCModel following a single session in a 3 T MRI scanner within the ACC, DLPFC, and OCC. Results: Participants with WAD-NP presented moderate pain intensity and interference compared with the WAD-noNP group. Single-voxel MRS analysis demonstrated a higher glutamate concentration in the ACC and lower total choline (tCho) in the DLPFC in the WAD-NP versus WAD-noNP group, with no intergroup metabolite difference detected in the OCC. Best fit and stepwise multiple regression revealed that the normalised ACC glutamate/total creatine (tCr) (p = 0.01), DLPFC n-acetyl-aspartate (NAA)/tCr (p = 0.001), and DLPFC tCho/tCr levels (p = 0.02) predicted NP components in the WAD-NP group (ACC r 2 = 0.26, α = 0.81; DLPFC r 2 = 0.62, α = 0.98). The normalised Glu/tCr concentration was higher in the healthy than the WAD-noNP group within the ACC (p < 0.05), but not in the DLPFC or OCC. Neither sex nor age affected key normalised metabolite concentrations related to WAD-NP components when compared to the WAD-noNP group. Discussion: This study demonstrates that elevated glutamate concentrations within the ACC are related to chronic WAD-NP components, while higher NAA and lower tCho metabolite levels suggest a role for increased neuronal-glial signalling and cell membrane dysfunction in individuals with chronic WAD-NP components.

Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality.

BACKGROUND: There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality. METHODS: Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters -including k, the number of phenotypes- were chosen automatically, by maximizing the average Silhouette score across the training set. RESULTS: We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] - age ∼ 57, Charlson comorbidity ∼ 1, pneumonia CURB-65 score ∼ 0 to 1, respiratory rate at admission ∼ 18 min-1, FiO2 ∼ 21%, C-reactive protein CRP ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] - age ∼ 75, Charlson ∼ 5, CURB-65 ∼ 1 to 2, respiration ∼ 20 min-1, FiO2 ∼ 21%, CRP ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] - age ∼ 71, Charlson ∼ 4, CURB-65 ∼ 0 to 2, respiration ∼ 30 min-1, FiO2 ∼ 38%, CRP ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. CONCLUSION: A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients - phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods.

BACKGROUND: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. OBJECTIVE: To develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. METHODS: Observational, prospective cohort study enrolling n=623 patients who underwent tracheal intubation: 53/623 difficult cases (prevalence 8.51%). First, we used our previously validated deep convolutional neural network (DCNN) to extract 2D image coordinates for 27 + 13 relevant anatomical landmarks in two preoperative photos (frontal and lateral views). Here we propose a method to determine the 3D pose of the camera with respect to the patient and to obtain the 3D world coordinates of these landmarks. Then we compute a novel set of dM=59 morphological features (distances, areas, angles and ratios), engineered with our anaesthesiologists to characterize each individual's airway anatomy towards prediction. Subsequently, here we propose four ad hoc ML pipelines for difficult intubation prognosis, each with four stages: feature scaling, imputation, resampling for imbalanced learning, and binary classification (Logistic Regression, Support Vector Machines, Random Forests and eXtreme Gradient Boosting). These compound ML pipelines were fed with the dM=59 morphological features, alongside dD=7 demographic variables. Here we trained them with automatic hyperparameter tuning (Bayesian search) and probability calibration (Platt scaling). In addition, we developed an ad hoc multi-input DCNN to estimate the intubation risk directly from each pair of photographs, i.e. without any intermediate morphological description. Performance was evaluated using optimal Bayesian decision theory. It was compared against experts' judgement and against state-of-the-art methods (three clinical formulae, four ML, four DL models). RESULTS: Our four ad hoc ML pipelines with engineered morphological features achieved similar discrimination capabilities: median AUCs between 0.746 and 0.766. They significantly outperformed both expert judgement and all state-of-the-art methods (highest AUC at 0.716). Conversely, our multi-input DCNN yielded low performance due to overfitting. This same behaviour occurred for the state-of-the-art DL algorithms. Overall, the best method was our XGB pipeline, with the fewest false negatives at the optimal Bayesian decision threshold. CONCLUSIONS: We proposed and validated ML models to assist clinicians in anaesthesia planning, providing a reliable calibrated estimate of airway intubation risk, which outperformed expert assessments and state-of-the-art methods. Our novel set of engineered features succeeded in providing informative descriptions for prognosis.

Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity.

OBJECTIVE: To study the suitability of costsensitive ordinal artificial intelligence-machine learning (AIML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. MATERIALS & METHODS: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a 'standard' (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. RESULTS: The study enrolled n = 1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. d = 131 variables were collected, becoming d ' = 148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of d ' features), c) 'Ordered Partitions' ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the 'standard' AI-ML baseline. DISCUSSION & CONCLUSION: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature.

Spectroscopic and Microestructural Evidence for T-2 Toxin Adsorption Mechanism by Natural Bentonite Modified with Organic Cations.

Aluminosilicates are adsorbents able to bind mycotoxins, and their chemical modification increases their affinity to adsorb low-polarity mycotoxins. To further investigate if the inclusion of salts in bentonite modifies its adsorptive capacity, we studied T-2 toxin adsorption in natural bentonite (NB) and when modified with quaternary ammonium salts differing in polarity and chain length: myristyl trimethyl ammonium bromide (B14), cetyl trimethyl ammonium bromide (B16) and benzyl dimethyl stearyl ammonium chloride (B18). The results showed that quaternary salts made bentonite: displace monovalent (Na+1, K+1) and divalent (Mg+2, Ca+2) ions; reduce its porosity; change its compaction and structure, becoming more crystalline and ordered; and modify the charge balance of sheets. T-2 adsorption was higher in all modified materials compared to NB (p ≤ 0.0001), and B16 (42.96%) better adsorbed T-2 compared to B18 (35.80%; p = 0.0066). B14 (38.40%) showed no differences compared to B16 and B18 (p > 0.05). We described the T-2 adsorption mechanism in B16, in which hydrogen bond interactions, Van der Waals forces and the replacement of the salt by T-2 were found. Our results showed that interaction types due to the inclusion in B16 might be more important than the hydrocarbon chain length to improve the adsorptive capacity of bentonite.

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

Brewing a Craft Belgian-Style Pale Ale Using Pichia kudriavzevii 4A as a Starter Culture.

There is an expanding market for beer of different flavors. This study aimed to prepare a craft Belgian-style pale ale with a non-Saccharomyces yeast. Pichia kudriavzevii 4A was used as a sole starter culture, and malted barley as the only substrate. The ingredients and brewing process were carefully monitored to ensure the quality and innocuousness of the beverage. During fermentation, the yeast consumed 89.7% of total sugars and produced 13.8% v/v of ethanol. The product was fermented and then aged for 8 days, adjusted to 5% v/v alcohol, and analyzed. There were no traces of mycotoxins, lead, arsenic, methanol, or microbiological contamination that would compromise consumer health. According to the physicochemical analysis, the final ethanol concentration (5.2% v/v) and other characteristics complied with national and international guidelines. The ethyl acetate and isoamyl alcohol present are known to confer sweet and fruity flavors. The sensory test defined the beverage as refreshing and as having an apple and pear flavor, a banana aroma, and a good level of bitterness. The judges preferred it over a commercial reference sample of Belgian-style pale ale made from S. cerevisiae. Hence, P. kudriavzevii 4A has the potential for use in the beer industry.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks.

BACKGROUND: A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology. OBJECTIVE: To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. METHODS: We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. RESULTS: With annotators' consensus as the 'gold standard', our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. CONCLUSIONS: We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

Antibody Response 3 Months after 2 Doses of BNT162b2 mRNA COVID-19 Vaccine in Residents of Long-Term Care Facilities.

BACKGROUND: Older adults living in long-term care facilities (LTCFs) are at increased risk for severe outcomes from COVID-19 and were identified as a priority group in COVID-19 vaccination strategies. Emerging evidence suggests vaccine effectiveness in LTCF populations, but data about median and long-term durability of immune response after vaccination are still limited. OBJECTIVES: In this study, we assessed the humoral response to BNT162b2 mRNA COVID-19 vaccine 3 months after the second dose, in a cohort of 495 residents aged ≥65 years from 11 LTCF in Granada, Spain. METHOD: Between April 19 and April 30, 2021, we measured anti-SARS-CoV-2 Spike IgG to evaluate the humoral vaccination response. Antibody titers were reported in binding antibody units (BAU/mL). Bivariate and multivariate logistic regression models were performed to investigate the impact of age, sex, underlying health conditions, and prior COVID-19 infection on the antibody levels. RESULTS: Over 96% of the participants developed an adequate humoral response. We detected higher antibody titers in previously infected individuals, compared with those previously uninfected (B: 1,150.059 BAU/mL, p < 0.001). Moreover, we found a significant inverse association between age and antibody levels (B: -7.943 BAU/mL, p < 0.05). This negative age-dependent response was more noticeable among residents over 85 years old. In contrast, baseline health conditions and cognitive status were not associated with different antibody levels. CONCLUSIONS: These findings support monitoring COVID-19 vaccination response trend in older adults, in order to optimize future disease prevention and control strategies in this vulnerable population.

Effect of jasmonic acid on major terpenes and density of glandular trichomes in Lippia graveolens kunth (Verbenaceae).

The effect of exogenous application of jasmonic acid (JA) on the concentration of main terpenes and density of glandular trichomes was investigated in the Mexican oregano, propagated from seeds from 3 localities. JA 1 mM was applied locally and to the whole plant. JA locally applied increased the number of trichomes, with a mean of 20 trichomes more with respect to the controls in plants from Tecomavaca and Zapotitlán Salinas, and significantly increased the thymol concentration by 185% systemically and 255% locally, compared to the control. JA applied to the whole plant decreased the number of trichomes and increased the concentration of caryophyllene from 0.79 to 1.7 mg g-1, and α-caryophyllene from 0.3 to 0.8 mg g-1 in plants from San Rafael with reference to water control. The results suggest a plasticity of morphologic and phytochemical responses, and a potential use of JA to improve phenolic monoterpenes and sesquiterpenes production.

Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes.

The management of postprandial glucose excursions in type 1 diabetes has a major impact on overall glycaemic control. In this work, we propose and evaluate various mechanistic models to characterize the disposal of meal-attributable glucose. Sixteen young volunteers with type 1 diabetes were subject to a variable-target clamp which replicated glucose profiles observed after a high-glycaemic-load ([Formula: see text]) or a low-glycaemic-load ([Formula: see text]) evening meal. [6,6-[Formula: see text]] and [U-[Formula: see text];1,2,3,4,5,6,6-[Formula: see text]] glucose tracers were infused to, respectively, mimic: (a) the expected post-meal suppression of endogenous glucose production and (b) the appearance of glucose due to a standard meal. Six compartmental models (all a priori identifiable) were proposed to investigate the remote effect of circulating plasma insulin on the disposal of those glucose tracers from the non-accessible compartments, representing e.g. interstitium. An iterative population-based parameter fitting was employed. Models were evaluated attending to physiological plausibility, posterior identifiability of their parameter estimates, accuracy-via weighted fitting residuals-and information criteria (i.e. parsimony). The most plausible model, best representing our experimental data, comprised: (1) a remote effect x of insulin active above a threshold [Formula: see text] = 1.74 (0.81-2.50) [Formula: see text] min[Formula: see text] [median (inter-quartile range)], with parameter [Formula: see text] having a satisfactory support: coefficient of variation CV = 42.33 (31.34-65.34) %, and (2) steady-state conditions at the onset of the experiment ([Formula: see text]) for the compartment representing the remote effect, but not for the masses of the tracer that mimicked endogenous glucose production. Consequently, our mechanistic model suggests non-homogeneous changes in the disposal rates for meal-attributable glucose in relation to plasma insulin. The model can be applied to the in silico simulation of meals for the optimization of postprandial insulin infusion regimes in type 1 diabetes.

Automatic Identification of Physical Activity Intensity and Modality from the Fusion of Accelerometry and Heart Rate Data.

BACKGROUND: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling. METHODS: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low / moderate / vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic / resistance / mixed). In total, 178.63 h of data about PA intensity (65.55 % low / 18.96 % moderate / 15.49 % vigorous) and 17.00 h about modality were collected in two experiments: one in free-living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall. RESULTS: The best scheme, which comprised a projection through Linear Discriminant Analysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65 %, versus up to 63.60 %. Errors tended to be brief and to appear around transients. CONCLUSIONS: The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation.

Quantifying the acute changes in glucose with exercise in type 1 diabetes: a systematic review and meta-analysis.

BACKGROUND: The acute impact of different types of physical activity on glycemic control in type 1 diabetes has not been well quantified. OBJECTIVES: Our objective was to estimate the rate of change (RoC) in glucose concentration induced acutely during the performance of structured exercise and at recovery in subjects with type 1 diabetes. METHODS: We searched for original articles in the PubMed, MEDLINE, Scopus, and Cochrane databases. Search terms included type 1 diabetes, blood glucose, physical activity, and exercise. Eligible studies (randomized controlled trials and non-randomized experiments) encompassed controlled physical activity sessions (continuous moderate [CONT], intermittent high intensity [IHE], resistance [RESIST], and/or a resting reference [REST]) and reported excursions in glucose concentration during exercise and after its cessation. Data were extracted by graph digitization to compute two RoC measures from population profiles: RoCE during exercise and RoCR in recovery. RESULTS: Ten eligible studies were found from 540 publications. Meta-analyses of exercise modalities versus rest yielded the following: RoCE -4.43 mmol/L h(-1) (p < 0.00001, 95% confidence interval [CI] -6.06 to -2.79) and RoCR +0.70 mmol/L h(-1) (p = 0.46, 95% CI -1.14 to +2.54) for CONT vs. REST; RoCE -5.25 mmol/L·h(-1) (p < 0.00001, 95 % CI -7.02 to -3.48) and RoCR +0.72 mmol/L h(-1) (p = 0.71, 95% CI -3.10 to +4.54) for IHE vs. REST; RoCE -2.61 mmol/L h(-1) (p = 0.30, 95% CI -7.55 to +2.34) and RoCR -0.02 mmol/L h(-1) (p = 1.00, 95% CI -7.58 to +7.53) for RESIST vs. REST. CONCLUSIONS: Novel RoC magnitudes RoCE, RoCR reflected rapid decays of glycemia during CONT exercise and gradual recoveries immediately afterwards. RESIST showed more constrained decays, whereas discrepancies were found for IHE.

Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes.

OBJECTIVE: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient's data using two different strategies to control nocturnal and postprandial periods. RESEARCH DESIGN AND METHODS: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. RESULTS: Time spent in normoglycemia (BG, 3.9-8.0 mmol/L) during the nocturnal period (12 a.m.-8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3-75%) with OL to 95.8% (73-100%) using the CL algorithm (P<0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0-21%) in the OL night to 0.0% (0.0-0.0%) in the CL night (P<0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9-10.0 mmol/L) 58.3% (29.1-87.5%) versus 50.0% (50-100%). CONCLUSIONS: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemia.

[Low prevalence of hepatitis B virus primary drug resistance in Southern Spain]. / Baja prevalencia de resistencias primarias frente al virus de la hepatitis B en el sur de España.

INTRODUCTION: To know the prevalence of primary resistance in chronic hepatitis B naïve patients is essential to decide on the need of routine laboratory testing. PATIENTS AND METHODS: The genetic sequence of the HBV polymerase from 105naïve patients was analysed. RESULTS: rtV173L, a lamivudine compensatory mutation, was detected in two patients (1.9%), rtI233V in one patient, and another one carried the sG145R vaccine escape mutation. CONCLUSION: Our study shows that studying HBV resistance in naïve patients should not be recommended in the routine laboratory setting, for the time being.

Quantification of viral loads lower than 50 copies per milliliter by use of the Cobas AmpliPrep/Cobas TaqMan HIV-1 test, version 2.0, can predict the likelihood of subsequent virological rebound to >50 copies per milliliter.

After 1 year of follow-up, patients on HAART with a baseline viral load (VL) of <20 copies/ml showed significantly lower odds of virological rebound to two consecutive VLs of >50 copies/ml than those with baseline VLs of 20 to 39 and 40 to 49 (P < 0.001). The time to virological rebound was also significantly shorter (P < 0.001) for the groups with baseline VLs of 20 to 39 and 40 to 49.

An Analytic Network Process approach for siting a municipal solid waste plant in the Metropolitan Area of Valencia (Spain).

In this paper the Analytic Network Process (ANP) is applied to select the best location for the construction of a municipal solid waste (MSW) plant in the Metropolitan area of Valencia (Spain). Selection of the appropriate MSW facility location can be viewed as a complex multicriteria decision-making problem that requires an extensive evaluation process of the potential MSW plant locations and other factors as diverse as economic, technical, legal, social or environmental issues. The decision-making process includes the identification of six candidate MSW plant sites and 21 criteria grouped into clusters for the construction of a network. Two technicians of the Metropolitan Waste Disposal Agency acted as decision makers (DMs). The influences between the elements of the network were identified and analyzed using the ANP multicriteria decision method. Two different ANP models were used: one hierarchy model (that considers AHP as a particular case of ANP) and another network-based model. The results obtained in each model were compared and analyzed. The strengths and weaknesses of ANP as a multicriteria decision analysis tool are also described in the paper. The main findings of this research have proved that ANP is a useful tool to help technicians to make their decision process traceable and reliable. Moreover, this approach helps DMs undertake a sound reflection of the siting problem.