Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data

OBJECTIVE

This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

MATERIAL AND METHODS

A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision.

RESULTS

A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders.

DISCUSSION

Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods.

CONCLUSION

The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.

Natural killer cells have an activated profile in early Parkinson's disease

Immune dysregulation is heavily implicated in Parkinson's disease (PD) but the role of Natural Killer (NK) cells has not been well characterised. Accumulating evidence indicates the immune response peaks early in the disease, hence this study focused on characterising NK cells in recently diagnosed PD. PBMCs were obtained from PD cases (< 2 years duration) and age-matched controls and immunophenotyped using flow cytometry. We found an increased proportion and number of NK cells (CD3-CD56+), mature cytotoxic NK cells (CD3-CD16 + CD56dim), and NK cells expressing the activation marker, NKG2D. This implies NK cells are activated in the earliest stages of PD.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets

INTRODUCTION

Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets.

MATERIALS AND METHODS

A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence.

RESULTS

The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important.

CONCLUSION

The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.

Motor symptoms in Parkinson's disease are related to the interplay between cortical curvature and thickness

INTRODUCTION

Brain atrophy in Parkinson's disease occurs to varying degrees in different brain regions, even at the early stage of the disease. While cortical morphological features are often considered independently in structural brain imaging studies, research on the co-progression of different cortical morphological measurements could provide new insights regarding the progression of PD. This study's aim was to examine the interplay between cortical curvature and thickness as a function of PD diagnosis, motor symptoms, and cognitive performance.

METHODS

A total of 359 de novo PD patients and 159 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) database were included in this study. Additionally, an independent cohort from four databases (182 PD, 132 HC) with longer disease durations was included to assess the effects of PD diagnosis in more advanced cases. Pearson correlation was used to determine subject-specific associations between cortical curvature and thickness estimated from T1-weighted MRI images. General linear modeling (GLM) was then used to assess the effect of PD diagnosis, motor symptoms, and cognitive performance on the curvature-thickness association. Next, longitudinal changes in the curvature-thickness correlation as well as the predictive effect of the cortical curvature-thickness association on changes in motor symptoms and cognitive performance across four years were investigated. Finally, Akaike information criterion (AIC) was used to build a GLM to model PD motor symptom severity cross-sectionally.

RESULTS

A significant interaction effect between PD motor symptoms and age on the curvature-thickness correlation was found (β_standardized = 0.11; t(350) = 2.12; p = 0.03). This interaction effect showed that motor symptoms in older patients were related to an attenuated curvature-thickness association. No significant effect of PD diagnosis was observed for the PPMI database (β = 0.03; t(510) = 0.35; p = 0.72). However, in patients with a longer disease duration, a significant effect of diagnosis on the curvature-thickness association was found (β_standardized = 0.31; t(306.7) = 3.49; p = 0.0006). Moreover, rigidity, but not tremor, in PD was significantly related to the curvature-thickness correlation (β_standardized = 0.11, t(350) = 2.24, p = 0.03; β_standardized = -0.03, t(350) = -0.58, p = 0.56, respectively). The curvature-thickness association was attenuated over time in both PD and HC, but the two groups did not show a significantly different effect (β_standardized = 0.03, t(184.7) = 0.78, p = 0.44). No predictive effects of the CC-CT correlation on longitudinal changes in cognitive performance or motor symptoms were observed (all p-values > 0.05). The best cross-sectional model for PD motor symptoms included the curvature-thickness correlation, cognitive performance, and putamen dopamine transporter (DAT) binding, which together explained 14 % of variance.

CONCLUSION

The association between cortical curvature and thickness is related to PD motor symptoms and age. This research shows the potential of modeling the curvature-thickness interplay in PD.

Molecular identification of Trypanosoma theileri in cattle from the Ecuadorian Amazon

Trypanosoma theileri is a cosmopolitan opportunistic haemoparasite described in wild and domestic ruminants, and also in arthropod vectors. The presence of this parasite has been reported in several South American countries, including Amazonian regions. Despite the importance of livestock production, Ecuador possesses scarce studies about trypanosomosis and no T. theileri reports in its territory. Here, we showed molecular evidences of the presence of T. theileri in cattle from a province located in the Ecuadorian Amazon. Bovine blood samples were collected from 2014 to 2019, during campaigns to detect haemoparasites in the Ecuadorian provinces of Orellana and Sucumbíos. DNA was extracted from the buffy coat and used in PCR assays with three different molecular markers, ITS1, 18S and Cathepsin L-like. T. theileri was detected only in the Sucumbíos province, with a specific molecular prevalence of 8.6% (3/35) using the three primers and an additional animal detected as positive (11.4% prevalence) only by the ITS1 marker. DNA sequences derived from the generated amplicons were subjected to phylogenetics maximum parsimony and maximum likelihood analysis, which indicate the presence of TthI and TthII genotypes circulating in the evaluated animals. Molecular surveillance should be continually implemented in Ecuador in order to deepen the epidemiological and evolutionary knowledge about T. theileri as well other haemoparasites in the amazon parts of the country.

The Community Engaged Digital Alzheimer's Research (CEDAR) Study: A Digital Intervention to Increase Research Participation of Black American Participants in the Brain Health Registry

BACKGROUND

Although Black/African American older adults bear significant inequities in prevalence, incidence, and outcomes of Alzheimer's disease and related dementias, they are profoundly under-included in Alzheimer's Disease research. Community-Engaged Research (e.g., equitable community/science partnerships) is an evidence-based approach for improving engagement of underrepresented populations into Alzheimer's Disease research, but has lacked scalability to the national level. As internet use among older adults from underrepresented populations continues to grow, internet-based research shows promise as a feasible, valid approach to engagement and longitudinal assessment. The Community Engaged Digital Alzheimer's Research (CEDAR) study utilizes a community-engaged research approach to increase the engagement and research participation of Black/African American adults in the Brain Health Registry (BHR) and Alzheimer Disease clinical research.

OBJECTIVES

To describe the methods and evaluate the feasibility of the CEDAR culturally-informed digital platform within BHR.

DESIGN

All Black/African American participants in BHR were invited to enroll in CEDAR and to consider serving on a newly convened Community-Scientific Partnership Board to guide the study. The community board guided the development a culturally-informed cadre of engagement materials and strategies to increase research participation. Engagement strategies included incentives for study task completion, culturally-informed communications (e.g., landing page, emails and social media), resources about brain health, and video and written testimonials by CEDAR participants.

SETTING

BHR, an Internet-based registry and cohort.

PARTICIPANTS

BHR participants self-identifying as Black/African American were invited to enroll. All participants who signed an online informed consent document were enrolled.

MEASUREMENTS

We report the number of participants invited, enrolled, completed tasks, and volunteered to join the community board. We compared the demographics, cognitive profile, and baseline BHR task completion rates between CEDAR participants and all those invited to join the study.

RESULTS

Of 3738 invited, 349 (9.34%) enrolled in CEDAR. 134 (37% of CEDAR participants) volunteered to join the community board, of which 19 were selected for the community board. Compared to those invited, the CEDAR cohort had a higher percentage of female participants (84.5%) and a lower percentage of participants who identify as belonging to more than one ethnocultural group (21.8%). Compared to those did not enroll in CEDAR, those enrolled in CEDAR had a higher percentage of participants completing all BHR tasks (22%) and a higher percentage of participants completing at least one cognitive test (76%). Those enrolled in CEDAR also had a higher percentage of participants having an enrolled study partner (18%).

CONCLUSIONS

A culturally-informed Community-Engaged Research approach, including a remotely-convened community board, to engagement of Black/African American participants in an online research registry is feasible. This approach can be adapted for use in various clinical studies and other settings. Future studies will evaluate the effectiveness of the engagement strategies.

Severe smallness as predictor of adverse perinatal outcome in suspected late small-for-gestational-age fetuses: systematic review and meta-analysis

OBJECTIVE

To investigate the performance of severe smallness in the prediction of adverse perinatal outcome among fetuses with suspected late-onset small-for-gestational age (SGA).

METHODS

A systematic search was performed to identify relevant studies in PubMed, Web of Science and Scopus. Late-onset SGA was defined as estimated fetal weight (EFW) or abdominal circumference (AC) < 10^th percentile diagnosed at or after 32 weeks' gestation, while severe SGA was defined as EFW or AC < 3^rd percentile or < 2 SD. Random-effects modeling was used to generate hierarchical summary receiver-operating-characteristics (HSROC) curves. The performance of severe SGA (as a presumptive diagnosis) in predicting adverse perinatal outcome among singleton pregnancies with suspected late-onset SGA was expressed as area under the HSROC curve (AUC), sensitivity, specificity and positive/negative likelihood ratios. The association between suspected severe SGA and adverse perinatal outcome was also assessed by random-effects modeling using the Mantel-Haenszel method and presented as odds ratio (OR). The non-exposed group was defined as non-severe SGA (EFW ≥ 3^rd centile).

RESULTS

Twelve cohort studies were included in this systematic review and meta-analysis. The studies included a total of 3639 fetuses with suspected late-onset SGA, of which 1246 had suspected severe SGA. Significant associations were found between suspected severe SGA and composite adverse perinatal outcome (OR, 1.97 (95% CI, 1.33-2.92)), neonatal intensive care unit admission (OR, 2.87 (95% CI, 1.84-4.47)) and perinatal death (OR, 4.26 (95% CI, 1.07-16.93)). However, summary ROC curves showed limited performance of suspected severe SGA in predicting perinatal outcomes, with AUCs of 60.9%, 66.9%, 53.6%, 57.2%, 54.6% and 64.9% for composite adverse perinatal outcome, neonatal intensive care unit admission, neonatal acidosis, Cesarean section for intrapartum fetal compromise, low Apgar score and perinatal death, respectively.

CONCLUSION

Although suspected severe SGA was associated with a higher risk of perinatal complications, it performed poorly as a standalone parameter in predicting adverse perinatal outcome. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Early constipation predicts faster dementia onset in Parkinson's disease

Constipation is a common but not a universal feature in early PD, suggesting that gut involvement is heterogeneous and may be part of a distinct PD subtype with prognostic implications. We analysed data from the Parkinson's Incidence Cohorts Collaboration, composed of incident community-based cohorts of PD patients assessed longitudinally over 8 years. Constipation was assessed with the MDS-UPDRS constipation item or a comparable categorical scale. Primary PD outcomes of interest were dementia, postural instability and death. PD patients were stratified according to constipation severity at diagnosis: none (n = 313, 67.3%), minor (n = 97, 20.9%) and major (n = 55, 11.8%). Clinical progression to all three outcomes was more rapid in those with more severe constipation at baseline (Kaplan-Meier survival analysis). Cox regression analysis, adjusting for relevant confounders, confirmed a significant relationship between constipation severity and progression to dementia, but not postural instability or death. Early constipation may predict an accelerated progression of neurodegenerative pathology.

0893 Myofunctional Therapy in Children With Mild Obstructive Sleep Apnea: A Meta-Analysis

Introduction

OSA affects 2-4% of children and untreated OSA can have adverse behavior and quality of life outcomes. 40% of children can have residual obstructive sleep apnea (OSA)despite first line treatment (adenotonsillectomy). Alternative modalities of treatment for OSA are limited. Myofunctional therapy comprises of exercises targeting upper airway muscles that can improve facial growth and have been shown to treat OSA in adults. There is paucity of data on the role of myofunctional therapy (MT) in children. The objective of this study was to systematically review the literature for articles evaluating myofunctional therapy (MT) as treatment for OSA in children and to perform a meta-analysis on the polysomnographic and mouth breathing data.

Methods

Medline, Embase, CINAHL, Scopus, Web of Science and Cochrane were searched from inception through October 1st, 2019. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was followed.

Results

Eight studies (91 patients) reported polysomnography and/or mouth breathing outcomes. The pre- and post-MT apnea hypopnea indices (AHI) decreased from a mean ± standard deviation (M ± SD) of 3.75± 3.14/h to 2.08 ± 2.48/h, mean difference (MD) -1.6 [95% confidence interval (CI) -2.42, -0.78], P =0.0001. Mean oxygen saturations improved from 96.03 ± 1.1% to 96.67 ± 0.95%, MD 0.42 (95% CI 0.21, 0.63), P &lt;0.0001. Lowest oxygen saturations improved from 86.6 ± 7.3% to 90.94 ± 3.05%, MD 1.01 (95% CI 0.25, 1.77), P = 0.009. Mouth breathing decreased in all three studies reporting subjective outcomes.

Conclusion

Current literature demonstrates that myofunctional therapy decreases apnea-hypopnea index by approximately 45% in children with mild obstructive sleep apnea. Mean oxygen saturations, lowest oxygen saturations and mouth breathing outcomes improved in children. Myofunctional therapy could serve as an adjunct to other obstructive sleep apnea treatments.

Support

None

Vitrification of Mouse Blastocysts by Open or Closed System and Warming in Sucrose-containing or Sucrose-free Diluent

BACKGROUND

Cryopreservation of embryos is of considerable relevance for the implementation of embryo transfer programs and the establishment of embryo banks in several mammalian species.

OBJECTIVE

The present investigation compares two different vitrification systems and two different warming solutions.

MATERIALS AND METHODS

Vitrification was performed using Open Pulled Straw (OPS) or CVM RingFibre plug (CVM) devices. Warming was carried out either in a warming solution containing 0.33 M sucrose or in a solution devoid of sucrose.

RESULTS

Differences between vitrification systems were not significant. Warming in sucrose-containing diluent resulted in an expansion rate of 64%, as compared to 86% in a solution devoid of sucrose; reported hatching rates were 45% vs. 9%, respectively (p<0.05). Upon transfer, implantation rates for OPS- and CVM were 50% and 27%, respectively, compared with 55% for freshly collected embryos. The implantation rate after warming was 43% for sucrose-containing and 33% for sucrose-free medium.

CONCLUSION

a) both vitrification systems are suitable for vitrifying mouse blastocysts; b) warming in sucrose-free diluent yields better embryo survival rates than in diluent containing 0.33 M sucrose.

Essentiality of the glnA gene in Haloferax mediterranei: gene conversion and transcriptional analysis

Glutamine synthetase is an essential enzyme in ammonium assimilation and glutamine biosynthesis. The Haloferax mediterranei genome has two other glnA-type genes (glnA2 and glnA3) in addition to the glutamine synthetase gene glnA. To determine whether the glnA2 and glnA3 genes can replace glnA in nitrogen metabolism, we generated deletion mutants of glnA. The glnA deletion mutants could not be generated in a medium without glutamine, and thus, glnA is an essential gene in H. mediterranei. The glnA deletion mutant was achieved by adding 40 mM glutamine to the selective medium. This conditional HM26-ΔglnA mutant was characterised with different approaches in the presence of distinct nitrogen sources and nitrogen starvation. Transcriptomic analysis was performed to compare the expression profiles of the strains HM26-ΔglnA and HM26 under different growth conditions. The glnA deletion did not affect the expression of glnA2, glnA3 and nitrogen assimilation genes under nitrogen starvation. Moreover, the results showed that glnA, glnA2 and glnA3 were not expressed under the same conditions. These results indicated that glnA is an essential gene for H. mediterranei and, therefore, glnA2 and glnA3 cannot replace glnA in the conditions analysed.

National Trends and Outcomes of Patients Bridged to Transplant With Continuous Flow Left Ventricular Assist Devices

BACKGROUND

Continuous flow left ventricular assist devices (CF-LVAD) are widely used as a bridge to transplantation (BTT) among patients with advanced heart failure. The primary outcome of the current study was to study the incidence of waitlist mortality and morbidity of CF-LVAD patients bridged to heart transplantation in the current BTT era and to determine the factors that increased their risk of delisting.

METHODS

Patients who were bridged to heart transplant with a CF-LVAD between April 2008 and September 2015 were identified from the United Network for Organ Sharing heart transplant registry. They were then categorized based on the development of complications. Cox proportional hazards and Kaplan-Meier survival curves were used for time-to-event analysis for the primary outcome.

RESULTS

Out of 7070 patients who were bridged to heart transplant, 2510 (36%) developed device-related complications. The primary outcome was present in 1631 of 7070 patients (23%). Independent predictors of primary outcome were age, ABO blood group, etiology of cardiomyopathy, and history of diabetes mellitus. Developing one device-related complication was associated with a hazard ratio (HR) of 2.59 of having the primary outcome. The HR increased to 3.45 when ≥2 of the defined complications occurred. In patients who developed the primary outcome, they most likely had a device infection (odds ratio 2.51).

CONCLUSION

Findings from the current study add to the existing literature about the incidence of morbidity and mortality in the current BTT era. Development of one device-related complication increases the risk of death or delisting among patients on the heart transplant waitlist; however, this risk almost doubles when 2 or more complications occur.

Tissue-Sparing Uvulopalatopharyngoplasty for OSA: Conservative, Compassionate and Possibly just as Effective

A common surgical treatment for obstructive sleep apnea (OSA) is uvulopalatopharyngoplasty (UPPP). Unfortunately, traditional UPPP can cause a foreign body sensation, chronic discomfort and in rare cases, nasopharyngeal stenosis or velopharyngeal insufficiency. Modifications to traditional UPPP have been developed over the years to help decrease side effects, while trying to maintain or improve OSA outcomes. Conservative, tissue-sparing UPPP techniques include preservation of soft palate tissues (muscle and/or mucosa), avoidance of plication or conservative plication of the uvula, partial instead of complete uvulectomy, and suture plication of the palatopharyngeus-superior pharyngeal constrictor-palatoglossus muscles with complete preservation of surrounding tissues after tonsillectomy.

Intestinal parasites: Associations with intestinal and systemic inflammation

The aim of the present study was to evaluate associations between intestinal parasitic infection with intestinal and systemic inflammatory markers in school-aged children with high rates of obesity. Plasma concentrations of C-Reactive Protein (CRP), leptin, TNF-α, IL-6 and IL-10 were measured as systemic inflammation markers and count of stool leukocytes as marker of intestinal inflammation in 291 children (6-10 years). Intestinal parasitic infection was measured by stool examination. Logistic regression analyses were performed to determine the odds of having high inflammatory markers for each parasite or group of parasites as compared to parasite-free children while adjusting for sex, age, mother's educational level and percentage of body fat. The prevalence of soil-transmitted helminths and intestinal protozoa infections was 12% and 36%, respectively. Parasitic infection was not associated with CRP, IL-6, IL-10 or TNF-α. Children infected with Ascaris lumbricoides (aOR: 5.91, 95% CI: 1.97-17.70) and Entamoeba coli (aOR: 8.46, 95% CI: 2.85-25.14) were more likely to have higher stool leucocytes than parasite-free children. Children with multiple infections (aOR: 10.60, 95% CI: 2.85-25.14) were more likely to have higher leptin concentrations than parasite-free children. Intestinal parasitic infection was not associated with systemic inflammation, but was associated with intestinal inflammation. Having multiple infections were associated with higher leptin concentrations.

Metallosomes for biomedical applications by mixing molybdenum carbonyl metallosurfactants and phospholipids

New photo-CORM metallosomes with low cell toxicity were prepared from organometallic metallosurfactants and phospholipids.

Assessing the role of endophytic bacteria in the halophyte Arthrocnemum macrostachyum salt tolerance

There is an increasing interest to use halophytes for revegetation of salt affected ecosystems, as well as in understanding their mechanisms of salt tolerance. We hypothesized that bacteria from the phyllosphere of these plants might play a key role in its high tolerance to excessive salinity. Eight endophytic bacteria belonging to Bacillus and closely related genera were isolated from phyllosphere of the halophyte Arthrocnemum macrostachyum growing in salty agricultural soils. The presence of plant-growth promoting (PGP) properties, enzymatic activities and tolerance towards NaCl was determined. Effects of inoculation on seeds germination and adult plant growth under experimental NaCl treatments (0, 510 and 1030 mM NaCl) were studied. Inoculation with a consortium including the best performing bacteria improved considerably the kinetics of germination and the final germination percentage of A. macrostachyum seeds. At high NaCl concentrations (1030 mM), inoculation of plants mitigated the effects of high salinity on plant growth and physiological performance and, in addition, this consortium appears to have increased the potential of A. macrostachyum to accumulate Na⁺ in its shoots, thus improving sodium phytoextraction capacity. Bacteria isolated from A. macrostachyum phyllosphere seem to play an important role in plant salt tolerance under stressing salt concentrations. The combined use of A. macrostachyum and its microbiome can be an adequate tool to enhance plant adaptation and sodium phytoextraction during restoration of salt degraded soils.

Low-toxicity metallosomes for biomedical applications by self-assembly of organometallic metallosurfactants and phospholipids

New photo-CORM metallosomes prepared by a straightforward method from organometallic metallosurfactants and phospholipids show a drastic diminution of cell toxicity.

Anaerobic Metabolism in Haloferax Genus: Denitrification as Case of Study

A number of species of Haloferax genus (halophilic archaea) are able to grow microaerobically or even anaerobically using different alternative electron acceptors such as fumarate, nitrate, chlorate, dimethyl sulphoxide, sulphide and/or trimethylamine. This metabolic capability is also shown by other species of the Halobacteriaceae and Haloferacaceae families (Archaea domain) and it has been mainly tested by physiological studies where cell growth is observed under anaerobic conditions in the presence of the mentioned compounds. This work summarises the main reported features on anaerobic metabolism in the Haloferax, one of the better described haloarchaeal genus with significant potential uses in biotechnology and bioremediation. Special attention has been paid to denitrification, also called nitrate respiration. This pathway has been studied so far from Haloferax mediterranei and Haloferax denitrificans mainly from biochemical point of view (purification and characterisation of the enzymes catalysing the two first reactions). However, gene expression and gene regulation is far from known at the time of writing this chapter.

[Recommendations for the use of methotrexate in patients with juvenile idiopathic arthritis]

OBJECTIVES

To develop a consensus document of recommendations for the use of methotrexate (MTX) in patients with juvenile idiopathic arthritis (JIA).

MATERIAL AND METHOD

A group of eleven experts proposed several clinical questions on the use of MTX in patients with JIA. A systematic review was conducted and the evidence and recommendations for each question were extracted. The results were discussed and validated by the experts in a work session to establish the final recommendations.

RESULTS

MTX is recommended as the first drug for inducing remission in JIA, and its indication should be made according to the clinical category of the patient. Prior to treatment, it is recommended to perform a complete blood count, including white cells, levels of liver enzymes, serum creatinine, and other analytical parameters according to specific risk factors. Treatment should be initiated with a dose of 10-15 mg/m(2)/week. In cases of uveitis or polyarthritis, an initial dose of 15 mg/m(2)/week should be considered. For a better bioavailability and tolerability, it is preferable to administer MTX parenterally if the dose is ≥15 mg/m(2)/week. It is necessary to periodically perform an analytical monitoring of the patient and to assess possible alterations in liver enzymes to make changes if necessary. Combinations with biological agents may be necessary, as well as the concomitant addition of folic or folinic acid.

CONCLUSIONS

This document describes the main recommendations for the appropriate use of MTX in JIA patients, according to scientific evidence and clinical experience.