Hasil untuk "Nutritional diseases. Deficiency diseases"

Nicholas A. Koemel, Matthew N. Ahmadi, Raaj Kishore Biswas et al.

Abstract Background Vigorous intermittent lifestyle physical activity (VILPA) completed through normal daily living may offer a time-efficient avenue to accrue physical activity in a behaviourally sustainable manner. However, no research to date has explored its association with mortality in a nationally representative population. This study aimed to examine the dose-response association between VILPA and mortality risk in a nationally representative sample of US adults. Methods This study included a nationally representative sample of 3293 US adults from the 2011-14 National Health and Nutrition Examination Survey (NHANES) who self-reported no participation in structured exercise (52.3% female; mean age: 50.7 [SD: 16.6 years]. The dose-response relationship between VILPA and all-cause mortality was estimated using multivariable-adjusted cubic splines. Average daily frequency (bouts/day) and duration (minutes/day) of VILPA bouts lasting up to one minute were measured using a wrist-worn accelerometer. Results Over the mean (SD) 6.7 (1.4) year follow-up period, 290 all-cause mortality events occurred. Compared to the referent point (0 bouts per day), there was an L-shaped dose-response association where the median frequency (5.3 bouts per day) was associated with a 44% lower risk of all-cause mortality (HR: 0.56; 95% CI: 0.39, 0.82). The dose response curve was less steep beyond approximately 8 bouts per day (HR: 0.46; 95% CI: 0.28, 0.77). Findings for the median frequency of VILPA bouts (5.3 bouts per day) remained consistent after excluding participants with poor health (HR: 0.51: 95% CI: 0.29, 0.87) and those who completed no VILPA (HR: 0.57: 95% CI: 0.38, 0.86). When excluding adults with prevalent cardiovascular disease or cancer at baseline (n = 2,731, 152 events), the dose-response relationship was similar, although the 95% CIs crossed unity for most of the curve (e.g. median frequency of VILPA bouts HR: 0.68: 95% CI: 0.41, 1.11). Conclusions In a nationally representative sample of US adults, short bursts of intermittent vigorous physical activity were associated with a lower risk of mortality. Excluding those with prevalent cardiovascular disease and cancer led to attenuated dose-response curves and wider confidence intervals, suggesting that the observed relationships may, at least partly, be driven by existing disease-induced reverse causation. While these results highlight some potential of VILPA as a time-efficient source of activity, additional observational studies with longer follow up and larger sample sizes are warranted.

Qingqing Zhang, Di Wu, Fengyun Guo et al.

Abstract Background Dietary fiber is inversely associated with metabolic syndrome (MetS); however, the mechanistic pathways and the consistency of this association across key demographic strata remain quantitatively unclear, which hinders the development of precise public health guidance. Methods This cross-sectional study included 7703 U.S. adults from the National Health and Nutrition Examination Survey (NHANES, 2010–2020). We employed survey-weighted logistic regression to assess the association between dietary fiber density (g/1000 kcal) and MetS (defined by NCEP-ATP III criteria). Mediation analysis was conducted to decompose the total association into pathways operating through systemic inflammation (log-transformed high-sensitivity C-reactive protein [hs-CRP]) and insulin resistance (HOMA-IR). We further evaluated effect modification by body mass index (BMI) categories. Results Each 1 g/1000 kcal increase in dietary fiber density was associated with a 4% reduction in the odds of MetS (OR = 0.96, 95% CI: 0.94–0.99). This inverse association was partially mediated by lower hs-CRP levels (proportion mediated: 7.4%, P = 0.023), with sensitivity analyses confirming robustness to additional lifestyle adjustments. The association was consistent across all BMI categories (interaction P > 0.62), suggesting homogeneous associations for normal-weight, overweight, and obese individuals. Conclusions In a nationally representative sample of U.S. adults, higher dietary fiber density is associated with a lower risk of metabolic syndrome. This association is partly explained by lower levels of systemic inflammation rather than by improvements in insulin resistance, and it is uniformly present across body weight statuses. These findings support the potential universal relevance of increasing fiber intake for MetS prevention and highlight anti-inflammatory pathways as a key area for mechanistic focus.

Anupama Chawla, Rosen Ling, Amy Canterella et al.

Teodora Todorova, Krassimir Boydzhiev, Ignat Ignatov et al.

The present study aimed to analyze the physicochemical properties and biological activity of an L-malic acid descaler. The treated water with L-malic acid descaler complies with EU Directive No. 2020/2184 for the quality of water intended for human consumption. The L-malic acid descaler contains L-malic acid as the active component, while polyethylene and activated charcoal function as structural and absorbent materials, respectively. The composition was analyzed in a licensed laboratory using Chemical Abstracts Service Number (CAS) and European List of Notified Chemical Substances (EINECS) standards. Fourier Transform Infrared (FT-IR) analysis confirmed the presence of hydroxyl (–OH), carbonyl (C=O), and carboxyl (–COOH) groups in L-malic acid descaler, which are connected with proton-donating ability, and redox activity. The biological activity was evaluated using Saccharomyces cerevisiae as a model system. The role of the YAP1 transcription factor, a key regulator of oxidative stress defense mechanisms, was also examined. The detrimental effects on a cellular level were induced by the well-known mutagen—methyl methanesulfonate (MMS). Our data revealed that yeast cells treated with such water decrease the MMS-induced superoxide anions (3.5-fold), total glutathione lipid peroxidation (1.5-fold), and total glutathione (3-fold) and increase cell survival (2-fold). In conclusion, water treated with L-malic acid descaler possesses antioxidant effects in yeast-cell-based tests, independent of YAP1 transcription factor activity. This study provides preliminary evidence that L-malic acid, when dissolved in water, significantly reduced MMS-induced superoxide anions, one of the biomarkers contributing to the genotoxic and carcinogenic effects of MMS.

Wei Guo, Shuo Zhao, Qinzheng Chang et al.

Abstract Background An association exists between obesity and reduced testosterone levels in males. The propose of this research is to reveal the correlation between 15 indices linked to obesity and lipid levels with the concentration of serum testosterone, and incidence of testosterone deficiency (TD) among adult American men. Methods The study utilized information gathered from the National Health and Nutrition Examination Survey (NHANES) carried out from 2011 to 2016. The condition known as TD is typically characterized by a total serum testosterone level that falls below 300 ng/dL. The analysis used weighted linear and logistic regression methods to announce the association between 15 obesity- and lipid-related factors and serum testosterone levels as well as TD. Subgroup analyses were further carried out to confirm and validate the findings. Additionally, restricted cubic spline plots were utilized to examine non-linear relationships. Receiver operating characteristic (ROC) curves were created for the 15 factors, and the area under the curves (AUC) was calculated to assess the efficacy of each factor in detecting TD. Results Among a group of 3,540 adult males, it was observed that all 15 obesity- and lipid-related indices showed a negative relationship with testosterone concentration and a direct correlation with the presence of TD. After accounting for all covariates, the analysis revealed that individuals within the highest quartile (Q4) for metabolic score for visceral fat (METS-VF) had the excellent probability of developing TD (OR = 13.412, 95%CIs: 4.222, 42.262, P < 0.001). Additionally, a non-linear relationship was detected between the METS-VF with TD. Within the model that incorporated all adjustments, the triglyceride glucose-waist to height ratio (TyG-WHtR) has the best performance for predicting TD (Overall: AUC = 0.762, 95%CIs: 0.743, 0.782, cut-off = 5.186). Conclusion Elevated levels of these 15 markers were inversely related to testosterone levels and were indicative of an elevated risk of TD. Among all indices analyzed, TyG-WHtR demonstrated the highest predictive value. Trial registration Not available.

Khaled Al-Saih, Fares Al-Shargie, Mohammed Isam Al-hiyali et al.

Worldwide, sight loss is commonly occurred by retinal diseases, with age-related macular degeneration (AMD) being a notable facet that affects elderly patients. Approaching 170 million persons wide-ranging have been spotted with AMD, a figure anticipated to rise to 288 million by 2040. For visualizing retinal layers, optical coherence tomography (OCT) dispenses the most compelling non-invasive method. Frequent patient visits have increased the demand for automated analysis of retinal diseases, and deep learning networks have shown promising results in both image and pixel-level 2D scan classification. However, when relying solely on 2D data, accuracy may be impaired, especially when localizing fluid volume diseases. The goal of automatic techniques is to outperform humans in manually recognizing illnesses in medical data. In order to further understand the benefit of deep learning models, we studied the effects of the input size. The dice similarity coefficient (DSC) metric showed a human performance score of 0.71 for segmenting various retinal diseases. Yet, the deep models surpassed human performance to establish a new era of advancement of segmenting the diseases on medical images. However, to further improve the performance of the models, overlapping patches enhanced the performance of the deep models compared to feeding the full image. The highest score for a patch-based model in the DSC metric was 0.88 in comparison to the score of 0.71 for the same model in non-patch-based for SRF fluid segmentation. The objective of this article is to show a fair comparison between deep learning models in relation to the input (Patch-Based vs. NonPatch-Based).

Mohammad Rafsan, Tamer Oraby, Upal Roy et al.

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.

Muhammad Ahmad, Sardar Usman, Ildar Batyrshin et al.

Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of such conditions. The most prevalent lung diseases, including pneumonia, asthma, allergies, chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, and lung cancer, represent significant public health challenges. Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A. (2023) [14], categorized into three classes: normal, lung opacity, and pneumonia. We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images. This approach aims to address diagnostic issues in lung abnormalities by reducing reliance on human intervention through automated classification systems. Our analysis was conducted in both binary and multiclass settings. Results: In the binary classification, we focused on distinguishing between normal and viral pneumonia cases, whereas in the multi-class classification, all three classes (normal, lung opacity, and viral pneumonia) were included. Our proposed methodology (ViT) achieved remarkable performance, with accuracy rates of 99% for binary classification and 95.25% for multiclass classification.

Soyang Kwon, Bridget Armstrong, Nina Wetoska et al.

Abstract Background The PROMIS® Early Childhood Physical Activity (PROMIS EC PA) scale is a recently developed PROMIS Early Childhood measure to assess PA among children aged 1–5 years. The purpose of this study was to examine test-retest reliability and convergent validity of the PROMIS EC PA scale among toddlers. Method An ancillary study was conducted in the toddler-mother dyad sample of the Child and Mother Physical Activity Study. Mothers completed the 7-item PROMIS EC PA scale twice: during a study visit (test) and on the last day when their child’s wore an ActiGraph accelerometer on the hip for 7 days (retest). The PROMIS EC PA summed score was calculated by totaling scores from items 1–5. Test-retest reliability was assessed using intraclass correlation coefficient (ICC) for test and retest PROMIS EC PA. Convergent validity was assessed using rank correlation coefficients (rho) between PROMIS EC PA scores and accelerometer-measured moderate- and vigorous-intensity PA (MVPA). Results Among 74 participants (56% female; 19 ± 4 months of mean age with range of 12–30 months), average accelerometer-measured MVPA was 76 ± 24 min/day. The median number of days between PROMIS EC PA test and retest was 8 days (IQR = 6 to 8), with an average PROMIS EC PA summed score of 11.0 ± 3.5 at test and 10.5 ± 3.4 at retest. ICC for the test-retest PROMIS EC PA summed scores was 0.72 (95% CI = 0.59–0.82). The rank correlation between the PROMIS EC PA summed score and accelerometer-measured MVPA was 0.13 (95% CI=-0.10 to 0.35; p = 0.28). Conclusion In a sample of children aged 12–30 months, test-retest reliability for the PROMIS EC PA scale was moderate and its convergent validity against accelerometer-measured MVPA was poor. Prior to a widespread use of the PROMIS EC PA scale in large-scale research and clinical practice, the tool should be further refined and validated to elucidate how young children’s lived PA experience as measured in the PROMIS EC PA scale is relevant to their health and wellbeing outcomes.

Emma H. Spence, Meredith T. Niles, Farryl Bertmann et al.

Abstract Background Federal nutrition assistance programs serve as safety nets for many American households, and participation has been linked to increased food security and, in some instances, improved diet quality and mental health outcomes. The COVID-19 pandemic brought new and increased economic, social, and psychological challenges, necessitating inquiry into how nutrition assistance programs are functioning and associated with public health outcomes. Methods Using data from a representative statewide survey administered in Vermont (n = 600) between July and September 2020, we examined participant experiences with major federal nutrition assistance programs: the Supplemental Nutrition Assistance Program (SNAP), the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), and school meal programs. We explored quantitative and qualitative responses regarding perceptions of program utility, and used nearest neighbors matching analyses in combination with bivariate statistical tests to assess associations between program participation and food insecurity, perceived stress, and fruit and vegetable intake as indicators of dietary quality. Results One in four respondents (27.3%) used at least one federal nutrition assistance program. As compared to non-participants, we found higher rates of food insecurity among program participants (57.5% vs. 18.1%; p < 0.001), an association that persisted even when we compared similar households using matching techniques (p ≤ 0.001). From matched analyses, we found that, compared to low-income non-participants, low-income program participants were less likely to meet fruit intake recommendations (p = 0.048) and that low-income SNAP and WIC participants were less likely to meet vegetable intake recommendations (p = 0.035). We also found lower rates of perceived stress among low-income school meal participant households compared to low-income non-participants (p = 0.039). Despite these mixed outcomes, participants broadly valued federal nutrition assistance programs, characterizing them as helpful or easy to use. Conclusions We found that federal nutrition assistance programs as a group were not sufficient to address food insecurity and stress or increase fruit and vegetable intake in the state of Vermont during the early months of the COVID-19 pandemic. Nonetheless, participants perceived benefits from participation in these programs. Optimizing the utility of nutrition assistance programs depends on critical examination of their functioning under conditions of great stress.

Harsh Shah, Ritchel B. Gannaban, Zobayda Farzana Haque et al.

Abstract Background High-protein diets are often enriched with branched-chain amino acids (BCAAs) known to enhance protein synthesis and provide numerous physiological benefits, but recent studies reveal their association with obesity and diabetes. In support of this, protein or BCAA supplementation is shown to disrupt glucose metabolism while restriction improves it. However, it is not clear if these are primary, direct effects of BCAAs or secondary to other physiological changes during chronic manipulation of dietary BCAAs. Methods Three-month-old C57Bl/6 mice were acutely treated with either vehicle/BCAAs or BT2, a BCAA-lowering compound, and detailed in vivo metabolic phenotyping, including frequent sampling and pancreatic clamps, were conducted. Results Using a catheter-guided frequent sampling method in mice, here we show that a single infusion of BCAAs was sufficient to acutely elevate blood glucose and plasma insulin. While pre-treatment with BCAAs did not affect glucose tolerance, a constant infusion of BCAAs during hyperinsulinemic–euglycemic clamps impaired whole-body insulin sensitivity. Similarly, a single injection of BT2 was sufficient to prevent BCAA rise during fasting and markedly improve glucose tolerance in high-fat-fed mice, suggesting that abnormal glycemic control in obesity may be causally linked to high circulating BCAAs. We further show that chemogenetic over-activation of AgRP neurons in the hypothalamus, as present in obesity, significantly impairs glucose tolerance that is completely normalized by acute BCAA reduction. Interestingly, most of these effects were demonstrated only in male, but not in female mice. Conclusion These findings suggest that BCAAs per se can acutely impair glucose homeostasis and insulin sensitivity, thus offering an explanation for how they may disrupt glucose metabolism in the long-term as observed in obesity and diabetes. Our findings also reveal that AgRP neuronal regulation of blood glucose is mediated through BCAAs, further elucidating a novel mechanism by which brain controls glucose homeostasis.

Samina Akhtar, Shahid Khan, Namra Aziz et al.

Abstract Background Childhood obesity and hypertension are growing concerns globally, especially in developing countries. This study investigated the association between overall and central obesity at baseline, and prehypertension or hypertension at follow-up among preadolescent school children in urban Karachi, Pakistan. Methods This is a sub study with cohort design embedded within a feasibility trial on School Health Education Program in Pakistan (SHEPP) in preadolescents aged 6–11 years, attending two private schools conducted from 2017 to 2019. Hypertension or prehypertension at follow-up were the outcomes and obesity or central obesity at baseline were the exposure variables. Hypertension was defined as systolic blood pressure and/or diastolic blood pressure ≥ 95th percentile for age, sex, and height. Obesity was defined as body mass index for-age and sex ≥ 95th percentile, whereas central obesity was determined by waist circumference measurements ≥ 85th percentile of age, sex, and height specific cut-offs. Logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to identify risk factors for hypertension and prehypertension. Results Analysis was conducted for 908 participants, evenly distributed with 454 boys and 454 girls. Hypertension was observed in 19.8% of the preadolescents, with rates of 18.5% in boys and 21.0% in girls. Prehypertension was found in 16.8% of preadolescents, with 18% among boys and 16% among girls. Additionally, 12.8% of preadolescents were classified as obese and 29.8% had central obesity. Obesity at baseline was associated with hypertension at followup (OR 8.7, 95% CI 3.5, 20.4) in the final model after adjusting for age, gender, physical activity, sedentary behavior, fruits, vegetable intake and hypertension at baseline. Central obesity at baseline also yielded high odds, with prehypertension (OR 1.9, 95% CI 1.4, 2.8) and hypertension (OR 2.7, 95% CI 1.9, 3.9) at follow up in the final model. Conclusion This study highlights a concerning prevalence of hypertension and prehypertension among preadolescent school-going children. Obesity and central obesity at baseline emerged as significant predictive factors for hypertension or prehypertension at followup within this cohort. The findings emphasize the urgency of implementing comprehensive school health education programs aimed at early detection and effective management of hypertension during childhood and adolescence in school settings.

Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena et al.

The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational Fluid Dynamics (CFD) is a commonly used tool for patient-specific simulations in the cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LV). The formation of an asymmetric vortex in a healthy LV shows an efficient way of blood transport. The vortex pattern changes before any change in the geometry of LV is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that understanding vortex dynamics in LV using various vortex indexes coupled with data-driven approaches can be used as an early diagnosis tool and improvement in DCM treatment.

Mei-Yen Chan, Arailym Yeltay, Antonio Sarria- Santa

Weizhi Nie, Chen Zhang, Dan Song et al.

The chest X-ray is often utilized for diagnosing common thoracic diseases. In recent years, many approaches have been proposed to handle the problem of automatic diagnosis based on chest X-rays. However, the scarcity of labeled data for related diseases still poses a huge challenge to an accurate diagnosis. In this paper, we focus on the thorax disease diagnostic problem and propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents and the model parameters can also be continuously updated as the data increases, like a person's learning process. Especially, 1) prior knowledge can be learned from the pre-trained model based on old data or other domains' similar data, which can effectively reduce the dependence on target domain data, and 2) the framework of reinforcement learning can make the diagnostic agent as exploratory as a human being and improve the accuracy of diagnosis through continuous exploration. The method can also effectively solve the model learning problem in the case of few-shot data and improve the generalization ability of the model. Finally, our approach's performance was demonstrated using the well-known NIH ChestX-ray 14 and CheXpert datasets, and we achieved competitive results. The source code can be found here: \url{https://github.com/NeaseZ/MARL}.

Francis G. T. Kamba, Leonard C. Eze, Jean Claude Kamgang et al.

We propose and analyze an epidemiological model for vector borne diseases that integrates a multi-stage vector population and several host sub-populations which may be characterized by a variety of compartmental model types: subpopulations all include Susceptible and Infected compartments, but may or may not include Exposed and/or Recovered compartments. The model was originally designed to evaluate the effectiveness of various prophylactic measures in malaria-endemic areas, but can be applied as well to other vector-borne diseases. This model is expressed as a system of several differential equations, where the number of equations depends on the particular assumptions of the model. We compute the basic reproduction number $\mathcal R_0$, and show that if $\mathcal R_0\leqslant 1$, the disease free equilibrium (DFE) is globally asymptotically stable (GAS) on the nonnegative orthant. If $\mathcal R_0>1$, the system admits a unique endemic equilibrium (EE) that is GAS. We analyze the sensitivity of $R_0$ and the EE to different system parameters, and based on this analysis we discuss the relative effectiveness of different control measures.

Malvika Choudhary, Mahima Choudhary

Paniz Anvarifard, Maryam Anbari, Alireza Ostadrahimi et al.

Abstract Background The present systematic review is conducted, focusing on the existing evidence of Propolis's effects due to its various health benefits, mainly antioxidant and anti-inflammatory properties on preserving renal function. Methods A systematic search of PubMed, Scopus, Embase, ProQuest, and Google Scholar was undertaken for relevant papers published from the start until January 2021. Results This review revealed that Propolis affects fasting blood sugar (FBS), postprandial blood glucose, advanced glycation end products (AGEs) concentrations, malondialdehyde (MDA) levels, urinary concentrations of reactive oxygen metabolites (Tbars), total oxidant status (TOS), oxidative stress index (OSI), and 8-hydroxy-2′-deoxyguanosine (8-OHdG) formation favorably. The findings on hemoglobin A1C (HbA1C), insulin, homeostasis model assessment of insulin resistance (HOMA-IR), β-cell function (HOMA-β), quantitative insulin sensitivity check index (QUICKI), and lipid profile were controversial. Moreover, a significant reduction in renal nuclear factor kappa B (NF-κB), serum immunoglobulins, renal ED-1+ cells, and urinary monocyte chemoattractant protein-1 (MCP-1) following Propolis supplementation has been reported, while the results on interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), nitric oxide (NO), nitric oxide synthetase (NOS), and high sensitivity C-reactive protein (hs-CRP) were controversial. Furthermore, included studies showed its anti- proteinuria and kidney restoring effects. Conclusion In this review, both human and animal studies provide us evidences that Propolis could potentially improve the glycemic status, oxidative stress, renal tissue damage, and renal function. Further studies are needed to determine the underlying mechanisms.

Ramy Hussein, Moss Zhao, David Shin et al.

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomography (PET) is currently regarded as the gold standard for the measurement of CBF in the human brain. PET imaging, however, is not widely available because of its prohibitive costs, use of ionizing radiation, and logistical challenges, which require a co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope. Magnetic resonance imaging (MRI), in contrast, is more readily available and does not involve ionizing radiation. In this study, we propose a multi-task learning framework for brain MRI-to-PET translation and disease diagnosis. The proposed framework comprises two prime networks: (1) an attention-based 3D encoder-decoder convolutional neural network (CNN) that synthesizes high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale 3D CNN that identifies the brain disease corresponding to the input MRI images. Our multi-task framework yields promising results on the task of MRI-to-PET translation, achieving an average structural similarity index (SSIM) of 0.94 and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In addition, we show that integrating multiple MRI modalities can improve the clinical diagnosis of brain diseases.

Sarah Valentin, Elena Arsevska, Aline Vilain et al.

This paper describes a method for annotation of epidemiological information in animal disease-related news articles. The annotation guidelines are generic and aim to embrace all animal or zoonotic infectious diseases, regardless of the pathogen involved or its way of transmission (e.g. vector-borne, airborne, by contact). The framework relies on the successive annotation of all the sentences from a news article. The annotator evaluates the sentences in a specific epidemiological context, corresponding to the publication of the news article.