Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Targeted prevention of common mental health disorders in university students: randomised controlled trial of a transdiagnostic trait-focused web-based intervention

Background: A large proportion of university students show symptoms of common mental disorders, such as depression, anxiety, substance use disorders and eating disorders. Novel interventions are required that target underlying factors of multiple disorders.<p></p> Aims: To evaluate the efficacy of a transdiagnostic trait-focused web-based intervention aimed at reducing symptoms of common mental disorders in university students.<p></p> Method: Students were recruited online (n = 1047, age: M = 21.8, SD = 4.2) and categorised into being at high or low risk for mental disorders based on their personality traits. Participants were allocated to a cognitive-behavioural trait-focused (n = 519) or a control intervention (n = 528) using computerised simple randomisation. Both interventions were fully automated and delivered online (trial registration: ISRCTN14342225). Participants were blinded and outcomes were self-assessed at baseline, at 6 weeks and at 12 weeks after registration. Primary outcomes were current depression and anxiety, assessed on the Patient Health Questionnaire (PHQ9) and Generalised Anxiety Disorder Scale (GAD7). Secondary outcome measures focused on alcohol use, disordered eating, and other outcomes.<p></p> Results: Students at high risk were successfully identified using personality indicators and reported poorer mental health. A total of 520 students completed the 6-week follow-up and 401 students completed the 12-week follow-up. Attrition was high across intervention groups, but comparable to other web-based interventions. Mixed effects analyses revealed that at 12-week follow up the trait-focused intervention reduced depression scores by 3.58 (p<.001, 95%CI [5.19, 1.98]) and anxiety scores by 2.87 (p = .018, 95%CI [1.31, 4.43]) in students at high risk. In high-risk students, between group effect sizes were 0.58 (depression) and 0.42 (anxiety). In addition, self-esteem was improved. No changes were observed regarding the use of alcohol or disordered eating.<p></p> Conclusions This study suggests that a transdiagnostic web-based intervention for university students targeting underlying personality risk factors may be a promising way of preventing common mental disorders with a low-intensity intervention

Effect of hemicellulose liquid phase on the enzymatic hydrolysis of autohydrolyzed Eucalyptus globulus wood

In this work, Eucalyptus globulus wood was pretreated under non-isothermal autohydrolysis process at 210, 220, and 230 °C, obtaining a pretreated solid with high cellulose content and a hemicellulosic liquid phase (HLP) containing mainly xylose, acetic acid, furfural, xylooligosaccharides, and phenolic compounds. The maximum concentration of xylooligosaccharides (8.97 g/L) and phenolic compounds (2.66 g/L) was obtained at 210 and 230 °C, respectively. To evaluate the effect of HLP addition on the enzymatic hydrolysis using unwashed pretreated solid as substrate, different proportions of HLP were studied. Also, in order to use the whole slurry on enzymatic hydrolysis, the supplementation of xylanases was evaluated. Glucose concentration of 107.49 g/L (corresponding to 74.65 % of conversion) was obtained using pretreated solid at 220 °C liquid/solid ratio (LSR) of 4 g/g and enzyme solid ratio (ESR) of 25 FPU/gwithout the addition of HLP. Thus, it was shown that the unwashed pretreated solids are susceptible to enzymatic hydrolysis contributing to reduce operational cost (water consumption). Additionally, the influence of the inhibitory compounds in the HLP was shown to affect the enzymatic hydrolysis. Results indicated that 82.52 g/L of glucose (59.37 % of conversion) was obtained, using 100 % of HLP at LSR of 4 g/g and ESR of 16 FPU/g at 210 °C of pretreated solid. However, a positive effect was shown on the enzymatic hydrolysis when the xylanases were added using 100 % of HLP, increasing to 35 and 27 % in the glucose production with respect to whole slurry without addition of xylanases.The authors A. Romani and F. B. Pereira thank to the Portuguese Foundation for Science and Technology (FCT, Portugal) for their fellowships (grant number, SFRH/BPD/77995/2011 and SFRH/BD/64776/2009, respectively)

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study

Background Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour

Copolymer-induced stabilizing effect of highly swollen hexagonal mesophases

We show quantitatively that tiny amounts of copolymer that decorate a oil/water interfaces can greatly enhance the stability of swollen surfactant hexagonal phases, comprising oil tubes regularly arranged in a water matrix. Such soft composite materials, whose both radius of the tubes and water channel between the tubes can be controlled independently over large ranges, offer a potential interest for the synthesis of mesoporous materials

Microbial degradation of furanic compounds: biochemistry, genetics, and impact

Microbial metabolism of furanic compounds, especially furfural and 5-hydroxymethylfurfural (HMF), is rapidly gaining interest in the scientific community. This interest can largely be attributed to the occurrence of toxic furanic aldehydes in lignocellulosic hydrolysates. However, these compounds are also widespread in nature and in human processed foods, and are produced in industry. Although several microorganisms are known to degrade furanic compounds, the variety of species is limited mostly to Gram-negative aerobic bacteria, with a few notable exceptions. Furanic aldehydes are highly toxic to microorganisms, which have evolved a wide variety of defense mechanisms, such as the oxidation and/or reduction to the furanic alcohol and acid forms. These oxidation/reduction reactions constitute the initial steps of the biological pathways for furfural and HMF degradation. Furfural degradation proceeds via 2-furoic acid, which is metabolized to the primary intermediate 2-oxoglutarate. HMF is converted, via 2,5-furandicarboxylic acid, into 2-furoic acid. The enzymes in these HMF/furfural degradation pathways are encoded by eight hmf genes, organized in two distinct clusters in Cupriavidus basilensis HMF14. The organization of the five genes of the furfural degradation cluster is highly conserved among microorganisms capable of degrading furfural, while the three genes constituting the initial HMF degradation route are organized in a highly diverse manner. The genetic and biochemical characterization of the microbial metabolism of furanic compounds holds great promises for industrial applications such as the biodetoxifcation of lignocellulosic hydrolysates and the production of value-added compounds such as 2,5-furandicarboxylic acid

High Tumour Cannabinoid CB1 Receptor Immunoreactivity Negatively Impacts Disease-Specific Survival in Stage II Microsatellite Stable Colorectal Cancer

BACKGROUND: There is good evidence in the literature that the cannabinoid system is disturbed in colorectal cancer. In the present study, we have investigated whether CB(1) receptor immunoreactive intensity (CB(1)IR intensity) is associated with disease severity and outcome. METHODOLOGY/PRINCIPAL FINDINGS: CB(1)IR was assessed in formalin-fixed, paraffin-embedded specimens collected with a consecutive intent during primary tumour surgical resection from a series of cases diagnosed with colorectal cancer. Tumour centre (n = 483) and invasive front (n = 486) CB(1)IR was scored from 0 (absent) to 3 (intense staining) and the data was analysed as a median split i.e. CB(1)IR <2 and ≥2. In microsatellite stable, but not microsatellite instable tumours (as adjudged on the basis of immunohistochemical determination of four mismatch repair proteins), there was a significant positive association of the tumour grade with the CB(1)IR intensity. The difference between the microsatellite stable and instable tumours for this association of CB(1)IR was related to the CpG island methylation status of the cases. Cox proportional hazards regression analyses indicated a significant contribution of CB(1)IR to disease-specific survival in the microsatellite stable tumours when adjusting for tumour stage. For the cases with stage II microsatellite stable tumours, there was a significant effect of both tumour centre and front CB(1)IR upon disease specific survival. The 5 year probabilities of event-free survival were: 85±5 and 66±8%; tumour interior, 86±4% and 63±8% for the CB(1)IR<2 and CB(1)IR≥2 groups, respectively. CONCLUSIONS/SIGNIFICANCE: The level of CB(1) receptor expression in colorectal cancer is associated with the tumour grade in a manner dependent upon the degree of CpG hypermethylation. A high CB(1)IR is indicative of a poorer prognosis in stage II microsatellite stable tumour patients

Transcriptional Analysis of Lactobacillus brevis to N-Butanol and Ferulic Acid Stress Responses

The presence of anti-microbial phenolic compounds, such as the model compound ferulic acid, in biomass hydrolysates pose significant challenges to the widespread use of biomass in conjunction with whole cell biocatalysis or fermentation. Currently, these inhibitory compounds must be removed through additional downstream processing or sufficiently diluted to create environments suitable for most industrially important microbial strains. Simultaneously, product toxicity must also be overcome to allow for efficient production of next generation biofuels such as n-butanol, isopropanol, and others from these low cost feedstocks.This study explores the high ferulic acid and n-butanol tolerance in Lactobacillus brevis, a lactic acid bacterium often found in fermentation processes, by global transcriptional response analysis. The transcriptional profile of L. brevis reveals that the presence of ferulic acid triggers the expression of currently uncharacterized membrane proteins, possibly in an effort to counteract ferulic acid induced changes in membrane fluidity and ion leakage. In contrast to the ferulic acid stress response, n-butanol challenges to growing cultures primarily induce genes within the fatty acid synthesis pathway and reduced the proportion of 19:1 cyclopropane fatty acid within the L. brevis membrane. Both inhibitors also triggered generalized stress responses. Separate attempts to alter flux through the Escherichia coli fatty acid synthesis by overexpressing acetyl-CoA carboxylase subunits and deleting cyclopropane fatty acid synthase (cfa) both failed to improve n-butanol tolerance in E. coli, indicating that additional components of the stress response are required to confer n-butanol resistance.Several promising routes for understanding both ferulic acid and n-butanol tolerance have been identified from L. brevis gene expression data. These insights may be used to guide further engineering of model industrial organisms to better tolerate both classes of inhibitors to enable facile production of biofuels from lignocellulosic biomass