Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). METHODS: We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. RESULTS: Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. CONCLUSION: This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.

30-day Morbidity and Mortality after Cholecystectomy for Benign Gallbladder Disease (AMBROSE): A Prospective, International Collaborative Cohort Study.

OBJECTIVE: This study aimed to assess 30-day morbidity and mortality rates following cholecystectomy for benign gallbladder disease and identify the factors associated with complications. SUMMARY BACKGROUND DATA: Although cholecystectomy is common for benign gallbladder disease, there is a gap in the knowledge of the current practice and variations on a global level. METHODS: A prospective, international, observational collaborative cohort study of consecutive patients undergoing cholecystectomy for benign gallbladder disease from participating hospitals in 57 countries between January 1 and June 30, 2022, was performed. Univariate and multivariate logistic regression models were used to identify preoperative and operative variables associated with 30-day postoperative outcomes. RESULTS: Data of 21,706 surgical patients from 57 countries were included in the analysis. A total of 10,821 (49.9%), 4,263 (19.7%), and 6,622 (30.5%) cholecystectomies were performed in the elective, emergency, and delayed settings, respectively. Thirty-day postoperative complications were observed in 1,738 patients (8.0%), including mortality in 83 patients (0.4%). Bile leaks (Strasberg grade A) were reported in 278 (1.3%) patients and severe bile duct injuries (Strasberg grades B-E) were reported in 48 (0.2%) patients. Patient age, ASA physical status class, surgical setting, operative approach and Nassar operative difficulty grade were identified as the five predictors demonstrating the highest relative importance in predicting postoperative complications. CONCLUSION: This multinational observational collaborative cohort study presents a comprehensive report of the current practices and outcomes of cholecystectomy for benign gallbladder disease. Ongoing global collaborative evaluations and initiatives are needed to promote quality assurance and improvement in cholecystectomy.

Unraveling brain fog in post-COVID syndrome: Relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms.

BACKGROUND AND PURPOSE: "Brain fog" is a frequent and disabling symptom that can occur after SARS-CoV-2 infection. However, its clinical characteristics and the relationships among brain fog and objective cognitive function, fatigue, and neuropsychiatric symptoms (depression, anxiety) are still unclear. In this study, we aimed to examine the characteristics of brain fog and to understand how fatigue, cognitive performance, and neuropsychiatric symptoms and the mutual relationships among these variables influence subjective cognitive complaints. METHODS: A total of 170 patients with cognitive complaints in the context of post-COVID syndrome were evaluated using a comprehensive neuropsychological protocol. The FLEI scale was used to characterize subjective cognitive complaints. Correlation analysis, regression machine-learning algorithms, and mediation analysis were calculated. RESULTS: Cognitive complaints were mainly attention and episodic memory symptoms, while executive functions (planning) issues were less often reported. The FLEI scale, a mental ability questionnaire, showed high correlations with a fatigue scale and moderate correlations with the Stroop test, and anxiety and depressive symptoms. Random forest algorithms showed an R2 value of 0.409 for the prediction of FLEI score, with several cognitive tests, fatigue and depression being the best variables used in the prediction. Mediation analysis showed that fatigue was the main mediator between objective and subjective cognition, while the effect of depression was indirect and mediated through fatigue. CONCLUSIONS: Brain fog associated with COVID-19 is mainly characterized by attention and episodic memory, and fatigue, which is the main mediator between objective and subjective cognition. Our findings contribute to understanding the pathophysiology of brain fog and emphasize the need to unravel the main mechanisms underlying brain fog, considering several aspects.

Evaluation of surface water quality in basins of the Chilean Altiplano-Puna and implications for water treatment and monitoring.

Water quality characterization and assessment are key to protecting human health and ecosystems, especially in arid areas such as northern Chile, where water resources are scarce and rich in pollutants. The objective of this study was to review and assess available official water quality data in the Chilean Altiplano-Puna basins for a 10-year period (2008-2018), including water treatment systems. Within the 43,600 km2 of Chilean Altiplano-Puna territory, only 16 official water quality monitoring stations had up-to-date data, and the sampling frequency was less than 3 per year. Most of the water samples collected at the evaluated stations exceeded the drinking and irrigation water Chilean standards for arsenic, boron, and electrical conductivity. Moreover, the characteristics of the Altiplano-Puna affect water quality inside and beyond the area, limiting water usage throughout the Altiplano-Puna basins. Drinking water treatment plants exist in urban and rural settlements; however, the drinking water supply in rural locations is limited due to the lack of adequate treatment and continuity of service. Wastewater treatment plants operate in some urban locations but rarely exist in rural locations. Limited data impede the proper assessment of water quality and thus the evaluation of the need for treatment systems. As such, the implementation of public policies that prioritize water with appropriate quantity and quality for local communities and ecosystems is imperative.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms.

BACKGROUND: Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests. METHODS: Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy. RESULTS: Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. CONCLUSIONS: Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Characterization of Orange Peel Waste and Valorization to Obtain Reducing Sugars.

Annually, millions of tons of foods are generated with the purpose to feed the growing world population. One particular eatable is orange, the production of which in 2018 was 75.54 Mt. One way to valorize the orange residue is to produce bioethanol by fermenting the reducing sugars generated from orange peel. Hence, the objective of the present work was to determine the experimental conditions to obtain the maximum yield of reducing sugars from orange peel using a diluted acid hydrolysis process. A proximate and chemical analysis of the orange peel were conducted. For the hydrolysis, two factorial designs were prepared to measure the glucose and fructose concentration with the 3,5-DNS acid method and UV-Visible spectroscopy. The factors were acid concentration, temperature and hydrolysis time. After the hydrolysis, the orange peel samples were subjected to an elemental SEM-EDS analysis. The results for the orange peel were 73.530% of moisture, 99.261% of volatiles, 0.052% of ash, 0.687% of fixed carbon, 19.801% of lignin, 69.096% of cellulose and 9.015% of hemicellulose. The highest concentration of glucose and fructose were 24.585 and 9.709 g/L, respectively. The results highlight that sugar production is increased by decreasing the acid concentration.

Assessment and potential-site determination of a wheat straw power plant by Aspen Plus and multi-criteria GIS model.

Wheat is one of the most important crops worldwide. Mexicali, Baja California, is an important wheat producer in Mexico with an average production of 507,543 t. Wheat straw is generated as a residue which could be used for different purposes such as bioenergy, heat and power generation. In this work, an assessment and potential site determination of a biomass power plant operating with wheat straw as fuel was performed. Aspen Plus was used to evaluate a plant capacity of at least 10 MW considering the physicochemical properties and an higher heating value of 14.86 MJ kg-1 of the wheat straw from the region. The combustion produced 39.76 MW, and the overall plant efficiency was 25.52%. The development of the multi-criteria geographic information system model allowed us to assess and analyse four factors and three restrictions to determine the potential site for the biomass power plant. The factors were raw material, wheat crops, electric transmission lines, paths and roads, water canals and aqueducts, while the restrictions were localities, Ramsar sites and faults. The biomass power plant is technically and geographically feasible. The geographical coordinates of the potential site of the biomass power plant that fulfils all the criteria are 32°29'29.72″N and 115°15'39.45″W.

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. RESULTS: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. CONCLUSIONS: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

Creosote Bush (Larrea tridentata) Extract Assessment as a Green Antioxidant for Biodiesel.

In this work, the antioxidant properties of methanolic extract of Larrea tridentata were assessed through the free radical scavenging method, ferric reducing antioxidant power and oxygen radical absorbance capacity. The phenolic acids content in the extract was quantified by high-performance liquid chromatography (HPLC) and the total phenol content by the Folin-Ciocalteu method. The extract was used as an antioxidant in biodiesel from canola oil composed mostly by fatty acid methyl esters identified and quantified by gas chromatography-mass spectrophotometry (GC-MS). The performance of the extract as an antioxidant was assessed by the oxidative stability index (OSI) with a Rancimat equipment at 100, 110, 120 and 130 °C. Additionally, the change of the peroxide value (PV) and the higher heating value under conditions of oxidative stress at 100 °C and air injection were measured. The antioxidant capacity of the extract reached 50,000 TAEC (micromole of Trolox antioxidant equivalent capacity per gram). The biodiesel was constituted by more than 70% of unsaturated fatty acid methyl esters (FAME), mainly methyl oleate. The time needed to reach a PV of 100 meqO2/kg was almost four times longer with an antioxidant concentration of 250 mg/L than the blank. The biodiesel showed an OSI time of 1.25 h at 110 °C, while it increased to 8.8, 15.89 and 32.27 h with the antioxidant at concentrations of 250, 500 and 1000 mg/L, respectively. The methanolic Larrea tridentata extract proved to have an antioxidant capacity and it is a green antioxidant in biodiesel to increase its oxidative stability. According to the results obtained, the L. tridentata methanolic extract is an alternative to the commercial synthetic antioxidants used in biodiesel nowadays.

Traditional Knowledge of Edible Wild Plants Used by Indigenous Communities in Zongolica, Mexico.

Edible wild plants (EWP) continue to be an important food source for indigenous communities. A survey was conducted to identify the consumption and management of EWP known as quelites in the Zongolica region of Mexico. 15 species of quelites are consumed mainly during the rainy season, whose local name is associated with the plant's shape, smell and flavor. Changes in food patterns and land use threaten the permanence and consumption of these species. Indigenous and local knowledge is crucial for the use, management and conservation of this group of plants, whose consumption can be leveraged to address malnutrition and unhealthy food use.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.

Proactive and reactive transmission power control for energy-efficient on-body communications.

In wireless body sensor network (WBSNs), the human body has an important effect on the performance of the communication due to the temporal variations caused and the attenuation and fluctuation of the path loss. This fact suggests that the transmission power must adapt to the current state of the link in a way that it ensures a balance between energy consumption and packet loss. In this paper, we validate our two transmission power level policies (reactive and predictive approaches) using the Castalia simulator. The integration of our experimental measurements in the simulator allows us to easily evaluate complex scenarios, avoiding the difficulties associated with a practical realization. Our results show that both schemes perform satisfactorily, providing overall energy savings of 24% and 22% for a case of study, as compared to the maximum transmission power mode.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data.

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

Detergent-labile, supramolecular assemblies of KcsA: relative abundance and interactions involved.

In this work, we illustrate the ability of the prokaryotic potassium channel KcsA to assemble into a variety of supramolecular clusters of defined sizes containing the tetrameric KcsA as the repeating unit. Such clusters, particularly the larger ones, are markedly detergent-labile and thus, disassemble readily upon exposure to the detergents commonly used in protein purification or conventional electrophoresis analysis. This is a reversible process, as cluster re-assembly occurs upon detergent removal and without the need of added membrane lipids. Interestingly, the dimeric ensemble between two tetrameric KcsA molecules are quite resistant to detergent disassembly to individual KcsA tetramers and along with the latter, are likely the basic building blocks through which the larger clusters are organized. As to the proteins domains involved in clustering, we have observed disassembly of KcsA clusters by SDS-like alkyl sulfates. As these amphiphiles bind to inter-subunit, "non-annular" sites on the protein, these observations suggest that such sites also mediate channel-channel interactions leading to cluster assembly.

Desmoplakin truncations and arrhythmogenic left ventricular cardiomyopathy: characterizing a phenotype.

AIMS: Risk stratification for sudden death in arrhythmogenic right ventricular cardiomyopathy (ARVC) is challenging in clinical practice. We lack recommendations for the risk stratification of exclusive left-sided phenotypes. The aim of this study was to investigate genotype-phenotype correlations in patients carrying a novel DSP c.1339C>T, and to review the literature on the clinical expression and the outcomes in patients with DSP truncating mutations. METHODS AND RESULTS: Genetic screening of the DSP gene was performed in 47 consecutive patients with a phenotype of either an ARVC (n = 24) or an idiopathic dilated cardiomyopathy (DCM), who presented with ventricular arrhythmias or a family history of sudden death (n = 23) (aged 40 ± 19 years, 62% males). Three unrelated probands with DCM were found to be carriers of a novel mutation (c.1339C>T). Cascade family screening led to the identification of 15 relatives who are carriers. Penetrance in c.1339C>T carriers was 83%. Sustained ventricular tachycardia was the first clinical manifestation in six patients and nine patients were diagnosed with left ventricular impairment (two had overt severe disease and seven had a mild dysfunction). Cardiac magnetic resonance revealed left ventricular involvement in nine cases and biventricular disease in three patients. Extensive fibrotic patterns in six and non-compaction phenotype in five patients were the hallmark in imaging. CONCLUSION: DSP c.1339C>T is associated with an aggressive clinical phenotype of left-dominant arrhythmogenic cardiomyopathy and left ventricular non-compaction. Truncating mutations in desmoplakin are consistently associated with aggressive phenotypes and must be considered as a risk factor of sudden death. Since ventricular tachycardia occurs even in the absence of severe systolic dysfunction, an implantable cardioverter-defibrillator should be indicated promptly.

Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

AIMS: The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aß(1-42), Aß(1-42)/Aß(1-40) ratio, tTau, and pTau. RESULTS: The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION: We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.