| 1. |
- Yulug, B., et al.
(författare)
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Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial
- 2023
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Ingår i: Translational Neurodegeneration. - : Springer Science and Business Media LLC. - 2047-9158. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.Methods Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.Results We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.Conclusion Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis.
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| 2. |
- Groenewold, Nynke A., et al.
(författare)
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Volume of subcortical brain regions in social anxiety disorder : mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group
- 2023
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Ingår i: Molecular Psychiatry. - : Springer Nature. - 1359-4184 .- 1476-5578. ; 28:3, s. 1079-1089
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Tidskriftsartikel (refereegranskat)abstract
- There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = −0.077, pFWE = 0.037; right: d = −0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = −0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = −0.141, pFWE < 0.001; right: d = −0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.
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| 3. |
- Peker, Yüksel, 1961, et al.
(författare)
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Effect of High-Risk Obstructive Sleep Apnea on Clinical Outcomes in Adults with Coronavirus Disease 2019 A Multicenter, Prospective, Observational Clinical Trial
- 2021
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Ingår i: Annals of the American Thoracic Society. - 1546-3222. ; 18:9, s. 1548-1559
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Tidskriftsartikel (refereegranskat)abstract
- Rationale: Coronavirus disease (COVID-19) is an ongoing pandemic, in which obesity, hypertension, and diabetes have been linked to poor outcomes. Obstructive sleep apnea (OSA) is associated with these conditions and may influence the prognosis of adults with COVID-19. Objectives: To determine the effect of OSA on clinical outcomes in patients with COVID-19. Methods: The current prospective observational study was conducted in three hospitals in Istanbul, Turkey from March 10 to June 22, 2020. The participants were categorized as high-risk or low-risk OSA according to the Berlin questionnaire that was administered in the out-patient clinic, in hospital, or shortly after discharge from hospital blinded to the clinical outcomes. A modified high-risk (mHR)-OSA score based on the snoring patterns (intensity and/or frequency), breathing pauses, and morning/daytime sleepiness, without taking obesity and hypertension into account, were used in the regression models. Results: The primary outcome was the clinical improvement defined as a decline of two categories from admission on a 7-category ordinal scale that ranges from 1 (discharged with normal activity) to 7 (death) on Days 7, 14, 21, and 28, respectively. Secondary outcomes included clinical worsening (an increase of 1 category), need for hospitalization, supplemental oxygen, and intensive care. In total, 320 eligible patients (median [interquartile range] age, 53.2 [41.3-63.0] yr; 45.9% female) were enrolled. In all, 121 (37.8%) were categorized as known (n = 3) or high-risk OSA (n = 118). According to the modified scoring, 70 (21.9%) had mHR-OSA. Among 242 patients requiring hospitalization, clinical improvement within 2 weeks occurred in 75.4% of the mHR-OSA group compared with 88.4% of the modified low-risk-OSA group (P = 0.014). In multivariate regression analyses, mHR-OSA (adjusted odds ratio [OR], 0.42; 95% confidence interval [CI], 0.19-0.92) and male sex (OR, 0.39; 95% CI, 0.17-0.86) predicted the delayed clinical improvement. In the entire study population (n = 320), including the nonhospitalized patients, mHR-OSA was associated with clinical worsening (adjusted hazard ratio, 1.55; 95% CI, 1.00-2.39) and with the need for supplemental oxygen (OR, 1.95; 95% CI, 1.06-3.59). Snoring patterns, especially louder snoring, significantly predicted delayed clinical improvement, worsening, need for hospitalization, supplemental oxygen, and intensive care. Conclusions: Adults with mHR-OSA in our COVID-19 cohort had poorer clinical outcomes than those with modified low-risk OSA independent of age, sex, and comorbidities.
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| 4. |
- Battisti, Umberto Maria, et al.
(författare)
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Ellagic Acid and Its Metabolites as Potent and Selective Allosteric Inhibitors of Liver Pyruvate Kinase
- 2023
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Ingår i: Nutrients. - : MDPI AG. - 2072-6643. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Liver pyruvate kinase (PKL) has recently emerged as a new target for non-alcoholic fatty liver disease (NAFLD), and inhibitors of this enzyme could represent a new therapeutic option. However, this breakthrough is complicated by selectivity issues since pyruvate kinase exists in four different isoforms. In this work, we report that ellagic acid (EA) and its derivatives, present in numerous fruits and vegetables, can inhibit PKL potently and selectively. Several polyphenolic analogues of EA were synthesized and tested to identify the chemical features responsible for the desired activity. Molecular modelling studies suggested that this inhibition is related to the stabilization of the PKL inactive state. This unique inhibition mechanism could potentially herald the development of new therapeutics for NAFLD.
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| 5. |
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| 6. |
- Zhang, Cheng, et al.
(författare)
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Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning
- 2022
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Ingår i: eBioMedicine. - : Elsevier BV. - 2352-3964. ; 83
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Tidskriftsartikel (refereegranskat)abstract
- Background Non-alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver pathologies. However, no medical treatment has been approved for the treatment of NAFLD. In our previous study, we found that PKLR could be a potential target for treatment of NALFD. Here, we investigated the effect of PKLR in in vivo model and performed drug repositioning to identify a drug candidate for treatment of NAFLD. Methods Tissue samples from liver, muscle, white adipose and heart were obtained from control and PKLR knock-out mice fed with chow and high sucrose diets. Lipidomics as well as transcriptomics analyses were conducted using these tissue samples. In addition, a computational drug repositioning analysis was performed and drug candidates were identified. The drug candidates were both tested in in vitro and in vivo models to evaluate their toxicity and efficacy. Findings The Pklr KO reversed the increased hepatic triglyceride level in mice fed with high sucrose diet and partly recovered the transcriptomic changes in the liver as well as in other three tissues. Both liver and white adipose tissues exhibited dysregulated circadian transcriptomic profiles, and these dysregulations were reversed by hepatic knockout of Pklr. In addition, 10 small molecule drug candidates were identified as potential inhibitor of PKLR using our drug repositioning pipeline, and two of them significantly inhibited both the PKLR expression and triglyceride level in in vitro model. Finally, the two selected small molecule drugs were evaluated in in vivo rat models and we found that these drugs attenuate the hepatic steatosis without side effect on other tissues. Interpretation In conclusion, our study provided biological insights about the critical role of PKLR in NAFLD progression and proposed a treatment strategy for NAFLD patients, which has been validated in preclinical studies. Copyright (C) 2022 The Authors. Published by Elsevier B.V.
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| 7. |
- Bayram, Firas, et al.
(författare)
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A domain-region based evaluation of ML performance robustness to covariate shift
- 2023
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Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35:24, s. 17555-17577
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Tidskriftsartikel (refereegranskat)abstract
- Most machine learning methods assume that the input data distribution is the same in the training and testing phases.However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpectedperformance of the learned model in deployment. The issue in which the training and test data inputs follow differentprobability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In thispaper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariateshift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function ofthe input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a twodimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on theexperimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing thelowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the resultsreveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that themodels exhibit high bias toward the region with high density in the input space domain of the training samples.
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| 8. |
- Bayram, Firas, et al.
(författare)
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A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes
- 2022
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Ingår i: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450394758 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learning (ML) software systems to the new conditions. In this paper, we investigate an Electroslag Remelting (ESR) use case process from the Uddeholms AB steel company. The use case involves predicting the minimum pressure value for a vacuum pumping event. Taking into account the long time required to collect new records and efficiently integrate the new machines with the built ML software system. Additionally, to accommodate the changes and satisfy the non-functional requirement of the software system, namely adaptability, we propose an automated and adaptive approach based on a drift handling technique called importance weighting. The aim is to address the problem of adding a new furnace to production and enable the adaptability attribute of the ML software. The overall results demonstrate the improvements in ML software performance achieved by implementing the proposed approach over the classical non-adaptive approach.
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| 9. |
- Bayram, Firas, et al.
(författare)
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DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.
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| 10. |
- Bayram, Firas, et al.
(författare)
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DQSOps : Data Quality Scoring Operations Framework for Data-Driven Applications
- 2023
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Ingår i: EASE '23: Proceedings of the 27<sup>th</sup> International Conference on Evaluation and Assessment in Software Engineering. - : Association for Computing Machinery (ACM). - 9798400700446 ; , s. 32-41
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Konferensbidrag (refereegranskat)abstract
- Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.
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