Short versus long-acting erythropoiesis-stimulating agents for anemia management in Egyptian hemodialysis patients.

BACKGROUND: Chronic kidney disease (CKD) often results in renal anemia, impacting the well-being of patients and causing various negative consequences. Erythropoiesis-stimulating agents (ESAs) offer promising solutions for managing anemia in CKD. This study aimed to evaluate and compare the effectiveness, safety profile, and cost-effectiveness of short-acting (Eprex®) and long-acting (Aranesp®) ESAs. METHOD: This comparative prospective cohort cost-effectiveness study was carried out over 6 months among adult Egyptian hemodialysis patients of either gender. Participants were categorized into two groups based on the type of ESA administered: the Eprex group, receiving epoetin alfa, and the Aranesp group, receiving darbepoetin alfa. These two treatment groups' efficacy, safety, and cost were analyzed and compared. RESULTS: Of 127 hemodialysis patients, 60 (47.2%) received Eprex, while 67 (52.8%) were treated with Aranesp. Target hemoglobin (Hb) was achieved by 50.6% of patients in the Eprex group versus 63.4% in the Aranesp group, with a significant difference (P < 0.001). Both treatment groups exhibited a similar safety profile, while Aranesp® was considered the cost-saving protocol. CONCLUSION: In hemodialysis Egyptian patients, Aranesp with extended dosing intervals proved to be more effective in achieving target Hb with comparable adverse effect profiles, a substantial cost-saving strategy, and offered time-saving advantages for medical staff workload compared to Eprex. TRIAL REGISTRATION: The Clinicaltrial.gov registration ID is NCT05699109 (26/01/2023).

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52744.

Graph neural networks for clinical risk prediction based on electronic health records: A survey.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition.

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.

BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. OBJECTIVE: The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. METHODS: The retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model's decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. RESULTS: The retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission among patients with HF. The evaluation of predictive model performance was based on commonly used metrics, with an area under the precision-recall curve of 66% for the deep model and 68% for the traditional model on the holdout data set. Importantly, the explanations provided by the traditional model offer actionable insights that have the potential to enhance care planning. CONCLUSIONS: This study found that a widely used deep prediction model did not outperform an explainable ML model when predicting readmissions among patients with HF. The results suggest that model transparency does not necessarily compromise performance, which could facilitate the clinical adoption of such models.

Domain Knowledge-Driven Generation of Synthetic Healthcare Data.

Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy.

A Framework for Evaluating Synthetic Electronic Health Records.

Synthetic data generation can be applied to Electronic Health Records (EHRs) to obtain synthetic versions that do not compromise patients' privacy. However, the proliferation of synthetic data generation techniques has led to the introduction of a wide variety of methods for evaluating the quality of generated data. This makes the task of evaluating generated data from different models challenging as there is no consensus on the methods used. Hence the need for standard ways of evaluating the generated data. In addition, the available methods do not assess whether dependencies between different variables are maintained in the synthetic data. Furthermore, synthetic time series EHRs (patient encounters) are not well investigated, as the available methods do not consider the temporality of patient encounters. In this work, we present an overview of evaluation methods and propose an evaluation framework to guide the evaluation of synthetic EHRs.

Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages.

Temporal Context Matters: An Explainable Model for Medical Resource Utilization in Chronic Kidney Disease.

The prediction of medical resource utilization is beneficial for effective healthcare resource planning and allocation. Previous work in resource utilization prediction can be categorized into two main classes, count-based and trajectory-based. Both of these classes have some challenges, in this work we propose a hybrid approach to overcome these challenges. Our initial results promote the value of temporal context in resource utilization prediction and highlight the importance of model explainability in understanding the main important variables.

The Clinical Significance of Septin 9 and Colon Cancer Specific Antigen-2 (CCSA-2) in Colorectal Cancer.

INTRODUCTION: Colorectal cancer (CRC) is a major health problem Worldwide, Egypt shows a high rate of early CRC in the world as 35% of 1,600 Egyptian CRC patients were under 40 with threefold increased risk of death within 5 years. DNA methylation-based biomarkers as methylated Septin9 (mSEPT9) has a promising role for detecting CRC. As well as set of nuclear matrix proteins associated with changes in the nuclear structure/architecture. detection of these nuclear proteins resulted in identification of biomarkers that are specific for colon cancer. Particular interest has been placed on colon cancer specific antigen-2(CCSA-2). METHODS: A total of 30 newly diagnosed CRC patients, 7 colonic adenoma patients, and 15 age- and sex-matched control subjects were recruited in this study. Plasma mSEPT9was assayed by Epi procolon kit, CCSA-2 by ELISA and, Occult blood in stool by Guaiac-based fecal occult blood test. The level of Colon Cancer mSEPT9 and CCSA-2 were carried on CRC patients both preoperatively and three months postoperatively. RESULTS: mSEPT9 has 96.7% sensitivity and 95.5% specificity in differentiating colorectal cancer patients from non-malignant cases. Also, our study showed a highly statistically significant difference between the pre and three months postoperative expression of mSEPT9 in colorectal cancer as there was a dramatically decrease in the expression of mSEPT9 postoperatively (p value < 0.001). The CCSA-2 at the cutoff level of >1.43 would provide 93.3% sensitivity and 90.9% specificity in differentiation between malignant and non-malignant cases. Also, the study showed that there is a statistically significant difference between colorectal cancer patients preoperatively and postoperatively according to CCSA-2 with dramatic decrease in its level postoperatively (p value > 0.001). CONCLUSION: The plasma SEPT9 DNA methylation level and Serum CCSA-2 could be used as promising non-invasive methods for observing the CRC patients postsurgical response to predict the occurrence of complete remission or relapses.

Glypican-3, Vascular Endothelial Growth Factor and Golgi Protein-73 for Differentiation between Liver Cirrhosis and Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) accounts for more than 80% of primary liver cancers. Moreover, in the next 10 years, more than one million patients are expected to die from liver cancer as estimated by the World Health Organization. The aim of the present study is to evaluate the clinical utility of using Glypican (GPC3), Vascular endothelial growth factor (VEGF) and Golgi protein 73 (GP73) in serum by Enzyme-Linked Immunosorbent Assay (ELISA) and by Real-Time Polymerase Chain Reaction (RT-PCR), as diagnostic markers to differentiate HCC from cirrhotic liver disease. METHODS: A total of 50 patients with histologically-proven HCC, 50 liver cirrhosis patients and 20 healthy volunteers as controls were enrolled in this study, blood samples were obtained from each patient. Expression of the studied biomarkers was evaluated by ELISA and Real-Time PCR. RESULTS: Statistical analysis of RT-PCR results showed that the expression of GPC3, VEGF and GP73 in serum of patients with HCC was significant (P value < 0.001, 0.01, and < 0.001) respectively and increased when compared to the cirrhotic group. Furthermore, the serum protein levels of GPC3 and VEGF in HCC and cirrhotic patients were significant when compared to the control group. While no significance was found between HCC and cirrhotic group. The serum protein level of GP73 was significantly increased in HCC and cirrhosis groups  compared to the control group (P value < 0.001). Moreover, a significant increase was  evident in HCC group compared to cirrhotic group (P value < 0.001). The results of the present study showed that the combination of VEGF and  GP73 could  discriminate HCC from cirrhosis. CONCLUSION: GPC3, VEGF and GP73 are reliable biomarkers for diagnosis of  HCC. The serum level of GP73 is a potential screening marker for discriminating HCC from liver cirrhosis.

Nucleated red blood cells as a biomarker for mortality in infants and neonates requiring veno-arterial extracorporeal membrane oxygenation for cardiac disease.

INTRODUCTION: Nucleated red blood cells (NRBC) are rare in the peripheral circulation of healthy individuals and their presence have been associated with mortality in adults and very low birth weight newborns, however, its value as a biomarker for mortality in infants requiring veno-arterial (VA) extracorporeal membrane oxygenation (ECMO) has yet to be studied. We sought to determine if NRBC can serve as a biomarker for ECMO mortality and inpatient mortality in infants requiring V-A ECMO. METHODS: A single-center retrospective chart review analyzing infants <1 year of age requiring VA ECMO due to myocardial dysfunction or post-cardiotomy between January 1, 2011 to June 30, 2020. RESULTS: One hundred two patients required VA ECMO. Sixty-five patients required ECMO post-cardiotomy, 19 for perioperative deterioration, and 18 for myocardial dysfunction. Fifty-one patients (50%) died (21 died on ECMO, 30 died post-ECMO decannulation). Multivariable analysis found Age <60 days (OR 13.0, 95% CI 1.9-89.6, p = 0.009), NRBC increase by >50% post-ECMO decannulation (OR 17.1, 95% CI 3.1-95.1, p = 0.001), Single Ventricle (OR 9.0, 95% CI 1.7-47.7, p = 0.01), and lactate at ECMO decannulation (OR 3.0, 95% CI 1.3-7.1, p = 0.011) to be independently associated with inpatient mortality. ROC curves evaluating NRBC pre-ECMO decannulation as a biomarker for mortality on ECMO (AUC 0.80, 95% CI 0.68-0.92, p ⩽ 0.001) and post-ECMO decannulation (AUC 0.75, 95% CI 0.65-0.84, p ⩽ 0.001) show NRBC to be an accurate biomarker for mortality. CONCLUSIONS: Greater than 50% increase in NRBC post-ECMO decannulation is associated with inpatient mortality. NRBC value pre-ECMO decannulation may be a useful biomarker for mortality while on ECMO and post-decannulation.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

Nucleated red blood cells as a biomarker for mortality in neonates following cardiac surgery.

INTRODUCTION: Nucleated red blood cells (NRBCs) are immature red cells that under normal conditions are not present in the peripheral circulation. Several studies have suggested an association between elevated NRBC and poor outcome in critically ill adults and neonates. We sought to determine if elevations in NRBC value following cardiac surgery and following clinical events during the hospital stay can be used as a biomarker to monitor for mortality risk in neonates post-cardiac surgery. MATERIALS AND METHODS: We constructed a retrospective study of 264 neonates who underwent cardiac surgery at Children's Hospital, New Orleans between 2011 and 2020. Variables included mortality and NRBC value were recorded following cardiac surgery and following peri-operative clinical events. The study was approved by LSU Health IRB. Sensitivity, specificity, receiver operating characteristic (ROC) curves with area under the curve (AUC) and logistic regression analysis were performed. RESULTS: Thirty-six patients (13.6%) died, of which 32 had an NRBC value ≥10/100 white blood cell (WBC) during hospitalisation. Multi-variable analysis found extracorporeal membrane oxygenation use (OR 10, 95% CI 2.9-33, p=<0.001), NRBC ≥10/100 WBC (OR 16.1, CI 4.1-62.5, p ≤ 0.001) and peak NRBC in the 14-day period post-cardiac surgery (continuous variable, OR 1.05, 95% CI 1.0-1.09, p = 0.03), to be independently associated with mortality. Using a cut-off NRBC value of 10/100 WBC, there was an 88.9% sensitivity and a 90.8% specificity, with ROC curve showing an AUC of 0.9 and 0.914 for peak NRBC value in 14 days post-surgery and entire hospitalisation, respectively. CONCLUSIONS: NRBC ≥10/100 WBC post-cardiac surgery is strongly associated with mortality. Additionally, NRBC trend appears to show promise as an accurate biomarker for mortality.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

Synergistic effect of growth-promoting microorganisms on bio-control of Fusarium oxysporum f. sp. pisi, growth, yield, physiological and anatomical characteristics of pea plants.

Fusarium root rot caused by Fusarium oxysporum is an aggressive disease-causing damping-off, root rot, and vascular wilt in all peas growing fields. The disease can cause 100% yield losses under favorable conditions. The present study aims to control Fusarium root rot using Trichoderma harzianum, Pseudomonas fluorescens, and arbuscular mycorrhizal fungi, singly or in combinations. The results showed that all treatments significantly enhanced not only the plant growth, total phenol, activities of antioxidant enzymes, but also, the yield and seed quality. Several changes in the anatomical, physiological, and characteristics of the treated plants were also recorded. Compared to the untreated control treatment, under greenhouse conditions, the maximum reduction of the disease severity (80%) was achieved by the synergistic triple treatment consists of arbuscular mycorrhizal fungi, Trichoderma harzianum, and Pseudomonas fluorescens, as they gave the best growth and yield parameters. The same combination showed the highest activity of the antioxidant enzyme peroxidase (57.1%), as well as the highest total phenol content (117.7%), over the control. The synergistic triple increased the contents of protein (64.6%), total soluble sugars (48.5%), and total carbohydrate (24.8%) in seeds of pea compared with the control. The synergistic triple treatment led to an increase in the thickness of the root section (25%), the thickness of the cortex (24.8%), the thickness of the vascular cylinder (31.5%), and the diameter of the xylem vessels (81.5%) of the root. Based on their efficiency and eco-safety, this synergistic triple might be very effective for controlling root rot disease of pea caused by F. oxysporum, as well as improve the growth, yield, and seed quality.

Neurotoxin-Induced Rodent Models of Parkinson's Disease: Benefits and Drawbacks.

Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterized by cardinal motor impairments, including akinesia and tremor, as well as by a host of non-motor symptoms, including both autonomic and cognitive dysfunction. PD is associated with a death of nigral dopaminergic neurons, as well as the pathological spread of Lewy bodies, consisting predominantly of the misfolded protein alpha-synuclein. To date, only symptomatic treatments, such as levodopa, are available, and trials aiming to cure the disease, or at least halt its progression, have not been successful. Wong et al. (2019) suggested that the lack of effective therapy against neurodegeneration in PD might be attributed to the fact that the molecular mechanisms standing behind the dopaminergic neuronal vulnerability are still a major scientific challenge. Understanding these molecular mechanisms is critical for developing effective therapy. Thirty-five years ago, Calne and William Langston (1983) raised the question of whether biological or environmental factors precipitate the development of PD. In spite of great advances in technology and medicine, this question still lacks a clear answer. Only 5-15% of PD cases are attributed to a genetic mutation, with the majority of cases classified as idiopathic, which could be linked to exposure to environmental contaminants. Rodent models play a crucial role in understanding the risk factors and pathogenesis of PD. Additionally, well-validated rodent models are critical for driving the preclinical development of clinically translatable treatment options. In this review, we discuss the mechanisms, similarities and differences, as well as advantages and limitations of different neurotoxin-induced rat models of PD. In the second part of this review, we will discuss the potential future of neurotoxin-induced models of PD. Finally, we will briefly demonstrate the crucial role of gene-environment interactions in PD and discuss fusion or dual PD models. We argue that these models have the potential to significantly further our understanding of PD.

Domain expertise-agnostic feature selection for the analysis of breast cancer data.

Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise-agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.

Adenosine role in brain functions: Pathophysiological influence on Parkinson's disease and other brain disorders.

Although adenosine plays a key role in multiple motor, affective, and cognitive processes, it has received less attention in the neuroscience field compared to other neurotransmitters (e.g., dopamine). In this review, we highlight the role of adenosine in behavior as well as its interaction with other neurotransmitters, such as dopamine. We also discuss brain disorders impacted by alterations to adenosine, and how targeting adenosine can ameliorate Parkinson's disease motor symptoms. We also discuss the role of caffeine (as an adenosine antagonist) on cognition as well as a neuroprotective agent against Parkinson's disease (PD).