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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)., Competing Interests: The authors declare no competing interests., (© 2024 Soliman, Magdy, Ayoub, Ebid, Licensee HBKU Press.)

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., (©Monika Nair, Lina E Lundgren, Amira Soliman, Petra Dryselius, Ebba Fogelberg, Marcus Petersson, Omar Hamed, Miltiadis Triantafyllou, Jens Nygren. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.03.2024.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

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., (©Amira Soliman, Björn Agvall, Kobra Etminani, Omar Hamed, Markus Lingman. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.10.2023.)

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.

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.

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 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.

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., (© 2022. The Author(s).)

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., (© 2021. The Author(s).)

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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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)., (Copyright © 2018 Institute of Pharmacology, Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.)

Background: Increasing evidence indicates that in hepatocellular carcinomas (HCCs) abnormal gene expression, for example of glypican-3 (GPC-3) and insulin-like growth factor-II (IGF-II), are associated with the occurrence and progression of HCC. The objective of this study was to evaluate the differential expression of GPC-3 and IGF-II mRNAs in HCC tissues with a background of chronic hepatitis C virus (HCV) genotype 4 cirrhosis, in relation to Ki-67 and alpha-feto protein (AFP) tissue markers. Methods: One hundred and five patients with HCCs who had undergone hepatectomy, were included, after obtaining informed consent. Total RNA was extracted from malignant and corresponding peri-malignant liver tissues, and GPC-3 and IGF-II mRNAs in addition to beta-actin mRNA as an internal control, were evaluated in all samples by reverse transcriptase-polymerase chain reactions (RT-PCR). Routine histopathological diagnosis as well as immunohistochemical (IHC) staining using monoclonal antibodies for Ki-67 and AFP were also performed. Result: Expression of GPC-3 mRNA was positive in all HCC malignant tissue, with overexpression in 86/105 (81.9%); in respect to the grade of the tumor (1-3 grades), while in peri-malignant tissue it was over expressed only in 20/105 (19%). The IGF-II mRNA was over expressed in only 10/105 (9.5%) malignant and peri-malignant samples. AFP was expressed in 33.3% of malignant samples but absent in peri-malignant tissues. Ki-67 expression was significantly increased in malignant compared to peri-malignant tissue. Conclusion: GPC-3 and IGF II mRNAs may be good molecular markers for HCC, especially with a background of cirrhosis due to chronic HCV infection. Significant correlations were noted with the pattern of AFP and Ki-67 expression., (Creative Commons Attribution License)

Several studies have addressed the possible role of hepatitis C virus genotype4 (HCV GT4) in apoptosis. However, this still not fully understood. In the current study a reconstructed clone of E1/E2 polyprotein region of the HCV GT4 was transfected into the Huh7 cell line and a human apoptotic PCR array of 84 genes was used to investigate its possible significance for apoptosis. Out of the 84 genes, only 35 showed significant differential expression, 12 genes being upregulated and 23 downregulated. The highestup regulated genes were APAF1 (apoptotic peptidaseactivating factor 1), BID (BH3 interacting domain death agonist) and BCL 10 (Bcell CLL/ lymphoma protein 10) with fold regulation of 33.2, 30.1 and 18.9, respectively. The most downregulated were FAS (TNF receptor super family), TNFRSF10B (tumor necrosis factor receptor superfamily member 10b) and FADD (FASassociated death domain) with fold regulation of 30.2, 27.7 and 14.9, respectively. These results suggest that the E1/E2 proteins may be involved in HCVinduced pathogenesis by modulating apoptosis through the induction of the intrinsic apoptosis pathway and disruption of the BCL2 gene family.

The effect of intestinal helminthes (IH) and Toxoplasma gondii (Tox) co-infection on anti-mycobacterium tuberculosis (MTB) immunity in patients with active pulmonary tuberculosis was studied in 96 patients of 4 groups. Thirteen patients had TB+IH+Tox (G1), 15 had TB+IH (G2), 21 had TB+Tox (G3) and 47 had TB (G4). The mean diameter of tuberculin and toxoplasmin tests was measured to assess cell mediated immunity. Anti-Toxoplasma IgG1 & IgG4 antibodies were sought in toxoplasmic patients by ELISA for Thelper1 (Th1) and Th2 cytokine responses respectively. All patients were treated by 6 months anti-MTB therapy. Specific anti-IH therapies were given for patients with concomitant IH. Sputum examination for acid fast bacilli was done 2 weeks post-treatment and duration of sputum clearance was recorded. The results showed that IH co-infection had significant negative effect on mean diameter of tuberculin test compared to G4 (5.87 +/- 0.08 vs 8.65 +/- 0.05 mm; P < 0.01). Concurrent Tox with TB significantly increased tuberculin test mean diameter (10.89 +/- 0.11 vs 8.65 +/- 0.05 mm; P < 0.05). Mean level of anti-Tox IgG1 among G3 was significantly higher than in G1 (0.88 +/- 0.05 vs 0.55 +/- 0.02; P < 0.001), but vice versa was with IgG4. Mean tuberculin diameter increased significantly post-treatment in all Gs except G3. Anti-Tox IgGC1 showed significant increase (0.55 +/- 0.02 to 0.82 +/- 0.03; P < 0.001) but IgG4 showed significant decrease (0.62 +/- 0.07 to 0.45 +/- 0.06, P < 0.01) post-treatment in G1, but G3 was insignificantly affected. The duration of sputum clearance was significantly longer in patients with IH co-infection compared to G4 (29 +/- 1 vs 21.8 +/- 4.6 days; P < 0.001).