Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

Cultural persistence and the 'herbal medicine paradox': Evidence from European data.

The use of herbal or traditional medicines has survived the proliferation of modern medicine. The phenomenon has been labeled as the 'herbal medicines paradox' (HMP). We study whether such HMP hypothesis can be explained by the persistence of attitudes across cultural boundaries. We undertake a secondary analysis of individual-level migration data to test the persistence of the use of herbal medicines in relation to norms in the person's country of birth (or home country). We study the association between attitudes towards herbal medicine treatments of both first (N = 3630) and second-generation (N = 1618) immigrants in 30 European countries, and the average attitudes of their sending country origins. We find robust evidence of an association that is stronger for the second-generation migrants. We document a stronger effect among maternal than paternal lineages, as well as significant heterogeneity based on migrants' country of origin. Our estimates are robust to different sample analysis. Our estimates are consistent with a cultural explanation for the HMP.

Exploration of the molecular mechanism guiding Xinfeng capsule regulatory mechanism for rheumatoid arthritis inflammation.

OBJECTIVES: Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joint synovium. The traditional Chinese medicine Xinfeng capsule (XFC) has a remarkable alleviating effect on inflammatory symptoms, such as joint pain and swelling, in patients with RA. However, the underlying mechanism of action remains to be elucidated. This study intended to conduct network pharmacology, animal experiments, data mining, and molecular docking to explore the molecular mechanism through which XFC can improve the inflammatory symptoms of RA. METHODS: The Apriori association rules and a random walk model were employed to evaluate the effect of XFC on the clinical inflammatory indexes of RA. The active ingredients and the potential target genes of XFC were obtained from public databases. Based on the search tool for recurring instances of neighboring genes (STRING) database, the Database for Annotation, Visualization and Integrated Discovery (DAVID) database, Cytoscape software, and molecular docking method, the molecular mechanism by which XFC acts on RA was also analyzed. Finally, an adjuvant arthritis rat model was established to verify the effects of XFC on inflammation-related signaling pathways and inflammatory factors. RESULTS: XFC significantly reduced the level of C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and the erythrocyte sedimentation rate (ESR). The docking space structures of the active ingredients in XFC, namely triptolide and quercetin, and the key targets were stable. Inflammation-related biological processes were identified as the key factors involved in the development of RA, and the regulation of the toll-like receptor (TLR) signaling pathway may be the key link for XFC toward improving the inflammatory state of RA. The expression levels of toll-like receptor 4 (TLR4), myeloid differentiation primary response protein MyD88 (MyD88), interleukin-1 receptor-associated kinase 1 (IRAK1), TNF receptor-associated factor 6 (TRAF6), TGF-beta-activated kinase 1 (TAK1), phospho-Inhibitor of NF-κB kinaseß (p-IKKß), phospho-Nuclear factor-k-gene binding (p-NF-κB), and interleukin-1ß (IL-1ß) can all be decreased by XFC. XFC improves joint inflammation symptoms by lowering pro-inflammatory factors tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (INF-γ) levels. CONCLUSIONS: XFC could effectively improve the clinical inflammatory indexes of RA. The active ingredients of XFC improved the inflammatory state of RA by regulating the TLR-signaling pathway.

Analysis of post-market adverse events of istradefylline: a real-world study base on FAERS database.

Analyze the adverse event (AE) signals of istradefylline based on the FAERS database. By extracting large-scale data from the FAERS database, this study used various signal quantification techniques such as ROR, PRR, BCPNN, and MGPS to calculate and evaluate the ratio and association between istradefylline and specific AEs. In the FAERS database, this study extracted data from the third quarter of 2019 to the first quarter of 2023, totaling 6,749,750 AE reports. After data cleansing and drug screening, a total of 3633 AE reports related to istradefylline were included for analysis. Based on four calculation methods, this study unearthed 25 System Organ Class (SOC) AE signals and 82 potential preferred terms (PTs) related to istradefylline. The analysis revealed new AEs during istradefylline treatment, including reports of Parkinsonism hyperpyrexia syndrome (n = 3, ROR 178.70, PRR 178.63, IC 1.97, EBGM 165.63), Compulsions (n = 5, ROR 130.12, PRR 130.04, IC 2.53, EBGM 123.02), Deep brain stimulation (n = 10, ROR 114.42, PRR 114.27, IC 3.33, EBGM 108.83), and Freezing phenomenon (n = 60, ROR 97.52, PRR 96.76, IC 5.21, EBGM 92.83). This study provides new risk signals and important insights into the use of istradefylline, but further research and validation are needed, especially for those AE that may occur in actual usage scenarios but are not yet explicitly described in the instructions.

Outpatient geriatric health care in the German federal state of Mecklenburg-Western Pomerania: a population-based spatial analysis of claims data.

BACKGROUND: Due to unidentified geriatric needs, elderly patients have a higher risk for developing chronic conditions and acute medical complications. Early geriatric screenings and assessments help to identify geriatric needs. Holistic and coordinated therapeutic approaches addressing those needs maintain the independence of elderly patients and avoid adverse effects. General practitioners are important for the timely identification of geriatric needs. The aims of this study are to examine the spatial distribution of the utilization of outpatient geriatric services in the very rural Federal State of Mecklenburg-Western Pomerania in the Northeast of Germany and to identify regional disparities. METHODS: Geographical analysis and cartographic visualization of the spatial distribution of outpatient geriatric services of patients who are eligible to receive basic geriatric care (BGC) or specialized geriatric care (SGC) were carried out. Claims data of the Association of Statutory Health Insurance Physicians in Mecklenburg-Western Pomerania were analysed on the level of postcode areas for the quarter periods between 01/2014 and 04/2017. A Moran's I analysis was carried out to identify clusters of utilization rates. RESULTS: Of all patients who were eligible for BGC in 2017, 58.3% (n = 129,283/221,654) received at least one BCG service. 77.2% (n = 73,442/95,171) of the patients who were eligible for SGC, received any geriatric service (BGC or SGC). 0.4% (n = 414/95,171) of the patients eligible for SGC, received SGC services. Among the postcode areas in the study region, the proportion of patients who received a basic geriatric assessment ranged from 3.4 to 86.7%. Several regions with statistically significant Clusters of utilization rates were identified. CONCLUSIONS: The widely varying utilization rates and the local segregation of high and low rates indicate that the provision of outpatient geriatric care may depend to a large extent on local structures (e.g., multiprofessional, integrated networks or innovative projects or initiatives). The great overall variation in the provision of BGC services implicates that the identification of geriatric needs in GPs' practices should be more standardized. In order to reduce regional disparities in the provision of BGC and SGC services, innovative solutions and a promotion of specialized geriatric networks or healthcare providers are necessary.

In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis.

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

Insights into the nutritional prevention of macular degeneration based on a comparative topic modeling approach.

Topic modeling and text mining are subsets of natural language processing (NLP) with relevance for conducting meta-analysis (MA) and systematic review (SR). For evidence synthesis, the above NLP methods are conventionally used for topic-specific literature searches or extracting values from reports to automate essential phases of SR and MA. Instead, this work proposes a comparative topic modeling approach to analyze reports of contradictory results on the same general research question. Specifically, the objective is to identify topics exhibiting distinct associations with significant results for an outcome of interest by ranking them according to their proportional occurrence in (and consistency of distribution across) reports of significant effects. Macular degeneration (MD) is a disease that affects millions of people annually, causing vision loss. Augmenting evidence synthesis to provide insight into MD prevention is therefore of central interest in this article. The proposed method was tested on broad-scope studies addressing whether supplemental nutritional compounds significantly benefit macular degeneration. Six compounds were identified as having a particular association with reports of significant results for benefiting MD. Four of these were further supported in terms of effectiveness upon conducting a follow-up literature search for validation (omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported by the follow-up literature search (niacin and molybdenum) also had scores in the lowest range under the proposed scoring system. Results therefore suggest that the proposed method's score for a given topic may be a viable proxy for its degree of association with the outcome of interest, and can be helpful in the systematic search for potentially causal relationships. Further, the compounds identified by the proposed method were not simultaneously captured as salient topics by state-of-the-art topic models that leverage document and word embeddings (Top2Vec) and transformer models (BERTopic). These results underpin the proposed method's potential to add specificity in understanding effects from broad-scope reports, elucidate topics of interest for future research, and guide evidence synthesis in a scalable way. All of this is accomplished while yielding valuable and actionable insights into the prevention of MD.

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Analysis of mechanism of core points in acupuncture and moxibustion treatment for epilepsy based on data mining. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŽ¢è®¨é’ˆç¸æ²»ç–—ç™«ç—«æ ¸å¿ƒç©´ä½çš„ä½œç”¨æœºåˆ¶.

OBJECTIVES: To explore the mechanism of core points in acupuncture and moxibustion treatment for epilepsy by using data mining technique, so as to provide a reference for clinical practice and experimental research. METHODS: The data comes from relevant documents collected from CNKI, Wanfang, SinoMed, VIP, PubMed, Embase, Cochrane Library, EBSCO, Web of Science databases. The selected acupoints were analyzed in descriptive statistics, high-frequency acupoints group and core acupoint prescription. Further, potential target mining, "core acupoint prescription-target-epilepsy" network construction, protein-protein interactions (PPI) network establishment and core target extraction, gene ontology (GO) and KEGG gene enrichment analysis of the core acupoint prescription were carried out to predict its anti-epileptic potential mechanism. RESULTS: A total of 122 acupoint prescriptions were included. The core acupoint prescriptions were Baihui (GV20), Hegu (LI4), Neiguan (PC6), Shuigou (GV26) and Taichong (LR3). 277 potential targets were identified, among which 134 were shared with epilepsy. The core targets were extracted by PPI network topology analysis, including signal transducer and activator of transcription 3, tumor necrosis factor (TNF), interleukin (IL)-6, protein kinase B1, c-Jun N-terminal kinase, brain-derived neurotrophic factor, tumor protein 53, vascular endothelial growth factor A, Caspase-3, epidermal growth factor receptor, etc. The main anti-epileptic pathways of the core acupoints were predicted by KEGG enrichment, including lipid and atherosclerosis, neurodegeneration, phosphatidylinositol-3-kinase/protein B kinase signaling pathway, mitogen-activated protein kinase signaling pathway, cyclic adenosine monophosphate signaling pathway, TNF signaling pathway, IL-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, apoptosis, etc., involving neuronal death, synaptic plasticity, oxidative stress, inflammation and other related biological process. CONCLUSIONS: The core acupoint prescription of acupuncture and moxibustion intervention for epilepsy can act on multiple targets and multiple pathways to exert anti-epileptic effects, which can provide a theoretical basis for further clinical application and mechanism research.

Analysis on acupoint selection rules in the treatment of metabolism-associated fatty liver disease based on data mining. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŠ€æœ¯åˆ†æžé’ˆç¸æ²»ç–—ä»£è°¢ç›¸å ³è„‚è‚ªæ€§è‚ç— çš„é€‰ç©´è§„å¾‹.

OBJECTIVES: To explore the rules of acupoint selection in the treatment of metabolic-associated fatty liver disease (MAFLD) with acupuncture and moxibustion by using data mining technology. METHODS: The clinical research literature on acupuncture treatment of MAFLD was collected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, VIP Database and China Biology Medicine from their inception to November 20, 2022. According to our inclusion and exclusion criteria, the literature was independently screened and re-screened by two research members, and the screened results were checked, followed by establishing an acupoint prescription database using Excel 2019. Descriptive statistics of acupoints applied frequency, involved meridians, locations and specific acupoints were perpormed. Then, SPSS Modeler18.0 software was used to conduct analysis about association rules, and the SPSS Statistics 26.0 software was used to perform cluster analysis on high-frequency acupoints, exploring the characteristics and rules of acupoint selection and combination in the treatment of MAFLD. RESULTS: Totally, 178 papers were collected, containing 130 acupoints, with a total application frequency of 1 305. The top five acupoints are Zusanli (ST36), Fenglong (ST40), Ganshu (BL18), Taichong (LR3) and Sanyinjiao (SP6). The commonly involved meridians are the Stomach Meridian of Foot Yangming, Bladder Meridian of Foot Taiyang, and Spleen Meridian of Foot Taiyin. The employed acupoints are mostly located in the lower limbs and abdomen, and the five Shu acupoints and crossing acupoints are in the majority. The association rule analysis of high frequency acupoints indicated that of the 16 qualified acupoint groups, the top 5 with close correlation degrees are ST36 and ST40, ST36 and LR3, ST36 and SP6, ST40 and LR3 and ST36, ST36 and SP6 and ST40. Further, 3 effective clusters were obtained by cluster analysis. CONCLUSIONS: Acupuncture and moxibustion treatment of MAFLD follows the therapeutic principles of soothing the liver, invigorating the spleen, tonifying the kidney, and resolving phlegm and removing dampness. The core acupoint group is ST36, ST40 and LR3, and the combination of acupoints is based on syndrome differentiation. These results may provide a useful reference for clinical practice.

Pitfalls in the lab assessment of hypopituitarism.

The diagnostic approach to hypopituitarism involves many disciplines. Clinical symptoms rarely are specific. Imaging techniques are helpful but cannot prove the specific functional defects. Therefore, the definitive diagnosis of pituitary insufficiency is largely based on laboratory tests. However, also laboratory methods come with inherent limitations, and it is essential for the clinician to know and recognize typical pitfalls. Most factors potentially impairing the quality of hormone measurements are introduced in the preanalytical phase, i.e. before the hormones are measured by the laboratory. For example, the timing of blood drawing with respect to circadian rhythm, stress, and medication can have an influence on hormone concentrations. During the actual analysis of the hormones, cross-reactions with molecules present in the sample presenting the same or similar epitopes than the intended analyte may affect immunoassays. Interference can also come from heterophilic or human anti-animal antibodies. Unexpected problems can also be due to popular nutritional supplements which interfere with the measurement procedures. An important example in this respect is the interference from biotin. It became only clinically visible when the use of this vitamin became popular among patients. The extreme serum concentrations reached when patients take it as a supplement can lead to incorrect measurements in immunoassays employing the biotin-streptavidin system. To some extent, hormone analyses using liquid chromatography mass spectrometry (LCMS) can overcome problems, although availability and cost-effectiveness of this method still imposes restrictions. In the post-analytical phase, appropriateness of reference intervals and cut-offs with respect to the specific analytical method used is of outmost importance. Furthermore, for interpretation, additional biological and pharmacological factors like BMI, age and concomitant diseases must be considered to avoid misinterpretation of the measured concentrations. It is important for the clinician and the laboratory to recognize when one or more laboratory values do not match the clinical picture. In an interdisciplinary approach, the search for the underlying cause should be initiated.

Monitorización de la glucemia en el paciente crítico adulto: tipo de muestra y método de análisis. Revisión sistemática y metanálisis / Blood glucose monitoring in critically ill adult patients: Type of sample and method of analysis. Systematic review and meta-analysis

IntroducciónLa guía clínica para el manejo de la sepsis recomienda usar muestras de sangre arterial para el control glucémico. Un estudio multicéntrico en 86 unidades de cuidados intensivos españolas reveló que el 85,4% de estas utilizaban punción capilar.ObjetivoAnalizar la fiabilidad de la glucemia comparando diferentes muestras sanguíneas (arterial, venosa, capilar) e instrumentos (glucómetros, gasómetros, laboratorio central). Secundariamente, estimar el efecto de variables confusoras y el rendimiento de los instrumentos de medición determinados por las diferentes normas de calidad.MetodologíaRevisión sistemática y metanálisis con búsqueda en las bases de datos PubMed, CINAHL y Embase en septiembre-2021 y septiembre-2022, sin límites temporales ni idiomáticos. Fuentes de literatura gris: DART-Europe, OpenGrey y Google Académico. Resultados resumidos mediante síntesis cualitativa (descripción de resultados, características de los estudios) y cuantitativa (metanálisis para evaluar la diferencia de medias estandarizadas). Calidad metodológica de artículos evaluada con Quality Assessment of Diagnostic Accuracy Studies-2. Protocolo: https://osf.io/ DOI 10.17605/OSF.IO/T8KYP.ResultadosSe incluyeron un total de 32 artículos y 5.451 pacientes. No se obtuvieron discrepancias entre muestras arteriales con glucómetro vs. laboratorio (sesgo [IC95%]: 0,01 [−0,12 a 0,14] mg/dL). En cambio, muestras arteriales con gasómetro sí sobreestimaron de forma significativa (sesgo [IC95%]: 0,12 [0,01 a 0,24] mg/dL). La misma tendencia presentan capilares con glucómetro, aunque no de forma significativa (sesgo [IC95%]: 0,07 [−0,02 a 0,15] mg/dL). Hay discrepancia entre los estudios sobre el efecto del hematocrito y el equilibrio ácido-base. El mayor consenso se da en la poca concordancia del glucómetro con muestras capilares vs. laboratorio en presencia de shock y soporte vasopresor, situación de fallo renal o durante el tratamiento con vitamina C.Conclusiones... (AU)

IntroductionThe clinical guideline for the management of sepsis recommends using arterial blood samples for glycaemic control. A multicentre study in 86 Spanish intensive care units revealed that 85.4% of these used capillary puncture.ObjectiveTo analyse the reliability of glycaemia by comparing different blood samples (arterial, venous, capillary) and instruments (glucometers, gasometers, central laboratory). Secondarily, to estimate the effect of confounding variables and the performance of measuring instruments as determined by different quality standards.MethodologySystematic review and meta-analysis with search in PubMed, CINAHL and Embase databases in September-2021 and September-2022, with no time or language limits. Grey literature sources: DART-Europe, OpenGrey and Google Scholar. Results summarised by qualitative (description of results, study characteristics) and quantitative (meta-analysis to assess standardised mean difference) synthesis. Methodological quality of articles assessed with Quality Assessment of Diagnostic Accuracy Studies-2. Protocol: https://osf.io/ DOI 10.17605/OSF.IO/T8KYP.ResultsA total of 32 articles and 5451 patients were included. No discrepancies were obtained between arterial glucometer vs. laboratory samples (bias [95%CI]: 0.01 [−0.12 to 0.14] mg/dL). In contrast, arterial samples with a gasometer did significantly overestimate (bias [95%CI]: 0.12 [0.01 to 0.24] mg/dL). The same trend is seen in capillaries with a glucometer, although not significantly (bias [95%CI]: 0.07 [−0.02 to 0.15] mg/dL). There is discrepancy between studies on the effect of haematocrit and acid-base balance. The greatest consensus is on the poor agreement of glucometer with capillary vs. laboratory samples in the presence of shock and vasopressor support, renal failure or during vitamin C treatment.Conclusions... (AU)

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Phosphoproteomic analysis of APP/PS1 mice of Alzheimer's disease by DIA based mass spectrometry analysis with PRM verification.

Traditional Chinese medicine has been utilized in China for approximately thousands of years in clinical settings to prevent Alzheimer's disease (AD) and enhance memory, despite the lack of a systematic exploration of its biological underpinnings. Exciting research has corroborated the beneficial effects of tetrahydroxy stilbene glycoside (TSG), an extract derived from Polygonum multiflorum, in delaying learning and memory impairment in a model that mimics AD. Therefore, the primary objective of this study is to investigate the major function of TSG upon protein regulation in AD. Herein, a novel approach, encompassing data independent acquisition (DIA), DIA phosphorylated proteomics, and parallel reaction monitoring (PRM), was utilized to integrate quantitative proteomic data collected from APP/PS1 mouse model exhibiting toxic intracellular aggregation of Aß. Initially, we deliberated upon both single and multi-dimensional data pertaining to AD model mice. Furthermore, we authenticated disparities in protein phosphorylation quantity and expression, phosphorylation function, and ultimately phosphorylation kinase analysis. In order to validate the results, we utilized PRM ion monitoring technology to identify potential protein or peptide biomarkers. In the mixed samples, targeted detection of 50 target proteins revealed that 26 to 33 target proteins were stably detected by PRM. In summary, our findings provide new candidates for AD biomarker, which have been identified and validated through protein researches conducted on mouse brains. This offers a wealth of potential resources for extensive biomarker validation in neurodegenerative diseases. SIGNIFICANCE: DIA phosphorylated proteomics technique was used to detect and analyze phosphorylated proteins in brain tissues of mice with AD. Data were analyzed by various bioinformatics tools to explore the phosphorylation events and characterize them related to TSG. The results of DIA were further verified by PRM. Besides, we mapped the major metabolite classes emerging from the analyses to key biological pathways implicated in AD to understand the potential roles of the molecules and the interactions in triggering symptom onset and progression of AD. Meanwhile, we clarified that in the context of AD onset and TSG intervention, the changes in proteins, protein phosphorylation, phosphorylation kinases, and the internal connections.

Acupoint selection rules of acupuncture for Tourette syndrome in children. / 针刺治疗儿童Tourette综合征选穴规律.

OBJECTIVES: To explore the rules of acupoint selection and compatibility of acupuncture for Tourette syndrome(TS) in children. METHODS: The relevant literature regarding acupuncture for Tourette syndrome in children included in CNKI, Wanfang, VIP, SinoMed, PubMed, Web of Science and Cochrane Library from the establishment of the database to March 31st, 2023 was retrieved.The information of acupuncture prescription, syndrome type, meridian affinity was extracted to set up database. The Microsoft Excel 2019 was used for descriptive statistical analysis, SPSS modeler18.0 was for association rule analysis, lantern5.0 was for latent structure analysis and comprehensive clustering. RESULTS: ①A total of 80 literature was included, and 112 acupuncture prescriptions were extracted, involving 104 acupoints, with a cumulative frequency of 859 times.②The acupoints with high use frequency were Taichong(LR 3), Baihui(GV 20), Fengchi(GB 20), Hegu(LI 4), Sishencong(EX-HN 1), Sanyinjiao(SP 6) and Zusanli(ST 36).③In the treatment of TS with acupuncture, the governor vessel acupoints were the most frequently used, the proportion of acupoints on the head, face, neck and lower limbs was higher. ④The association rule analysis showed that Fengchi(GB 20)-Hegu(LI 4) and Taichong(LR 3)-Hegu(LI 4) had the highest support degree, both were 47.32%.⑤Five comprehensive clustering models were obtained by analyzing the latent structure of high-frequency acupoints, corresponding to yin deficiency disturbing wind, liver hyperactivity and spleen deficiency, liver yang transforming into wind, phlegm-heat harassing the interior and qi stagnation transformed fire. CONCLUSIONS: Acupuncture for TS in children is based on the principle of soothe the liver and extinguish the wind, regulating qi and blood, and paying attention to regulating spirit and qi. The core acupoints are Fengchi(GB 20), Hegu(LI 4), Taichong(LR 3), Baihui(GV 20), Sanyinjiao(SP 6) , Zusanli(ST 36), acupoints should be selected according to different syndrome in clinical.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis.

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Electrolyte disorders induced by six multikinase inhibitors therapy for renal cell carcinoma: a large-scale pharmacovigilance analysis.

To provide evidence for optimization of multi-kinase inhibitors (MKIs) use in the clinic, we use the public database to describe and evaluate electrolyte disorders (EDs) related to various MKIs treated for renal cell carcinoma. We analyzed spontaneous reports submitted to the Food and Drug Administration Adverse Events Reporting System (FAERS) in an observational and retrospective manner. Selecting electrolyte disorders' adverse events to multikinase inhibitors (axitinib, cabozantinib, lenvatinib, pazopanib, sunitinib, and sorafenib). We used Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms to analyze suspected adverse reactions of electrolyte disorders induced by MKIs (which were treated for renal cell carcinoma) between January 2004 and December 2022. As of December 2022, 2772 MKIs (which were treated for renal cell carcinoma) ICSRs were related to electrolyte disorders AEs. In general, there were more AEs cases in males, except lenvatinib and 71.8% of the cases were submitted from North America. ICSRs in this study, the age group most frequently affected by electrolyte disorders AEs was individuals aged 45-64 years for axitinib, cabozantinib, pazopanib, and sunitinib, whereas electrolyte disorders AEs were more common in older patients (65-74 years) for sorafenib and lenvatinib. For all EDs documented in ICSRs (excluding missing data), the most common adverse outcome was hospitalization(1429/2674, 53.4%), and the most serious outcome was death/life-threat(281/2674, 10.5%). The prevalence of mortality was highest for sunitinib-related EDs (145/616, 23.5%), excluding missing data (n = 68), followed by cabozantinib-related EDs (20/237, 8.4%), excluding missing data (n = 1). The distribution of time-to-onset of Each drug-related ICSRs was not all the same, and the difference was statistically significant (P = 0.001). With the criteria of ROR, the six MKIs were all significantly associated with electrolyte disorders AEs, the strongest association was the association between cabozantinib and hypermagnesaemia. MKIs have been reported to have significant electrolyte disorders AEs. Patients and physicians need to recognize and monitor these potentially fatal adverse events.

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets.

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.

Multimodal risk prediction with physiological signals, medical images and clinical notes.

The broad adoption of electronic health record (EHR) systems brings us a tremendous amount of clinical data and thus provides opportunities to conduct data-based healthcare research to solve various clinical problems in the medical domain. Machine learning and deep learning methods are widely used in the medical informatics and healthcare domain due to their power to mine insights from raw data. When adapting deep learning models for EHR data, it is essential to consider its heterogeneous nature: EHR contains patient records from various sources including medical tests (e.g. blood test, microbiology test), medical imaging, diagnosis, medications, procedures, clinical notes, etc. Those modalities together provide a holistic view of patient health status and complement each other. Therefore, combining data from multiple modalities that are intrinsically different is challenging but intuitively promising in deep learning for EHR. To assess the expectations of multimodal data, we introduce a comprehensive fusion framework designed to integrate temporal variables, medical images, and clinical notes in EHR for enhanced performance in clinical risk prediction. Early, joint, and late fusion strategies are employed to combine data from various modalities effectively. We test the model with three predictive tasks: in-hospital mortality, long length of stay, and 30-day readmission. Experimental results show that multimodal models outperform uni-modal models in the tasks involved. Additionally, by training models with different input modality combinations, we calculate the Shapley value for each modality to quantify their contribution to multimodal performance. It is shown that temporal variables tend to be more helpful than CXR images and clinical notes in the three explored predictive tasks.