Tracking dendritic cells: use of an in situ method to label all blood leukocytes

Here we describe an in situ procedure with a labeling index (percent of labeled blood leukocytes) >98%, which is high enough to permit the direct tracking of dendritic cell (DC) precursors from blood into lymphoid tissues, while circumventing the pitfalls associated with in vitro labeling. DC and lymphocytes have similar blood to afferent lymph migratory capabilities. This method has additional applications in tracking other rare cell populations in both normal and pathological state

Influence of body condition score and ultrasound-determined thickness of body fat deposit in holstein-friesian cows on the risk of lameness developing

The aim of this study was to examine the correlations between ultrasound measurement of thickness of fat over the tuber ischiadicum (TFT), body condition scoring (BCS) and the risk of lameness developing in Holstein-Friesian dairy cows. The 100 cows were enrolled from a population of dry cows on one farm. TFT was measured with ultrasound, and BCS and locomotion score were determined during lactation. Of the 100 cows, 31% developed lameness during lactation. The highest proportion of lame cows was in cows with BCS≥4.25 (66.7%). The risk of lameness developing was higher in cows with BCS≥4.25 (OR=7) and ≤3.25 (OR=2) than in cows with optimal BCS=3.75. Cows in the lower TFT quartile had a higher proportion of lameness, but not those in the upper quartile. TFT may have some value as a predictor of lameness in thin cows. The best prediction of lameness in both fat and thin cows (ROCAUC=0.8725, P<0.01) occurred when both BCS and TFT values were used together. The risk of developing lameness was positively correlated with BCS, negatively correlated with TFT and negatively correlated with their interaction. For fat cows, BCS assessment is a suitably strong predictor of lameness. In normal or thin cows, lameness prediction required the combination of both BCS and TFT measurements

An Archival Framework for Sharing of Cultural Heritage 3D Survey Data: OpenHeritage3D.org

Photogrammetry and LiDAR have become increasingly accessible methods for documentation of Cultural Heritage sites. Academic and government agencies recognize the utility of high-resolution 3D models supporting long-term asset management through visualization, conservation planning, and change detection. Though detailed models can be created with increasing ease, their potential for future use can be constrained by a lack of accompanying topographic data, data collector skill level, and incomplete recording of the key metadata and paradata which make such survey data useful to future endeavors. In this paper, informed by various international survey organizations and data archives, we present a framework to record and communicate Cultural Heritage - focusing on architectures based on 3D metric survey - to first describe the data and metadata which should be included by surveyors to enable data usage and to communicate the expected utility of this data

Orthodontics in the era of big data analytics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/1/ocr12279_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/2/ocr12279.pd

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Integration heterogener medizinischer und biologischer Daten in elektronische Patientenakten

The shortage of data for patients with chronic and other diseases and previous medical treatments shows significant weakness in the diagnosis and treatment of patients. Due to the healthcare system insufficiency, patients with comorbidities might not survive the diseases, especially when the disease is novel. The lack of information on patients' genetic disorders, especially when they are unaware of them, also contributes to increased patient deaths. This conveys the necessity to integrate medical and health data with various biological omics and other data, especially in pandemic circumstances. Patients' health data matters are apparent, but they are stored in multiple hospitals and health systems such as electronic health records (EHRs), healthcare institutions, and laboratories. Furthermore, biological data are often not integrated and cannot be used by patients, physicians, and specialists to treat particular diseases. Although the urgent need for healthcare and medical data integration is apparent, personal data protection regulations are severe. They do not allow much progress in the area without implementing security and privacy standards for patient healthcare data. One solution for this issue is setting a personal health record (PHR) as an integrative system for the patient. Many ontological frameworks have been proposed to unify the record formats, but none of them is accepted as a healthcare standard. The efforts toward approving the Health Level Seven (HL7) standards and the common medical coding systems ensure further data integration. Some efforts are made to associate particular diseases with data obtained from external environmental sensors that measure disease-associated data. Using these data, which are called exposome, the increasing symptoms of particular diseases influenced by external factors can be clarified. This paper suggests a cloud-based model for integrating healthcare and medical data from different sources such as EHRs, health information systems, and measurement sensors into the PHR as the first stage toward integrating patient health data. Besides the patients' personal and clinical data, various omics data should be integrated for improved individualized disease prognosis and treatment of the patients. These data are stored in the cloud following the required data security and privacy standards.Der Mangel an Daten über PatientInnen mit chronischen und anderen Krankheiten und medizinischen Vorbehandlungen zeigt eine erhebliche Schwäche bei der Diagnose und Behandlung vieler PatientInnen auf. Aufgrund der Unzulänglichkeit des Gesundheitssystems kann es sein, dass PatientInnen mit Komorbiditäten die Krankheiten nicht überleben, insbesondere wenn es sich um eine neue Krankheit handelt. Der Mangel an Informationen über die genetischen Störungen der PatientInnen, vor allem wenn sie sich derer nicht bewusst sind, trägt ebenfalls zu einer erhöhten PatientInnensterblichkeit bei. Daraus ergibt sich die Notwendigkeit, medizinische und gesundheitliche Daten mit verschiedenen biologischen Omics und anderen Daten zu integrieren, insbesondere unter Pandemiebedingungen. Die Relevanz des Themas der Gesundheitsdaten von PatientInnen ist offensichtlich, aber die Daten werden in verschiedenen Krankenhäusern und Gesundheitssystemen wie der elektronischen Patientenakte (ePA), Gesundheitseinrichtungen und Laboren gespeichert. Darüber hinaus werden biologische Daten oft nicht integriert und können von PatientInnen, ÄrztInnen und SpezialistInnen nicht zur Behandlung bestimmter Krankheiten genutzt werden. Obwohl der dringende Bedarf an der Integration von Gesundheits- und medizinischen Daten offensichtlich ist, sind die Vorschriften zum Schutz personenbezogener Daten streng. Sie lassen keine großen Fortschritte in diesem Bereich zu, ohne dass Sicherheits- und Datenschutzstandards für Gesundheitsdaten von PatientInnen eingeführt werden. Eine Lösung für dieses Problem ist die Einrichtung eines Personal Health Records (PHR) als integratives System für die PatientInnen. Viele ontologische Rahmenwerke wurden vorgeschlagen, um die Datensatzformate zu vereinheitlichen, aber keines von ihnen ist als Standard im Gesundheitswesen anerkannt. Die Bemühungen um die Annahme der Health Level Seven (HL7)-Standards und der gängigen medizinischen Codierungssysteme sorgen für eine weitere Datenintegration. Es gibt Bestrebungen, bestimmte Krankheiten mit Daten in Verbindung zu bringen, die von externen Umweltsensoren gewonnen werden, die krankheitsassoziierte Daten messen. Anhand dieser Daten, die als Exposom bezeichnet werden, können die zunehmenden Symptome bestimmter Krankheiten, die durch externe Faktoren beeinflusst werden, geklärt werden. In diesem Artikel wird ein Cloud-basiertes Modell zur Integration von Gesundheits- und medizinischen Daten aus verschiedenen Quellen wie der ePA, Gesundheitsinformationssystemen und Messsensoren in den PHR als erster Schritt zur Integration von Gesundheitsdaten vorgeschlagen. Neben den persönlichen und klinischen Daten der PatientInnen sollen auch verschiedene Omics-Daten integriert werden, um eine bessere individualisierte Krankheitsprognose und Behandlung der PatinentInnen zu ermöglichen. Diese Daten werden in der Cloud unter Einhaltung der erforderlichen Datensicherheits- und Datenschutzstandards gespeichert