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<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>

Durch ihren Einfluß auf die Genexpression reguliert die zirkadiane Uhr physiologische Funktionen vieler Organe. Obwohl der zugrundeliegende allgemeine Uhrmechanismus gut untersucht ist, bestehen noch viele Unklarheiten über die gewebespezifische Regulation zirkadianer Gene. Neben ihrer gemeinsamen 24-h-Periode im Expressionsmuster unterscheiden diese sich darin, zu welcher Tageszeit sie am höchsten exprimiert sind und in welchem Gewebe sie oszillieren. Mittels Überrepräsentationsanalyse lassen sich Bindungsstellen von Transkriptionsfaktoren identifizieren, die an der Regulation ähnlich exprimierter Gene beteiligt sind. Um diese Methode auf zirkadiane Gene anzuwenden, ist es nötig, Untergruppen ähnlich exprimierter Gene genau zu definieren und Vergleichsgene passend auszuwählen. Eine hierarchische Methode zur Kontrolle der FDR hilft, aus der daraus entstehenden Menge vieler Untergruppenvergleiche signifikante Ergebnisse zu filtern. Basierend auf mit Microarrays gemessenen Zeitreihen wurde durch Promotoranalyse die gewebespezifische Regulation von zirkadianen Genen zweier Zelltypen in Mäusen untersucht. Bindungsstellen der Transkriptionsfaktoren CLOCK:BMAL1, NF-Y und CREB fanden sich in beiden überrepräsentiert. Diesen verwandte Transkriptionsfaktoren mit spezifischen Komplexierungsdomänen binden mit unterschiedlicher Stärke an Motivvarianten und arrangieren dabei Interaktionen mit gewebespezifischeren Regulatoren (z.B. HOX, GATA, FORKHEAD, REL, IRF, ETS Regulatoren und nukleare Rezeptoren). Vermutlich beeinflußt dies den Zeitablauf der Komplexbildung am Promotor zum Transkriptionsstart und daher auch gewebespezifische Transkriptionsmuster. In dieser Hinsicht sind der Gehalt an Guanin (G) und Cytosin (C) sowie deren CpG-Dinukleotiden wichtige Promotoreigenschaften, welche die Interaktionswahrscheinlichkeit von Transkriptionsfaktoren steuern. Grund ist, daß die Affinitäten, mit denen Regulatoren zu Promotoren hingezogen werden, von diesen Sequenzeigenschaften abhängen. / A circadian clock in peripheral tissues regulates physiological functions through gene expression timing. However, despite the common and well studied core clock mechanism, understanding of tissue-specific regulation of circadian genes is marginal. Overrepresentation analysis is a tool to detect transcription factor binding sites that might play a role in the regulation of co-expressed genes. To apply it to circadian genes that do share a period of about 24 hours, but differ otherwise in peak phase timing and tissue-specificity of their oscillation, clear definition of co-expressed gene subgroups as well as the appropriate choice of background genes are important prerequisites. In this setting of multiple subgroup comparisons, a hierarchical method for false discovery control reveals significant findings. Based on two microarray time series in mouse macrophages and liver cells, tissue-specific regulation of circadian genes in these cell types is investigated by promoter analysis. Binding sites for CLOCK:BMAL1, NF-Y and CREB transcription factors are among the common top candidates of overrepresented motifs. Related transcription factors of BHLH and BZIP families with specific complexation domains bind to motif variants with differing strengths, thereby arranging interactions with more tissue-specific regulators (e.g. HOX, GATA, FORKHEAD, REL, IRF, ETS regulators and nuclear receptors). Presumably, this influences the timing of pre-initiation complexes and hence tissue-specific transcription patterns. In this respect, the content of guanine (G) and cytosine (C) bases as well as CpG dinucleotides are important promoter properties directing the interaction probability of regulators, because affinities with which transcription factors are attracted to promoters depend on these sequence characteristics.