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© 2015 IEEE. Terahertz (THz) spectral properties of human cornea are explored as a function of central corneal thickness (CCT) and corneal water content, and the clinical utility of THz-based corneal water content sensing is discussed. Three candidate corneal tissue water content (CTWC) perturbations, based on corneal physiology, are investigated that affect the axial water distribution and total thickness. The THz frequency reflectivity properties of the three CTWC perturbations were simulated and explored with varying system center frequency and bandwidths (Q-factors ). The modeling showed that at effective optical path lengths on the order of a wavelength the cornea presents a lossy etalon bordered by air at the anterior and the aqueous humor at the posterior. The simulated standing wave peak-to-valley ratio is pronounced at lower frequencies and its effect on acquired data can be modulated by adjusting the bandwidth of the sensing system. These observations are supported with experimental spectroscopic data. The results suggest that a priori knowledge of corneal thickness can be utilized for accurate assessments of corneal tissue water content. The physiologic variation of corneal thickness with respect to the wavelengths spanned by the THz band is extremely limited compared to all other structures in the body making CTWC sensing unique amongst all proposed applications of THz medical imaging.

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Cutaneous lupus erythematosus (CLE) is a disfiguring autoimmune skin disease characterized by an inflammatory infiltrate rich in T cells, which are strongly implicated in tissue damage. How these cells adapt to the skin environment and promote tissue inflammation and damage is not known. In lupus nephritis, we previously identified an inflammatory gene program in kidney-infiltrating T cells that is dependent on HIF-1, a transcription factor critical for the cellular and developmental response to hypoxia as well as inflammation-associated signals. In our present studies using a mouse model of lupus skin disease, we find that skin-infiltrating CD4+ and CD8+ T cells also express high levels of HIF-1. Skin-infiltrating T cells demonstrated a strong cytotoxic signature at the transcript and protein levels, and HIF-1 inhibition abrogated skin and systemic diseases in association with decreased T cell cytotoxic activity. We also demonstrate in human CLE tissue that the T cell-rich inflammatory infiltrate exhibited increased amounts of HIF-1 and a cytotoxic signature. Granzyme B-expressing T cells were concentrated at sites of skin tissue damage in CLE, suggesting relevance of this pathway to human disease.

Competing Interests: None.

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design., (© 2022. The Author(s).)

Context.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established., Objective.—: To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis., Design.—: We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation., Results.—: Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data., Conclusions.—: These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist-augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.

A method to register THz and visible images of cutaneous burn wounds and to calibrate THz image data is presented. Images of partial and full thickness burn wounds in 9 rats were collected over 435 mins. = 7.25 hours following burn induction. A two-step process was developed to reference the unknown structure of THz imaging contrast to the known structure and the features present in visible images of the injury. This process enabled the demarcation of a wound center for each THz image, independent of THz contrast. Threshold based segmentation enabled the automated identification of air (0% reflectivity), brass (100% reflectivity), and abdomen regions within the registered THz images. Pixel populations, defined by the segmentations, informed unsupervised image calibration and contrast warping for display. The registered images revealed that the largest variation in THz tissue reflectivity occurred superior to the contact region at ~0.13%/min. Conversely the contact region showed demonstrated an ~6.5-fold decrease at ~0.02%/min. Exploration of occlusion effects suggests that window contact may affect the measured edematous response., Competing Interests: The authors declare that there are no conflicts of interest related to this article.

We have developed a system for evaluating magnetic resonance imaging of prostate cancer, using patient-specific 3D printed molds to facilitate MR-histology correlation. Prior to radical prostatectomy a patient receives a multiparametric MRI, which an expert genitourinary radiologist uses to identify and contour regions suspicious for disease. The same MR series is used to generate a prostate contour, which is the basis for design of a patient-specific mold. The 3D printed mold contains a series of evenly spaced parallel slits, each of which corresponds to a known MRI slice. After surgery, the patient's specimen is enclosed within the mold, and all whole-mount levels are obtained simultaneously through use of a multi-bladed slicing device. The levels are then formalin fixed, processed, and delivered to an expert pathologist, who identifies and grades all lesions within the slides. Finally, the lesion contours are loaded into custom software, which elastically warps them to fit the MR prostate contour. The suspicious regions on MR can then be directly compared to lesions on histology. Furthermore, the false-negative and false-positive regions on MR can be retrospectively examined, with the ultimate goal of developing methods for improving the predictive accuracy of MRI. This work presents the details of our analysis method, following a patient from diagnosis through the MR-histology correlation process. For this patient MRI successfully predicted the presence of cancer, but true lesion volume and extent were underestimated. Most cancer-positive regions missed on MR were observed to have patterns of low T2 signal, suggesting that there is potential to improve sensitivity.