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Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability ofex vivomouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDS_e), and late OCT correlation decay speed (OCDS_l) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making. 

Abstract Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization‐sensitive optical coherence tomography (PS‐OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS‐OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross‐testing accuracy of 91.53%. These results showed the improved precision by PS‐OCT in guiding epidural anesthesia needle placement. 

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies. 

Double cropping winter camelina (Camelina sativa (L.) Crantz) with maize (Zea mays L.) and soybean (Glycine max L. (Merr.)) is a diversification strategy in northern regions. Winter camelina is reported to have low nutrient requirements, but its nitrogen (N) needs are not well understood. Studies on winter camelina without (Study 1) and with (Study 2) N fertilization were used to compare growth, seed yield and quality, and effects on soil N. Study 1 was conducted from 2015 to 2017 at one location and Study 2 was conducted from 2018 to 2020 at two locations. Grain yield was as much as six times higher in Study 2 compared with Study 1; averaged across treatments, winter camelina yielded 1157 kg ha−1 in Study 2 and 556 kg ha−1 without N. Oil and protein content ranged from 26.4 to 27.2% and 19.4 to 27.1%, respectively, in Study 1 and from 31.7 to 35.9% and 14.9 to 20.8%, respectively, in Study 2. N fertilizer increased winter camelina biomass and grain yield and soil N when double cropped with maize and soybean. Our study indicates that grain yield of winter camelina respond positively to N fertilization in a northern location. The drawback of this is the increase in residual soil N, which suggests the need for further research to balance agronomic practices with environmental outcomes. 

ABSTRACT Ovarian cancer (OvCa) remains the leading cause of gynecological cancer mortality, with most patients developing chemoresistance. Drug repurposing offers promising alternatives, with mebendazole (MBZ) showing anticancer activity. This study evaluates MBZ efficacy using Spectral Domain Optical Coherence Tomography (SD‐OCT). We conducted longitudinal imaging of 40 wild‐type (WT) and cisplatin‐resistant (CPR) OVCAR8 multicellular tumor spheroids over 11 days. Four analyses were performed: volume analysis, optical attenuation analysis, uniformity analysis, and texture feature analysis. Volume analysis showed MBZ reduced spheroid growth in both groups, with greater effects in CPR‐MCTs. Optical attenuation analysis revealed increased necrotic tissue ratios in treated spheroids. Uniformity analysis demonstrated MBZ targets heterogeneous tissues effectively. Texture analysis identified significant structural changes, with 866 altered features in CPR spheroids versus 124 in WT spheroids. Cell viability assays confirmed MBZ's effectiveness against standard and chemo‐resistant OVCAR8 tumors. This study demonstrates SD‐OCT's utility for noninvasive therapy monitoring in 3D cancer models. 

Optical coherence tomography (OCT) imaging enables high resolution visualization of sub-surface tissue microstructures. However, OCT image analysis using deep learning is hampered by limited diverse training data to meet performance requirements and high inference latency for real-time applications. To address these challenges, we developed Octascope, a lightweight domain-specific convolutional neural network (CNN) - based model designed for OCT image analysis. Octascope was pre-trained using a curriculum learning approach, which involves sequential training, first on natural images (ImageNet), then on OCT images from retinal, abdominal, and renal tissues, to progressively acquire transferable knowledge. This multi-domain pre-training enables Octascope to generalize across varied tissue types. In two downstream tasks, Octascope demonstrated notable improvements in predictive accuracy compared to alternative approaches. In the epidural tissue detection task, our method surpassed single-task learning with fine-tuning by 9.13% and OCT-specific transfer learning by 5.95% in accuracy. Octascope outperformed VGG16 and ResNet50 by 5.36% and 6.66% in a retinal diagnosis task, respectively. In comparison to a Transformer-based OCT foundation model - RETFound, Octascope delivered 2 to 4.4 times faster inference speed with slightly better predictive accuracies in both downstream tasks. Octascope represented a significant advancement for OCT image analysis by providing an effective balance between computational efficiency and diagnostic accuracy for real-time clinical applications.