Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning

In vitro and in vivo studies of the therapeutic potential of Tinospora crispa extracts in osteoarthritis: Targeting oxidation, inflammation, and chondroprotection

ETHNOPHARMACOLOGICAL RELEVANCE

The increasing incidence of osteoarthritis (OA), especially among the elderly population, highlights the need for more efficacious treatments that go beyond mere symptomatic relief. Tinospora crispa (L.) Hook. f. & Thomson (TC) boasts a rich traditional heritage, widespread use in Ayurveda, traditional Chinese medicine (TCM), and diverse indigenous healing practices throughout Southeast Asia for treating arthritis, rheumatism, fever, and inflammation.

AIM OF THE STUDY

This study investigates the anti-inflammatory and chondroprotective potential of TC stem extracts, including ethanolic TC extract (ETCE) and aqueous TC extract (ATCE), in modulating OA pathogenesis through in vitro and in vivo approaches.

MATERIALS AND METHODS

The study utilized LC-MS/MS to identify key compounds in TC stem extracts. In vitro experiments assessed the antioxidative and anti-inflammatory properties of ETCE and ATCE in activated macrophages, while an in vivo monoiodoacetate (MIA)-induced OA rat model evaluated the efficacy of ETCE treatment. Key markers of oxidative stress, such as superoxide dismutase (SOD) and catalase (CAT), were assessed alongside pro-inflammatory cytokines TNF-α and IL-1β, and matrix-degrading enzymes, matrix metalloproteinase (MMP 13 and MMP 3), to evaluate the therapeutic effects of TC stem extracts on OA.

RESULTS

Chemical profiling of the extracts was conducted using LC-MS/MS in positive ionization, identifying seven compounds, including pseudolaric acid B, stylopine, and reticuline, which were reported for the first time in this species. The study utilized varying concentrations of TC stem extracts, specifically 6.25-25 μg/mL for in vitro assays and 500 mg/kg for in vivo studies. Our findings also revealed that both ETCE and ATCE exhibit dose-dependent reduction in reactive oxygen species (41%-52%) and nitric oxide (NO) levels (50% and 72%), with ETCE displaying superior antioxidative efficacy and marked anti-inflammatory properties, significantly reducing TNF-α and IL-6 at concentrations above 12.5 μg/mL. In the MIA-induced OA rat model, ETCE treatment notably outperformed ATCE, markedly lowering TNF-α (1.91 ± 0.37 pg/mL) and IL-1β (26.30 ± 3.68 pg/mL) levels and effectively inhibiting MMP 13 and MMP 3 enzymes. Furthermore, macroscopic and histopathological assessments, including ICRS scoring and OARSI grading, indicate that TC stem extracts reduce articular damage and proteoglycan loss in rat knee cartilage. These results suggest that TC stem extracts may play a role in preventing cartilage degradation and potentially alleviating inflammation and pain associated with OA, though further studies are needed to confirm these effects.

CONCLUSION

This study highlights the potential of TC stem extracts as a novel, chondroprotective therapeutic avenue for OA management. By targeting oxidative stress, pro-inflammatory cytokines, and cartilage-degrading enzymes, TC stem extracts promise to prevent cartilage degradation and alleviate inflammation and pain associated with OA.

Pathological progression of osteoarthritis: a perspective on subchondral bone

Osteoarthritis (OA) is a degenerative bone disease associated with aging. The rising global aging population has led to a surge in OA cases, thereby imposing a significant socioeconomic burden. Researchers have been keenly investigating the mechanisms underlying OA. Previous studies have suggested that the disease starts with synovial inflammation and hyperplasia, advancing toward cartilage degradation. Ultimately, subchondral-bone collapse, sclerosis, and osteophyte formation occur. This progression is deemed as "top to bottom." However, recent research is challenging this perspective by indicating that initial changes occur in subchondral bone, precipitating cartilage breakdown. In this review, we elucidate the epidemiology of OA and present an in-depth overview of the subchondral bone's physiological state, functions, and the varied pathological shifts during OA progression. We also introduce the role of multifunctional signal pathways (including osteoprotegerin (OPG)/receptor activator of nuclear factor-kappa B ligand (RANKL)/receptor activator of nuclear factor-kappa B (RANK), and chemokine (CXC motif) ligand 12 (CXCL12)/CXC motif chemokine receptor 4 (CXCR4)) in the pathology of subchondral bone and their role in the "bottom-up" progression of OA. Using vivid pattern maps and clinical images, this review highlights the crucial role of subchondral bone in driving OA progression, illuminating its interplay with the condition.

Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning

The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that:1)SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(P < 0.01).2)In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (P < 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed.3)Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively. In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice.

Open-source pipeline for automatic segmentation and microstructural analysis of murine knee subchondral bone

μCT images are commonly analysed to assess changes in bone density and microstructure in preclinical murine models. Several platforms provide automated analysis of bone microstructural parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop an automated end-to-end pipeline, combining segmentation and microstructural analysis, to evaluate subchondral bone in the mouse proximal knee.

METHODS

A segmented dataset of μCT scans from 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice was used to train a U-Net type architecture to automate segmentation of the subchondral trabecular bone. These segmentations were used in tandem with the original scans as input for microstructural analysis along with thresholded trabecular bone. Manually and U-Net segmented ROIs were fed into two available pipelines for microstructural analysis: the ITKBoneMorphometry library and CTan (SKYSCAN). Outcome parameters were compared between pipelines, including: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV).

RESULTS

There was good agreement for all bone measures comparing the manual and U-Net pipelines utilizing ITK (R = 0.88-0.98) and CTAn (R = 0.91-0.98). ITK and CTAn showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R = 0.9-0.98). However, limited agreement was seen between TbN (R = 0.73) and TbSb (R = 0.59) due to methodological differences in how spacing is evaluated. Microstructural parameters generated from manual and automatic segmentations showed high correlation across all measures. Using the CTAn pipeline yielded strong R² values (0.83-0.96) and very strong agreement based on ICC (0.90-0.98). The ITK pipeline yielded similarly high R² values (0.91-0.96, except for TbN (0.77)), and ICC values (0.88-0.98). The automated segmentations yield lower average values for BV, TV and BV/TV (ranging from 14 % to 6.3 %), but differences were not found to be influenced by the mean ROI values.

CONCLUSIONS

This integrated pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone microstructure. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of microstructural analysis of trabecular bone across different research groups.

Artificial intelligence in veterinary diagnostic imaging: A literature review

Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.

High-resolution micro-CT for 3D infarct characterization and segmentation in mice stroke models

Characterization of brain infarct lesions in rodent models of stroke is crucial to assess stroke pathophysiology and therapy outcome. Until recently, the analysis of brain lesions was performed using two techniques: (1) histological methods, such as TTC (Triphenyltetrazolium chloride), a time-consuming and inaccurate process; or (2) MRI imaging, a faster, 3D imaging method, that comes at a high cost. In the last decade, high-resolution micro-CT for 3D sample analysis turned into a simple, fast, and cheaper solution. Here, we successfully describe the application of brain contrasting agents (Osmium tetroxide and inorganic iodine) for high-resolution micro-CT imaging for fine location and quantification of ischemic lesion and edema in mouse preclinical stroke models. We used the intraluminal transient MCAO (Middle Cerebral Artery Occlusion) mouse stroke model to identify and quantify ischemic lesion and edema, and segment core and penumbra regions at different time points after ischemia, by manual and automatic methods. In the transient-ischemic-attack (TIA) mouse model, we can quantify striatal myelinated fibers degeneration. Of note, whole brain 3D reconstructions allow brain atlas co-registration, to identify the affected brain areas, and correlate them with functional impairment. This methodology proves to be a breakthrough in the field, by providing a precise and detailed assessment of stroke outcomes in preclinical animal studies.

Structural clues to articular calcified cartilage function: A descriptive review of this crucial interface tissue

Articular calcified cartilage (ACC) has been dismissed, by some, as a remnant of endochondral ossification without functional relevance to joint articulation or weight-bearing. Recent research indicates that morphologic and metabolic ACC features may be important, reflecting knee joint osteoarthritis (OA) predisposition. ACC is less investigated than neighbouring joint tissues, with its component chondrocytes and mineralised matrix often being either ignored or integrated into analyses of hyaline articular cartilage and subchondral bone tissue respectively. Anatomical variation in ACC is recognised between species, individuals and age groups, but the selective pressures underlying this variation are unknown. Consequently, optimal ACC biomechanical features are also unknown as are any potential locomotory roles. This review collates descriptions of ACC anatomy and biology in health and disease, with a view to revealing its structure/function relationship and highlighting potential future research avenues. Mouse models of healthy and OA joint ageing have shown disparities in ACC load-induced deformations at the knee joint. This raises the hypothesis that ACC response to locomotor forces over time may influence, or even underlie, the bony and hyaline cartilage symptoms characteristic of OA. To effectively investigate the ACC, greater resolution of joint imaging and merging of hierarchical scale data will be required. An appreciation of OA as a 'whole joint disease' is expanding, as is the possibility that the ACC may be a key player in healthy ageing and in the transition to OA joint pathology.

Resolving complex cartilage structures in developmental biology via deep learning-based automatic segmentation of X-ray computed microtomography images

The complex shape of embryonic cartilage represents a true challenge for phenotyping and basic understanding of skeletal development. X-ray computed microtomography (μCT) enables inspecting relevant tissues in all three dimensions; however, most 3D models are still created by manual segmentation, which is a time-consuming and tedious task. In this work, we utilised a convolutional neural network (CNN) to automatically segment the most complex cartilaginous system represented by the developing nasal capsule. The main challenges of this task stem from the large size of the image data (over a thousand pixels in each dimension) and a relatively small training database, including genetically modified mouse embryos, where the phenotype of the analysed structures differs from the norm. We propose a CNN-based segmentation model optimised for the large image size that we trained using a unique manually annotated database. The segmentation model was able to segment the cartilaginous nasal capsule with a median accuracy of 84.44% (Dice coefficient). The time necessary for segmentation of new samples shortened from approximately 8 h needed for manual segmentation to mere 130 s per sample. This will greatly accelerate the throughput of μCT analysis of cartilaginous skeletal elements in animal models of developmental diseases.