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Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Nevertheless, the conventional human-based approaches to carrying out industrial radiography are prone to challenges that negatively impact the accuracy and efficiency of defect detection. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these ADR algorithms demand large volumes of qualitative data that should be representative of real-world cases to be expected during deployment. However, the availability of digital X-ray radiography data, especially for open research, is limited by non-disclosure contractual terms in the industry. In this study, we present a pipeline that is capable of modelling synthetic images based on real digital X-ray radiography images. This is achieved through a systematic analysis of the intensity distribution, considering grey value (GV) statistical uniqueness related to exposure conditions used during image acquisition, type of imaged component, material thickness variations, X-ray beam divergence, anode heel effect, scatter radiation, edge delineation, etc. The generated synthetic images were exclusively utilized to train a deep learning model, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96 when tested on real unseen digital X-ray radiography images. The presented methodology is scalable and adaptable, making it suitable for diverse industrial applications.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

The thermographic inspection of buildings is a powerful and non-invasive method for monitoring and diagnosing building performance and structural integrity. It can effectively detect moisture, evaluate heat loss, and assess building’s roofs. The early detection of problems in a building allows owners to fix issues before they become more severe and costly. One of the challenges in automating thermal analysis for building inspections comes in the form of distinguishing between different surfaces. This paper presents an automated process pipeline using coupled thermal and visible images for building inspections to assist inspectors in adapting strategies and methods to discriminate the thermal signatures between different kinds of surfaces. A deep learning method is employed to segment visible images texturally. Thermal images are then analyzed based on the resulting segmentation. Moreover, a multi-modal dataset is introduced, presenting coupled thermal and visible images acquired during multiple building inspections.

Defects in in-service pipelines, including corrosion under insulation (CUI) and thickness loss, pose significant challenges to asset integrity in the oil and gas industry. These defects are particularly hazardous as they often remain unnoticed. The automation of defect detection processes can assist inspectors in reducing analysis time, costs, and human error. However, recent attempts to adopt machine learning for automated defect detection from thermal images have been hindered by limited data availability. This paper presents a novel approach to address this issue by utilizing thermal data augmentation, generating synthetic sub-surface defects via finite element modeling. The resulting synthetic thermal images, combined with real images, are then used to train a deep learning model for the automatic detection of potential defects. Additionally, this study explores the efficacy of synthetic thermal images in enhancing the generalization of the detection model.

(1) Background: Currently, there are no pharmacological treatments that can modify the course of osteoarthritis (OA). For this reason, the present work is focused on generating knowledge for the development of new therapeutic alternatives for the treatment of OA. The objective of this work was to develop an articular hybrid implant with mesenchymal stem cells (MSCs) from sheep. The cells were differentiated into cartilage and bone using a bioabsorbable polymer with 3D printing Technology. (2) Methods: MSCs pre-differentiated to chondrocytes and osteoblasts were seeded on the 3D-printed scaffolds using polylactic acid (PLA). These were later implanted for 3 months in the thoracic ribs area and for 6 months inside the femoral head and outside of the joint capsule. After recovery, we analyzed the expressions of specific markers for bone and cartilage in the implants (3) Results: After 3 months, in lateral implants, the expression for bone markers (OPN, RUNX2) was similar to that of the control; at 6 months, we obtained a higher expression of bone markers in the implants with pre-differentiated MCS to osteoblasts outside and inside the joint. For cartilage markers, three months after the placement of the lateral implant, the expressions of Aggrecan and SOX9 COL2A1 were similar to those of the control, but the expression of COL2A1 was less; at 6 months, the three cartilage markers SOX9, Aggrecan, and COL2A1 showed significant expressions in the implant inside joint with pre-differentiated MCS to chondrocytes. (4) Conclusions: In this study, we demonstrated that the presence of pre-differentiated MSCs in the implants was a determinant factor for the expression of bone- and cartilage-specific markers at three and six months. We managed to generate a practical and easy-to-implement articular surface repair model.

Thermographic has proven to be effective for the early detection of breast cancer and with clinical breast examination (CBE). There are many matrix factorization methods developed for computational thermography that can be used to extract thermal variations across the acquisition time. These methods are often used to summarize thermographic sequences and simultaneously highlight predominant thermal patterns. Finding a single predominant infrared image capturing the prevalent patterns of changes remains a challenging task in the field. This study presents the applications of convex factor analysis combined with the bell-curve membership function embedding approach to tackle this task and generate one image to represent the entire sequence. This low-dimensional (LD) representation of a thermal sequence was then used to extract thermomics and train tuned hyperparameters random forest model for early breast cancer diagnosis. A comparative analysis of different embedding methods and factorization approaches is also provided. The results of the proposed method combining clinical information, and demographics yield 78.9% (75.7% and 85.9%), while the convex-nonnegative matrix factorization (NMF) alone gave 76.9% (73.7% and 86.1%). The result of the proposed method suggests that the embedding can help preserve important thermal patterns, which significantly aid CBE and early detection of breast cancer.

Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR ≥ 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (z ≈ 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT).

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask–RCNN (Mask Region-based Convolutional Neural Networks) and Center–Mask; 2. The independent semantic segmentation methods: U-net and Resnet–U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

Background: The arthroscopic approach to acromioclavicular (AC) dislocation with methods such as AC TightRope fixation has reported radiographic failure rates between 18% and 50% with functional results graded as good or excellent. Our objective was to review the outcomes after arthroscopic fixation for acute AC joint dislocation using the TightRope device. Methods: We reviewed the records of 52 patients, with a mean age of 31 years, who underwent arthroscopic fixation with the TightRope device for acute AC joint dislocation. Outcomes were evaluated using the Constant and University of California, Los Angeles scores. The coracoclavicular (CC) distance before and after surgery was compared by radiography. Results: The mean follow-up period was 36.7 months (range, 6-65 months). Postoperatively, the mean Constant score was 97.13 and the mean University of California, Los Angeles score was 33.2. The CC distance was maintained in 73% of the patients, whereas partial loss of reduction occurred in 19.2% and failure of reduction occurred in 7.7%. Conclusion: Arthroscopic fixation using the TightRope device for acute AC joint dislocation achieves satisfactory clinical outcomes. However, CC reconstruction appears to result in subluxation in cases with AC dislocation for a period of more than 10 days.

The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities.

Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.

Common peroneal nerve injury is present in 40% of knee dislocations, and foot drop is the principal complication. Posterior tibial tendon transfer is a viable solution to replace the function of the anterior tibial tendon (ATT) in the mid-foot. Several techniques for posterior tibial tendon transfer exist today, with variable results reported. However, adding augmentation with side-to-side tenorrhaphy of ATT to the transferred posterior tibial tendon (PTT) enhances anterior tissue balance and load sharing stress between native ATT enthesis and PTT tenodesis, allowing early rehabilitation and improving functional outcomes. Side-to-side tenorrhaphy is performed after PTT tenodesis in the lateral cuneiform to improve reliability in foot drop. This technique allows shorter immobilization time (from 6 to 2 weeks), earlier rehabilitation, sooner weight-bearing, and decreased risk of arthrofibrosis, scar formation, and muscle atrophy.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

Evaluar la evolución clínica, la condroprotección y la reacción inmunológica del trasplante de menisco (TM) con aloinjerto gama irradiado (GI) versus fresco congelado (FC) a veinticuatro meses. Veinte TM mediales en veinte pacientes, se evaluaron escalas de rodilla, Mapeo-T2 y segunda vista artroscópica, así como identificación de reacciones inmunológicas con la medición de citocinas inflamatorias por PCR en sangre y líquido sinovial. Trece trasplantes con injerto FC y siete GI, edad promedio de treinta y dos años. Mejoría significativa en escalas a veinticuatro meses: KOOS (dolor 67.80/79.30; síntomas 60.80/82.10; AVD 8.05/92.40; deportes 37/63.35; CV 28.90/71.30), Lysholm (62.20/85.80), IKDCs (50.17/72.12), EVA (3.35/0.4). El cartílago del compartimento trasplantado se mantuvo dentro de valores normales, sin diferencia a los veinticuatro meses (fémur: 33.43 versus 33.50 ms, p = 0.16) (tibia: 33.57 versus 34.35 ms, p = 0.21). Todos los pacientes mostraron integridad del injerto a los doce meses en la segunda vista artroscópica. Solo se observó aumento en las citoquinas plasmáticas IL-6 e IL-17 en un paciente del grupo GI, sin repercusión clínica. Mejoría clínica, adecuada integración y condroprotección significativa a veinticuatro meses en ambos tipos de injertos.

BackgroundMexico is one of the countries with the highest number of deaths from the COVID-19 pandemic. In spite of this high mortality, in Mexico the number of confirmed cases and diagnostic tests per million population are lower than for other comparable countries, which leads to uncertainty about the actual extent of the pandemic. In Mexico City, healthcare workers represent an important fraction of individuals with SARS-CoV-2 infection. We performed a cross-sectional study whose objective was to estimate the frequency of antibodies to SARS-CoV-2 and identify associated factors in healthcare workers at a large hospital in Mexico City.MethodsWe conducted a serological survey in a non-COVID national referral teaching hospital. The study population included all the personnel that works, in any capacity, in the hospital. From this population we selected a representative sample of 300 individuals. Blood samples were collected and questionnaires were applied between August 10th and September 9th, 2020.ResultsELISA results indicate a serological prevalence of SARS-CoV-2 infection of 13.0%. Working in the janitorial and security groups, having an educational level below a university degree, and living with a larger number of people, were all identified as sociodemographic factors that increase the probability of having SARS-CoV-2 infection.ConclusionsLess favored socioeconomic groups face significantly higher prospects of experiencing SARS-CoV-2 infection and in institutions such as ours, providing janitorial and security workgroups with additional testing and counseling could help to limit the spread of contagion. The rate from the official number of confirmed cases in Mexico City is substantially smaller than the seropositive rate identified in this work.

This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

Hamstring tendon autograft remains a popular graft choice for anterior cruciate ligament reconstruction. Although the technique of hamstring autograft harvest is relatively straightforward, it is critical to pay attention to several technical steps to avoid iatrogenic anatomic or neurovascular damage as well as to reduce the risk of premature amputation of the graft when using a tendon stripper. We describe a technique of hamstring autograft harvesting using only 2 anatomic references that makes it a simple and reproducible technique for surgeons, especially those in training.

Abstract Composite materials are being successfully used in aeronautics for more than two decades. This great success is mainly because this kind of materials is typically lighter when compared to metals and more resistant to corrosion. Resistance and stiffness of composite materials are usually related to their fiber orientation. The development of non-destructive methods is required to assess this kind of property without damaging the sample. In this work, a non-destructive method based on infrared thermography is presented for fiber orientation assessment. More specifically, two approaches are presented and results are compared for fiber orientation assessment of randomly oriented strand parts. The first one is a point-by-point inspection based on pulsed thermal ellipsometry and the second one is a line approach based on flying laser spot inspection combined with neural artificial networks. Results showed that the second approach provides similar accuracy when compared to the first one; however, it is much faster which makes the second approach very interesting for industrial applications.

Abstract Background Treatment of severe or chronic skin wounds is an important challenge facing medicine and a significant health care burden. Proper wound healing is often affected by bacterial infection; where biofilm formation is one of the main risks and particularly problematic because it confers protection to microorganisms against antibiotics. One avenue to prevent bacterial colonization of wounds is the use of silver nanoparticles (AgNPs); which have proved to be effective against non-multidrug-resistant and multidrug-resistant bacteria. In addition, the use of mesenchymal stem cells (MSC) is an excellent option to improve wound healing due to their capability for differentiation and release of relevant growth factors. Finally, radiosterilized pig skin (RPS) is a biomatrix successfully used as wound dressing to avoid massive water loss, which represents an excellent carrier to deliver MSC into wound beds. Together, AgNPs, RPS and MSC represent a potential dressing to control massive water loss, prevent bacterial infection and enhance skin regeneration; three essential processes for appropriate wound healing with minimum scaring. Results We synthesized stable 10 nm-diameter spherical AgNPs that showed 21- and 16-fold increase in bacteria growth inhibition (in comparison to antibiotics) against clinical strains Staphylococcus aureus and Stenotrophomonas maltophilia, respectively. RPS samples were impregnated with different AgNPs suspensions to develop RPS-AgNPs nanocomposites with different AgNPs concentrations. Nanocomposites showed inhibition zones, in Kirby–Bauer assay, against both clinical bacteria tested. Nanocomposites also displayed antibiofilm properties against S. aureus and S. maltophilia from RPS samples impregnated with 250 and 1000 ppm AgNPs suspensions, respectively. MSC were isolated from adipose tissue and seeded on nanocomposites; cells survived on nanocomposites impregnated with up to 250 ppm AgNPs suspensions, showing 35% reduction in cell viability, in comparison to cells on RPS. Cells on nanocomposites proliferated with culture days, although the number of MSC on nanocomposites at 24 h of culture was lower than that on RPS. Conclusions AgNPs with better bactericide activity than antibiotics were synthesized. RPS-AgNPs nanocomposites impregnated with 125 and 250 ppm AgNPs suspensions decreased bacterial growth, decreased biofilm formation and were permissive for survival and proliferation of MSC; constituting promising multi-functional dressings for successful treatment of skin wounds.

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

Dimensional reduction methods have significantly improved the simplification of Pulsed Thermography (PT) data while improving the accuracy of the results. Such approaches reduce the quantity of data to analyze and improve the contrast of the main defects in the samples contributed to their popularity. Many works have been proposed in the literature mainly based on improving the Principal Component Thermography (PCT). Recently the Independent Component Analysis (ICA) has been a topic of attention. Many different approaches have been proposed in the literature to solve the ICA. In this paper, we investigated several recent ICA methods and evaluated their influence on PT data compared with the state-of-the-art methods. We conducted our evaluation on reference CFRP samples with known defects. We found that ICA outperform PCT for small and deep defects. For other defects ICA results are often not far from the results obtained by PCT. However, the frequency of acquisition and the ICA methods have a great influence on the results.

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.

It was recently demonstrated that a coplanar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, finite element (FE) modeling was employed to simulate the electric field distribution from a coplanar capacitive sensor and the way it interacts with a nonconducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with subsurface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.

Only select tissues and organs are able to spontaneously regenerate after disease or trauma, and this regenerative capacity diminishes over time. Human stem cell research explores therapeutic regenerative approaches to treat various conditions. Mesenchymal stem cells (MSCs) are derived from adult stem cells; they are multipotent and exert anti-inflammatory and immunomodulatory effects. They can differentiate into multiple cell types of the mesenchyme, for example, endothelial cells, osteoblasts, chondrocytes, fibroblasts, tenocytes, vascular smooth muscle cells, and sarcomere muscular cells. MSCs are easily obtained and can be cultivated and expanded in vitro; thus, they represent a promising and encouraging treatment approach in orthopedic surgery. Here, we review the application of MSCs to various orthopedic conditions, namely, orthopedic trauma; muscle injury; articular cartilage defects and osteoarthritis; meniscal injuries; bone disease; nerve, tendon, and ligament injuries; spinal cord injuries; intervertebral disc problems; pediatrics; and rotator cuff repair. The use of MSCs in orthopedics may transition the practice in the field from predominately surgical replacement and reconstruction to bioregeneration and prevention. However, additional research is necessary to explore the safety and effectiveness of MSC treatment in orthopedics, as well as applications in other medical specialties.

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

This work is focused on the use of line-scan thermography (LST) method for the inspection of an ancient book cover. Three widely used image post-processing techniques (i.e., pulsed phase thermography, partial least square thermography and principal component thermography) were applied to the acquired thermal sequences. Flash thermography (FT) anticipated the LST results in order to have a comparison of the results. It was concluded that LST is an effective technique for paper-based materials, and it can additionally provide a higher image contrast if compared to classical FT technique.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was given.

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

Bicycle frames made of carbon fibre are extremely popular for high-performance cycling due to the stiffness-to-weight ratio, which enables greater power transfer. However, products manufactured using carbon fibre are sensitive to impact damage. Therefore, intelligent nondestructive evaluation is a required step to prevent failures and ensure a secure usage of the bicycle. This work proposes an inspection method based on active thermography, a proven technique successfully applied to other materials. Different configurations for the inspection are tested, including power and heating time. Moreover, experiments are applied to a real bicycle frame with generated impact damage of different energies. Tests show excellent results, detecting the generated damage during the inspection. When the results are combined with advanced image post-processing methods, the SNR is greatly increased, and the size and localization of the defects are clearly visible in the images.

La lesión medular (LM) es un problema que afecta sobre todo a la población en edad laboral y, por lo tanto, sus repercusiones rebasan el ámbito familiar. La LM es irreversible para la mitad de las víctimas y en la actualidad los tratamientos existentes consisten en la asistencia y la estabilización espinal. Con el reconocimiento de la existencia de células madre (CM), el tratamiento de la LM ha recibido otro enfoque. Las CM se encargan de la renovación de los tejidos durante la vida del individuo y su reparación en caso de lesión. Las CM más atractivas desde el punto de vista terapéutico son las capaces de generar diversos tejidos, obtenibles con facilidad, y cuya manipulación es aceptable en términos éticos. En este artículo se presentan algunos de los estudios realizados con CM de diversos orígenes y su aplicación al tratamiento de la LM.Spinal cord injury (SCI) is a trauma problem striking mainly working age adults, therefore affecting society beyond the victim’s family circle. Most of the victims of SCI will never recover; therapy for this type of injury consists basically on spinal cord support and stabilization. With the discovery of stem cells (SC), SCI treatment has been given another chance. Stem cells are responsible for tissue renewal throughout the individual’s life, as well as tissue repair when needed. From the therapeutic point of view, the most appealing SC are those capable of generating a variety of tissues, those easily harvested, and finally, those ethically unquestioned. This article summarizes some studies carried with SC of various origins and their application to SCI treatment.

The Integrin β1 family is the major receptors of the Extracellular matrix (ECM), and the synthesis and degradation balance of ECM is seriously disrupted during Osteoarthritis (OA). In this scenario, integrins modify their pattern expression and regulate chondrocyte differentiation in the articular cartilage. Members of the Transforming growth factor beta (Tgf-β) Superfamily, such as Growth differentiation factor 5 (Gdf-5) and Bone morphogenetic protein 7 (Bmp-7), play a key role in joint formation and could regulate the integrin expression during chondrocyte differentiation and osteoarthritis progression in an experimental OA rat model. Decrease of α5 integrin expression in articular cartilage was related with chondrocyte dedifferentiation during OA progression, while increase of α1, α2, and α3 integrin expression was related with fibrous areas in articular cartilage during OA. Hypertrophic chondrocytes expressed αV integrin and was increased in the articular cartilage of rats with OA. Integrin expression during chondrocyte differentiation was also analyzed in a micromass culture system of mouse embryo mesenchymal cells, micromass cultures was treated with Gdf-5 or Bmp-7 for 4 and 6 days, respectively. Gdf-5 induced the expression of the α5 sub-unit, while Bmp-7 induced the expression of the αV sub-unit. This suggests a switch in signaling for prehypertrophic chondrocyte differentiation towards hypertrophy, where Gdf-5 could maintain the articular chondrocyte phenotype and Bmp-7 would induce hypertrophy. Decrease of Ihh expression during late stages of OA in rat model suggest that the ossification in OA rat knees and endochondral ossification could be activated by Bmp-7 and αV integrin in absence of Ihh. Thus, chondrocyte phenotype in articular cartilage is similar to prehypetrophic chondrocyte in growth plate, and is preserved due to the presence of Indian hedgehog (Ihh), Gdf-5 and α5 integrin to maintain articular cartilage and prevent hypertrophy.