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Mesenchymal stem cells (MSC) hold great potential for regenerative medicine because of their ability for self‐renewal and differentiation into tissue‐specific cells such as osteoblasts, chondrocytes, and adipocytes. MSCs orchestrate tissue development, maintenance and repair, and are useful for musculoskeletal regenerative therapies to treat age‐related orthopedic degenerative diseases and other clinical conditions. Importantly, MSCs produce secretory factors that play critical roles in tissue repair that support both engraftment and trophic functions (autocrine and paracrine). The development of uniform protocols for both preparation and characterization of MSCs, including standardized functional assays for evaluation of their biological potential, are critical factors contributing to their clinical utility. Quality control and release criteria for MSCs should include cell surface markers, differentiation potential, and other essential cell parameters. For example, cell surface marker profiles (surfactome), bone‐forming capacities in ectopic and orthotopic models, as well as cell size and granularity, telomere length, senescence status, trophic factor secretion (secretome), and immunomodulation, should be thoroughly assessed to predict MSC utility for regenerative medicine. We propose that these and other functionalities of MSCs should be characterized prior to use in clinical applications as part of comprehensive and uniform guidelines and release criteria for their clinical‐grade production to achieve predictably favorable treatment outcomes for stem cell therapy. Stem Cells Translational Medicine 2017;6:2173–2185 Adopting a multifaceted approach for characterizing mesenchymal stem cells (MSCs) is critical for the selection of best‐in‐class cells for therapeutic use. Assaying for surfactome, secretome, self‐renewal, colony formation, trophic factor secretion, and multilineage differentiation, all form part of an assessment of cellular naivety or “stemness.” When this status is combined with functional assays such as immunomodulation, and other key parameters of cellular health that include telomere length, they collectively help provide a robust assessment of characteristics that are increasingly becoming important indicators of clinical efficacy.

The suitability of human mesenchymal stem cells (hMSCs) in regenerative medicine relies on retention of their proliferative expansion potential in conjunction with the ability to differentiate toward multiple lineages. Successful utilisation of these cells in clinical applications linked to tissue regeneration requires consideration of biomarker expression, time in culture and donor age, as well as their ability to differentiate towards mesenchymal (bone, cartilage, fat) or non-mesenchymal (e.g., neural) lineages. To identify potential therapeutic suitability we examined hMSCs after extended expansion including morphological changes, potency (stemness) and multilineage potential. Commercially available hMSC populations were expanded in vitro for > 20 passages, equating to > 60 days and > 50 population doublings. Distinct growth phases (A-C) were observed during serial passaging and cells were characterised for stemness and lineage markers at representative stages (Phase A: P+5, approximately 13 days in culture; Phase B: P+7, approximately 20 days in culture; and Phase C: P+13, approximately 43 days in culture). Cell surface markers, stem cell markers and lineage-specific markers were characterised by FACS, ICC and Q-PCR revealing MSCs maintained their multilineage potential, including neural lineages throughout expansion. Co-expression of multiple lineage markers along with continued CD45 expression in MSCs did not affect completion of osteogenic and adipogenic specification or the formation of neurospheres. Improved standardised isolation and characterisation of MSCs may facilitate the identification of biomarkers to improve therapeutic efficacy to ensure increased reproducibility and routine production of MSCs for therapeutic applications including neural repair.

The problem of bone regeneration has engaged both physicians and scientists since the beginning of medicine. Not only can bone heal itself following most injuries, but when it does, the regenerated tissue is often indistinguishable from healthy bone. Problems arise, however, when bone does not heal properly, or when new tissue is needed, such as when two vertebrae are required to fuse to stabilize adjacent spine segments. Despite centuries of research, such procedures still require improved therapeutic methods to be devised. Autologous bone harvesting and grafting is currently still the accepted benchmark, despite drawbacks for clinicians and patients that include limited amounts, donor site morbidity, and variable quality. The necessity for an alternative to this “gold standard” has given rise to a bone-graft and substitute industry, with its central conundrum: what is the best way to regenerate bone? In this review, we dissect bone anatomy to summarize our current understanding of its constituents. We then look at how various components have been employed to improve bone regeneration. Evolving strategies for bone regeneration are then considered.

Bone morphogenetic protein-2 (BMP-2) is considered a promising adjuvant for the treatment of skeletal non-union and spinal fusion. However, BMP-2 delivery in a conventional collagen scaffold necessitates a high dose to achieve an efficacious outcome. To lower its effective dose, we precomplexed BMP-2 with the glycosaminoglycans (GAGs) dermatan sulfate (DS) or heparin (HP), prior to loading it into a hyaluronic acid (HA) hydrogel. In vitro release studies showed that BMP-2 precomplexed with DS or HP had a prolonged delivery compared to without GAG. BMP-2-DS complexes achieved a slightly faster release in the first 24 h than HP; however, both delivered BMP-2 for an equal duration. Analysis of the kinetic interaction between BMP-2 and DS or HP showed that HP had approximately 10 times higher affinity for BMP-2 than DS, yet it equally stabilized the protein, as determined by alkaline phosphatase activity. Ectopic bone formation assays at subcutaneous sites in rats demonstrated that HA hydrogel-delivered BMP-2 precomplexed with GAG induced twice the volume of bone compared with BMP-2 delivered uncomplexed to GAG.

Alternaria solani is the second most devastating foliar pathogen of potato crops worldwide, causing premature defoliation of the plants. This disease is currently prevented through the regular application of detrimental crop protection products and is guided by early warnings based on weather predictions and visual observations by farmers. To reduce the use of crop protection products, without additional production losses, it would be beneficial to be able to automatically detect Alternaria solani in potato fields. In recent years, the potential of deep learning in precision agriculture is receiving increasing research attention. Convolutional Neural Networks (CNNs) are currently the state of the art, but also come with challenges, especially regarding in-field robustness. This stems from the fact that they are often trained on datasets that are limited in size or have been recorded in controlled environments, not necessarily representative of real-world settings. We collected a dataset consisting of ultra-high-resolution modified RGB UAV-imagery of both symptomatic and non-symptomatic potato crops in the field during various years and disease stages to cover the great variability in agricultural data. We developed a convolutional neural network to perform in-field detection of Alternaria , defined as a binary classification problem. Our model achieves a similar accuracy as several state-of-the-art models for disease detection, but has a much lower inference time, which enhances its practical applicability. By using training data of three consecutive growing seasons (2019, 2020 and 2021) and test data of an independent fourth year (2022), an F1 score of 0.93 is achieved. Furthermore, we evaluate how different properties of the dataset such as its size and class imbalance impact the obtained accuracy.

Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model.

Mechanical weeding is an important part of integrated weed management. It destroys weeds between (interrow) and in (intrarow) crop rows. Preventing crop damage requires precise detection and tracking of the plants. In this work, a detection and tracking algorithm was developed and integrated on an intrarow hoeing prototype. The algorithm was developed and validated on 12 rows of 950 cauliflower plants. Therefore, a methodology was provided to automatically generate a label based on the crop plants’ Global Navigation Satellite System (GNSS) position during data collection with a robot platform. A CenterNet architecture was adjusted for plant centre detection by comparing different encoder networks and selecting the optimal hyperparameters. The monocular camera projection error of the plant centre detections in pixel to 3D coordinates was evaluated and used in a position- and velocity-based tracking algorithm to determine the timing for intrarow hoeing knife actuation. A dataset of 53k labelled images was created. The best CenterNet model resulted in an F1 score on the test set of 0.986 for detecting cauliflower centres. The position tracking had an average variation of 1.62 cm. Velocity tracking had a standard deviation of 0.008 with respect to the robot’s operational target velocity. Overall, the entire integration showed effective actuation of the prototype in field conditions. Only one false positive detection occurred during operation in two test rows of 135 cauliflowers.

It is challenging to regenerate periodontal tissues fully. We have previously reported a heparan sulfate variant with enhanced affinity for bone morphogenetic protein-2, termed HS3, that enhanced periodontal tissue regeneration in a rodent model. Here we seek to transition this work closer to the clinic and investigate the efficacy of the combination HS3 collagen device in a non-human primate (NHP) periodontitis model. Wire-induced periodontitis was generated in ten Macaca fascicularis, and defects were treated with Emdogain or collagen (CollaPlug) loaded with (1) distilled water, (2) HS low (36 µg of HS3), or (3) HS high (180 µg of HS3) for 3 months. At the endpoint, microscopic assessment showed significantly less epithelial down-growth, greater alveolar bone filling, and enhanced cementum and periodontal ligament regeneration following treatment with the HS-collagen combination devices. When evaluated using a periodontal regeneration assessment score (PRAS) on a scale of 0–16, collagen scored 6.78 (± 2.64), Emdogain scored 10.50 (± 1.73) and HS low scored 10.40 (± 1.82). Notably, treatment with HS high scored 12.27 (± 2.20), while healthy control scored 14.80 (± 1.15). This study highlights the efficacy of an HS-collagen device for periodontal regeneration in a clinically relevant NHP periodontitis model and warrants its application in clinical trials.

Heparan sulfate proteoglycans (HSPGs) are an evolutionarily ancient subclass of glycoproteins with exquisite structural complexity. They are ubiquitously expressed across tissues and have been found to exert a multitude of effects on cell behavior and the surrounding microenvironment. Evidence has shown that heterogeneity in HSPG composition is crucial to its functions as an essential scaffolding component in the extracellular matrix as well as a vital cell surface signaling co-receptor. Here, we provide an overview of the significance of HSPGs as essential regulators of stem cell function. We discuss the various roles of HSPGs in distinct stem cell types during key physiological events, from development through to tissue homeostasis and regeneration. The contribution of aberrant HSPG production to altered stem cell properties and dysregulated cellular homeostasis characteristic of cancer is also reviewed. Finally, we consider approaches to better understand and exploit the multifaceted functions of HSPGs in influencing stem cell characteristics for cell therapy and associated culture expansion strategies.