Reduction of Matrix Metallopeptidase 13 and Promotion of Chondrogenesis by Zeel T in Primary Human Osteoarthritic Chondrocytes

Objectives: Zeel T (Ze14) is a multicomponent medicinal product. Initial preclinical data suggested a preventive effect on cartilage degradation. Clinical observational studies demonstrated that Ze14 reduced symptoms of osteoarthritis (OA), including stiffness and pain. This study aimed to explore these effects further to better understand the mode of action of Ze14 on human OA chondrocytes in vitro. Methods: Primary chondrocytes were obtained from the knees of 19 OA patients and cultured either as monolayers or in alginate beads. The cultures were treated with 20% or 10% (v/v) Ze14 or placebo. For RNA-seq, reads were generated with Illumina NextSeq5000 sequencer and aligned to the human reference genome (UCSC hg19). Differential expression analysis between Ze14 and placebo was performed in R using the DESeq2 package. Protein quantification by ELISA was performed on selected genes from the culture medium and/or the cellular fractions of primary human OA chondrocyte cultures. Results: In monolayer cultures, Ze14 20% (v/v) significantly modified the expression of 13 genes in OA chondrocytes by at least 10% with an adjusted p-value < 0.05: EGR1, FOS, NR4A1, DUSP1, ZFP36, ZFP36L1, NFKBIZ, and CCN1 were upregulated and ATF7IP, TXNIP, DEPP1, CLEC3A, and MMP13 were downregulated after 24 h Ze14 treatment. Ze14 significantly increased (mean 2.3-fold after 24 h, p = 0.0444 and 72 h, p = 0.0239) the CCN1 protein production in human OA chondrocytes. After 72 h, Ze14 significantly increased type II collagen pro-peptide production by mean 27% (p = 0.0147). For both time points CCN1 production by OA chondrocytes was correlated with aggrecan (r = 0.66, p = 0.0004) and type II collagen pro-peptide (r = 0.64, p = 0.0008) production. In alginate beads cultures, pro-MMP-13 was decreased by Ze14 from day 7–14 (from −16 to −25%, p < 0.05) and from day 17–21 (−22%, p = 0.0331) in comparison to controls. Conclusion: Ze14 significantly modified the expression of DUSP1, DEPP1, ZFP36/ZFP36L1, and CLEC3A, which may reduce MMP13 expression and activation. Protein analysis confirmed that Ze14 significantly reduced the production of pro-MMP-13. As MMP-13 is involved in type II collagen degradation, Ze14 may limit cartilage degradation. Ze14 also promoted extracellular matrix formation arguably through CCN1 production, a growth factor well correlated with type II collagen and aggrecan production

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role

Olfactory-induced locomotion in lampreys

The olfactory system allows animals to navigate in their environment to feed, mate, and escape predators. It is well established that odorant exposure or electrical stimulation of the olfactory system induces stereotyped motor responses in fishes. However, the neural circuitry responsible for the olfactomotor transformations is only beginning to be unraveled. A neural substrate eliciting motor responses to olfactory inputs was identified in the lamprey, a basal vertebrate used extensively to examine the neural mechanisms underlying sensorimotor transformations. Two pathways were discovered from the olfactory organ in the periphery to the brainstem motor nuclei responsible for controlling swimming. The first pathway originates from sensory neurons located in the accessory olfactory organ and reaches a single population of projection neurons in the medial olfactory bulb, which, in turn, transmit the olfactory signals to the posterior tuberculum and then to downstream brainstem locomotor centers. A second pathway originates from the main olfactory epithelium and reaches the main olfactory bulb, the neurons of which project to the pallium/cortex. The olfactory signals are then conveyed to the posterior tuberculum and then to brainstem locomotor centers. Olfactomotor behavior can adapt, and studies were aimed at defining the underlying neural mechanisms. Modulation of bulbar neural activity by GABAergic, dopaminergic, and serotoninergic inputs is likely to provide strong control over the hardwired circuits to produce appropriate motor behavior in response to olfactory cues. This review summarizes current knowledge relative to the neural circuitry producing olfactomotor behavior in lampreys and their modulatory mechanisms

Opinions from five oral specialists on 3D printing in the challenge of customized oral healthcare. What can plasma technology bring?

International audienceThe field of "plasma medicine" is expanding. It is based on advances in plasma physics and chemistry; a better knowledge of the species produced, a better control in the creation of gaseous plasmas, enable different plasma processes with potential different clinical applications. In the medical field, progress is being made towards predictive, preventive, personalized, participatory, and populational medicine. In particular, three-dimensional printing is useful in the challenge of customized oral healthcare. For example, plasma technology offers many options to optimize the use of custom-made prothesis, particularly in the area of oral medicine and oral surgery

Evolution et maintenance du système d'Information Agrosyst pour l'INRA : Cahier des clauses techniques particulières (CCTP)

Evolution et maintenance du système d'Information Agrosyst pour l'INRA : Cahier des clauses techniques particulières (CCTP

The Damage-Associated Molecular Patterns (DAMPs) as Potential Targets to Treat Osteoarthritis: Perspectives From a Review of the Literature.

During the osteoarthritis (OA) process, activation of immune systems, whether innate or adaptive, is strongly associated with low-grade systemic inflammation. This process is initiated and driven in the synovial membrane, especially by synovium cells, themselves previously activated by damage-associated molecular patterns (DAMPs) released during cartilage degradation. These fragments exert their biological activities through pattern recognition receptors (PRRs) that, as a consequence, induce the activation of signaling pathways and beyond the release of inflammatory mediators, the latter contributing to the vicious cycle between cartilage and synovial membrane. The primary endpoint of this review is to provide the reader with an overview of these many molecules categorized as DAMPs and the contribution of the latter to the pathophysiology of OA. We will also discuss the different strategies to control their effects. We are convinced that a better understanding of DAMPs, their receptors, and associated pathological mechanisms represents a decisive issue for degenerative joint diseases such as OA

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role