Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.

PURPOSE: A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted. METHODS: A retrospective monocentric cohort study of patients with lumbar spinal stenosis treated with microsurgical (MSD) and full-endoscopic (FED) decompression was conducted. The study included 206 patients with lumbar spinal stenosis who underwent FED (63; 30.6%) and MSD (118; 57.3%). Prolonged LOS and OT were defined as those exceeding the 75th percentile of the cohort. Furthermore, complications were assessed as a dependent variable. Using unsupervised learning, clusters were identified in the data, which helped distinguish between the early learning curve (ELC) and the late learning curve (LLC). From 15 algorithms, the top five algorithms that best fit the data were selected for each prediction task. We calculated the accuracy of prediction (Acc) and the area under the curve (AUC). The most significant predictors were determined using a feature importance analysis. RESULTS: For the FED group, the median number of surgeries with case surgery type at the time of surgery was 72 in the ELC group and 274 in the LLC group. FED patients did not significantly differ in outcome variables (LOS, OT, complication rate) between the ELC and LLC group. The random forest model demonstrated the highest mean accuracy and AUC across all folds for each classification task. For OT, it achieved an accuracy of 76.08% and an AUC of 0.89. For LOS, the model reached an accuracy of 83.83% and an AUC of 0.91. Lastly, in predicting complications, the random forest model attained the highest accuracy of 89.90% and an AUC of 0.94. Feature importance analysis indicated that LOS, OT, and complications were more significantly affected by patient characteristics than the surgical technique (FED versus MSD) or the surgeon's learning curve. CONCLUSIONS: A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.

Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery.

BACKGROUND: Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. METHODS: We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). RESULTS: The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. CONCLUSIONS: Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.

Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning.

Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.

Metabolic Fingerprinting of Different Sideritis Taxa Infusions and Their Neurogenic Activity.

Over the last years, Sideritis extracts were shown to improve memory. However, their potential to promote the generation of new neurons, starting with the neuronal differentiation of neural stem cells, remains unexplored. Therefore, the present study aimed to evaluate the neurogenic effects of different Sideritis infusions in neural stem and precursor cells and their impact on cell viability. Moreover, the metabolic fingerprints were recorded using LC-DAD, LC-HRESIMS, and GC-MS. The neurogenic potential of infusions of the eight Sideritis taxa tested was as potent as the classical neuronal inducer combination of retinoic acid and valproic acid. Further cytotoxicity assays revealed that the IC50 values of the extracts were between 163 and 322 µg/mL. Hierarchical cluster analyses of the metabolic fingerprints unveiled that the two Sideritis taxa with the lowest IC50 values were the most divergent in the analytical techniques used. As the analysis focused on polyphenols, it is reasonable to assume that these compounds are responsible for the effect on the cell viability of SH-SY5Y neuroblastoma cells. This study is the first report on the neurogenic potential of Sideritis taxa and might support the use of Sideritis herbal preparations in the context of neurodegenerative diseases.

Case-matched radiological and clinical outcome evaluation of interlaminar versus microsurgical decompression of lumbar spinal stenosis.

PURPOSE: Endoscopic spine surgery is a globally expanding technique advocated as less invasive for spinal stenosis treatment compared to the microsurgical approach. However, evidence on the efficiency of interlaminar full-endoscopic decompression (FED) vs. conventional microsurgical decompression (MSD) in patients with lumbar spinal stenosis is still scarce. We conducted a case-matched comparison for treatment success with consideration of clinical, laboratory, and radiologic predictors. METHODS: We included 88 consecutive patients (FED: 36/88, 40.9%; MSD: 52/88, 59.1%) presenting with lumbar central spinal stenosis. Surgery-related (operation time, complications, length of stay (LOS), American Society of Anesthesiologists physical status (ASA) score, C-reactive protein (CRP), white blood cell count, side of approach (unilateral/bilateral), patient-related outcome measures (PROMs) (Oswestry disability index (ODI), numeric rating scale of pain (NRS; leg-, back pain), EuroQol questionnaire (eQ-5D), core outcome measures index (COMI)), and radiological (dural sack cross-sectional area, Schizas score (SC), left and right lateral recess heights, and facet angles, respectively) parameters were extracted at different time points up to 1-year follow-up. The relationship of PROMs was analyzed using Spearman's rank correlation. Surgery-related outcome parameters were correlated with patient-centered and radiological outcomes utilizing a regression model to determine predictors for propensity score matching. RESULTS: Complication (most often residual sensorimotor deficits and restenosis due to hematoma) rates were higher in the FED (33.3%) than MSD (13.5%) group (p < 0.05), while all complications in the FED group were observed within the first 20 FED patients. Operation time was higher in the FED, whereas LOS was higher in the MSD group. Age, SC, CRP revealed significant associations with PROMs. We did not observe significant differences in the endoscopic vs. microsurgical group in PROMs. The correlation between ODI and COMI was significantly high, and both were inversely correlated with eQ-5D, whereas the correlations of these PROMs with NRS findings were less pronounced. CONCLUSIONS: Endoscopic treatment of lumbar spinal stenosis was similarly successful as the conventional microsurgical approach. Although FED was associated with higher complication rates in our single-center study experience, the distribution of complications indicated surgical learning curves to be the main factor of these findings. Future long-term prospective studies considering the surgical learning curve are warranted for reliable comparisons of these techniques.

Full-endoscopic versus conventional microsurgical therapy of lumbar disc herniation: a prospective, controlled, single-center, comprehensive cohort trial (FEMT-LDH trial).

BACKGROUND: Lumbar disc herniation is one of the leading causes of chronic low back pain. Surgery remains the therapy of choice when conservative approaches fail. Full-endoscopic approaches represent a promising alternative to the well-established microsurgical technique. However, high-grade evidence comparing these techniques is still scarce. METHODS: Patients presenting with lumbar disc herniation will be included. The intervention group will obtain full-endoscopic disc decompression, whereas the control group will be treated by microsurgical disc decompression. We will apply a comprehensive cohort study design involving a randomized and a prospective non-randomized study arm. Patients who do not consent to be randomized will be assigned to the non-randomized arm. The primary outcome will be the Oswestry Disability Index (ODI). Secondary outcomes involve the visual analog scale (VAS) of pain and the SF-36 health questionnaire. Furthermore, clinical characteristics including duration of hospital stay, operation time, and complications as well as laboratory markers, such as C-reactive protein, white blood cell counts, and interleukin 6 will be determined and compared. DISCUSSION: This study will significantly contribute to the current evidence available in the literature by evaluating the outcome of the full-endoscopic technique against the gold standard for lumbar disc herniation in a clinically relevant study setup. Additionally, the study design allows us to include patients not willing to be randomized in a prospective parallel study arm and to evaluate the impact of randomization on outcomes and include. The results could help to improve the future therapy in patients suffering from lumbar disc herniation. TRIAL REGISTRATION: This study was prospectively registered in The German Clinical Trials Register (DRKS), a German WHO primary registry, under the registration number: DRKS00025786. Registered on July 7, 2021.

Performance of Artificial Intelligence-Based Algorithms to Predict Prolonged Length of Stay after Lumbar Decompression Surgery.

BACKGROUND: Decompression of the lumbar spine is one of the most common procedures performed in spine surgery. Hospital length of stay (LOS) is a clinically relevant metric used to assess surgical success, patient outcomes, and socioeconomic impact. This study aimed to investigate a variety of machine learning and deep learning algorithms to reliably predict whether a patient undergoing decompression of lumbar spinal stenosis will experience a prolonged LOS. METHODS: Patients undergoing treatment for lumbar spinal stenosis with microsurgical and full-endoscopic decompression were selected within this retrospective monocentric cohort study. Prolonged LOS was defined as an LOS greater than or equal to the 75th percentile of the cohort (normal versus prolonged stay; binary classification task). Unsupervised learning with K-means clustering was used to find clusters in the data. Hospital stay classes were predicted with logistic regression, RandomForest classifier, stochastic gradient descent (SGD) classifier, K-nearest neighbors, Decision Tree classifier, Gaussian Naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), multilayer perceptron artificial neural network (MLP), and radial basis function neural network (RBNN) in Python. Prediction accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Further, we developed a decision tree based on the Chi-square automatic interaction detection (CHAID) algorithm to investigate cut-offs of predictors for clinical decision-making. RESULTS: 236 patients and 14 feature variables were included. K-means clustering separated data into two clusters distinguishing the data into two patient risk characteristic groups. The algorithms reached AUCs between 67.5% and 87.3% for the classification of LOS classes. Feature importance analysis of deep learning algorithms indicated that operation time was the most important feature in predicting LOS. A decision tree based on CHAID could predict 84.7% of the cases. CONCLUSIONS: Machine learning and deep learning algorithms can predict whether patients will experience an increased LOS following lumbar decompression surgery. Therefore, medical resources can be more appropriately allocated to patients who are at risk of prolonged LOS.

One-Year Clinical Outcomes of Minimal-Invasive Dorsal Percutaneous Fixation of Thoracolumbar Spine Fractures.

Introduction: Minimal-invasive instrumentation techniques have become a workhorse in spine surgery and require constant clinical evaluations. We sought to analyze patient-reported outcome measures (PROMs) and clinicopathological characteristics of thoracolumbar fracture stabilizations utilizing a minimal-invasive percutaneous dorsal screw-rod system. Methods: We included all patients with thoracolumbar spine fractures who underwent minimal-invasive percutaneous spine stabilization in our clinics since inception and who have at least 1 year of follow-up data. Clinical characteristics (length of hospital stay (LOS), operation time (OT), and complications), PROMs (preoperative (pre-op), 3-weeks postoperative (post-op), 1-year postoperative: eq5D, COMI, ODI, NRS back pain), and laboratory markers (leucocytes, c-reactive protein (CRP)) were analyzed, finding significant associations between these study variables and PROMs. Results: A total of 68 patients (m: 45.6%; f: 54.4%; mean age: 76.9 ± 13.9) were included. The most common fracture types according to the AO classification were A3 (40.3%) and A4 (40.3%), followed by B2 (7.46%) and B1 (5.97%). The Median American Society of Anesthesiologists (ASA) score was 3 (range: 1−4). Stabilized levels ranged from TH4 to L5 (mean number of targeted levels: 4.25 ± 1.4), with TH10-L2 (12/68) and TH11-L3 (11/68) being the most frequent site of surgery. Mean OT and LOS were 92.2 ± 28.2 min and 14.3 ± 6.9 days, respectively. We observed 9/68 complications (13.2%), mostly involving screw misalignments and loosening. CRP increased from 24.9 ± 33.3 pre-op to 34.8 ± 29.9 post-op (p < 0.001), whereas leucocyte counts remained stable. All PROMs showed a marked significant improvement for both 3-week and 1-year evaluations compared to the preoperative situation. Interestingly, we did not find an impact of OT, LOS, lab markers, complications, and other clinical characteristics on PROMs. Notably, a higher number of stabilized levels did not affect PROMs. Conclusions: Minimal-invasive stabilization of thoracolumbar fractures utilizing a dorsal percutaneous approach resulted in significant PROM outcome improvements, although we observed a complication rate of 13.2% for up to 1 year of follow-up. PROMs were not significantly associated with clinicopathological characteristics, technique-related variables, or the number of targeted levels.

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

On the Acceptance of "Fake" Histopathology: A Study on Frozen Sections Optimized with Deep Learning.

BACKGROUND: The fast acquisition process of frozen sections allows surgeons to wait for histological findings during the interventions to base intrasurgical decisions on the outcome of the histology. Compared with paraffin sections, however, the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep neural networks are capable of modifying specific characteristics of digital histological images. Particularly, generative adversarial networks proved to be effective tools to learn about translation between two modalities, based on two unconnected data sets only. The positive effects of such deep learning-based image optimization on computer-aided diagnosis have already been shown. However, since fully automated diagnosis is controversial, the application of enhanced images for visual clinical assessment is currently probably of even higher relevance. METHODS: Three different deep learning-based generative adversarial networks were investigated. The methods were used to translate frozen sections into virtual paraffin sections. Overall, 40 frozen sections were processed. For training, 40 further paraffin sections were available. We investigated how pathologists assess the quality of the different image translation approaches and whether experts are able to distinguish between virtual and real digital pathology. RESULTS: Pathologists' detection accuracy of virtual paraffin sections (from pairs consisting of a frozen and a paraffin section) was between 0.62 and 0.97. Overall, in 59% of images, the virtual section was assessed as more appropriate for a diagnosis. In 53% of images, the deep learning approach was preferred to conventional stain normalization (SN). CONCLUSION: Overall, expert assessment indicated slightly improved visual properties of converted images and a high similarity to real paraffin sections. The observed high variability showed clear differences in personal preferences.

Allergy-induced systemic inflammation impairs tendon quality.

BACKGROUND: Treatment of degenerating tendons still presents a major challenge, since the aetiology of tendinopathies remains poorly understood. Besides mechanical overuse, further known predisposing factors include rheumatoid arthritis, diabetes, obesity or smoking all of which combine with a systemic inflammation. METHODS: To determine whether the systemic inflammation accompanying these conditions contributes to the onset of tendinopathy, we studied the effect of a systemic inflammation induced by an allergic episode on tendon properties. To this end, we induced an allergic response in mice by exposing them to a timothy grass pollen allergen and subsequently analysed both their flexor and Achilles tendons. Additionally, we analysed data from a health survey comprising data from more than 10.000 persons for an association between the occurrence of an allergy and tendinopathy. FINDINGS: Biomechanical testing and histological analysis revealed that tendons from allergic mice not only showed a significant reduction of both elastic modulus and tensile stress, but also alterations of the tendon matrix. Moreover, treatment of 3D tendon-like constructs with sera from allergic mice resulted in a matrix-remodelling expression profile and the expression of macrophage-associated markers and matrix metalloproteinase 2 (MMP2) was increased in allergic Achilles tendons. Data from the human health study revealed that persons suffering from an allergy have an increased propensity to develop a tendinopathy. INTERPRETATION: Our study demonstrates that the presence of a systemic inflammation accompanying an allergic condition negatively impacts on tendon structure and function. FUNDING: This study was financially supported by the Fund for the Advancement of Scientific Research at Paracelsus Medical University (PMU-FFF E-15/22/115-LEK), by the Land Salzburg, the Salzburger Landeskliniken (SALK, the Health Care Provider of the University Hospitals Landeskrankenhaus and Christian Doppler Klinik), the Paracelsus Medical University, Salzburg and by unrestricted grants from Bayer, AstraZeneca, Sanofi-Aventis, Boehringer-Ingelheim.

PSA Depletion Induces the Differentiation of Immature Neurons in the Piriform Cortex of Adult Mice.

Immature neurons are maintained in cortical regions of the adult mammalian brain. In rodents, many of these immature neurons can be identified in the piriform cortex based on their high expression of early neuronal markers, such as doublecortin (DCX) and the polysialylated form of the neural cell adhesion molecule (PSA-NCAM). This molecule plays critical roles in different neurodevelopmental events. Taking advantage of a DCX-CreERT2/Flox-EGFP reporter mice, we investigated the impact of targeted PSA enzymatic depletion in the piriform cortex on the fate of immature neurons. We report here that the removal of PSA accelerated the final development of immature neurons. This was revealed by a higher frequency of NeuN expression, an increase in the number of cells carrying an axon initial segment (AIS), and an increase in the number of dendrites and dendritic spines on the immature neurons. Taken together, our results demonstrated the crucial role of the PSA moiety in the protracted development of immature neurons residing outside of the neurogenic niches. More studies will be required to understand the intrinsic and extrinsic factors affecting PSA-NCAM expression to understand how the brain regulates the incorporation of these immature neurons to the established neuronal circuits of the adult brain.

Granulocyte colony-stimulating factor in traumatic spinal cord injury.

Granulocyte colony-stimulating factor (G-CSF) is a cytokine used in pharmaceutical preparations for the treatment of chemotherapy-induced neutropenia. Evidence from experimental studies indicates that G-CSF exerts relevant activities in the central nervous system (CNS) in particular after lesions. In acute, subacute, and chronic CNS lesions, G-CSF appears to have strong anti-inflammatory, antiapoptotic, antioxidative, myelin-protective, and axon-regenerative activities. Additional effects result in the stimulation of angiogenesis and neurogenesis as well as in bone marrow stem cell mobilization to the CNS. There are emerging preclinical and clinical data indicating that G-CSF is a safe and effective drug for the treatment of acute and chronic traumatic spinal cord injury (tSCI), which we summarize in this review.

Myogenic progenitor cell transplantation for muscle regeneration following hindlimb ischemia and reperfusion.

BACKGROUND: Muscle is severely affected by ischemia/reperfusion injury (IRI). Quiescent satellite cells differentiating into myogenic progenitor cells (MPC) possess a remarkable regenerative potential. We herein established a model of local application of MPC in murine hindlimb ischemia/reperfusion to study cell engraftment and differentiation required for muscle regeneration. METHODS: A clamping model of murine (C57b/6 J) hindlimb ischemia was established to induce IRI in skeletal muscle. After 2 h (h) warm ischemic time (WIT) and reperfusion, reporter protein expressing MPC (TdTomato or Luci-GFP, 1 × 106 cells) obtained from isolated satellite cells were injected intramuscularly. Surface marker expression and differentiation potential of MPC were analyzed in vitro by flow cytometry and differentiation assay. In vivo bioluminescence imaging and histopathologic evaluation of biopsies were performed to quantify cell fate, engraftment and regeneration. RESULTS: 2h WIT induced severe IRI on muscle, and muscle fiber regeneration as per histopathology within 14 days after injury. Bioluminescence in vivo imaging demonstrated reporter protein signals of MPC in 2h WIT animals and controls over the study period (75 days). Bioluminescence signals were detected at the injection site and increased over time. TdTomato expressing MPC and myofibers were visible in host tissue on postoperative days 2 and 14, respectively, suggesting that injected MPC differentiated into muscle fibers. Higher reporter protein signals were found after 2h WIT compared to controls without ischemia, indicative for enhanced growth and/or engraftment of MPC injected into IRI-affected muscle antagonizing muscle damage caused by IRI. CONCLUSION: WIT-induced IRI in muscle requests increased numbers of injected MPC to engraft and persist, suggesting a possible rational for cell therapy to antagonize IRI. Further investigations are needed to evaluate the regenerative capacity and therapeutic advantage of MPC in the setting of ischemic limb injury.

Generation of myogenic progenitor cell-derived smooth muscle cells for sphincter regeneration.

BACKGROUND: Degeneration of smooth muscles in sphincters can cause debilitating diseases such as fecal incontinence. Skeletal muscle-derived cells have been effectively used in clinics for the regeneration of the skeletal muscle sphincters, such as the external anal or urinary sphincter. However, little is known about the in vitro smooth muscle differentiation potential and in vivo regenerative potential of skeletal muscle-derived cells. METHODS: Myogenic progenitor cells (MPC) were isolated from the skeletal muscle and analyzed by flow cytometry and in vitro differentiation assays. The differentiation of MPC to smooth muscle cells (MPC-SMC) was evaluated by immunofluorescence, flow cytometry, patch-clamp, collagen contraction, and microarray gene expression analysis. In vivo engraftment of MPC-SMC was monitored by transplanting reporter protein-expressing cells into the pyloric sphincter of immunodeficient mice. RESULTS: MPC derived from human skeletal muscle expressed mesenchymal surface markers and exhibit skeletal myogenic differentiation potential in vitro. In contrast, they lack hematopoietic surface marker, as well as adipogenic, osteogenic, and chondrogenic differentiation potential in vitro. Cultivation of MPC in smooth muscle differentiation medium significantly increases the fraction of alpha smooth muscle actin (aSMA) and smoothelin-positive cells, while leaving the number of desmin-positive cells unchanged. Smooth muscle-differentiated MPC (MPC-SMC) exhibit increased expression of smooth muscle-related genes, significantly enhanced numbers of CD146- and CD49a-positive cells, and in vitro contractility and express functional Cav and Kv channels. MPC to MPC-SMC differentiation was also accompanied by a reduction in their skeletal muscle differentiation potential. Upon removal of the smooth muscle differentiation medium, a major fraction of MPC-SMC remained positive for aSMA, suggesting the definitive acquisition of their phenotype. Transplantation of murine MPC-SMC into the mouse pyloric sphincter revealed engraftment of MPC-SMC based on aSMA protein expression within the host smooth muscle tissue. CONCLUSIONS: Our work confirms the ability of MPC to give rise to smooth muscle cells (MPC-SMC) with a well-defined and stable phenotype. Moreover, the engraftment of in vitro-differentiated murine MPC-SMC into the pyloric sphincter in vivo underscores the potential of this cell population as a novel cell therapeutic treatment for smooth muscle regeneration of sphincters.

Dimethylsulfoxide Inhibits Oligodendrocyte Fate Choice of Adult Neural Stem and Progenitor Cells.

Several clinical trials address demyelinating diseases via transplantation of mesenchymal stromal cells (MSCs). Published reports detail that administration of MSCs in patients may provide a beneficial immunomodulation, and that factors secreted by MSCs are potent inducers of oligodendrogenesis. Dimethylsulfoxide (DMSO) is widely used in life science and medicine as solvent, vehicle or cryoprotectant for cells used in transplantation. Importantly, most transplantation protocols do not include the removal of DMSO before injecting the cell suspension into patients. This indifferent application of DMSO is coming under increasing scrutiny following reports investigating its potential toxic side-effects. While the impact of DMSO on the central nervous system (CNS) has been partially studied, its effect on oligodendrocytes and oligodendrogenesis has not been addressed yet. Consequently, we evaluated the influence of DMSO on oligodendrogenesis, and on the pro-oligodendrogenic effect of MSCs' secreted factors, using adult rat neural stem and progenitor cells (NSPCs). Here, we demonstrate that a concentration of 1% DMSO robustly suppressed oligodendrogenesis and drove the fate of differentiating NSPCs toward astrogenesis. Furthermore, the pro-oligodendrogenic effect of MSC-conditioned medium (MSCCM) was also nearly completely abolished by the presence of 1% DMSO. In this condition, inhibition of the Erk1/2 signal transduction pathway and high levels of Id2 expression, a specific inhibitor of oligodendrogenic differentiation, were detected. Furthermore, inflammatory demyelinating diseases may even potentiate the impact of DMSO on oligodendrogenesis. Our results demonstrate the imperative of considering the strong anti-oligodendrogenic activity of DMSO when designing future clinical trial protocols.

Aging restricts the ability of mesenchymal stem cells to promote the generation of oligodendrocytes during remyelination.

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS) that leads to severe neurological deficits. Due to their immunomodulatory and neuroprotective activities and their ability to promote the generation of oligodendrocytes, mesenchymal stem cells (MSCs) are currently being developed for autologous cell therapy in MS. As aging reduces the regenerative capacity of all tissues, it is of relevance to investigate whether MSCs retain their pro-oligodendrogenic activity with increasing age. We demonstrate that MSCs derived from aged rats have a reduced capacity to induce oligodendrocyte differentiation of adult CNS stem/progenitor cells. Aging also abolished the ability of MSCs to enhance the generation of myelin-like sheaths in demyelinated cerebellar slice cultures. Finally, in a rat model for CNS demyelination, aging suppressed the capability of systemically transplanted MSCs to boost oligodendrocyte progenitor cell (OPC) differentiation during remyelination. Thus, aging restricts the ability of MSCs to support the generation of oligodendrocytes and consequently inhibits their capacity to enhance the generation of myelin-like sheaths. These findings may impact on the design of therapies using autologous MSCs in older MS patients.

Early functional connectivity deficits and progressive microstructural alterations in the TgF344-AD rat model of Alzheimer's Disease: A longitudinal MRI study.

The development and characterization of new improved animal models is pivotal in Alzheimer's Disease (AD) research, since valid models enable the identification of early pathological processes, which are often not accessible in patients, as well as subsequent target discovery and evaluation. The TgF344-AD rat model of AD, bearing mutant human amyloid precursor protein (APPswe) and Presenilin 1 (PSEN1ΔE9) genes, has been described to manifest the full spectrum of AD pathology similar to human AD, i.e. progressive cerebral amyloidosis, tauopathy, neuronal loss and age-dependent cognitive decline. Here, AD-related pathology in female TgF344-AD rats was examined longitudinally between 6 and 18 months by means of complementary translational MRI techniques: resting state functional MRI (rsfMRI) to evaluate functional connectivity (FC) and diffusion tensor imaging (DTI) to assess the microstructural integrity. Additionally, an evaluation of macroscopic changes (3D anatomical MRI) and an image-guided validation of ex vivo pathology were performed. We identified slightly decreased FC at 6 months followed by severe and widespread hypoconnectivity at 10 months of age as the earliest detectable pathological MRI hallmark. This initial effect was followed by age-dependent progressive microstructural deficits in parallel with age-dependent ex vivo AD pathology, without signs of macroscopic alterations such as hippocampal atrophy. This longitudinal MRI study in the TgF344-AD rat model of AD revealed early rsfMRI and DTI abnormalities as seen in human AD patients. The characterization of AD pathology in this rat model using non-invasive MRI techniques further highlights the translational value of this model, as well as its use for potential treatment evaluation.