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.

Neuron and Brain Maturation 2.0.

The mammalian central nervous system (CNS) is built up during embryogenesis by neural stem cells located in the periventricular germinal layers which undergo multiple division cycles [...].

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.

Age-related changes in layer II immature neurons of the murine piriform cortex.

The recent identification of a population of non-newly born, prenatally generated "immature" neurons in the layer II of the piriform cortex (cortical immature neurons, cINs), raises questions concerning their maintenance or depletion through the lifespan. Most forms of brain structural plasticity progressively decline with age, a feature that is particularly prominent in adult neurogenesis, due to stem cell depletion. By contrast, the entire population of the cINs is produced during embryogenesis. Then these cells simply retain immaturity in postnatal and adult stages, until they "awake" to complete their maturation and ultimately integrate into neural circuits. Hence, the question remains open whether the cINs, which are not dependent on stem cell division, might follow a similar pattern of age-related reduction, or in alternative, might leave a reservoir of young, undifferentiated cells in the adult and aging brain. Here, the number and features of cINs were analyzed in the mouse piriform cortex from postnatal to advanced ages, by using immunocytochemistry for the cytoskeletal marker doublecortin. The abundance and stage of maturation of cINs, along with the expression of other markers of maturity/immaturity were investigated. Despite a marked decrease in this neuronal population during juvenile stages, reminiscent of that observed in hippocampal neurogenesis, a small amount of highly immature cINs persisted up to advanced ages. Overall, albeit reducing in number with increasing age, we report that the cINs are present through the entire animal lifespan.

The awakening of dormant neuronal precursors in the adult and aged brain.

Beyond the canonical neurogenic niches, there are dormant neuronal precursors in several regions of the adult mammalian brain. Dormant precursors maintain persisting post-mitotic immaturity from birth to adulthood, followed by staggered awakening, in a process that is still largely unresolved. Strikingly, due to the slow rate of awakening, some precursors remain immature until old age, which led us to question whether their awakening and maturation are affected by aging. To this end, we studied the maturation of dormant precursors in transgenic mice (DCX-CreERT2 /flox-EGFP) in which immature precursors were labelled permanently in vivo at different ages. We found that dormant precursors are capable of awakening at young age, becoming adult-matured neurons (AM), as well as of awakening at old age, becoming late AM. Thus, protracted immaturity does not prevent late awakening and maturation. However, late AM diverged morphologically and functionally from AM. Moreover, AM were functionally most similar to neonatal-matured neurons (NM). Conversely, late AM were endowed with high intrinsic excitability and high input resistance, and received a smaller amount of spontaneous synaptic input, implying their relative immaturity. Thus, late AM awakening still occurs at advanced age, but the maturation process is slow.

Semi-Synthesis of Different Pyranoflavonoid Backbones and the Neurogenic Potential.

Flavonoids and chalcones are known for their manifold biological activities, of which many affect the central nervous system. Pyranochalcones were recently shown to have a great neurogenic potential, which is partly due to a specific structural motif-the pyran ring. Accordingly, we questioned if other flavonoid backbones with a pyran ring as structural moiety would also show neurogenic potential. Different semi-synthetic approaches starting with the prenylated chalcone xanthohumol, isolated from hops, led to pyranoflavanoids with different backbones. We identified the chalcone backbone as the most active backbone with pyran ring using a reporter gene assay based on the promoter activity of doublecortin, an early neuronal marker. Pyranochalcones therefore appear to be promising compounds for further development as a treatment strategy for neurodegenerative diseases.

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.

Depolarization and Hyperexcitability of Cortical Motor Neurons after Spinal Cord Injury Associates with Reduced HCN Channel Activity.

A spinal cord injury (SCI) damages the axonal projections of neurons residing in the neocortex. This axotomy changes cortical excitability and results in dysfunctional activity and output of infragranular cortical layers. Thus, addressing cortical pathophysiology after SCI will be instrumental in promoting recovery. However, the cellular and molecular mechanisms of cortical dysfunction after SCI are poorly resolved. In this study, we determined that the principal neurons of the primary motor cortex layer V (M1LV), those suffering from axotomy upon SCI, become hyperexcitable following injury. Therefore, we questioned the role of hyperpolarization cyclic nucleotide gated channels (HCN channels) in this context. Patch clamp experiments on axotomized M1LV neurons and acute pharmacological manipulation of HCN channels allowed us to resolve a dysfunctional mechanism controlling intrinsic neuronal excitability one week after SCI. Some axotomized M1LV neurons became excessively depolarized. In those cells, the HCN channels were less active and less relevant to control neuronal excitability because the membrane potential exceeded the window of HCN channel activation. Care should be taken when manipulating HCN channels pharmacologically after SCI. Even though the dysfunction of HCN channels partakes in the pathophysiology of axotomized M1LV neurons, their dysfunctional contribution varies remarkably between neurons and combines with other pathophysiological mechanisms.

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.

Effects of Extracorporeal Shockwave Therapy on Functional Recovery and Circulating miR-375 and miR-382-5p after Subacute and Chronic Spinal Cord Contusion Injury in Rats.

Extracorporeal shockwave therapy (ESWT) can stimulate processes to promote regeneration, including cell proliferation and modulation of inflammation. Specific miRNA expression panels have been established to define correlations with regulatory targets within these pathways. This study aims to investigate the influence of low-energy ESWT-applied within the subacute and chronic phase of SCI (spinal cord injury) on recovery in a rat spinal cord contusion model. Outcomes were evaluated by gait analysis, µCT and histological analysis of spinal cords. A panel of serum-derived miRNAs after SCI and after ESWT was investigated to identify injury-, regeneration- and treatment-associated expression patterns. Rats receiving ESWT showed significant improvement in motor function in both a subacute and a chronic experimental setting. This effect was not reflected in changes in morphology, µCT-parameters or histological markers after ESWT. Expression analysis of various miRNAs, however, revealed changes after SCI and ESWT, with increased miR-375, indicating a neuroprotective effect, and decreased miR-382-5p potentially improving neuroplasticity via its regulatory involvement with BDNF. We were able to demonstrate a functional improvement of ESWT-treated animals after SCI in a subacute and chronic setting. Furthermore, the identification of miR-375 and miR-382-5p could potentially provide new targets for therapeutic intervention in future studies.

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.

Spinal Cord Injury and Loss of Cortical Inhibition.

After spinal cord injury (SCI), the destruction of spinal parenchyma causes permanent deficits in motor functions, which correlates with the severity and location of the lesion. Despite being disconnected from their targets, most cortical motor neurons survive the acute phase of SCI, and these neurons can therefore be a resource for functional recovery, provided that they are properly reconnected and retuned to a physiological state. However, inappropriate re-integration of cortical neurons or aberrant activity of corticospinal networks may worsen the long-term outcomes of SCI. In this review, we revisit recent studies addressing the relation between cortical disinhibition and functional recovery after SCI. Evidence suggests that cortical disinhibition can be either beneficial or detrimental in a context-dependent manner. A careful examination of clinical data helps to resolve apparent paradoxes and explain the heterogeneity of treatment outcomes. Additionally, evidence gained from SCI animal models indicates probable mechanisms mediating cortical disinhibition. Understanding the mechanisms and dynamics of cortical disinhibition is a prerequisite to improve current interventions through targeted pharmacological and/or rehabilitative interventions following SCI.

Why Would the Brain Need Dormant Neuronal Precursors?

Dormant non-proliferative neuronal precursors (dormant precursors) are a unique type of undifferentiated neuron, found in the adult brain of several mammalian species, including humans. Dormant precursors are fundamentally different from canonical neurogenic-niche progenitors as they are generated exquisitely during the embryonic development and maintain a state of protracted postmitotic immaturity lasting up to several decades after birth. Thus, dormant precursors are not pluripotent progenitors, but to all effects extremely immature neurons. Recently, transgenic models allowed to reveal that with age virtually all dormant precursors progressively awaken, abandon the immature state, and become fully functional neurons. Despite the limited common awareness about these cells, the deep implications of recent discoveries will likely lead to revisit our understanding of the adult brain. Thus, it is timely to revisit and critically assess the essential evidences that help pondering on the possible role(s) of these cells in relation to cognition, aging, and pathology. By highlighting pivoting findings as well as controversies and open questions, we offer an exciting perspective over the field of research that studies these mysterious cells and suggest the next steps toward the answer of a crucial question: why does the brain need dormant neuronal precursors?

Neuronal and Brain Maturation.

Can plasticity be considered as an extension of "immaturity" [...].

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.