Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

This report delineates the intricate diagnostic journey and therapeutic conundrum presented by a 61-year-old male who exhibited atypical neurological deterioration shortly after lumbar fusion surgery, manifesting clinical and radiological features suggestive of pseudohypoxic encephalopathy, an entity characterized by symptoms mimicking cerebral hypoxia in the absence of a discernible hypoxic insult. Following an initially unremarkable recovery from an elaborate spinal surgery, the patient’s postoperative condition was confounded by a perplexing decline in consciousness, unresponsive to conventional therapeutic interventions and devoid of clear etiological indicators on standard neuroimaging. The subsequent diagnostic odyssey unraveled a cerebrospinal fluid leak as the putative reason, positing a nuanced clinical paradigm wherein the cerebrospinal fluid leak engendered a state mimicking pseudohypoxic brain swelling. This report underscores the clinical challenges and emphasizes the need for an astute diagnostic approach in postoperative patients with unexplained neurological symptoms advocating for a comprehensive evaluation to identify underlying cerebrospinal fluid leaks and mitigate potential morbidity.

Background: Artificial intelligence (AI) is rapidly transforming clinical decision-making, yet its role in orthopedic trauma surgery remains fragmented and unevenly validated in clinical practise. Methods: We conducted a PRISMA-SCR–compliant scoping review using a systematic search of the Semantic Scholar, OpenAlex and PubMed corpus via Elicit (497 records). Studies were eligible if they applied AI or machine-learning methods to traumatic orthopedic conditions, included ≥ 10 human subjects, reported quantitative performance metrics, and represented original research. After title/abstract and full-text screening, 146 studies were included. Data on study characteristics, AI methodology, clinical application, validation strategy, performance metrics, explainability and translational maturity were synthesized descriptively. Results: Research output increased sharply after 2017, with 52% of all studies published since 2022. Most studies were retrospective (≈ 99%). Deep learning dominated the field (61%), particularly for fracture detection and classification, while classical machine-learning models were mainly used for outcome prediction. Internal validation was reported in 85% of studies, whereas only 15% clearly performed external or multicenter validation; true prospective clinical testing was rare (1.4%), and only a small subset of models had been implemented in practice (3.4%). Diagnostic models frequently achieved very high technical accuracy (AUC 0.90–1.00 in constrained tasks), while prognostic models showed moderate-to-high performance (AUC 0.75–0.95). Explainability was underreported, only 24% used any form of saliency mapping, Grad-CAM or feature importance analysis. Conclusions: AI in orthopedic trauma surgery demonstrates strong technical feasibility but remains overwhelmingly at the proof-of-concept stage. The field is characterized by limited external validation, minimal prospective evidence, scarce explainability, and insufficient workflow integration, factors that collectively hinder clinical translation. To bridge the gap from laboratory performance to real-world impact, future research must emphasize multicenter datasets, rigorous external and prospective validation, explainable AI, and user-centered implementation studies. AI has the potential to augment, rather than replace, orthopedic trauma care, but its safe and effective adoption requires substantial methodological maturation.

Background: Pediatric and adult glioblastomas (GBM) represent biologically distinct entities requiring age-tailored therapeutic strategies. However, rapid and non-invasive methods to distinguish these molecular subtypes remain an unmet clinical need. This study evaluates the potential of confocal Raman spectroscopy combined with deep learning as a label-free diagnostic tool to differentiate pediatric from adult GBM based on intrinsic biochemical fingerprints. Methods: We acquired n=1,382 Raman spectra from a cohort of six patient-derived GBM neurosphere cell lines, comprising a pediatric model (SF188) and five adult-origin lines. A multilayer perceptron (MLP) neural network was trained to classify spectra by age group. To ensure rigorous validation and generalizability, performance was assessed on a strictly held-out external test set (20% of data), completely excluded from model optimization. Results: The deep learning model successfully differentiated pediatric from adult GBM signatures with an overall accuracy of 83.6% and an ROC AUC of 0.855 on the independent test set. Spectral analysis revealed distinct vibrational modes, highlighting significant variations in lipid, protein, and nucleic acid content between age groups. Notably, the model achieved a high sensitivity for the pediatric phenotype (91.4% identification rate) . Conclusion: This proof-of-concept study demonstrates that Raman spectroscopy, augmented by deep learning, can identify age-related molecular variations in GBM without extrinsic labeling. By capturing the unique biochemical landscape of pediatric versus adult tumors, this approach lays the foundation for rapid, automated, and objective diagnostic workflows in precision neuro-oncology.

Procalcitonin (PCT) and C-reactive protein (CRP) are considered markers used in clinical practice to differentiate bacterial infections from autoimmune origin. Here we evaluate a rare case of a male patient diagnosed with juvenile idiopathic arthritis. The patient presented repeatedly to our department with atraumatic femoral head necrosis, traumatic medial femoral neck fracture and peri-implant femoral fracture. While undergoing repeated surgical interventions including a removal of osteosynthesis material and total endoprosthesis of his right hip including double subtrochanteric osteotomy, the patient developed drastically increasing infection parameters of PCT and CRP. After a completely inconspicuous revision we revealed the untypical genesis of a rheumatic cause. Consequently, we emphasize this etiology to be considered in further decision making for trauma surgery.

Spinal epidural lipomatosis (SEL) is a rare cause of lumbar canal stenosis, typically linked to obesity or corticosteroid use. Severe cases often require decompression. We report a 48-year-old man with chronic low back pain and morbid obesity (153 kg) who presented with extensive SEL and critical stenosis. Previous conservative therapies failed. A non-surgical strategy using GLP-1–induced weight loss (semaglutide) and medical cannabis for pain control was initiated. Over a follow-up of one year, his weight dropped to 93.8 kg, with major improvement in pain and mobility. MRI demonstrated near-complete regression of epidural fat and resolution of stenosis, avoiding surgery. The patient remained neurologically intact. This appears to be the first documented case of SEL reversal achieved through medical weight loss alone. The findings highlight GLP-1 agonists as a potential therapeutic option in obesity-related spinal disorders and support cannabis as part of non-opioid pain management.