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OBJECTIVE Coexisting medical conditions are increasingly prevalent in surgical populations. The impact of multiple comorbidities on patient-reported outcomes (PROs) and endotypes of frequently co-occurring conditions for cervical spondylotic myelopathy (CSM) remain unclear. This study explores whether CSM patients with multimorbidity have worse baseline and postoperative PROs and less functional improvement after surgery compared to those with few or no comorbidities. The authors also investigated whether distinct comorbidity endotypes exist among CSM surgical patients and whether they influence postoperative outcomes. METHODS The prospective Quality Outcomes Database (QOD) was used to assess patients undergoing surgery for CSM. Multimorbidity was defined as ≥ 2 chronic conditions, including diabetes, coronary artery disease, peripheral vascular disease, arthritis, chronic renal disease, chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, depression, and anxiety. Baseline characteristics and 24-month PROs were assessed across multiple-comorbidity status, including modified Japanese Orthopaedic Association (mJOA), Neck Disability Index (NDI), visual analog scale for neck and arm pain, EQ-5D, and patient satisfaction scores. Clusters were identified from the full cohort using k-medoids, revealing subgroups with similar comorbidity endotypes. RESULTS The final cohort included 1141 CSM patients (83.1% reaching 24-month follow-up), with 761 (66.7%) having 0 or 1 comorbidity and 380 (33.3%) ≥ 2 comorbidities. The multimorbidity cohort was older (mean age 62.6 ± 11.2 vs 59.5 ± 12.0 years, p < 0.001), more likely to be female (52.9% vs 44.7%, p = 0.011), and had a higher BMI (mean 31.1 ± 6.7 vs 29.7 ± 6.2 kg/m 2 , p < 0.001). Multimorbidity patients exhibited worse mJOA, NDI, and EQ-5D scores at baseline and 24 months (p < 0.05). On multivariable analysis, the total number of comorbidities was not significantly associated with any PRO measures. Four comorbidity clusters were identified: low burden, arthritis, diabetes, and high burden. On one-way ANOVA, the baseline mJOA score was significantly different across clusters (p = 0.003). At 24 months, the mJOA score was significantly lower in the diabetes and high-burden endotypes. Twenty-four-month score change and minimal clinically important difference (MCID) achievement of all PROs remained similar across clusters (p > 0.05). CONCLUSIONS While patients with multimorbidity have worse baseline and postoperative PROs, they achieve similar functional and pain-related improvements following CSM surgery. Similarly, the comorbidity endotypes identified in this QOD cohort suggest that certain patterns of coexisting chronic conditions, such as overlapping diabetes and arthritis, are associated with different levels of disability but may not diminish the effectiveness of surgical intervention.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

Abstract INTRODUCTION Neurofilament light chain (NfL) quantification aids in diagnosing and predicting neurological disorders, but clinical and laboratory practices vary across centers. Differences in result interpretation and reporting further challenge test commutability. This study aimed to review the global analytical and post‐analytical methods used for NfL measurement in routine clinical practice across different contexts. METHODS We established an international working group (WG) and distributed a survey to its members to gather information on context of use (COU), (pre) analytical methods, cutoff usage, as well as the interpretation and reporting of NfL measurements. RESULTS Among the centers, 63% measured NfL in cerebrospinal fluid (CSF), 87% in blood, and 53% in both. COU was widespread, with 50% defining pathological cutoffs based on publications and 42% considering age. Reporting was primarily done through numeric results (95%). DISCUSSION Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice. Highlights Unique international overview of current analytical and post‐analytical methods for neurofilament light chain (NfL) measurement in routine clinical practice. Tailored sheets for each neurological application. Strategies to harmonize cutoffs, reporting, and interpretation of NfL's measurement.

Abstract Introduction: Opioid use disorder constitutes a significant health crisis in the United States, contributing to high rates of opioid overdose-related deaths. A major driver of these fatalities is fentanyl, a potent synthetic opioid with both sedative and analgesic properties. These properties that make fentanyl clinically effective also increase its addictive potential. Early in opioid addiction, abnormal increases in extracellular dopamine in the nucleus accumbens (NAc) reinforce excessive drug-seeking behaviors which can lead to fatal respiratory depression. Given this mechanism, we investigated whether high frequency stimulation (HFS), mimicking human deep brain stimulation (DBS) of the ventral tegmental area (VTA) could block NAc dopamine increase following an acute lethal dose of fentanyl. We hypothesized that VTA DBS would mitigate these dopaminergic responses and prevent fentanyl-induced respiratory failure. Methods: Multiple cyclic square wave voltammetry (M-CSWV), was applied via a carbon fiber microelectrode in the NAc of urethane-anesthetized male Sprague-Dawley rats. Dopamine levels were recorded at baseline and following acute fentanyl (30 µg/kg, i.v.). HFS (130 Hz frequency, 200 µsec pulse width, and 0.2 mA amplitude) was administered to the VTA before and during fentanyl exposure. Results: Acute fentanyl administration resulted in a 178.2% increase in NAc dopamine levels from baseline, accompanied by a decline in respiratory rates to critically low levels (45 breaths per minute vs. 102 bpm), eventually resulting in 100% mortality. HFS of the VTA did not significantly alter baseline tonic dopamine levels or prevent fentanyl-induced dopamine increase in the NAc but was able to fully rescue fentanyl-induced respiratory failure.

Introduction Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a high 5-year survival rate of approximately 98%. Despite advances in diagnosis and treatment, up to 20% of patients experience recurrence, adversely affecting their quality of life. Predictive models have been developed to assess recurrence risk and guide clinical decision-making, but these models often face limitations such as retrospective design, lack of diversity in study populations and absence of external validation. The primary aim is to externally validate existing predictive models for DTC recurrence using prospective data from a diverse Latin American cohort. The secondary aim is to explore opportunities for model recalibration to improve their performance in our population. Methods and analysis The CaTaLiNA study is a multicentre prospective observational study conducted across 10 hospitals in five Latin American countries, including Ecuador, Peru, Uruguay and Mexico. Patients aged 18 years or older receiving treatment for DTC, such as the first thyroid surgery, active surveillance or radiofrequency ablation will be included. Recruitment will occur from November 2023 to June 2025, with follow-up extending until June 2028. Data collection will include baseline clinical, surgical and histological characteristics, treatment details and follow-up outcomes. Statistical analysis will follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines, using imputation strategies for missing data and evaluating calibration and discrimination of the prediction models. Calibration measures include the ratio of expected and observed events, calibration slope and calibration plot, while discrimination will be assessed using the C-index and area under the receiver operating characteristic curve. Ethics and dissemination This study protocol was approved by Comité de Ética de Investigación en Seres Humanos de la Universidad San Francisco de Quito USFQ ‘CEISH-USFQ’ APO-010–2023-CEIHS-USFQ Oficio No. 161-2023-CA-23030M-CEISH-USFQ. Results will be disseminated via peer-reviewed publications.

SUMMARY Tau aggregates propagate through the brain in a prion-like manner in Alzheimer’s disease (AD) and other tauopathies, but the molecular identity and functional partners of the seeding-competent Tau species remain poorly defined. Here, we present an unbiased proteomic profiling of a high-molecular-weight (HMW) Tau-seed isolated from AD patient brains. We contrast this interactome with that of a biochemically similar, seeding-incompetent HMW-Tau species from age-matched healthy controls. Despite comprising less than 5% of total Tau in the brain, Tau-seed associates with a distinct set of proteins enriched in synaptic, mitochondrial, and vesicle-trafficking functions. Cross-species functional screening in Drosophila and mouse models identifies interactors that modulate Tau toxicity and seeding. Spatially resolved analysis of postmortem AD brains reveals heterogenous co-deposition of these proteins with Tau aggregates, suggesting functionally distinct Tau-seed complexes. Together, this dataset provides a framework for understanding selective Tau-seed toxicity and identifies candidate regulators of Tau propagation with therapeutic potential.

Activation of oncogenes, such as through mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS), triggers profound disruptions in cellular homeostasis that set off a cascade of stress responses. These responses enable cells to cope with the array of challenges encountered during tumorigenesis by activating defense mechanisms that promote adaptation and survival. Key components of this oncogenic stress response include heat shock proteins, the ubiquitin-proteasome system, autophagy, nuclear factor erythroid 2-related factor 2–antioxidant response element signaling, DNA damage response proteins, p53, redox-regulating proteins, and stress granules. This review concentrates on KRAS-driven oncogenic transformation, as KRAS mutations are among the most common in human cancers, accounting for over 90% of pancreatic ductal adenocarcinoma cases, around 30% of lung cancers, and approximately 50% of colorectal cancers. We examine the intricate molecular interplay between oncogenic stress and the associated cellular defense mechanisms, emphasizing the key molecular events that follow KRAS activation. Importantly, the very pathways that allow cancer cells to adapt to oncogenic stress also offer novel therapeutic opportunities. By selectively targeting pivotal regulators within these stress response pathways, we can potentially disrupt the survival mechanisms of cancer cells. This strategy not only promises to enhance the effectiveness of existing treatments but also paves the way for the development of innovative therapies designed to combat tumor progression. In essence, exploiting oncogenic stress responses represents an original and promising therapeutic approach in the fight against cancer.