Citations

ABSTRACT Background Inflammation plays a critical role in cancer cell proliferation; however, the specific role of inflammasomes, multiprotein complexes that regulate inflammation‐associated signaling pathways, in prostate cancer (PCa) remains insufficiently explored. This study aims to characterize the expression of inflammasome‐related genes in PCa and evaluate their association with clinical outcomes. Methods De‐identified transcriptome data from the Decipher GRID RP, a cohort of 52,266 radical prostatectomy (RP) samples tested (2016–2024) with the Decipher prostate genomic classifier (Veracyte, San Diego, CA), were retrieved from the GRID registry (NCT02609269). Expression analysis of 34 genes involved in inflammatory pathways was conducted to associate their expression with clinical and genomic variables. Outcomes analyses were conducted on a retrospective cohort of 855 patients treated with RP (META855). Results Analysis of inflammasome gene expression in the GRID RP cohort revealed that most genes exhibit low baseline expression, whereas HSP90AB1, APP, TXN, and TXNIP demonstrate strong expression signals. Additionally, higher expression of most genes was associated with Gleason Grade Group 4–5 and very high Decipher scores. On survival analysis of the META855 cohort, higher expressions of AIM2 and HSP90AB1 were significantly associated with worse metastasis‐free survival. Conversely, both high and low expression levels of NLRP3 were associated with better metastasis‐free survival outcomes following RP compared to average expression. On multivariable Cox regression analysis, higher expressions of AIM2 (HR 1.75) and HSP90AB1 (HR 1.60) were significantly associated with shorter time to metastasis following RP. Conclusions There is molecular heterogeneity within pro‐inflammatory genes among patients with PCa. Our findings showed there is a potential association between the expression levels of certain inflammasomes, such as AIM2, HSP90AB1, and NLRP3, and oncological outcomes following RP.

ABSTRACT Leptomeningeal disease (LMD) is a rapidly fatal complication of systemic cancer for which sensitive diagnostic tools and informative biomarkers remain limited. Here, we introduce CSF-Seq, a method for whole-transcriptome sequencing of cell-free RNA (cfRNA) from human cerebrospinal fluid (CSF), designed to enable molecular profiling of LMD and other central nervous system (CNS) conditions. Using a prospectively collected CSF biobank, we analyzed 125 samples spanning multiple pathologies, including breast and lung LMD, glioblastoma, traumatic brain injury, and non-cancer neurological controls. Through optimized RNA extraction, library preparation, and deep sequencing, CSF-Seq generated robust and reproducible transcriptome-wide profiles despite the low abundance and fragmentation of cfRNA in CSF. CSF transcriptomes exhibited disease-specific expression, separating LMD from non-cancer controls and from non-LMD cancers, independent of CSF collection modality. Tumor-associated epithelial transcripts, including CEACAM6 and MUC1, were consistently enriched in LMD samples, whereas immune and CNS-associated transcripts were broadly detected across disease states, consistent with contributions from both tumor and non-tumor sources. Cross-site processing of matched samples demonstrated high concordance, indicating preservation of sample-specific transcriptional signatures across independent workflows. Importantly, we identified a collection method– independent LMD gene expression signature that was significantly associated with overall survival, supporting its potential prognostic relevance. Together, these findings establish CSF-Seq as a technically robust and clinically informative platform for transcriptomic biomarker discovery in CNS metastatic disease, offering a minimally invasive approach for disease characterization, risk stratification, and longitudinal monitoring in patients with LMD.

Abstract Background Stroke is a time-sensitive neurological emergency in which early EMS activation and presentation to definitive care are cornerstones of effective therapy. Large language models (LLMs) are increasingly consulted by the public for medical advice, but the veracity of the guidance provided by commercially available models responding to potential stroke symptoms is not well understood. Methods We performed a cross-model benchmarking study comparing the triage choices of three frontier LLMs (Claude Sonnet 4.6, GPT-4o, and Llama 3.3-70b-versatile) on first-person vignettes describing a unilateral arm symptom on waking, across 10 symptom descriptors, and two clinical phases (before and after a partially reassuring self-examination), with or without a clinical distractor (n=50 per condition). Results Claude sought emergency care most often, Llama least, and GPT-4o in between, diverging most sharply in the post-examination phase where Claude called 911 in 100% of runs, Llama called for non-emergency help in 100%, and GPT-4o was symptom-dependent. A distractor shifted behavior away from emergency care in almost all conditions: calling 911 fell from 37.9% to 14.6% and waiting rose from 0% to 45.9% in the post-examination vignette. Responses were also sensitive to symptom word: weak, limp, heavy, and clumsy generated higher alarm, whereas numb, tingly, odd, strange, and weird generated less urgent responses. Conclusions The increasing use of LLMs for medical advice has significant public health implications. Commercially available LLMs show significant model-to-model variability and framing sensitivity when confronted with potential stroke symptoms, including under-recognition of canonical CDC warning descriptors, underscoring the need for systematic benchmarking as these tools become de facto first points of contact for patients experiencing neurological emergencies.

Abstract Purpose Crisis periods—defined as sustained disruptions to the clinical environment resulting in increased workload, exposure to high-acuity patient care, or limited access to coping resources—can lead to posttraumatic stress disorder (PTSD) among residents and fellows (trainees). By examining self-reported pandemic-related PTSD symptoms, associated risk factors, and their consequences, this study aims to identify vulnerable groups and inform institutional strategies to support trainee well-being during and after crisis events. Method A multi-institutional cross-sectional survey was conducted at 3 U.S. academic medical centers between April and May 2022. Measures included the Primary Care PTSD Screen for DSM-5 (range 0 to 5, scores ≥4 indicate probable PTSD), 4-item PROMIS Social Isolation scale (normed T-score range 0 to 100 scale with higher scores indicating greater isolation), items exploring intent to stay at one’s institution, and demographics. Multivariable analysis adjusted for demographics, specialty, and postgraduate year. Results Of 6,057 eligible trainees, 3,185 (53%) participated in the survey, with 3,109 completing the PTSD screening. A total of 190 (6%) trainees screened positive for self-reported PTSD symptoms. Multivariable analysis revealed that female trainees had a higher odds of screening positive for self-reported PTSD symptoms compared to males (odds ratio [OR] 1.45; 95% confidence interval [CI], 1.03-2.04; P = .03). Greater social isolation was independently associated with increased PTSD risk (OR 1.09; 95% CI, 1.07-1.11; P < .001). Additionally, trainees who did not intend to remain at their institution after training were more likely to screen positive for PTSD (OR 1.74; 95% CI, 1.17-2.58; P = .01). Conclusions Female gender, greater social isolation, and reduced intention to remain at one’s institution are independently associated with self-reported PTSD symptoms in trainees. Recognizing these relationships may enable health care leaders to proactively identify trainees at higher risk during crisis periods and to implement targeted, evidence-based interventions that promote well-being and workforce retention.

PURPOSE Taxanes are associated with a higher incidence of immediate hypersensitivity reactions (HSRs) compared with other classes of chemotherapeutic agents, even with premedication. This study evaluates whether a titrated infusion protocol reduces the rate of first-dose HSRs to taxanes. METHODOLOGY This was a retrospective cohort study conducted at a single-site hematology/oncology infusion center. The fixed-rate arm consisted of patients treated from January 2022 to July 2023 who received a fixed infusion rate for their first dose. The titration arm comprised patients treated from August 2023 to July 2024 who underwent a three-step titration protocol for their first dose. The primary end point was the rate of immediate HSRs during the first lifetime dose of docetaxel or paclitaxel. Secondary end points included HSR severity, HSRs during the first or second dose, and rate of treatment discontinuation because of HSRs. RESULTS Of 479 patients included (240 fixed-rate; 239 titration), HSRs occurred in 7.1% of the fixed-rate group and 11.3% of the titration group (odds ratio, 1.67 [95% CI, 0.89 to 3.15]; P = .1104). There were no differences in the severity of HSRs or in the rate of treatment discontinuation among both groups. CONCLUSION The use of a titrated infusion protocol for first lifetime dose taxanes did not significantly reduce the incidence or severity of immediate HSRs. These findings suggest that a titration protocol may not confer additional benefits over fixed-rate in this setting.

ABSTRACT The Burkholderia cepacia complex (BCC) is comprised of 24 species of Gram-negative bacteria that cause opportunistic infections. While antimicrobial susceptibility testing (AST) has historically been used to guide treatment for BCC infections, recent work highlighting problems with AST for these organisms led the Clinical and Laboratory Sciences Institute (CLSI) to remove disk diffusion (DD) and minimal inhibitory concentration (MIC) breakpoints for BCC from its M100 standards document. Epidemiological cut-off values (ECVs) may be helpful to clinicians in the absence of breakpoints, as they may be used to determine whether an isolate has a wild-type or non-wild-type phenotype. Here we present an analysis of BCC ECVs for ceftazidime (CAZ), levofloxacin (LVX), meropenem (MEM), minocycline (MIN), and trimethoprim-sulfamethoxazole (TMP-SMX). ECVs were calculated using MIC data from 3 previous studies and 3 independent laboratories for 1,896 BCC isolates. ECVs were 16 μg/ml for CAZ, 8 μg/ml for LVX, 16 μg/ml for MEM, and 8 μg/ml for MIN. The ECV for TMP-SMX varied depending on the analysis from 2 μg/ml, 8 μg/ml, and 16 μg/ml and therefore could not be reliably established. Challenges with establishing ECVs for BCC include limitations with the pooled MIC dataset, broad MIC distributions, and high ECVs that are above the obsolete susceptible MIC breakpoints. These challenges limit the clinical utility of ECVs for these organisms and supported removal of ECVs from the CLSI M100 standards document. IMPORTANCE The Burkholderia cepacia complex is a group of bacterial species that cause difficult-to-treat opportunistic infections. Recently, clinical breakpoints, which are used to determine whether organisms are susceptible to certain antimicrobials, were removed from Clinical and Laboratory Standards Institute (CLSI) standards for these organisms due to problems with antimicrobial susceptibility testing performance. Clinicians are now faced with the challenge of how to treat these complex infections without clinical breakpoints. Here we determine epidemiological cut-off values (ECVs) for relevant antimicrobials for the B. cepacia complex. While we established ECVs for four antimicrobials, we encountered significant challenges in our analyses, including limitations with data for these organisms and high ECVs that are not clinically useful. These challenges limit the practical use of these ECVs in helping guide clinicians on treatment and supported the eventual removal of ECVs from the CLSI M100 standards document.

OBJECTIVE Intramedullary spinal cord tumors (IMSCTs) are typically treated with maximal safe resection, during which neurosurgeons often monitor for neurological injury using muscle motor evoked potential (mMEP) and direct wave (D-wave) neuromonitoring. The predictive value of changes in D-waves for identifying motor outcomes is underexplored. This study evaluated the utility of D-waves for predicting postoperative motor deficits. METHODS Patients who underwent resection of a primary IMSCT with mMEP neuromonitoring from 2003 to 2023 at a tertiary care hospital were identified. Patients who underwent D-wave monitoring in addition to mMEP monitoring were compared to those who underwent mMEP monitoring alone using the Mann-Whitney U-test, chi-square test, and Fisher’s exact test. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of D-wave and mMEP monitoring for identifying new neurological deficits immediately postoperatively and at 1 month, 6 months, and last follow-up were calculated. RESULTS After matching, 125 patients were included (median age 42.0 years; 57.6% male; median follow-up 34.0 months), of whom 88 had both mMEP and D-wave data. The most common pathologies were ependymoma (64.0%) and astrocytoma (17.6%). Patients who did and did not undergo D-wave neuromonitoring had similar preoperative neurological function, primary pathology, tumor grade, and tumor location. D-wave use was associated with increased gross-total resection (88.6% vs 64.9%, p = 0.002) and reduced mortality (5.7% vs 24.3%, p = 0.007), length of stay (5.0 vs 6.0 days, p = 0.033), and 30-day readmission (2.3% vs 13.5%, p = 0.013) and reoperation (1.1% vs 10.8%, p = 0.012). At the 6-month follow-up, D-wave monitoring alone was superior to mMEP and combination monitoring for detecting new motor deficits. D-wave monitoring had peak sensitivity (77.8%) and NPV (96.5%) at 6 months and peak specificity (95.8%) and PPV (76.9%) in the immediate postoperative period. CONCLUSIONS D-wave monitoring was associated with reduced mortality and was more accurate than mMEP monitoring alone or combination monitoring for detecting new postoperative neurological deficits. Further prospective studies are needed to validate these results.

Background and Objectives: Obstructive sleep apnea (OSA) is a heterogeneous disease with diverse clinical presentations and high global prevalence. Obesity is a common comorbidity in OSA, but its relationship with symptom subtypes remains unclear. This study aimed to evaluate the association between OSA symptom subtypes and obesity in a clinical cohort. Methods: This observational study analyzed data from the Santiago Obstructive Sleep Apnea (SantOSA) prospective clinical cohort, including adults with OSA confirmed by home sleep apnea testing. Symptom subtypes were identified using latent class analysis. Associations between obesity and symptom subtypes were evaluated using multivariable regression models adjusted for age, sex, tobacco use, RDI, T90%, TST, hypertension, and diabetes. Statistical significance was set at p < 0.05. Results: A total of 1167 patients were included (943 men). Latent class analysis identified three symptom subtypes: non-sleepy, disturbed sleep, and excessive daytime sleepiness. Among obese patients, 30.7%, 50.0%, and 19.3% were classified into these subtypes, respectively. Obesity prevalence was 50.2%, and compared with non-obese OSA patients, obese patients showed a higher prevalence of severe OSA (46.1% vs. 26.9%), hypertension (54.4% vs. 34.9%), and diabetes (37.2% vs. 19.4%), as well as higher ESS scores and higher RDI and T90% values (all p < 0.01). In adjusted analyses, obesity remained independently associated with the excessive daytime sleepiness subtype after controlling for age, sex, tobacco use, RDI, T90%, TST, and comorbidities. Conclusions: Obesity is highly prevalent in OSA and is associated with specific symptom-defined phenotypes, particularly excessive daytime sleepiness and disturbed sleep. These findings support the relevance of considering symptom profiles alongside traditional severity metrics.