Citations

ABSTRACT Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine learning approaches. These technical barriers have left much of the potential of MRSI unrealized. We acquired MRSI data from 4 human subjects with a diagnosis of multiple sclerosis (MS), incorporating experimental design into an informed machine learning approach. MRSI acquisitions were registered to anatomical MRI to label 105k spectra from brain tissue and 162 spectra from white matter hyperintensities (WMHs), an imaging biomarker associated with MS lesions. Spectral labels were then used in contrastive principal component analysis (cPCA) to filter artifacts and background features in the MRSI data from lesion salient features and clustered into statistically significant states based on features that could be interpreted from the original data. Our approach renders MRSI data into testable representations of neurometabolism, enabling the method for fundamental and clinical research. Graphical Abstract Analysis workflow for neurometabolic profiling of MS lesions. MRSI and anatomical MRI is acquired and processed in parallel for spectral data and anatomical labels. Spectra are then labeled and separated into experimental vs background data for contrastive PCA. Spectra are clustered for similarity, further labeled, and projected onto a brain atlas for a neurometabolic view.

INTRODUCTION: Chronic gastroduodenal disorders are highly prevalent and encompass heterogeneous pathophysiology. Autonomic dysfunction may contribute, but its relative contribution to disease pathophysiology and symptoms is unclear. This study aimed to clarify the contributions of autonomic dysfunction in gastroduodenal disorders and symptom associations using multimodal testing, hypothesizing that distinct disorder subgroups would be revealed. METHODS: This prospective observational cohort study included 80 patients with chronic gastroduodenal symptoms. Participants underwent expert clinician-led autonomic evaluation, body surface gastric mapping (BSGM; Gastric Alimetry, New Zealand), and gastric emptying scintigraphy testing. BSGM phenotyping, gastric emptying cutoffs, and autonomic criteria followed standard criteria. RESULTS: Gastroparesis was diagnosed in 27 (33.8%) patients. Autonomic dysfunction was present in 21 (26.3%) patients, and abnormal BSGM in 20 (25%). Autonomic dysfunction and abnormal BSGM were both more prevalent in gastroparesis (51.9% vs 13.2%, P = 0.001; and 44.4% vs 15.1%, P = 0.005, respectively). However, these diagnoses tended not to overlap, with patients having autonomic dysfunction generally exhibiting normal BSGM tests. Autonomic dysfunction independently predicted delayed gastric emptying (OR 13.8, P < 0.001) and was associated with earlier meal responses (higher meal response ratio, P = 0.04) and worse postprandial distress symptoms ( P < 0.05). DISCUSSION: Autonomic dysfunction independently predicted delayed gastric emptying and revealed a gastroparesis mechanism distinct from gastric myoelectrical impairment. Autonomic dysfunction altered the postprandial gastric motor response and was associated with a greater symptom burden. Multimodal physiological testing including autonomic profiling reveals distinct mechanistic subgroups in patients with chronic gastroduodenal symptoms.

ABSTRACT Background Patients with gastric dysmotility exhibit abnormal transit and reduced contractility, for which quantification is limited with current methodologies. The Atmo Capsule is a wireless motility capsule that measures regional gastrointestinal transit. We tested its technical capabilities to evaluate gastric contractility by detecting changes in capsule acceleration during transit. Methods Data from 91 healthy controls and 182 patients with suspected dysmotility who ingested the Atmo Capsule following a standardized protocol were included. Gastric emptying times (GET) ≥ 5 h were considered delayed. Contraction frequencies (cycles per minute [cpm]) and amplitudes (as arbitrary units [au]) were identified following Fast Fourier Transformation of accelerometer data using 5‐min windows and were assessed in 30‐min windows post‐ingestion (early gastric phase) and before gastric emptying (late gastric phase). Data were compared via ANOVA, using Bonferroni corrections, between healthy controls, patients with normal gastric emptying, and patients with delayed gastric emptying. Key Results Gastric emptying was delayed in 53/182 (29%) patients. Three cpm contraction frequencies were identified across both gastric phases in controls and patients. Contraction amplitudes did not differ between groups in the early gastric phase. In the late phase, mean amplitudes were lower in patients with delayed gastric emptying (6.4 au) compared with subjects with normal gastric emptying (controls, 9.7 au, p < 0.0001; patients, 8.6 au, p < 0.0001). Conclusions & Inference The Atmo Capsule enables precise quantification of gastric contraction profiles, permitting differentiation of normal from pathological motor patterns. Findings of this technical assessment support its potential for evaluating gastric physiology and detecting pathophysiological patterns of gastric dysmotility.

NF-κB activating protein (NKAP) plays important roles in various cancers, including breast cancer. However, its expression and prognosis value in breast cancer remains uncertain. Gene expression profiling interactive analysis, Human protein atlas database, and University of Alabama at Birmingham Cancer data analysis portal database were used to predict the expression and prognostic value of NKAP in breast cancer. Immunohistochemistry, quantitative real time polymerase chain reaction (qRT-PCR) and western blot were performed to detect NKAP expression. The effects of NKAP on cell proliferation, migration and drug sensitivity were investigated in MDA-MB-231 and SK-BR-3 cells. NKAP protein expression differed in breast cancer tissues and paraneoplastic tissues based on the cancer genome atlas data. The high NKAP expression was significantly correlated with a poor prognosis in breast cancer patients. The results of immunohistochemistry, western blot assay, and qRT-PCR proved that NKAP was highly expressed in breast cancer tissues compared with paraneoplastic tissues. In addition, qRT-PCR results showed that high expression of NKAP was significantly correlated with the larger tumor size and higher TNM stage. Moreover, knockdown of NKAP significantly inhibited the proliferation, migration, and enhanced drug sensitivity of MDA-MB-231 and SK-BR-3 cells. NKAP is highly expressed in breast cancer tissues, and its high expression is closely associated with poor prognosis. NKAP also promotes proliferation, migration, and inhibits drug sensitivity of breast cancer cells.

INTRODUCTION: This study used the novel C-reactive protein-albumin-lymphocyte (CALLY) index to explore its relationship with the risk of developing gallstones. METHODS: This study conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey data from 2017 to 2020; multivariable logistic regression examined the CALLY-gallstone association. Restricted cubic splines tested nonlinearity. Subgroup and mediation analyses explored population variations and mediating effects (Conicity, lipid accumulation product, and a body shape index). Receiver operating characteristic curves compared CALLY, systemic immune inflammation index, systemic inflammatory response index, and aggregate index of systemic inflammation predictive performance. RESULTS: After comprehensive adjustments, the highest CALLY quartile had significantly lower gallstone risk vs the lowest (odds ratio 0.56, 95% confidence interval 0.34–0.94). Restricted cubic splines revealed a linear inverse correlation. Subgroup analyses generally supported this inverse relationship. Mediation analysis identified the Conicity index as the strongest mediator (21.47%, P < 0.001), followed by lipid accumulation product and a body shape index (10.72% each, P < 0.001). Receiver operating characteristic analysis showed CALLY (area under the curve = 0.604) outperformed systemic immune inflammation index (0.540), systemic inflammatory response index (0.550), and aggregate index of systemic inflammation (0.546). DISCUSSION: A significant inverse association exists between the CALLY index and gallstone prevalence. The CALLY index demonstrates superior predictive ability compared with other indices, suggesting its potential utility as an objective biomarker for early gallstone risk identification.

OBJECTIVE Deep brain stimulation (DBS) is emerging as an additional surgical option for refractory, multifocal epilepsy. Four-lead DBS systems can be used when seizure onset is poorly localized, generalized, or involves multiple targetable circuits. The goal of this study was to assess preliminary outcomes and complication rates in 4-lead DBS systems for treating epilepsy. METHODS A consecutive series of 32 patients with 4-lead DBS implants who had at least 6 months of follow-up was reviewed, and demographics, outcomes, and complications were abstracted. RESULTS Thirty-two patients implanted with 4 DBS leads (including off-label use) were included. The median age at surgery was 28.5 years, the median age at epilepsy diagnosis was 7.5 years, and 18 patients (56%) were women. The median epilepsy duration prior to implantation was 21 years. The most common seizure localization was frontotemporal (53%), followed by generalized (28%) and parietoccipital (13%). Prior vagus nerve stimulation had been trialed in 20 patients (63%), and an invasive stereotactic EEG recording was performed in 17 patients (59%). Eight patients had prior epilepsy surgery (25%). DBS targets included the anterior thalamic nucleus in combination with the centromedian thalamic nucleus, hippocampus, pulvinar nucleus, cingulate, insula, and globus pallidus. The median surgical duration was 5 hours 22 minutes, with a median operating room time of 8 hours 21 minutes. The median postoperative follow-up was 27 (interquartile range 17–41) months. Three patients experienced transient neurological deficits and 2 patients required intraoperative lead repositioning. There were no infections or hemorrhages. Most patients benefited from stimulation (22% Engel class I, 22% class II, 28% class III, and 28% class IV). The median patient-reported seizure reduction rate was 70%, with a responder rate of 72%. One patient elected to have the system explanted. CONCLUSIONS Four-lead DBS is safe and can achieve meaningful improvement in challenging cases of multifocal epilepsy with a low complication rate.

Abstract Background Large language models (LLMs) are increasingly used in medication-related tasks despite limited evidence supporting their accuracy and safety. LLMs are vulnerable to adversarial attacks and may confabulate in response to fabricated information in the prompt. Given the substantial risk of harm from medication errors, the purpose of this study was to establish a benchmark task to evaluate LLM performance in distinguishing fabricated from real medications. Methods Two datasets (brand and generic), each consisting of 250 medication lists were developed, each including 4-6 medications and one fabricated medication (specifically a Pokémon character), with complete dosing information provided. Six LLMs were evaluated (GPT-5-Chat, GPT-4o-mini, DrugGPT, Gemma-3-27B-IT, Llama-3.3-70B-Instruct, and Qwen3-32B). For each medication list, models were queried for dosing information and disease indication across three experimental conditions: standard decoding, standard decoding with mitigation prompting, and deterministic decoding (temperature = 0). Each experiment was performed in triplicate. Outputs were deemed confabulations if the LLM failed to recognize and reject the fabricated medication. The primary outcome was the rate of confabulations. Exact paired-permutation tests were used to compare confabulation rates across LLMs and prompting approaches. Results Confabulation rates under standard conditions ranged from 2.7% to 99.6%. DrugGPT demonstrated the lowest baseline confabulation rates (2.7-6.4%), whereas other models demonstrated substantially higher rates (47.2-99.6%). Mitigation prompting significantly reduced confabulation rates compared to standard and deterministic decoding across all tasks and datasets (p < 0.001): GPT-5-Chat achieved confabulation rates of 0% with mitigation prompting from baseline rates of 66.4-88.5%. Deterministic decoding did not substantially impact confabulation rates. Conclusions LLMs performing medication-related tasks are susceptible to confabulation in response to invalid or fabricated inputs, raising important safety concerns for clinical use. While mitigation prompting reduced confabulation rates, this study underscores the importance of robust safeguards when deploying LLMs in medication-related decision-making.

103 Background: Clinicians spend significant time extracting and synthesizing information to risk-stratify and make adjuvant chemotherapy (AC) recommendations for CRC. By automating parts of this process, LLMs could help reduce documentation burden and variability in decision-making. Methods: We selected 144 stage III, pMMR CRC patients treated at Mayo Clinic (2017–2021). HIPAA-compliant Gemini-2.0-Flash-001 and GPT-4o extracted clinical variables (age, neuropathy grade, pathological stage) from EHRs and generated AC recommendations. Two approaches were compared: (1) a rule-based algorithm with nested if/else statements translating variables into NCCN-based recommendations, and (2) a dynamic LLM agent generating recommendations using a structured prompt informed by NCCN guidelines. Concordance with treating physicians was assessed, with disagreements reviewed by a blinded GI oncologist. Results: The dataset included 144 patients with median age 67 (IQR 66–68), 48.6% female. The LLM pipeline extracted neuropathy grade with 100% accuracy and pathological T/N stages with 99.3% accuracy. Concordance with physician recommendations was 93.1% for the rule-based algorithm and 62.5% for the LLM agent. In discordant cases, blinded adjudication by a GI oncologist favored the algorithm and LLM agent over physicians in most cases (Table 1). Conclusions: This exploratory work shows LLM agents can reliably extract variables and generate evidence-based treatment recommendations for stage III CRC. The high rate of expert preference for LLM recommendations in discordant cases is intriguing but limited by small sample size and possible adjudicator bias. Discordant physician recommendations reflect the complexity of decision-making, including patient preferences, performance status, comorbidities, and subtle findings not fully captured in EHRs. Rather than replacing judgment, we envision LLMs as decision support tools—analogous to an advanced trainee reviewing charts and presenting evidence-based recommendations to the attending physician with ultimate authority. Larger multi-institutional studies with multiple adjudicators are needed to validate these findings and clarify AI’s role in oncology. Patient characteristics, LLM extraction accuracy, and concordance with physician recommendations. Metric Result Total patients 144 Median age (IQR) 67 (66-68) Female (%) 48.6% LLM extraction accuracy – Neuropathy grade 100% LLM extraction accuracy – Pathological T and N stages 99.3% Primary Concordance – Rule-based algorithm vs. physician 93.1% Adjudicator preference of rule-based algorithm over treating physician for discordant cases 100% Concordance – LLM agent vs. physician 62.5% Adjudicator preference of LLM agent over treating physician for discordant cases 82.0%

Abstract Background The mechanisms of left ventricular outflow tract obstruction (LVOTO) in hypertrophic cardiomyopathy (HCM) with moderate septal hypertrophy (≤ 18 mm) remain uncertain with therapeutic implications. This study investigated the role of three-dimensional echocardiography (3DE)-derived myocardial mechanics in LVOTO. Methods We retrospectively analyzed 216 HCM patients with moderate septal hypertrophy, stratified into nonobstructive (n = 38), provokable LVOTO (n = 63), and resting LVOTO (n = 115). Transthoracic echocardiography assessed LV geometry, LVOT diameter, anterior mitral leaflet length, and papillary muscle abnormalities (via a composite SubMV score). 3DE-derived strain parameters, including global longitudinal (GLS), circumferential, radial, area (GAS) strain, twist, and torsion, were quantified. Determinants of LVOTO were evaluated using multivariate regression, restricted cubic splines (RCS), and receiver operating characteristic (ROC) analysis. Results Both LVOTO subgroups showed significantly augmented strain mechanics versus nonobstructive patients, greatest in resting LVOTO (p < 0.05); twist and torsion were the strongest discriminators (p < 0.001). Multivariate regression showed that, beyond LVOT diameter and SubMV score, torsion (B = 9.47), twist (B = 1.92), and GAS (B = 1.29) independently predicted provoked LVOT gradients (all p < 0.05); RCS demonstrated nonlinear twist/torsion–LVOTO relationships, with obstruction risk rising sharply above twist = 15° and torsion = 3°/cm, especially under provocation. Torsion discriminated provokable LVOTO achieving 81% specificity (cutoff 2.4°/cm, AUC = 0.72), outperforming all structural parameters. Integrating 3DE-derived mechanics with structural metrics improved diagnostic accuracy, especially in provokable LVOTO (AUC 0.84 vs. 0.76, DeLong p = 0.003). Conclusions In moderate-hypertrophy HCM, 3DE-derived strain mechanics, especially twist and torsion, independently determine LVOTO beyond structural narrowing, particularly under provocation, strengthening mechanistic insight and clinical applicability.

Key Points α Klotho as an antiaging protein can extend the lifespan of healthy animals; serum α Klotho was lower in patients with autosomal dominant polycystic kidney disease. Transgenic α Klotho and recombinant α Klotho delayed cyst growth in Pkd1 mutant mouse kidneys. α Klotho exerted its proapoptotic and anti-inflammatory function through interacting with and destabilizing cellular inhibitor of apoptosis protein 1. Background α -Klotho ( α Klotho) is expressed mainly in the kidney, which is encoded by the KL gene, existing in a membrane-bound form and a soluble circulating form. Soluble serum α Klotho was lower in patients with autosomal dominant polycystic kidney disease (ADPKD). However, the role of α Klotho in ADPKD remains unknown. Methods To investigate the roles of α Klotho in ADPKD, we generated transgenic α Klotho and Pkd1 mutant Pkd1-HOMO-KL Tg mice to evaluate cyst growth and investigated underlying mechanisms with single-cell RNA sequencing analysis. Results We showed that α Klotho was lower in diverse kidney cell types in Pkd1 mutant kidneys, which was restored by transgenic α Klotho. We identified differential expression genes and pathways mediated by transgenic α Klotho at single-cell resolution and determined the cytokines and their receptors mediated cell-to-cell communication networks in Pkd1 mutant and Pkd1-HOMO-KL Tg kidneys by CellChat analysis. We further showed that α Klotho exerted a proapoptotic and anti-inflammatory function through interacting with and destabilizing cellular inhibitor of apoptosis protein 1, which disturbed TNF and its receptor mediated prosurvival complex, leading to ( 1 ) the formation of predeath complex to increase cystic cell death and ( 2 ) the decrease of the activation of NF-κB, the expression of cytokines, and the recruitment of macrophages, resulting in a delay of cyst growth. Treatment with recombinant α Klotho also delayed cyst growth in Pkd1 mutant kidneys. Conclusions This study defined an unknown role and mechanism of α Klotho in the regulation of a TNF-dependent cystic cell apoptosis and inflammation in Pkd1 mutant kidneys.