There is an unmet need for objective disease-specific biomarkers in the heterogeneous autoimmune neuromuscular disorder myasthenia gravis (MG). This cross-sectional study identified a signature of 23 inflammatory serum proteins with proximity extension assay (PEA) that distinguishes acetylcholine receptor antibody seropositive (AChR+) MG patients from healthy controls (HCs). CCL28, TNFSF14, 4E-BP1, transforming growth factor alpha (TGF-α), and ST1A1 ranked top biomarkers. TGF-β1 and osteoprotegerin (OPG) differed between early- and late-onset MG, whereas CXCL10, TNFSF14, CCL11, interleukin-17C (IL-17C), and TGF-α differed significantly with immunosuppressive treatment. MG patients with moderate to high disease severity had lower uPA. Previously defined MG-associated microRNAs, miR-150-5p, miR-30e-5p, and miR-21-5p, correlated inversely with ST1A1 and TNFSF14. The presented inflammatory proteins that distinguish AChR+ MG are promising serum biomarkers for validation in prospective studies to allow for molecular signatures for patient subgroup stratification and monitoring of treatment response.

INTRODUCTION: Ulcerative colitis (UC) associated with primary sclerosing cholangitis (PSC-UC) is considered a unique inflammatory bowel disease (IBD) entity. PSC diagnosis in an IBD individual entails a significantly higher risk of gastrointestinal cancer; however, biomarkers for identifying patients with UC at risk for PSC are lacking. We, therefore, performed a thorough PSC-UC biomarker study, starting from archived colonic tissue. METHODS: Proteins were extracted out of formalin-fixed paraffin-embedded proximal colon samples from PSC-UC (n = 9), UC (n = 7), and healthy controls (n = 7). Patients with IBD were in clinical and histological remission, and all patients with UC had a history of pancolitis. Samples were processed by the multienzyme digestion FASP and subsequently analyzed by liquid chromatography–tandem mass spectrometry. Candidate proteins were replicated in an independent cohort (n: PSC-UC = 16 and UC = 21) and further validated by immunohistochemistry. RESULTS: In the discovery step, 7,279 unique proteins were detected. The top 5 most differentiating proteins (PSC-UC vs UC) based on linear regression analysis were selected for replication. Of these, 1-acetylglycerol-3-phosphate O-acyltransferase 1 (AGPAT1) was verified as higher in PSC-UC than UC (P = 0.009) in the replication cohort. A difference on the group level was also confirmed by immunohistochemistry, showing more intense AGPAT1 staining in patients with PSC-UC compared with UC. DISCUSSION: We present AGPAT1 as a potential colonic biomarker for differentiating PSC-UC from UC. Our findings have possible implication for future PSC-IBD diagnostics and surveillance.

Predicting disease progression in metabolic dysfunction-associated steatotic liver disease (MASLD) is challenging, and current non-invasive tests (NITs) lack the precision to replace liver biopsy. This study aimed to identify plasma biomarkers for different stages of fibrosis using affinity-based proteomics in two biopsy-proven cohorts. The primary objective was to identify biomarkers capable of distinguishing between low-to-no fibrosis (F0-1) and significant fibrosis (F2-4) in MASLD. Participants in the discovery cohort were recruited from Uppsala University Hospital and Swedish CArdioPulmonary bioImage Study (SCAPIS), while the validation cohort was included from Linköping University Hospital. All participants diagnosed with MASLD underwent liver biopsy and were categorized by fibrosis stage (F0-1 or F2-4). A total of 276 plasma proteins were analyzed using Olink^® panels, with biomarkers identified through ordinal logistic regression, random forest (RF) analysis and the Boruta algorithm. The discovery cohort included 60 participants, with 60% having fibrosis stage F0-1 and 40% having F2-4. The validation cohort had 59 participants, of whom 35 had fibrosis stage F0-1 (59.3%) and 24 had stage F2-4 (40.7%). Five biomarkers were significantly associated with fibrosis stage in the discovery cohort, with four confirmed in the validation cohort. A model combining angiotensin converting enzyme-2 (ACE2), hepatocyte growth factor (HGF) and insulin-like growth factor-binding protein-7 (IGFBP-7) demonstrated strong predictive performance for significant fibrosis (c-statistics 0.82-0.83), outperforming fibrosis-4 (FIB-4) (c-statistics 0.61-0.72). A biomarker model including ACE2, HGF and IGFBP7 shows promise in distinguishing between low-stage and significant fibrosis.

Background and Purpose: Myasthenia gravis (MG) lacks disease‐specific biomarkers that can support monitoring of disease activity or guide treatment decisions. This study aimed to validate serum inflammatory proteins as MG‐specific biomarkers by comparing their specificity to controls and individuals with other autoimmune neurological disorders, including multiple sclerosis (MS) and chronic inflammatory demyelinating polyneuropathy (CIDP). Methods: In this multicentre cross‐sectional study, serum from 200 acetylcholine receptor antibody seropositive (AChR+) MG patients, 192 matched controls, 93 MS patients, and 51 CIDP patients was analyzed using a 92‐plex inflammation panel (Olink PEA). Logistic regression, principal component analysis, and Boruta machine learning algorithms identified differentially expressed proteins. MG subgroups were defined by age at onset, disease severity, and immunosuppressive treatment. Results: Fourteen proteins significantly distinguished MG from controls, including AXIN1 (OR: 0.24), IL7 (OR: 9.38), ST1A1 (OR:0.42), IL10 (OR:3.62), CASP‐8 (OR:1.61), and TNFSF14 (OR:0.50) (Bonferroni‐corrected p < 0.00135). AXIN1, ST1A1, STAMBP, CDCP1, and SIRT2 were specific for MG, separating it from MS and CIDP. Shared markers across disorders included IL6, IL8, STAMBP, and TNFSF14. A 15‐protein profile, including FGF‐23 and CXCL9, correlated with MG severity. Subgroup analyses revealed distinct protein patterns by age and treatment. TRANCE and CD6 were reduced in immunosuppressed patients, whereas EN‐RAGE, IL10, and TNFRSF9 varied in those receiving biologicals. Conclusions: This study validates the MG‐specific serum proteomic biomarkers AXIN1, STAMBP, ST1A1, CDCP1, and SIRT2 and identifies signatures associated with severity, onset, and treatment. These findings support the use of blood‐based biomarkers for monitoring and stratification in MG clinical trials and care.

Methods for estimating radiological consequences in terms of radiation doses and cancer risks are needed for informed decisions on mitigation efforts after a radionuclide event. The 1986 Chernobyl Nuclear Power Plant accident fallout in Sweden was used as a case study. Open-source data on annual sex-specific population data in 1-y classes by municipality (n = 290), counties (n = 21), and future projection were retrieved from Statistics Sweden from 1986 to 2035. Published organ dose coefficients, cancer risk coefficients, and established methods for dose calculations and cancer risk projections were applied to estimate organ absorbed doses (mGy), effective dose (mSv), collective dose (person-Sv), and lifetime attributable risk (LAR). Due to the geographically variable Chernobyl fallout in Sweden, the variability in absorbed organ doses was greater between municipalities and counties than between organs or sexes. LAR was translated into 377 male and 448 female extra cancer cases over 50 y post-Chernobyl. Overall, 38% of these cancer cases could be attributed to the internal dose in males and 32% in females. The highest number of cancer cases was estimated for Västernorrland county, with only 3% of the Swedish population in 1986, but 18% of the excess cancer cases 1986 to 2035. The collective dose was calculated to 6,028 person-Sv, whereas 2,148 person-Sv (36%) was internal dose. Like for LAR, the population of Västernorrland county got 18% of the total collective dose. The excess number of cancer cases derived from LAR and collective dose gave similar results. Our methods can be adopted to other countries and different fallout scenarios.

Background: Drug‐coated balloons (DCB) are frequently used during percutaneous coronary intervention. Despite this there are limited data from studies comparing different types of DCB, particularly for a paclitaxel‐coated balloon (PCB) using urea as the excipient. This study aimed to assess the clinical efficacy of the PCB with urea compared with other frequently used DCB, attempting to emulate a target trial using observational data. Methods: SCAAR (Swedish Coronary Angiography and Angioplasty Registry) is a nationwide registry including data on all patients in Sweden undergoing coronary angiography. For this study, all procedures with DCB in Sweden between August 2021 and May 2024 were included. Propensity score weighted multivariable Cox regression analysis and Kaplan–Meier estimates were used to assess outcomes through 2 years. Results: A total of 1797 (27.7%) procedures with the PCB with urea and 4691 (72.3%) procedures with Other DCB were eligible for this analysis. DCB treatment was performed in a variety of patients and lesions including 32.4% diabetes, 64.2% acute coronary syndrome, 20.5% bifurcations and 33.0% in‐stent restenosis. After 2 years, the event rates for the PCB with urea and Other DCB were 16.4% versus 18.4% for major adverse cardiovascular events (hazard ratio [HR] 1.01 [95% CI 0.84–1.22]), 7.2% versus 8.2% for all‐cause mortality (HR 1.05 [95% CI 0.80–1.39]), 6.1% versus 8.0% for myocardial infarction (HR 0.81 [95% CI 0.59–1.11]), 14.6% versus 14.9% for new revascularization with percutaneous coronary intervention (HR 1.02 [95% CI 0.83–1.25]), 0.8% versus 1.3% for target lesion definite thrombosis (HR 0.93 [95% CI 0.41–2.11]), 7.4% versus 8.7% for target lesion revascularization (HR 0.95 [95% CI 0.70–1.29]), and 10.5% versus 11.3% for target vessel revascularization (HR 0.99 [95% CI 0.76–1.29]). Conclusions: In this analysis evaluating 2‐year clinical outcomes after DCB, in a broad patient population, the PCB with urea had comparable outcomes with other contemporary DCB. This data further support the safety and efficacy of the PCB with urea.