imatinib mesilate

RETRACTION: Depletion of lncRNA MEG3 Ameliorates Imatinib-Induced Injury of Cardiomyocytes via Regulating miR-129-5p/HMGB1 Axis.

Pathology Analytical Cellular AC

Diagnostic value of EBUS-TBNA for intrathoracic tuberculous lymphadenitis: a retrospective cohort analysis stratified by anti-tuberculosis treatment duration.

Bai Zhexin Z, Sun Zeyi Z, Dong Yujie Y, Nie Wenjuan W et al.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is widely adopted to diagnose intrathoracic lymphadenopathy, including tuberculous lymphadenitis (TBLA). However, the influence of anti-tuberculosis (anti-TB) treatment duration on its diagnostic yield remains unclear. In this retrospective cohort study, 78patients with suspected intrathoracic TBLA who underwent EBUS-TBNA between June 2017 and January 2024 were included. Patients were categorized into four groups by prior anti-TB treatment duration. Composite diagnostic criteria incorporating histopathology, microbiology, and treatment response were used as the reference standard. Diagnostic performance across lymph node stations and modalities was analyzed. Safety outcomes were also evaluated. The overall diagnostic sensitivity of EBUS-TBNA based on composite criteria was 96.2%. Stratified analysis showed no statistically significant difference in diagnostic positivity among untreated (95.9%), 1-30 days (83.3%), 31-180 days (100%), and > 180 days (100%) groups (P = 0.42). Diagnostic yield was consistent across mediastinal, hilar, and combined lymph node stations (P = 0.289). Among diagnostic modalities, TB-DNA, histopathology TB-SPOT and sputum Xpert demonstrated high positivity rates. No major complications occurred; minor self-limited bleeding was observed in 15.4% of patients. EBUS-TBNA is a safe and effective diagnostic tool for intrathoracic TBLA, with consistent performance in both treatment-naïve and pretreated patients, regardless of anti-TB therapy duration.

Incremental contribution of bronchoalveolar lavage fluid data to multidisciplinary discussion of interstitial lung disease.

Peixoto Bianca B, Graça Nadja Polisseni NP, Dos Reis Visconti Nina Rocha Godinho NRG, de Carvalho Bethlem Marcos M et al.

Multidisciplinary discussion (MDD) integrating clinical, radiological, and laboratory information is considered the reference standard for the diagnosis of interstitial lung disease (ILD). Bronchoalveolar lavage fluid (BALF) analysis is recommended in several diagnostic algorithms, but its incremental contribution during multidisciplinary evaluation remains incompletely characterized. In this study, we aimed to assess the impact of BALF findings on diagnostic consensus and confidence during MDD for suspected ILD. In this cross-sectional study, patients with suspected ILD at a tertiary referral center who remained without a definitive diagnosis after clinical evaluation and high-resolution computed tomography (HRCT) underwent bronchoscopy with BALF collection (n = 29). Blinded pulmonologists and radiologists evaluated each case using a sequential four-step framework: (1) radiological assessment; (2) radiological assessment with clinical data; (3) multidisciplinary discussion without BALF information; and (4) multidisciplinary discussion including BALF results. Interobserver agreement was assessed using Fleiss' kappa, and diagnostic confidence was recorded at each stage. Diagnostic agreement increased across the sequential evaluation steps (Fleiss' κ: 0.056, 0.565, 0.905, and 0.965, respectively; p < 0.001). Consensus diagnoses were achieved in 24 cases after MDD without BALF and in 27 cases following incorporation of BALF findings. The proportion of evaluations with high diagnostic confidence (≥ 90%) increased from 3.4% before BALF availability to 20.7% after its inclusion. Within a multidisciplinary diagnostic framework for ILD, BALF information were associated with improved diagnostic consensus and increased clinician confidence. BALF may therefore serve as a complementary diagnostic tool in the multidisciplinary assessment of ILD, particularly in cases that remained uncertain after clinical and radiological evaluation.

Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex.

Hart Steven N SN

To present a five-criterion calibration framework for evaluating artificial intelligence (AI) tools in pathology centered on preserving "brain capital" - the finite cognitive resources available to clinicians - and stabilizing the "diagnostic apex," the point where histological evidence meets clinical judgment. Using cognitive load theory (CLT) and implementation science, we developed the Pathology AI Diagnostic Balance - a conceptual model mapping the physician-specimen interaction within working memory and IT infrastructure constraints - and examined it in the context of peer-reviewed case studies of implemented pathology AI tools. The framework identifies five criteria: Contextual Literacy (integration of clinical history), Responsibility (preservation of signing authority), Evidence Gap Analysis (closure of diagnostic uncertainty), Verification (feasibility of quality control), and Opportunity Cost (return on cognitive investment). Successful tools, such as Ki-67 quantification and breast cancer screening, reduce cognitive burden by automating routine tasks or enabling rapid verification. Conversely, tools requiring an exhaustive re-review or producing opaque recommendations deplete cognitive bandwidth, causing decision fatigue and diagnostic errors. Pathologists are positioned to exercise evaluative authority over tools that increase extraneous cognitive load. Clinical expertise represents pathologists' most distinctive contribution to AI development: defining which failure modes matter clinically, what context an AI must integrate, and whether a tool serves patient care. By prioritizing cognitive sustainability over purely technical metrics, the profession can ensure AI functions as a genuine enhancement of diagnostic capability rather than an additional burden on diagnostic workflows.