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ABSTRACT Background: Esophageal cancer remains one of the most aggressive malignancies of the gastrointestinal tract, with high rates of recurrence and mortality despite curative-intent surgery and adjuvant therapies. Identifying factors associated with recurrence is crucial for improving outcomes and guiding personalized treatment. Aims: The aim of this study was to evaluate pretreatment and treatment-related variables associated with recurrence in patients with esophageal cancer undergoing surgical resection. Methods: This retrospective study analyzed data from patients with stage I–III esophageal carcinoma who underwent esophagectomy between 2000 and 2025, using the Fundação Oncocentro de São Paulo (FOSP) database. Clinical, histological, and treatment-related variables were evaluated. Disease-free survival and recurrence patterns were assessed using Cox proportional hazards models and Fine–Gray subdistribution hazard models. Results: A total of 2,057 patients were included, with a mean follow-up of 36.5 months (±44.8). In the multivariate analysis, advanced tumor stage (stage II: HR 1.68, 95%CI 1.21–2.33; stage III: HR 3.23, 95%CI 2.29–4.56; both p<0.01), location (middle esophagus: HR 1.31, 95%CI 1.11–1.54; p=0.001; upper esophagus: HR 1.54, 95%CI 1.21–1.96; p<0.001), and histological subtype (rare histologies: HR 2.17, 95%CI 1.35–3.49; p=0.001) were associated with worse disease-free survival. Multimodal therapy improved disease-free survival (HR 0.40, 95%CI 0.24–0.66) in stage III tumors. Squamous cell carcinoma was independently associated with locoregional recurrence (SHR 1.52, 95%CI 1.05–2.20; p=0.027). For distant recurrence, squamous cell carcinoma showed a protective effect (SHR 0.52, 95%CI 0.31–0.88; p=0.015), while high tumor grade (grade II: SHR 3.65, 95%CI 1.98–6.72; p<0.001) was associated with an increased risk. Multimodal treatments influenced recurrence patterns but did not independently predict outcomes after adjustment. Conclusions: Tumor stage, location, and histology were strong predictors of disease-free survival after surgery for esophageal cancer. Histological subtypes significantly influenced recurrence patterns. Squamous cell carcinoma was associated with a higher risk of locoregional recurrence but a lower risk of distant metastasis compared to adenocarcinoma. Multimodal therapy demonstrated a protective effect in stage III disease.

Intronic GGGGCC hexanucleotide repeat expansions in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Despite its intronic location, this repeat avidly supports synthesis of pathogenic dipeptide repeat (DPR) proteins via repeat-associated non-AUG (RAN) translation. However, the template RNA species that undergoes RAN translation endogenously remains unclear. Using long-read based 5′ RNA ligase-mediated rapid amplification of cDNA ends (5′ Repeat-RLM-RACE), we identified C9orf72 transcripts initiating within intron 1 in a C9BAC mouse model, patient-derived iNeurons, and iNeuron-derived polysomes. These cryptic m 7 G-capped mRNAs are at least partially polyadenylated and are more abundant than transcripts derived from intron retention or circular intron lariats. In RAN translation reporter assays, intronic template transcripts–even those with short (32 nucleotide) leaders–exhibited robust expression compared to exon–intron and repeat-containing lariat reporters. To assess endogenous repeat-containing lariat RNA contributions to RAN translation, we enhanced endogenous lariat stability by knocking down the lariat debranching enzyme Dbr1. However, this modulation did not impact DPR production in patient-derived iNeurons. These findings identify cryptic, linear, m 7 G-capped intron-initiating C9orf72 mRNAs as an endogenous template for RAN translation and DPR production, with implications for disease pathogenesis and therapeutic development.

Tumour-infiltrating lymphocyte (TIL) therapy has emerged as a promising adoptive cell transfer strategy for solid tumours. The recent accelerated approval of lifileucel by the Food and Drug Administration marks a significant milestone in the clinical application of TIL therapy. This review comprehensively examines the historical development, biology, clinical efficacy, safety and limitations of TIL therapy. We explore advancements in TIL manufacturing, including novel culture techniques, genetic modifications and automation, to enhance scalability and effectiveness. Despite promising results, TIL therapy faces challenges such as high-dose interleukin-2 toxicity, complex manufacturing processes and immune evasion mechanisms. Emerging strategies, including checkpoint inhibitor combinations, engineered TIL constructs and metabolic reprogramming, aim to improve TIL therapeutic efficacy. This review provides insights into the evolving landscape of TIL therapy and its potential to enhance current cancer immunotherapy.

Abstract Background Despite advances in diagnosis and management, the interstitial lung disease (ILD) lexicon is plagued by ambiguous and inconsistent terminology that complicates communication and impedes knowledge generation. The objective of this Fleischner Society Consensus Statement was to produce standardized terminology for ILD multidisciplinary diagnoses and major phenotypes. Methods Interviews with 10 experts were used to identify ILD clinical diagnoses and major phenotypes. The preferred terms for each entity and potential alternatives were identified, alongside the rationale for the preferred term. Entities with more than one potential term were the subject of an online modified Delphi survey posed to the 29 committee members, aiming to achieve consensus. Committee members rated their agreement with the initially preferred term—5 (strongly agree), 4 (agree), 3 (neutral/unsure), 2 (disagree), and 1 (strongly disagree)—with the option to provide additional comments. Median score ≥4 and interquartile range ≤1 were considered consensus agreement. Terms not reaching agreement were discussed by video conference, followed by an additional survey that incorporated feedback. Results From the 60 initial terms, there were two root terms that required upfront consensus before survey initiation (ILD and interstitial pneumonia) and another eight terms that had no alternative suggested by the committee or in the literature. Agreement was met by 47/50 terms (94%) in Round 1 of the survey. The three terms (6%) that did not reach agreement met agreement in Round 2. Conclusions This document provides standardized recommended terms for ILD multidisciplinary diagnoses and major phenotypes that will facilitate communication among clinicians, researchers, patients, and other stakeholders.

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed.