OA01.4. Multimodal Early Warning System for Postoperative Complications After Esophagectomy: Integrating Patient-Reported Outcomes, Laboratory Trajectories, and Clinical Text Mining
Esophageal Cancer: Surgical Treatment of Esophageal Cancer – early outcomes and complications
Postoperative complications after esophagectomy remain a major source of morbidity, yet current surveillance relies predominantly on clinician-initiated assessments and structured database fields that systematically underdetect complications. We aimed to develop and internally validate a multimodal early warning system (MEWS-Eso) integrating patient-reported outcomes (PROs), laboratory trajectories, and large language model (LLM)-extracted clinical text features to enable real-time, automated complication surveillance.
This prospective cohort study enrolled 323 consecutive patients undergoing esophagectomy for esophageal cancer at a single tertiary center (2019–2024). PROs were collected using the MD Anderson Symptom Inventory (MDASI) at 10 timepoints from preoperative baseline through 6 months postoperatively (completion rate 91.3%). Laboratory values (complete blood count, C-reactive protein, procalcitonin, albumin) were extracted at 7 perioperative timepoints. Free-text clinical notes (nursing records, physician progress notes, operative reports; median 3,247 words/patient for POD0–POD7) were processed using GPT-4o for structured information extraction and semantic embedding generation. NLP was independently applied to identify complications missed by structured fields. Six models of incrementally increasing modality were compared using 5-fold cross-validation with 100 bootstrap iterations: M1 (PRO-only), M2 (PRO + clinical baseline), M3 (PRO + laboratory), M4 (PRO + text embeddings), M5 (PRO + laboratory + text), and M6 (full multimodal). The primary endpoint was postoperative complications within 90 days under both standard and NLP-augmented outcome definitions.
NLP text mining identified 72 complications missed by structured fields, most notably anastomotic stricture (43 NLP-detected vs. 3 structured-field recorded; p < 0.001). Under the NLP-augmented definition, complication prevalence increased from 52.3% to 61.0%. AUROC improved progressively with modality addition: M1 (PRO-only) 0.658 [95% CI 0.601–0.715], M3 (PRO + laboratory) 0.761 [0.712–0.810], M5 (PRO + laboratory + text) 0.824 [0.779–0.869], and M6 (full multimodal) 0.847 [0.805–0.889]. The CRP trajectory (POD1–POD5 slope) was the single strongest laboratory predictor (OR 2.41, 95% CI 1.78–3.26). LLM-extracted text features contributed an incremental AUROC gain of +0.063 beyond PRO + laboratory. At the RED-alert threshold (probability ≥0.70), MEWS-Eso achieved sensitivity 72.8%, specificity 83.5%, and positive predictive value 78.3%, with a median early warning lead time of 4.2 days. Removing the PRO module caused the largest performance drop (ΔAUROC = −0.089), followed by laboratory (−0.074) and text (−0.063).
A multimodal early warning system integrating PROs, laboratory trajectories, and LLM-processed clinical text substantially outperforms single-modality approaches for detecting postoperative complications after esophagectomy. PROs provide the most irreplaceable modality, while NLP-based text mining both corrects systematic outcome misclassification and contributes independent predictive signals. This framework demonstrates the feasibility of automated, patient-centered multimodal surveillance in surgical oncology.
This study constructed a pH-responsive P-TN/SF@Fe-Cur composite coating that demonstrated significant anti-infective, anti-inflammatory, antioxidant, pro-angiogenic, and pro-osteogenic effects in rat subcutaneous infection and femoral defect models.
ProteinReasoner is developed, a multimodal generative protein foundation model that sequentially connects amino acid sequence, evolutionary constraints and three-dimensional structure within a shared autoregressive architecture and suggests a general route towards reasoning across interdependent representations in other scientific domains.
Chaozhong Liu, Linlin Chao, Shaomin Ji et al.· bioRxiv· 1 citation
Due to its importance and wide adoption, wheat cultivation is promptly required to shift towards sustainable practices, reducing the dependency on chemical components. Among bio-based solutions aimed at securing the sustainability of wheat cultivation, biostimulants offer a versatile platform of eco-friendly tools assuring sustainability and profitability. Microalgae present a concrete example of a biostimulant source due to their richness in metabolites and high value products. Therefore, this study evaluated the biostimulant potential of eleven eco-extracts prepared from soil-isolated microalgae strains. Eco-extracts applied via soil drench at low dose (0.1 g/L) were investigated for their biostimulant effects on wheat growth, physiology, yield, and quality under controlled conditions. Results demonstrated significant ameliorations in treated plants as compared to the control, with no phytoinhibitory effects. Remarkable enhancements were notable in growth parameters such as shoot and root lengths (+40-70%), physiological traits such as total chlorophyll and stomatal conductance (+7-52%), yield components in the example of grain number per spike and thousand grain weight (+17-103%), and grain quality namely protein and polyphenol content (+2-fold to 4-fold). Similarly, phosphorus accumulation and uptake were significantly improved, while soil physicochemical status was ameliorated, indicating enhanced fertility. Multivariate analysis and composite index ranking marked Chlorella sp. GA18, Chlorella sp. GA65, Scenedesmus sp. GA69, and Chlorococcum sp. GA63 as eco-extracts with consistent performances across all plant traits. These findings highlighted the promising potential of integrating microalgae-based eco-friendly extracts in sustainable wheat cultivation.
Amer Chabili, Z. Hakkoum, F. Minaoui et al.· Plant Science· 1 citation
Effective control of fluid flows is critical across transportation, energy and medicine, where it can increase lift, reduce drag, enhance mixing and attenuate noise1-3. Yet fluids are notoriously difficult to control because they involve high-dimensional, nonlinear and multiscale dynamics that resist conventional approaches4-6. Reinforcement learning has driven remarkable progress in fields such as protein folding and complex games, which have shared benchmarks and standardized environments7-10. Fluid dynamics has lacked such infrastructure, so each controller is typically tuned to a single geometry and operating condition, making progress difficult to accumulate, transfer and compare11-13. Here we introduce HydroGym, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions. Across these environments, agents repeatedly discover robust control principles, including boundary layer manipulation, disruption of acoustic feedback and reorganization of turbulent wakes. Critically, we demonstrate a proof of concept for zero-shot transfer, in which agents that are trained exclusively in inexpensive surrogate environments are deployed to challenging real-world scenarios such as a three-dimensional wing section. We achieve a 38% reduction in local skin friction while reducing exploration costs by four orders of magnitude compared with direct on-wing optimization. As this transfer exploits shared near-wall physics, the breadth of generalization remains open, suggesting a new pathway for research toward policy generalization across computationally prohibitive simulation environments. By offering a common, extensible foundation for reproducible research, HydroGym moves flow control from isolated case studies toward a cohesive community effort.
Christian Lagemann, Sajeda Mokbel, Miro Gondrum et al.· Nature· 1 citation
ABSTRACT Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyethylene terephthalate (PET) under physiological conditions. We leveraged deep learning to mine unexplored sequence space across 246 million proteins, discovering AhPETase, an evolutionarily distinct hydrolase with low homology (<50% sequence identity) to known PET‐degrading enzymes. This noncanonical biocatalyst efficiently depolymerizes PET at 37°C, outperforming all typical PETases and achieving a 7.76‐fold enhancement over IsPETase, one of the most representative mesophilic PETases. Additionally, engineered variant AhPETaseM1 retains functional activity for over 20 days under physiological conditions and can degrade post‐consumer PET MPs 34‐fold faster than recombinant human‐derived enzyme MG8 (rMG8) under equal enzyme loading. Critically, it reversed PET‐induced toxicity in human lung and colon cells, establishing the first proof‐of‐concept for enzymatic MPs detoxification.
Yuxuan Wang, Shijie He, Yuheng Chang et al.· Advancement of science· 0 citations
BACKGROUND
Electrical muscle stimulation (EMS) is used in critically ill patients to prevent intensive care unit-acquired weakness. It promotes anabolic responses partly through interleukin-6 (IL-6) signaling under non-inflammatory conditions; however, its effects during systemic inflammation remain unclear. We hypothesized that EMS applied during lipopolysaccharide (LPS)-induced systemic inflammation exacerbates skeletal muscle atrophy through activation of IL-6-mediated catabolic signaling.
METHODS
Male C57BL/6J mice were randomly assigned to control, EMS, LPS, or EMS/LPS groups. Intraperitoneal LPS (2 mg/kg) or phosphate-buffered saline was administered, followed by EMS applied to the left hindlimb 8 h later (80 Hz, 5 mA, 30 min). Gastrocnemius muscle fiber cross-sectional area (CSA) was measured as an index of muscle atrophy, with three mice analyzed per group. Gastrocnemius muscles and blood samples were collected after treatment, and muscle morphology and CSA were analyzed histologically. Protein expression of Atrogin-1, MuRF1, C/EBPδ, phosphorylated mTOR, p70S6K, and STAT3 was assessed by Western blot, and IL-6 expression by qRT-PCR and ELISA. Data are presented as mean ± standard deviation (SD).
RESULTS
Compared with control, EMS alone increased CSA (mean ± SD,1610 ± 468 vs 1350 ± 437 μm2; P < .0001), and the phosphorylation of mTOR (1.72 ± 0.234-fold; P < .0001) and p70S6K (2.34 ± 0.559-fold; P < .0001). In contrast, compared with LPS alone, EMS applied under LPS did not enhance the phosphorylation of mTOR or p70S6K, but reduced muscle fiber CSA (680 ± 327 vs 991 ± 453 μm2; P < .0001), and upregulated Atrogin-1 (13.1 ± 3.72 vs 7.85 ± 2.26-fold; p = 0.0014) and MuRF1 (3.77 ± 1.45 vs 2.50 ± 0.998 -fold; p = 0.0094) expression. These catabolic changes were accompanied by increased STAT3 phosphorylation and C/EBPδ expression (8.28 ± 4.16 vs 4.56 ± 1.88 -fold; p = 0.0098, 39.2 ± 16.4 vs 22.8 ± 12.3-fold; p = 0.0107). Additionally, IL-6 expression was elevated in both stimulated muscle (335 ± 242 vs 140 ± 23.1-fold; p = 0.0095) and serum (28.5 ± 4.60 vs 9.35 ± 3.19 ng/mL; P < .0001) in the EMS/LPS group. Similar atrophic changes were observed in the contralateral, non-stimulated limb.
CONCLUSIONS
EMS applied during LPS-induced systemic inflammation exacerbated skeletal muscle atrophy and was associated with activation of IL-6/STAT3-C/EBPδ signaling and proteolytic pathways. These findings provide mechanistic insight into the effects of EMS under inflammatory conditions and warrant further investigation.
Shino Matsukawa, Shinichi Kai, Hideya Seo et al.· Anesthesia and Analgesia· 0 citations