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Nitrogen-doped carbon dots (CDs) were prepared from essential fluorescent amino acids, such as lysine (Lys), phenylalanine (Phe), tyrosine (Tyr), and tryptophan (Trp) by two routes, microwave and teflon-lined autoclave. The fluorescent properties of these amino acids were significantly improved in their corresponding CD forms, especially for those prepared via the hydrothermal process. A significantly high fluorescent intensity was measured in the emission range of 420-470 nm at the excitation wavelengths of 300-350 nm. While a negatively zeta potential value of - 9.8 ± 2.6 mV was obtained for Lys CDs suspension, the CDs of Phe, Tyr, and Trp afforded strong positive zeta potentials, + 24.0 ± 2.4, + 12.5 ± 3.1, and + 31.9 ± 3.8 mV, respectively. The Lys CDs were found not to be antimicrobial even at a 10 mg/mL concentration, which is likely due to their negative surface charge that weakens electrostatic interactions with negatively charged microbial membranes. Whereas the Phe, Tyr, and Trp CDs had a 2.5 mg/mL minimum inhibition concentration (MIC) against a Gram-negative bacterium, Klebsiella pneumoniae. The Phe CDs commenced the highest antibacterial effect against Bacillus subtilis (ATCC 6633) Gram-positive bacteria, but the lowest MIC value of 1.25 mg/mL was determined for Tyr CDs against Candida albicans (ATCC 10231) fungus. Furthermore, a UV light exposure, 30 min treatment of UV-A light with a 6.88 mW/cm2 irradiance value on amino acids CD exhibited improved photodynamic activity. The natural amino acid-derived CDs show great biocompatibility on L929 fibroblast cells with > 86% cell viability, for all formulations except Tyr CDs, retaining 78 ± 3% viability even at 1000 μg/mL concentration, and blood compatibility at 500 μg/mL concentration. Therefore, these CDs derived from fluorescent amino acids are photoactivated and are of excellent nanosized materials in a variety of biomedical in vitro and in vivo uses, including diagnostic, sensor, and therapeutic applications.

The search for high-efficiency antimicrobial nanomaterials has intensified due to the escalating threat of antibiotic resistance. In this study, titanium dioxide nanoparticles (TiO2 NPs) were synthesized using aqueous leaf extract of Pterolobium hexapetalum. Physicochemical characterization confirmed the formation of a pure nanocrystalline anatase phase (XRD) with a primary crystallite size of 10-15 nm and an optical band gap of 3.18 eV. While UV-Visible spectroscopy showed characteristic absorption bands at 209.5 and 270.5 nm, DLS and Zeta potential (+ 9 mV) measurements indicated that the nanoparticles exist as aggregated colloidal clusters with a hydrodynamic diameter of 399.2 nm. FTIR and SEM verified the presence of plant-derived functional groups and a clustered, rough surface morphology. Biological evaluation via agar well diffusion, MIC, and MBC assays demonstrated that the TiO2 NPs significantly outperformed the parent leaf extract. At a dose of 100 µg/well, the TiO2 NPs produced inhibition zones of 22.67 mm for Staphylococcus aureus and 27.67 mm for Escherichia coli, surpassing the absolute zones produced by the ciprofloxacin (5 µg/well) control. The MIC values (31.25-62.5 µg/mL) and an MBC/MIC ratio of 2 indicate a potent, primarily bactericidal mode of action, achieving a two- to four-fold increase in potency over the crude extract. These findings establish P. hexapetalum-mediated TiO2 NPs as a promising platform for antimicrobial applications in wound care and decontamination technologies.

The infiltration and cytotoxicity of T lymphocytes are critical for cancer immunotherapy efficacy; however, the behavior of these immune cells has not been thoroughly investigated. Herein, a Tumor-Immune-On-Chip is established using cells acquired from the tissues of a patient with colorectal cancer to monitor T lymphocytes. Through the Tumor-Immune-On-Chip, the interaction between tumor spheroid and either T lymphocytes expanded from tumors (tumor-infiltrating lymphocytes; TILs) or lymph nodes (lymph node-derived lymphocytes; LN T cells) are investigated. Although initial 24-h analysis showed no statistical differences, extended 48-h observation revealed a significant deviation in T cell-mediated cell death signals between TILs and LN T cells. TILs demonstrated more potent cytotoxic effects than LN T cells after 48 h. The number of tumor-infiltrating CD3+ cells and cleaved caspase-3 expression levels were 4- and 2.1-fold higher, respectively, in TIL co-cultures compared to LN T cell co-cultures. Therefore, this proof-of-concept platform allows us to explore the patient-specific tumor-immune microenvironment, focusing on different types of T lymphocytes and establishing methodology for future clinical applications. ClinicalTrial.gov identifier: NCT02589496.

This study aimed to examine 8th-grade students' views on the concepts of nanotechnology and nanoscience through the use of the Metaverse in science courses. The study group sample consists of five students from both the before- and after-experience groups, all of whom are in 8th grade. This study employed a qualitative research method with a case study design. Observation, interview, and document analysis were used as data collection tools. Necessary measures have been taken to ensure the validity and reliability of the research within its scope. The data were analyzed using a content analysis approach. As a result of the interviews, data were collected and analyzed. As a result of the textual examinations, code, category, and theme were determined. The findings were presented in categories through tables, and the participants' answers were included in direct quotations. Upon reviewing the literature, it becomes apparent that most studies in nanotechnology and nanoscience are conducted for informational purposes, typically presented as presentations or reports. Given the limited availability of nanotechnology and metaverse education, the study was divided into two groups: a before-experience group and an after-experience group. As a result of the survey, 8th-grade students experience the metaverse and have future expectations for nanotechnology and nanoscience. Their cognitive and affective interests have increased, as evidenced by their questioning why these applications cannot be applied to all courses and by their correct expression of the concepts. At the same time, it has been concluded that using rich materials to concretize abstract concepts, such as nanotechnology, facilitates their teaching. The study provides qualitative evidence that Metaverse-based instruction can enhance both cognitive and affective dimensions of science learning, offering design implications for integrating immersive technologies into middle school curricula to teach abstract concepts.

Parabiotics (also termed paraprobiotics) are defined as non-viable microbial cells or their components, including peptidoglycans, teichoic acids, surface proteins, that confer health benefits without requiring viability which distinguishes them from traditional probiotics. Their non-viable nature eliminates risks such as microbial translocation, bacteremia, and sepsis, making them suitable for vulnerable populations including immunocompromised, critically ill, paediatric and elderly individuals. In addition, parabiotic exhibit improved thermal stability, extended shelf life, and easier incorporation into functional foods, nutraceuticals, and pharmaceutical formulations without cold-chain requirements. Mechanistically, parabiotics retain immunomodulatory, anti-inflammatory and have barrier-enhancing activities through interactions with host pattern recognition receptors, including Toll-like receptors, modulation of cytokine responses, and reinforcement of gut epithelial integrity. Preclinical and clinical studies support their therapeutic potential such as in case of heat-killed Lactobacillus acidophilus LB (L. acidophilus) has shown efficiency in managing acute paediatric diarrhoea, while heat-inactivated Lacticaseibacillus paracasei PS23 (Lcb. paracasei) has demonstrated improvements in muscle strength and inflammatory markers, including reduced C-reactive protein and interleukin-6 and increased interlukin-10 in elderly individuals. Similarly, inactivated Lactiplantibacillus plantarum (Lpb. plantarum) and Bifidobacterium strains have been associated with benefits in irritable bowel syndrome, atopic dermatitis, respiratory infections, visceral fat reduction, and antibiotic-associated dysbiosis. Synergistic combinations with prebiotics, postbiotics and related bioactives further enhance therapeutic outcomes in inflammatory, metabolic and infectious conditions. Advances in metagenomics, next-generation sequencing, proteomics, metabolomics, CRISPR-Cas systems, and synthetic biology are accelerating strain characterization, functional evaluation, and scalable production. Despite ongoing challenges in standardization and regulated harmonization, parabiotics represent a safe and effective approach for microbiome-targeted interventions. This review synthesizes current evidence on their therapeutic applications, technological advancements, and translational potential, highlighting their role in precision health and next-generation functional nutrition.

The plastic pollution crisis urges innovative recycling solutions. Promising approaches especially for polyester-containing wastes include enzymatic hydrolysis and microbial upcycling. For efficient enzymatic hydrolysis of polyesters, elevated temperatures (70-80 °C) are required, necessitating thermophilic microbial chassis for consolidated bioprocessing (CBP). In this study, we engineered Geobacillus thermoleovorans through adaptive laboratory evolution (ALE) for robust growth on adipic acid (AA) and 1,4-butanediol (BDO), two relevant monomers for example derived from poly(butylene adipate-co-terephthalate) (PBAT), enabling growth rates of up to 0.10 h-1 on AA and 0.13 h-1 on BDO. Based on a high-quality annotated genome sequence of the wild type, genomic mutations and gene expression levels were characterized in mutants grown on the respective substrates compared to glucose. For BDO, an alcohol dehydrogenase (Gth_001044) and an aldehyde dehydrogenase (Gth_001082) were identified to be likely responsible for its oxidative degradation. AA uptake appears to be mediated by a dicarboxylate transporter (Gth_003270), followed by CoA activation and β-oxidation involving a CoA transferase (Gth_003192) and several upregulated CoA-family dehydrogenases. To demonstrate applicability of these strains in plastic upcycling, they were co-cultivated with PBAT as the sole carbon source in combination with the cutinase HiC for PBAT hydrolysis. This resulted in growth on the released AA and BDO. Given the potential to purify the remaining terephthalate (TA), this approach highlights the feasibility of selective monomer valorization in bioprocesses. Additional ALE enabled co-utilization of AA and BDO by a single strain and improved AA consumption at lower concentrations, underscoring the strains' adaptability and high potential for plastic upcycling applications. KEY POINTS: • G. thermoleovorans evolved for robust growth on adipate and 1,4-butanediol at 60 °C. • Genome and transcriptome analyses revealed underlying pathways and enzymes involved. • Co-cultivation of the evolved strains on PBAT with HiC as the sole carbon source.

Antibody-drug conjugates (ADCs) combine the target specificity of monoclonal antibodies with the cytotoxic potency of small-molecule payloads. In recent years ADCs have emerged as a clinically validated component of modern precision oncology. To date, more than a dozen ADCs have received FDA approval for oncologic indications, with additional agents approved regionally and hundreds of ADC-based regimens in clinical development. Collectively, these therapies have demonstrated clinical benefit across hematologic malignancies and solid tumors, including significant overall survival improvements in advanced phase clinical trials. In this Trial Watch, we provide an overview on available ADCs from early preclinical development to current clinical applications. We also summarize design principles underpinning clinically successful ADCs, including epitope targeting, linker chemistry, payload toxicity and drug-to-antibody ratio, and discuss how these features can influence pharmacokinetics, intracellular trafficking, bystander effect and toxicity. Finally, we discuss results from advanced-stage clinical trials and approved agents to define future directions.

Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food security. While much has been done recently, many deep learning-based approaches will still fail in real-world tests because of challenges such as background clutter, differences in illumination, occlusion, or the fact that visual&#x2002;symptoms for these diseases can be very subtle early on. Traditional CNN- and Transformer-based architectures generally lack accurate lesion localisation and interpretability, hindering their practical deployment in agricultural decision-support tools. To address these issues, we present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage. It then applies preprocessing steps guided by ROI, based on leaf segmentation from the U-Net, followed by a compact CNN-based spatial feature-extraction framework. We arrange spatial feature embeddings extracted from lesion regions into an ordered sequence and employ a Bi-LSTM with attention to model structured contextual dependencies, allowing progression-aware feature learning without requiring actual temporal image sequences. Lastly, Grad-CAM-based post-hoc explainability is employed to interpret model decisions, enabling transparent visualisation of disease-relevant regions. We conduct extensive experiments on the PlantVillage benchmark and the FieldPlant dataset and show that LDDHybridNet consistently outperforms representative CNN, transformer, and hybrid baselines across multiple evaluation metrics. Although the near-ceiling performance on PlantVillage reveals the dataset's artificial nature, the proposed framework achieves 95.37% accuracy under real-world field conditions and 92.84% on weak-lesion early-stage samples, demonstrating the method's robustness and early-stage detection potential. The performance boosts are statistically significant (P&#x2009;<&#x2009;0.01). In general, LDDHybridNet is an interpretable and robust deep learning framework for leaf disease detection, which can support data-driven crop protection and precision agriculture&#x2002;applications.

Cutaneous gene therapy has the potential to treat a wide range of skin disorders, but effective delivery remains limited by the skin's barrier properties and immune surveillance. Here, we identify AAVrh32.33 as a potent vector for targeting dermal stromal compartments. Following systemic administration in mice, AAVrh32.33 mediated robust and durable transgene expression, with preferential targeting of dermal fibroblasts and hair follicle bulge cells. Expression peaked at one month and persisted for up to two years, highlighting its suitability for chronic conditions. To reduce immunogenicity, a dominant CD8+ T cell epitope was disrupted, generating the IDP&#x394; variant. This modification attenuated peptide-specific T cell responses while preserving stromal transduction. In human skin explants, IDP&#x394; achieved high levels of gene expression, primarily in dermal fibroblasts and precursors, confirming translational relevance. Finally, vectors encoding CCL17, CCL20, and CCL22 demonstrated localized targeted therapeutic gene delivery in both healthy and inflamed skin, underscoring the feasibility of using this platform to reshape local immune responses. Together, these findings establish AAVrh32.33 and IDP&#x394; as promising platforms for durable cutaneous gene therapy, with direct applications in diseases such as vitiligo where long-term modulation of the dermal microenvironment is essential.

With the advent of the immunotherapy era, the combination of radiotherapy and immunotherapy has become a critical strategy to enhance patient outcomes. In addition to its direct cytotoxicity, radiotherapy modulates the immune response within the tumor and its surrounding microenvironment by stimulating the body's anti-tumor immune response. This interplay provides the rationale for combining radiotherapy with immunotherapy. This review will summarize the immunomodulatory mechanisms of radiation therapy, evaluate the clinical efficacy and safety of combining radiotherapy with immunotherapy, and outline its current applications, challenges, and future potential. In the future, the combination of radiotherapy and immunotherapy holds immense potential in esophageal cancer treatment. Through additional prospective clinical trials exploring optimal combinations, timing, and biomarkers, we can further refine treatment strategies and enhance patient survival.

This article presents the design and the numerical analysis of a smart label-free Surface Plasmon Resonance (SPR) sensor to detect the concentration of haemoglobin in blood and the concentration of glucose in the urine samples. The suggested sensor uses a thin film of silver (Ag) on the prism's surface to excite the surface plasmons. The finite element method (FEM) was used to do numerical simulations to optimize the layer thickness and to analyse the sensor's performance in terms of the sensitivity and the Figure of merit (FOM). The results of the simulation showed that there is a linear correlation between resonance wavelength shift and change in analyte refractive index. The optimised design obtained a sensitivity of 288.29 &#xb0;/RIU, QF of 780.80 [Formula: see text], SNR of 15.62, FoM of 492.51 [Formula: see text] and CSF of 539.20. The label-free methodology involves no chemical tagging and thus allows biosensing that is quick, real-time and economical. The suggested SPR sensor has great possibilities to be implemented in the non-invasive biomedical applications, diagnostics and point-of-care monitoring. In addition, machine learning models were employed to predict sensor sensitivity based on structural and optical parameters, demonstrating the strong capability of data-driven approaches for rapid performance estimation and design optimization.

Visual impairment affects over 2.2 billion people worldwide and the major causes include age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy. For research in these areas, although animal models offer a more physiologically complex system than in vitro approaches, their use raises ethical considerations, and species-specific differences such as variations in protein sequences and signaling pathways. This can limit the direct translatability of the outcomes. Traditional 2-D cell cultures, in contrast, lack the multicellular organization and dynamic microenvironment necessary to replicate human retinal complexity. Retinal organoids (ROs), three-dimensional tissue constructs derived from pluripotent stem cells, have emerged as a promising model due to their human origin and complex cellular interactions that cannot be achieved in conventional 2-D/3-D co-culture models. In this review, we provide a brief overview of the evolution from 2-D to 3-D retinal models, highlight the structural and functional features of ROs including the presence of layered retinal architecture, photoreceptor outer segment formation, and light-responsive electrophysiological activity and summarize their applications in disease modeling, drug discovery, and gene and cell therapy. ROs represent a significant advancement over traditional models by enabling the recapitulation of human-specific retinal development, facilitating the study of patient-derived disease phenotypes, and providing a platform for personalized therapeutic screening. Their development has deepened understanding of pathological mechanisms in conditions such as retinitis pigmentosa and AMD, while enabling preclinical testing of targeted interventions like CRISPR-based gene editing and photoreceptor cell replacement. Nonetheless, challenges remain in fully replicating retinal vascularization, long-term functional maturation, and synaptic connectivity, underscoring the need for continued refinement and integration with complementary model systems.

Functional validation of candidate genes in congenital anomalies of the kidneys and urinary tract (CAKUT) and other disorders is essential for translating genetic discoveries into clinical applications. Conditional knockout mouse models are indispensable for studying gene function in complex organ systems. The Short Conditional intrON (SCON) system accelerates the generation of such models by inserting the artificial SCON into a coding exon. SCON is designed to be spliced out after transcription, without affecting gene expression. Upon Cre activity, SCON is converted into the &#x394;SCON allele which cannot be spliced out, introducing premature termination codons (PTCs) to inactivate the gene. Previous validation of the SCON system in mice has focused primarily on phenotypic outcomes. Here, we provide a molecular characterization of the SCON system in Cdh12-a candidate gene implicated in kidney damage in CAKUT. We found that both Cdh12SCON and Cdh12&#x394;SCON alleles caused unintended skipping of the exon downstream of the insertion site, culminating in a frameshift and PTC. Consequently, the Cdh12SCON allele led to a&#x2009;~&#x2009;25% reduction in mRNA expression, indicating that it was not transcriptionally inert as designed. Despite unintended exon skipping, the Cdh12&#x394;SCON allele still effectively suppressed mRNA expression. These findings highlight the importance of transcript-level characterization of engineered alleles prior to functional studies, as artefactual splicing events may occur across multiple gene-targeting strategies, including artificial intron-based conditional alleles as shown here.

Lung cancer is one of the most common malignancies and the leading cause of cancer-related mortality worldwide, posing a major public health challenge. Flavonoids, a large and diverse group of plant metabolites, exhibit various anticancer properties, making them promising candidates for therapeutic applications. This study evaluated the anticancer efficacy of methoxy flavonoids and elucidated their underlying mechanisms of action in A549 lung cancer&#xa0;cells. A549 cells were treated with various flavonoids (AKC1-AKC5), and their effects were analyzed using an MTT assay, DAPI staining, mitochondrial membrane potential (MMP), reactive oxygen species (ROS) production, colony formation, and wound scratch tests. Molecular docking was also performed to confirm the binding of AKC1 and AKC3 to EGFR, BCL-2, and CDK-2 proteins. AKC1 and AKC3 prevented the growth of A549 lung cancer cells with IC50 of 64.57 and 19.80&#x202f;&#x3bc;M among 5 methoxy flavonoids. AKC1 and AKC3 triggered notable alterations in the shape and reduced the colony-forming potential of A549 cells. The DAPI staining experiment demonstrated that AKC1 and AKC3 impede the growth of cancer cells through activation of apoptotic cell death. Moreover, the anticancer properties of AKC1 and AKC3 were attributed to significant inhibition of MMP and a notable ROS enhancement in a dose-related pattern. The wound scratch assay demonstrated that AKC1 and AKC3 suppressed A549 lung cancer cell migration, suggesting their anti-metastatic properties. Molecular docking studies confirmed that AKC-1 and AKC-3 bind strongly to EGFR, BCL-2, and CDK2, suggesting a multi-target mechanism that underlies their anti-proliferative and pro-apoptotic effects in A549&#x202f;cells. AKC1 and AKC3 exhibited significant anticancer activity against A549 cells and may serve as promising therapeutic drugs for lung cancer treatment.

Auger heating represents a major bottleneck for hot carrier (HC) relaxation in colloidal quantum wells (CQWs), delaying carrier accumulation in band-edge states and diminishing performance in light-emitting applications. To address this issue, we introduce copper doping in CdSe CQWs to create midgap states, which efficiently suppresses Auger heating without altering their intrinsic structural or optical properties. Ultrafast spectroscopy demonstrates pump-intensity-invariant HC cooling dynamics in Cu-doped CQWs, occurring within &#x223c;0.21&#xa0;ps at a consistent energy-loss rate of &#x223c;610 meV/ps, even under high exciton densities (<N> &#x2248; 4). In contrast, undoped samples exhibit significant cooling deceleration as excitation intensity increases. Combined experimental and theoretical results attribute this ultrafast cooling to rapid hole trapping at Cu1 + sites, which disrupts the biexcitonic energy-transfer mechanism responsible for Auger reheating. This work establishes a practical strategy for achieving rapid carrier cooling essential for high-performance CdSe CQW-based optoelectronics.

Postoperative gastrointestinal (GI) bleeding is a serious complication after hip fracture surgery in older adults, yet perioperative risk stratification remains limited because commonly used GI-bleeding scores are not tailored to orthopedic settings. This study aimed to develop and internally validate an interpretable model to predict postoperative GI bleeding risk in elderly hip fracture patients, using data routinely available during the perioperative period. We retrospectively included 342 elderly patients who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January to December 2023. The outcome was GI bleeding within 1 month after surgery, confirmed by medical records and/or telephone follow-up. Patients were randomly split into a training set (n&#x2009;=&#x2009;242) and a validation set (n&#x2009;=&#x2009;100). Predictors were screened using LASSO with 10-fold cross-validation, followed by multivariable logistic regression to identify independent risk factors. Ten prediction algorithms were trained and compared. Model performance was assessed by AUC, calibration, and decision curve analysis, and interpretability was evaluated using SHAP. GI bleeding occurred in 38 patients (11.1%). Multivariable analysis identified four independent predictors: alcohol consumption history (OR 8.109, 95% CI 2.463-26.69), glucocorticoid use (OR 4.922, 95% CI 1.055-22.97), NSAID use (OR 6.851, 95% CI 1.811-25.915), and higher systemic immune-inflammation index (SII) (OR 1.001, 95% CI 1.000-1.002). Among the tested models, LightGBM showed the best overall performance, with AUCs of 0.843 (training) and 0.817 (validation), good calibration, and the highest net benefit on decision curve analysis. SHAP results ranked feature importance as SII, NSAID use, alcohol consumption history, and glucocorticoid use, consistent with regression findings. We developed and validated an interpretable LightGBM model that predicts postoperative GI bleeding risk in elderly hip fracture patients using routinely available clinical data. The final model incorporates only preoperative variables, systemic inflammation, NSAID use, alcohol history, and glucocorticoid use, supporting its application for early risk stratification prior to surgery.

Peritoneal fibrosis, driven by M2 macrophage polarization, limits the long-term application of peritoneal dialysis (PD). Although ADAM19 is known to mediate fibrosis in other organs, its specific role in PD-associated peritoneal fibrosis remains unclear. PD patients were enrolled in a single center and divided into three groups depending on the PD time. Demographic and clinical data were collected. We detected the expressions of ADAM19, Notch1, Fibrosis-associated protein, chemokines and inflammatory factors in the peritoneum dialysis effluent by real-time PCR and western-blot assays. Macrophages were identified through flow cytometry. Then we analysis the relationship between ADAM19 and clinical data in PD patients. Furthermore, we established mouse models for peritoneal fibrosis to verify the biological function of ADAM19 in regulating macrophage polarization. In the long-term group, the fibrotic proteins (Fibronectin, &#x3b1;-SMA) and inflammatory factors (IL-6, IL-10) and chemokines (CCL5, CCL2, CXCL16) were higher than short-term group and more macrophages polarized towards M2. ADAM19 expression was linearly correlated with dialysis time and Kt/v. The AUROC of ADAM19 was 0.738 to identify the predictive value for peritoneal dialysis adequacy. The cut-off of ADAM19 RNA level was 7.84. In logistic regression models, higher ADAM19 (&#x2265;&#x2009;7.84) was also independently associated with lower Kt/v (<&#x2009;1.67). Additionally, the results revealed a moderate increment of M1 macrophage (CD86+) and enormous rise of M2 macrophage (CD206+) with high-glucose dialysis fluid in mice model. Furthermore, the 8-week G4.25% group showed significant growth of M2 macrophage compared to the 4-week G4.25% group, indicating that prolonged dialysis duration has a more pronounced effect on promoting M2 polarization of macrophages via ADAM19/Notch1 signaling pathway. Through stimulating chemokines and inflammatory factors, ADAM19 regulated macrophage polarization and was correlated to the progression of peritoneal fibrosis. ADAM19 is expected to be a novel indicator for detecting peritoneal ultrafiltration function in PD patients.

Surgical bleeding is often associated with the surgeon's actions during procedures and may be preventable if high-risk behaviors are identified in advance. Here, we aim to develop a real-time tool for predicting intraoperative bleeding risk using an event-log-based framework, and we demonstrate its feasibility in laparoscopic cholecystectomy with potential for broader generalization. We represent the surgeon's workflow as an event log composed of triplets (action, instrument, target), their durations, recent bleeding history, and surgical phase information extracted from the preceding minutes of the procedure. Using real-time event logs from laparoscopic cholecystectomy procedures in the CholecTrack20 and CholecT50 datasets, we preprocess these data and train a Transformer-based model to estimate the probability of bleeding in the immediate future. Our Transformer-based model outperforms LSTM- and TCN-based approaches, achieving its best performance for short-term bleeding prediction. For 30-60&#xa0;s ahead, the F1-score reaches 68.6%, surpassing TCN (60.1%) by 8%. For 60-90&#xa0;s, it remains competitive at 64.4%, about 6% higher than TCN, with performance gradually declining over longer horizons. Predicting bleeding events from real-time event logs based on recent surgical activity shows strong potential for effective intraoperative decision support. To further enhance model relevance and enable reliable real-time deployment, additional data (i.e., more surgical procedures) and more detailed bleeding annotations (e.g., bleeding intensity, the location of the bleeding, and whether the bleeding is active) will be beneficial.