The American Journal of Pathology

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

Under homeostatic conditions, the cornea is avascular and contains few immune cells, but this changes rapidly following injury. Although the long-term consequences of corneal damage are well characterized, the earliest vascular and immune responses remain poorly understood. Here, we used a murine corneal alkali-burn model to examine limbal vascular activation and leukocyte recruitment immediately and at 2, 6, and 24 hours after injury. Limbal blood vessels underwent immediate dilation; however, vascular leakage into the corneal stroma occurred only in males. Lymphatic capillaries rapidly formed directed extensions toward the injury without significantly increasing their total vascular area, with males exhibiting longer extensions than females. Fluorescent dextran uptake provided evidence that these lymphatic vessels were functionally engaged in early tracer drainage. Despite pronounced vascular activation, early recruitment of neutrophils, monocytes, dendritic cells, macrophages, T cells, B cells, and natural killer cells remained limited. Thus, limbal blood and lymphatic vessels initiate the earliest response to corneal alkali injury before substantial leukocyte infiltration. These findings reveal sex-dependent differences in vascular permeability and lymphatic remodeling and identify the limbal vasculature as an early regulator of corneal inflammation and tissue repair.

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling lesion severity) and diffuse pathologies (modeling lesion extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET's performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,249 WSIs from three cohorts, BanffNET demonstrates consistent performance on 11,028 WSIs across five external test sets, performing on par or exceeding expert consensus across lesions. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering a transparent, biologically grounded framework for computational pathology with relevance beyond transplantation.

Background & AimsHaematoxylin and eosin (H&E) staining remains the diagnostic gold standard for solid cancers, including hepatocellular carcinoma, and is increasingly complemented by genomic profiling for precision medicine. Inferring genomic alterations directly from H&E images could streamline testing, but heterogeneity and biases in human training data limit interpretation of genotype-phenotype associations. Here, we aimed to relate histologic to genomic pathology to provide biological explainability for mutation prediction models and assess the impact of germline variation on model performance. MethodsWe analysed 597 murine liver tumours with matched whole-genome sequencing and histopathology (163,835 image tiles; 22.9 million nuclei). Our controlled in vivo design accounted for germline variation, biological sex, and causal mutagen (N-diethylnitrosamine), removing confounding factors present in human cohorts. We trained and evaluated deep learning and supervised machine learning models to predict germline variation and cancer driver alterations from H&E. ResultsModelling accurately predicted germline and somatic alterations from histology, at both locus-specific and genome-wide scales. Quantitative image analysis revealed an unexpected association between Egfr driver mutations and hepatic steatosis, linking genotype to an interpretable morphological phenotype. While model performance declined when applied to tumours from unrepresented genetic backgrounds, this limitation was biologically informative, revealing strain-dependent differences in tumour evolution, notably the prevalence of whole-genome duplication. ConclusionsMachine learning integration of histological and genomic pathology enables accurate, interpretable inference of genetic alterations from H&E, potentially reducing reliance on costly ancillary molecular assays. Our predictions are supported by human-interpretable biological features, addressing concerns around "black-box" technologies. However, caution is required when applying such methods to samples with a genetic background that, even if closely related, is beyond the genetic horizon of training data.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder worldwide, yet therapeutic options remain limited. TMPRSS6, a liver serine protease best known for its role in iron homeostasis, has recently emerged as a potential therapeutic target for MASLD. However, the molecular mechanisms linking TMPRSS6 to hepatic lipid metabolism remain incompletely understood. To identify novel TMPRSS6 substrates, we performed extracellular proteomic analyses of TMPRSS6-overexpressing cells. Among the proteins identified, {beta}-klotho (KLB), a co-receptor required for FGF19 and FGF21 signaling, emerged as a compelling candidate substrate. We demonstrate that TMPRSS6 interacts with KLB and promotes its proteolytic shedding in a catalytic activity-dependent manner. Functionally, TMPRSS6 reduced full-length KLB abundance at the cell surface and attenuated FGF19-dependent FGFR4 signaling in a heterologous expression system. Together, these findings identify KLB as a novel functional substrate of TMPRSS6, providing a mechanistic framework through which this protease may influence hepatic lipid metabolism. These results provide a rationale for investigating the regulation of KLB and other candidate substrates by TMPRSS6 in physiological models and further support its evaluation as a therapeutic target for MASLD.

Excessive alcohol intake is frequently associated with hypertriglyceridemia, a condition that increases the risk of severe complications including acute pancreatitis and cardiovascular disease. The very low-density lipoprotein (VLDL) receptor (VLDLR) promotes uptake of apoE-containing VLDL particles by peripheral tissues and plays an important role in maintaining plasma triglyceride (TG) homeostasis. Brown adipose tissue (BAT) is a major metabolic organ that contributes to circulating lipid clearance during thermogenic activation. It was reported that cold-induced thermogenesis upregulates VLDLR expression in BAT and reduces plasma TG via VLDL uptake. However, whether BAT VLDLR-mediated VLDL uptake regulates alcohol-induced hypertriglyceridemia remains unknown. Here, we generated BAT-specific fatty acid synthase (FASN) knockout mice (FASNBKO) and subjected them to binge and acute-on-chronic alcohol feeding paradigms. We found that BAT FASN deficiency enhanced thermogenic function and promoted VLDL uptake, resulting in attenuation of alcohol-induced elevations in plasma TG. Consistent with these findings, pharmacological inhibition of FASN by TVB3664 treatment in differentiated brown adipocytes (bADs) increased thermogenic gene expression and VLDL uptake under both control and alcohol-exposed conditions. In addition, FASNBKO mice were protected from alcohol-induced hepatic steatosis, which was accompanied by increased hepatic AMP-activated-protein kinase (AMPK) activation and enhanced {beta}-oxidation. Furthermore, FASNBKO mice exhibited upregulated FGF21 mRNA expression in the BAT and elevated circulating FGF21 levels. Similarly, TVB3664-treated differentiated bADs showed higher FGF21 expression and increased FGF21 content in culture medium. Taken together, these findings identify the important role of brown adipocyte FASN in regulating thermogenic function and TG homeostasis during alcohol exposure and suggest that enhancing thermogenic lipid utilization in BAT may represent a potential therapeutic strategy for mitigating alcohol-associated increases in plasma TG and hepatic fat accumulation.

Objective: To evaluate the case-level performance of deep-learning segmentation models for detecting extrahepatic bile duct stones on representative intraoperative cholangiography images (IOC) and to characterize the completeness of individual-stone localization. Background: Retained bile duct stones can cause biliary obstruction, cholangitis, and pancreatitis. However false-positive interpretation of filling defects may prompt additional downstream procedures. Computer vision has been applied to biliary anatomy recognition and IOC adequacy assessment, but patient-level stone detection and individual-stone localization remain insufficiently studyed. Methods: Representative IOC images were annotated for extrahepatic biliary anatomy and stones, with case-level stone status established using a composite clinical reference standard. Two deep-learning models were developed to delineate the common bile duct and common hepatic duct and to detect and localize stones. Case-level diagnostic performance was evaluated against the composite clinical reference standard, and individual-stone localization was evaluated against expert-reviewed annotations. Results: On the held-out 125 patients test set, MiT-B2-UNet identified 23 of 25 stone-positive cases and 95 of 100 stone-negative cases, corresponding to a sensitivity of 0.920, specificity of 0.950, and AUC of 0.986. nnU-Net identified 19 of 25 stone-positive cases and 98 of 100 stone-negative cases, corresponding to a sensitivity of 0.760, specificity of 0.980, and AUC of 0.959. At the individual-stone level, MiT-B2-UNet and nnU-Net localized 31 of 59 and 25 of 59 annotated stones, respectively; all annotated stones were localized in 13 of 25 and 12 of 25 stone-positive cases. Conclusions: Deep-learning models can identify stone-positive IOC cases and localize individual stones. This technology may help inte

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.

Background IgA vasculitis (IgAV) is the most common systemic vasculitis in children, and its prognosis is largely determined by renal involvement (IgAV nephritis). No routine blood test identifies IgAV or stratifies the risk of nephritis, although aberrant O-glycosylation of the IgA1 hinge region is central to its pathogenesis. We developed a mass-spectrometry assay to profile IgA1 hinge O-glycoforms and define signatures of disease activity and renal involvement. Methods IgA was affinity-purified from 5 uL of plasma from 91 children (27 with acute IgAV, 26 in remission, and 38 age-matched healthy controls; 24 with and 29 without nephritis), trypsin-digested, and hinge-region O-glycopeptides were quantified by LC-MS. Sixty-nine glycoforms were normalized to a total-IgA1 tryptic peptide. Duplicate measurements showed good analytical repeatability, with a median coefficient of variation of 6%. Groups were compared using Mann-Whitney and Kruskal-Wallis tests (Benjamini-Hochberg FDR). Discrimination was assessed by ROC analysis and cross-validated logistic regression panels. Results Acute IgAV showed broad remodeling of the hinge glycoform profile (35 glycoforms differed with excellent discrimination (AUC 0.93-0.95) for the best ones), with an increase in low-sialylated, agalactosylated species and a decrease in complex sialylated species. The profile was normalized in remission (no glycoform differed from the controls). Two distinct renal patterns emerged: disease-associated glycoforms already altered without nephritis and renal-specific glycoforms altered only in nephritis (H2N2S1, H3N3S5, H3N4S4, and H4N4S3). A four-marker panel discriminated nephritis among IgAV children with a cross-validated AUC of 0.86 (IC95 % 0.75-0.94). Conclusions A single mass-spectrometry assay, from a small blood volume, captures an IgAV-associated IgA1 hinge O-glycoform signature that normalizes in remission, together with a distinct renal involvement associated signature. These findings identify candidate IgA1 O-glycoform signatures associated with IgAV activity and documented renal involvement. Prospective longitudinal studies are required to determine whether the renal-associated panel can predict subsequent nephritis.

Background: Gastric carcinoma remains a major cause of cancer related mortality worldwide, with tumor progression increasingly recognized as a consequence of complex interactions within the tumor microenvironment. Hypoxia induced signaling, cancer associated fibroblast (CAF) heterogeneity, and immune checkpoint activation play critical roles in tumor progression and immune evasion. However, their integrated relationship in gastric carcinoma remains insufficiently characterized. Objectives: To evaluate the expression of Hypoxia inducible factor 1 alpha and its association with cancer-associated fibroblast subtypes and Programmed death-ligand 1 expression in gastric carcinoma. Methods: This cross sectional analytical study included 100 histologically confirmed gastric carcinoma cases from Satkhira Medical College. Immunohistochemistry was performed for HIF 1 alpha, smooth muscle actin (SMA), fibroblast activation protein (FAP), and PD L1. CAFs were subclassified into myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs). Associations between biomarkers and clinicopathological variables were analyzed using chi square test, Spearman correlation, and multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis was used to assess model performance. Result: High HIF 1 alpha expression was observed in 55% of cases and demonstrated significant association with poor differentiation (p = 0.001), advanced tumor stage (p = 0.002), and lymph node metastasis (p = 0.001). iCAF predominance was significantly associated with poor differentiation (p = 0.003), advanced stage (p = 0.004), and nodal metastasis (p = 0.004). High PD L1 expression was significantly associated with poor differentiation (p = 0.03), advanced stage (p = 0.001), and lymph node metastasis (p = 0.002). Multivariate logistic regression identified high HIF 1 alpha expression (OR = 3.8, p = 0.001), iCAF dominance (OR = 4.5, p < 0.001), and advanced tumor stage (OR = 2.9, p = 0.004) as independent predictors of high PD L1 expression. Combined high HIF 1 alpha expression and CAF activation demonstrated the highest rate of PD L1 positivity (76.7%, p < 0.001). ROC curve analysis demonstrated good predictive performance of the model with an area under the curve of 0.81. Conclusion: The present study demonstrates a significant interaction between hypoxia, stromal remodeling, and immune checkpoint activation in gastric carcinoma. High HIF 1 alpha expression and inflammatory CAF predominance are strongly associated with aggressive clinicopathological features and increased PD L1 expression, supporting the existence of a coordinated hypoxia stroma immune axis in gastric carcinoma progression. These findings may have potential implications for prognostic stratification and combined targeted therapeutic strategies.

Objective: Lymphatic dysfunction has been implicated in exacerbating fibrosis in numerous diseases, yet the role of the lymphatic system in laryngotracheal injury has not previously been explored. This study aims to evaluate lymphatic vascular remodeling in a murine model of laryngotracheal stenosis (LTS) and determine how pharmacologic blockade of lymphangiogenesis impacts airway healing after mucosal injury. Methods: LTS was induced in C57BL6 mice using an established chemomechanical injury model. Lymphatic density was quantified using LYVE-1 immunohistochemistry. Mice were treated with the VEGFR-3-selective tyrosine kinase inhibitor SAR131675 to block lymphangiogenesis after injury. Outcomes assessed included survival, histopathology, immunohistochemistry, and Evans blue dye vascular leakage. Results: Laryngotracheal injury induced a substantial increase in subepithelial lymphatic vessel density concomitant with fibrotic remodeling. Pharmacologic inhibition of VEGFR-3 signaling with SAR131675 abrogated this lymphangiogenic response and resulted in markedly increased mortality, impaired epithelial repair with obstructive sloughing, increased edema, and persistent histopathologic evidence of tissue injury. A qualitative increase in pathologic fibrocellular remodeling was also observed, though with no measurable difference in lamina propria thickness. Conclusion: These findings establish lymphatic remodeling as an essential component of successful airway repair following mucosal injury. Lymphatic dysfunction is a common feature of known risk factors for LTS including diabetes, obesity, and prematurity, and can be exacerbated by positive pressure ventilation. Disruption of the lymphangiogenic response to airway injury may lead to stasis of pro-inflammatory factors that result in chronic inflammation, maladaptive remodeling, and pathologic tissue changes. The lymphatic vasculature is a viable target for future mechanistic study and potential therapeutic intervention following airway injury.

Pigment epithelium-derived factor (PEDF) promotes photoreceptor survival through its receptor PEDF-R, a phospholipase involved in retinal lipid metabolism. To define the in vivo function of the PEDF/PEDF-R axis, we generated mice lacking Serpinf1 (PEDF) and Pnpla2 (PEDF-R). Combined loss of Serpinf1 and Pnpla2 resulted in severe retinal degeneration characterized by outer nuclear layer (ONL) thinning, outer segment (OS) shortening, reduced rhodopsin and cone opsin expression, increased TUNEL-positive nuclei, and enhanced retinal autofluorescence associated with altered lipid distribution. Lipid-associated markers, including TIP47, PLIN5, and BODIPY, exhibited abnormal distribution patterns in mutant retinas, indicating disrupted lipid storage and trafficking. Loss of PEDF/PEDF-R signaling also impaired photoreceptor-rod bipolar cell connectivity, as demonstrated by reduced PKC/synaptophysin colocalization, and resulted in diminished electroretinographic responses. Lipid Imaging mass spectrometry revealed decreases in some lipid abundances in photoreceptor outer segment and inner segment/outer nucleus layer, while lipids containing arachidonic acid and docosahexaenoic acid-containing lipids showed increased abundance. Together these findings identify the PEDF/PEDF-R signaling axis as a key regulator of retinal phospholipid homeostasis that couples lipid metabolism to photoreceptor survival and visual function.

Mustard keratopathy, caused by exposure of the cornea to sulfur or nitrogen mustard vesicants, chemical warfare agents, can lead to severe and often irreversible vision loss. Despite considerable efforts to develop medical countermeasures, including anti-inflammatory, antioxidant, anti-fibrotic, and anti-angiogenic therapies, no treatment effectively targets the underlying mechanisms responsible for mustard-induced tissue injury or prevents long-term disease progression. In the present study, we comprehensively define mitochondrial mechanisms underlying nitrogen mustard-induced corneal injury in both our cell culture model in vitro and a mouse model in vivo. DNM1L (aka Drp1) is a mitochondria-localized dynamin-related GTPase that executes mitochondrial fission and facilitates the autophagic elimination of damaged mitochondrial components. Using complementary in vitro and in vivo models, we demonstrate that nitrogen mustard rapidly induces excessive mitochondrial fragmentation, bioenergetic collapse, membrane depolarization, oxidative stress, intracellular acidification, mitophagy, and apoptotic cell death. Pharmacological inhibition of DNM1L with Mdivi-1 preserves mitochondrial structure and function, restores cellular metabolism, reduces oxidative damage, and markedly improves corneal epithelial integrity, and tissue repair following nitrogen mustard exposure. Collectively, these findings establish mitochondrial dysfunction as a central pathological mechanism in mustard keratopathy and identify DNM1L-mediated mitochondrial remodeling as a therapeutically actionable target. Our work provides strong preclinical evidence supporting mitochondrial-directed therapy as a promising strategy for treating mustard keratopathy.

Pathologic diagnoses of hematopoietic diseases require immunohistochemistry (IHC) stains selected by pathologists upon preview of H&E-stained slides. This multi-step workflow can delay diagnostic turnaround time by days. Hence, we developed the Hematopathology Automatic Triaging System (HATS), which automates IHC panel ordering directly from H&E whole-slide images using pretrained pathology foundation model representations combined with attention-based multiple-instance learning. After the most comprehensive evaluation of pathology foundation models for hematologic malignancy classification to date, encompassing seven publicly available models, we trained HATS on 4,996 whole-slide images from 1,607 patients spanning the ten most common lymphoma diagnostic categories. HATS achieves 84% case-level subtype classification accuracy (0.962 ROC-AUC), translating to 92% IHC panel ordering accuracy. In a blinded reader study, HATS outperforms practicing pathologists at predicting lymphoma subtypes from morphology alone (85% vs 65%). In an independent real-world validation of 230 clinical cases, after directing 7 cases with scant tissue for manual review, HATS-ordered IHC panels were sufficient for diagnosis in 72.6% of cases. By automating the triaging step while preserving full pathologist oversight, HATS offers a safe and practical entry point for clinical AI adoption in pathology.

Background: Lung cancer is leading cause of cancer related death globally. It is second most prevalent cancer among women worldwide and ranks third among females in Nepal. Contributing factors are smoking, tobacco use, air pollution, and delayed diagnosis. Image-guided fine needle aspiration cytology (FNAC) is rapid diagnostic technique for evaluating lung lesions. It is minimally invasive procedure with less complications. This study examine histocytologic makeup of lung lesions and link the results. Materials and Methods: This cross-sectional observational study included 65 patients irrespective of age and sex presenting with lung masses at Chitwan Medical College and Teaching Hospital from April 2023 to September 2024. After clinical and radiologic evaluation, all cases underwent image-guided FNAC and biopsy. Only specimens with unequivocal malignant features were classified positive. Histopathology served as diagnostic reference standard. Results: FNAC diagnosed 90.8% as malignant and 9.2% as benign. Biopsy confirmed malignancy in 92.3% of cases. FNAC demonstrated a sensitivity of 98.33%, specificity of 100%, positive predictive value(PPV) of 100%, and negative predictive value (NPV) of 83.33%. Concordance between FNAC and histopathological subtyping was 98.46%. Adenocarcinoma was most common subtype, followed by Squamous cell carcinoma(SCC) and small cell carcinoma. Smoking was most common contributing factor associated with malignancy. Conclusion and implications: Image-guided FNAC is an excellent diagnostic accuracy tool which possess higher level of concordance with biopsy in evaluating lung masses. It should be considered as frontline diagnostic tool, especially in resource limited settings. Keywords: FNAC, Lung cancer, Biopsy, SCC, Adenocarcinoma, Small cell carcinoma, Nepal

Purpose To estimate the per-protocol effect of red blood cell (RBC) transfusion strategies on ICU-acquired infection in critically ill adults with sepsis using a target trial emulation framework. We evaluated whether restrictive strategy and liberal strategy, defined by hemoglobin (Hgb) thresholds, differ in their effect on ICU-acquired infection during ICU stay. Methods We conducted a target trial emulation using the MIMIC-IV database and included adults who met Sepsis criteria at ICU admission. Clones were assigned to restrictive or liberal transfusion strategies. Under the restrictive strategy, RBC transfusion was permitted only when Hgb was [&le;]7.0 g/dL, whereas under the liberal strategy, transfusion was permitted when Hgb was >7.0 g/dL. The primary outcome was the first ICU-acquired infection occurring at least 72 hours after ICU admission. Per-protocol effects were estimated using a clone-censor-weight approach with a marginal structural model. A parametric g-formula was used as a complementary analysis that jointly modeled ICU discharge and ICU mortality as competing events to derive strategy-specific 28-day cumulative incidences and risk differences. Results 8 Among 4,013 eligible ICU stays, the liberal-versus-restrictive comparison provided little evidence of a difference in the risk of ICU-acquired infection (adjusted conditional OR, 0.954; 95% CI, 0.797 to 1.142). In the complementary g-formula analysis, the 28-day risk difference for the liberal versus restrictive comparison was -0.02 percentage points (95% CI, -0.15 to 0.11), consistent with the primary analysis. Findings were generally robust across prespecified subgroup and sensitivity analyses. Conclusion In this target trial emulation of adults with sepsis, we observed no clinically meaningful difference in ICU-acquired infection between RBC transfusion strategies defined by hemoglobin thresholds.

Interstitial lung diseases (ILDs) are heterogeneous pulmonary disorders characterized by chronic inflammation and/or fibrosis. 30-40% of ILD patients develop fibrotic disease that is associated with progressive respiratory decline and poor prognosis, particularly in idiopathic pulmonary fibrosis. Current antifibrotic therapies slow disease progression but do not reverse fibrosis, highlighting the need for improved therapeutic strategies. Robust histopathological evaluation in preclinical models is essential for drug development; however, conventional scoring systems are semi-quantitative, labor-intensive, subject to inter-observer variability, and rely on limited field sampling. Here, we introduce FibroSight, a standalone platform for compartment-resolved quantification of lung remodeling in Sirius Red-stained sections. By integrating deep learning- based structural segmentation with color-based feature extraction, FibroSight enables highly automated whole-lobe analysis without requiring complex computational setup. The platform quantifies complementary remodeling parameters, including parenchymal collagen fraction, parenchymal tissue density, nuclear area fraction, parenchymal airspace fraction, and airway- and vascular-associated remodeling. Validated in the bleomycin-induced fibrosis model, FibroSight-derived metrics strongly correlated with expert Ashcroft scoring and showed stronger associations with histological severity than corresponding outputs from a semi-automated ImageJ-based workflow. The platform further distinguished inflammatory from fibrotic remodeling in influenza-induced lung injury and demonstrated translational proof-of-concept applicability in human ILD biopsy specimens. By enabling scalable, reproducible, and multi-compartment histological quantification, FibroSight provides a practical framework for objective assessment of lung remodeling. This approach expands conventional fibrosis evaluation by integrating fibrotic, inflammatory, airway, and vascular-associated readouts, supporting more precise analysis of disease mechanisms and therapeutic responses in preclinical and translational ILD research.

Severe dengue can damage the liver through unestablished mechanisms. We investigated the role of myeloperoxidase (MPO), a neutrophil enzyme, in dengue through patients, fatal liver samples, and mouse infection models. Observations from two independent clinical cohorts revealed elevated plasma MPO levels in dengue and, in one cohort, MPO was further linked to liver injury markers during the critical phase of disease, whereas livers from dengue fatal cases revealed MPO build-up in the vicinity of CD177+ activated neutrophils. In mice, dengue led to MPO overexpression, oxidative damage, and broad activation of innate and systemic inflammatory pathways in livers. Blocking MPO activity alleviated these and improved survival in one model and delayed disease progression without preventing death in another. These findings establish MPO as a functional mediator of severe dengue-associated liver injury and inflammation, which warrants further preclinical investigation into its hepatic pathogenic mechanism and its validity as target for therapeutic intervention.

Background Therapy resistance in pancreatic ductal adenocarcinoma (PDAC) is facilitated by the desmoplastic tumor microenvironment (TME) orchestrated by cancer associated fibroblasts (CAFs). Upon activation, pancreatic stellate cells (PSCs) deplete their intracellular retinoic acid (RA)-containing lipid droplets and secrete stromal remodeling proteins like pentraxin 3 (PTX3), leading to cancer progression. Preclinical evidence indicates that all-trans RA (ATRA) reprograms the TME, while circulating vitamin A and PTX3 were proposed as biomarkers for ATRA response in PDAC. To support further clinical development of RA-based therapies in PDAC, we studied the effects of ATRA on CAFs and patient-derived organoids (PDO) and evaluated the clinical relevance of these biomarkers in PDAC patients. Methods We employed viability assays in human and murine organoid mono- and co-culture models to explore the efficacy of adding ATRA to gemcitabine (GEM). In parallel, we conducted a prospective observational study and assessed vitamin A and PTX3 as response biomarkers in peripheral blood collected before first treatment and at cycles 2 and 4 of treatment among patients with advanced PDAC receiving GEM with or without nab-paclitaxel (NAB-P). Results In PDO monocultures, a significant additive effect of ATRA in combination with GEM on viability was observed in 5 (41%) of 12 PDOs and this effect was numerically more frequent in organoids from patients who had clinically responded to GEM. In human and murine 3D PDO+PSC/CAF co-cultures, ATRA demonstrated an additional direct impact on the viability of stromal cells. Clinically, among 18 patients with PDAC treated with GEM+/-NAB-P, patients with no treatment response (n=10) showed an increase in PTX3 and concomitant decrease in vitamin A levels under therapy. In contrast, response was associated with stable vitamin A levels and a trend towards lower PTX3 levels during chemotherapy. Conclusions Our preclinical data support the repurposing of ATRA, an agent with favorable toxicity profile, to potentiate the efficacy of GEM in PDAC treatment. Complementing these results, our clinical data suggest vitamin A and PTX3 as promising response biomarkers in PDAC treatment, not restricted to ATRA containing regimens.

Background. In Europe, first-trimester combined screening with the Fetal Medicine Foundation (FMF) algorithm identifies women at increased risk of preeclampsia who may benefit from personalized aspirin prophylaxis. However, a substantial proportion of early-onset preeclampsia (EOPE) remains undetected at clinically acceptable specificity. Objective. To evaluate the first-trimester performance of MaiRa for early-onset preeclampsia (EOPE) risk stratification by benchmarking it against FMF screening in the same women, characterizing discordant patient-level classification profiles and exploring potential implementation strategies. Study Design. This secondary case-control analysis was nested within the prospective, multicentre PREMOM cohort [NCT04990141], which enrolled women with singleton pregnancies across 14 tertiary hospitals in Spain. First-trimester MaiRa and FMF risk estimates were evaluated in the same 126 pregnant women, comprising 99 uncomplicated controls and 27 EOPE cases, defined by disease onset before 34 weeks. Discrimination was compared using a stratified paired bootstrap analysis of the areas under the receiver-operating-characteristic curves. Performance was assessed at prespecified clinical thresholds, and detection rates were evaluated at fixed false-positive rates. Universal and contingent MaiRa implementation strategies were also evaluated. Results. MaiRa showed greater first-trimester discrimination for EOPE than FMF combined screening (AUC, 0.974 vs 0.900; P=.040) and consistently achieved higher detection rates across fixed false-positive rates. At false-positive rates of 5% and 10%, MaiRa detected 85.2% and 92.6% of EOPE cases, compared with 44.4% and 70.4% for FMF, respectively. Patient-level analysis demonstrated that MaiRa identified 12 of 27 EOPE cases (44.4%) classified as low risk by FMF; these pregnancies generally exhibited less abnormal conventional first-trimester profiles, including fewer maternal risk factors, lower mean arterial pressure and lower uterine artery pulsatility index, yet 8 of 12 (66.7%) subsequently developed severe EOPE. Exploratory implementation analyses showed that universal MaiRa screening achieved the highest EOPE detection, whereas a contingent strategy using FMF for triage and reflex MaiRa testing reduced molecular testing to 35.7% of pregnancies while maintaining 77.8% sensitivity and 97.0% specificity. Conclusion. MaiRa provided greater first-trimester discrimination for EOPE than conventional combined screening and detected additional pregnancies that later developed severe disease despite less abnormal conventional screening profiles. The findings suggest that maternal plasma cfRNA profiling captures biological alterations not fully reflected by combined first-trimester screening and support further prospective evaluation in an independent, unselected obstetric population. Key words: early-onset preeclampsia; first-trimester screening; cell-free RNA; liquid biopsy; Fetal Medicine Foundation algorithm; combined screening; risk stratification; aspirin prophylaxis.