npj Breast Cancer

Tumor-infiltrating lymphocytes (TILs) are widely used to assess antitumor immunity in breast cancer but may not reflect the functional competence of adaptive immune responses. We show that immune organization, reflected by tertiary lymphoid structures (TLS) and coordinated humoral and cellular immune programs, represents a distinct dimension of tumor immunity beyond lymphocyte abundance. By integrating histologic, transcriptomic, spatial, and immune receptor profiling analyses across multiple breast cancer cohorts, we show that immune organization is associated with greater immune repertoire diversity, evidence of therapy-induced clonal selection, and improved clinical outcomes, independent of immune infiltration. Transcriptomic measures of immune organization retained independent prognostic value across external cohorts, whereas measures of immune infiltration did not. Furthermore, treatment-induced increases in immune organization, but not immune infiltration, were associated with therapeutic response. These findings identify immune organization as a dynamic and clinically measurable state of adaptive antitumor immunity with implications for prognosis, treatment monitoring, and therapeutic development in breast cancer.

Population-based mammography screening improves breast cancer outcomes, but its impact on real-world treatment pathways and quality indicators (QIs) remains incompletely described. We conducted a retrospective nationwide cohort study using linked data from the Czech National Cancer Registry and the National Registry of Reimbursed Health Services. Women aged [≥]18 years with a first breast cancer diagnosis between 2017 and 2024 were classified as screen-detected (SCR) or diagnostically-detected (DIG) according to the imaging modality preceding histological verification. Outcomes included stage distribution, untreated cases, first-line treatment, main treatment modality, time to treatment, multidisciplinary team discussion (MDT), centralization to Comprehensive Cancer Centres (COCs), and survival patterns. The verified cohort included 47,648 women: 26,817 SCR cases (56.3 %) and 20,831 DIG cases (43.7 %). In this nationwide analysis, SCR breast cancer was associated with earlier stage at diagnosis and better survival patterns, but also with longer time to treatment and longer time to MDT discussion than DIG-detected disease. Although treatment rates were high and centralization improved over time, substantial regional variation persisted in care pathways, MDT use, and access to COCs. These findings support continued strengthening of screening participation, monitoring of care intervals, and quality assurance of MDT reporting and regional oncology care delivery.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of cancer worldwide. While most cSCCs are not life-threatening, 2-5% of patients develop metastases. To better understand what causes some cSCCs to progress to metastatic disease, we assembled a nationwide cohort of 19,120 patients with clinico-pathologically annotated tumors linked to metastatic outcome. RNA-sequencing was performed on 378 tumors, and whole-exome sequencing on 147, with balanced numbers of tumors that progressed to metastatic disease (cases) and did not (controls). UV radiation was the dominant mutational signature with additional contributions from aging, APOBEC activity, and, in immunosuppressed patients, azathioprine exposure. We identified 38 genes under selection across a core set of signaling pathways. Gene expression clusters were primarily associated with the differentiation state of tumor cells and secondarily with the composition of the tumor microenvironment. Several mutational and transcriptional programs were associated with metastasis, including a dedifferentiated gene expression signature, activating mutations in the RAS signaling pathway, loss-of-function alterations in the SWI/SNF chromatin remodeling complex, and specific arm-level copy number alterations. A 23-gene expression signature was built to predict metastasis from primary cSCC tissue. The signature was validated in two independent cohorts (N=102 and 52), where it predicted metastasis independently of staging systems. Together, these findings provide the most detailed molecular portrait of cSCC to date and establish an assay for risk stratification suitable for clinical implementation.

Background The clinical significance of clonal hematopoiesis of indeterminate potential (CHIP) in melanoma remains incompletely defined, particularly with respect to CHIP genotype, clone size, and somatic mutations (e.g BRAF mutations). We integrated human cohort data and a syngeneic melanoma mouse model to evaluate whether CHIP is associated with melanoma risk, tumor growth, and differential clinical outcomes. Methods We analyzed CHIP prevalence and survival in a large treatment-unselected melanoma cohort (n=2,480), evaluated tumor growth in a syngeneic BRAF-mutant (BRAFmut) melanoma murine model of TET2-CHIP and DNMT3A-CHIP, and assessed survival outcomes in an immune checkpoint inhibitor (ICI)-treated advanced melanoma cohort (n=361). Associations with progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan-Meier analyses and multivariable Cox proportional hazards models. Results CHIP was enriched among patients with treatment-unselected melanoma compared with age/sex-matched healthy controls, and larger CHIP clone size showed an age-adjusted association with inferior OS. In a syngeneic BRAFmut melanoma murine model, TET2-CHIP, but not DNMT3A-CHIP, was associated with significantly increased primary melanoma tumor growth. Among patients with ICI-treated advanced melanoma, CHIP was associated with worse OS compared with patients without CHIP. TET2-CHIP had the strongest adverse association with survival, whereas DNMT3A-CHIP was not significantly associated with PFS or OS. Conclusions CHIP is enriched in melanoma and exploratory analyses demonstrate genotype-specific differences in melanoma tumor growth and clinical outcomes. These findings support further investigation of genotype-specific CHIP profiling as a potential biomarker for melanoma risk stratification and immunotherapy outcomes.

Purpose: To compare the cross-site generalization of radiomic features and deep learning embeddings for MRI prediction of substantial lymphovascular space invasion (LVSI) in endometrial cancer. Materials and Methods: This retrospective two-center study included 206 women (mean age, 59.8 years) with endometrial cancer who underwent preoperative 3-T MRI from March 2016 to March 2023. Hospital A (n = 130) was used for development and Hospital B (n = 76) for strict external testing. T2-weighted, reduced field-of-view diffusion-weighted, and apparent diffusion coefficient images were manually segmented. Radiomic features and seed-pooled embeddings from 3D ResNet18, DenseNet121, and U-NEXtractor were modeled with elastic-net logistic regression or XGBoost. Out-of-fold Platt calibration and sensitivity-targeted thresholds were estimated using development data only. AUCs were summarized with 95% bootstrap confidence intervals. Results: External radiomics with elastic-net achieved an AUC of 0.609 (95% CI: 0.464, 0.740) and sensitivity of 0 of 12 (0%). DenseNet121 with elastic-net had the highest external AUC (0.685; 95% CI: 0.538, 0.822) but sensitivity of 3 of 12 (25%). U-NEXtractor with elastic-net detected 10 of 12 positive cases (83.3%) with specificity of 32 of 64 (50.0%) and balanced accuracy of 0.667. XGBoost showed higher apparent development performance but weaker external operating behavior. Conclusion: Under real-world cross-site MRI acquisition shift, DenseNet121 and U-NEXtractor embeddings showed better external generalization than handcrafted radiomic features for substantial LVSI prediction.

Objectives. Radiomics pipelines extract hundreds of quantitative features that are widely known to be redundant, but the structure of this redundancy is usually treated as a per-dataset nuisance to be pruned away. We tested the alternative hypothesis that a substantial number of feature-feature correlations are universal: they persist across patients and across anatomically distinct structures because they reflect shared mathematical and image-statistical properties of how the image is summarised, rather than properties of the tissue being imaged. Materials and Methods. We re-analysed the publicly available Radiomics Atlas Dataset of normal Abdominal and Pelvic CT (RADAPT), restricting the analysis to the 526 non-contrast-enhanced examinations of the 531-subject atlas and to the 107 original (non-filtered) PyRadiomics features. The 53 segmented structures were grouped into four broad anatomical categories -- bones, muscles, vessels, and parenchymal organs. RADAPT is distributed as one Excel file per structure, with patients as rows and features as columns. Within each structure file we z-score-normalised every feature across patients, computed the absolute Spearman correlation matrix, and retained edges with |{rho}| [≥] {tau} for {tau} in {0.70, 0.80, 0.90}. We then intersected the edge sets across all structure files to obtain a "universal" correlation graph, in which an edge survives only if it exceeds the threshold in every structure (each estimated across the full patient sample). Stable feature communities were defined as the maximal cliques of this graph. Robustness to patient sampling was tested by repeating the entire pipeline on five independent random splits of each file into two patient halves (10 sub-cohorts per threshold), and the implementation was independently reproduced in R. Results. Despite the strictness of the global-intersection criterion, 34, 24, and 14 stable feature communities survived at {tau} = 0.70, 0.80, and 0.90 respectively, with the largest cliques containing six members at {tau} = 0.70 and {tau} = 0.80 and five members at {tau} = 0.90. The community structure was clearly interpretable: separate cliques captured (i) variance-like intensity dispersion, (ii) long-run / low-frequency (coarse) texture, (iii) high gray-level texture, (iv) low gray-level texture, (v) volume and surface shape, and (vi) local-homogeneity and energy/entropy duals. On random-half resampling the exact-match recovery rate of these communities was 81.5 %, 86.7 %, and 80.7 % across the three thresholds; departures from exact recovery were almost always a single boundary feature added or dropped, consistent with finite-sample fluctuation of near-threshold edges rather than structural instability. The R re-implementation reproduced the Python results exactly. Conclusion. A substantial portion of radiomics feature collinearity is universal across patients and tissues. We distinguish two layers within it: trivial near-algebraic duals that are universal by construction, and non-trivial cross-matrix-family communities that are the genuine empirical finding. Together they provide an interpretable, definition-grounded basis for aggressive dimensionality reduction, for retrospectively reconciling apparently different feature selections in the literature, and for moving radiomics pipelines toward organ-agnostic, more reproducible models. Clinical relevance statement. Selecting a single representative feature from each universal community shrinks the original-feature space by roughly an order of magnitude without sacrificing biologically distinct information. For example, the five variance-family members (first-order Variance, GLCM SumSquares, GLCM ClusterTendency, GLDM and GLRLM GrayLevelVariance) can be replaced by a single representative, removing redundant degrees of freedom that would otherwise inflate model variance; and labelling each retained feature by its community lets two studies that selected different variance-family names be recognised as having found the same signal, simplifying model development and improving cross-cohort generalisability in clinical CT workflows.

This review evaluates the biological pathways linking soft drink consumption with the risk of several cancers within the framework of the Global Cancer Update Programme (CUP Global). Soft drink consumption has been associated with increased risk of multiple cancers, and glucose or insulin dysregulation has been proposed as a potential underlying mechanism. We applied a three-stage framework. In the first stage, we identified insulin sensitivity as the key biological process potentially linking soft drink consumption (sugar-sweetened or artificially sweetened) to cancer risk, with glucose-related and insulin-related biomarkers as potential intermediate phenotypes, using a combination of expert knowledge and a web-based text mining tool. In the second stage, we conducted targeted PubMed searches to identify studies examining associations between consumption of soft drinks and these intermediate phenotypes (IPs) and between these IPs and the risk of several cancers in adult humans. In the third stage, the evidence was evaluated by the Expert Committee on Cancer Mechanisms (MEC), who assessed the strength of the evidence for these associations. The MEC concluded that there was weak evidence supporting a role of glucose or insulin-related processes as a potential mechanistic pathway linking the consumption of sugar-sweetened or artificially sweetened beverages to the risk of various cancers evaluated.

The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scientific literature. We characterized thematic shifts in HCC treatment and prognosis research by conducting a retrospective bibliometric analysis of influential publications from 2023 and 2024. Using the OpenAlex database, we identified the 50 most highly cited papers from each year based on eighteen-month post-publication citation counts. Large language models were deployed to extract, normalize, and classify concepts from unstructured text into canonical topics and parent themes, enabling quantitative year-over-year frequency comparisons. Analysis of these 100 papers revealed a distinct maturation in research focus. Although broad categories like general immunotherapy remained prevalent, their relative frequency declined in favor of specific dual immune checkpoint regimens, notably CTLA-4 inhibition and the durvalumab plus tremelimumab combination. Concurrently, parent themes related to radiomics, imaging, and health systems exhibited significant growth in the 2024 cohort. These findings demonstrate a thematic transition in high-impact HCC research from foundational immuno-oncology toward optimized combination therapies and precision diagnostics. Furthermore, this study highlights the utility of artificial intelligence-driven bibliometrics for objectively tracking dynamic conceptual shifts in oncology. A web interface for exploring the data is available at https://pri.pepkio.com/.

Introduction: This study aimed to provide population level survival trends during the era in which new systemic therapies transformed treatment guidelines for metastatic cancer, as well as insights on the real world use of these treatments and associated survival. Methods: Adults diagnosed with synchronous metastatic solid cancer in 2008 until 2022 (22 cancer types) were identified from the Netherlands Cancer Registry. Median overall survival (OS) was assessed by five year diagnostic period. For 2018 until 2022, systemic therapy use in any treatment line was analyzed and survival percentiles within treatment and cancer types were estimated with Kaplan Meier survival analyses. Results: Median OS in the overall cohort (n=280,419 patients) improved from 6 to 8 months between the period 2008 until 2012 and 2018 until 2022. Among patients diagnosed in 2018 until 2022, 15% received immunotherapy, 15% targeted therapy, 29% chemotherapy and/or traditional hormone therapy only, and 39% no systemic therapy. In some cancer types, a relatively large proportion of treated patients had longterm survival (e.g., immunotherapy in melanoma: p50 = 67 months). Other cancer types had a smaller subset of treated patients (p10 and p25) with substantially better outcomes than the median (e.g., targeted therapy in NSCLC: p50 = 22 months, p10 = 96 months). Conclusion: Population level survival for patients with synchronous metastatic solid cancer has modestly improved over time. The marked survival heterogeneity within cancer and treatment types highlights both the potential and uncertainty associated with (novel) treatments. Improved prediction of treatment effects and clear communication regarding survival expectation remain critical. Presenting multiple survival scenarios over median survival alone can support decision making.

Background: Early-onset colorectal cancer (EOCRC) is increasing globally, particularly among Hispanic/Latino (H/L) populations, yet the contribution of tumor-colonizing microbiota to age-associated colorectal cancer (CRC) biology remains poorly understood. Most microbiome studies have focused on fecal communities or non-Hispanic populations, leaving the intratumoral microbial landscape of H/L patients largely unexplored. Methods: We performed an exploratory characterization of tumor-colonizing microbiota using whole-exome sequencing (WES) data from four primary colorectal tumors obtained from H/L patients treated at City of Hope, including two EOCRC (<50 years) and two late-onset colorectal cancer (LOCRC; [&ge;]50 years) cases. Following removal of host-derived sequences, microbial taxonomic profiling was conducted at the family, genus, and species levels, and microbial metabolic pathways were inferred. Clinical and pathological data were integrated to evaluate age-associated differences in microbial composition and predicted function. Results: Family-, genus-, and species-level analyses consistently demonstrated greater microbial diversity in LOCRC than EOCRC. LOCRC contained more than twice the number of unique bacterial families, nearly three times as many unique genera, and more than twice as many unique bacterial species. A conserved core microbiota, including Fusobacteriaceae, Prevotellaceae, Fusobacterium, and Prevotella, was identified across both age groups, whereas LOCRC was enriched in CRC-associated taxa including Fusobacterium nucleatum, Bacteroides fragilis, Parvimonas micra, Porphyromonas asaccharolytica, and Dialister pneumosintes. Species-level analyses revealed only a single shared bacterial species between EOCRC and LOCRC, indicating progressive microbial divergence with increasing taxonomic resolution. In contrast, functional profiling identified 11 predicted microbial metabolic pathways, of which nine were shared between age groups, two were unique to EOCRC, and none were exclusive to LOCRC. Core metabolic pathways involved in energy metabolism, amino acid biosynthesis, phospholipid metabolism, and central carbon metabolism exhibited comparable abundance across both groups, demonstrating substantial functional conservation despite pronounced taxonomic differences. Conclusions: Tumor-colonizing microbiota differ markedly between EOCRC and LOCRC in H/L patients, with late-onset tumors exhibiting substantially greater microbial richness and taxonomic complexity. Despite these compositional differences, microbial metabolic functions remain largely conserved, supporting the concept of functional redundancy within the colorectal tumor microenvironment (TME). Although exploratory, this proof-of-concept study provides one of the first characterizations of intratumoral microbiota in H/L EOCRC and establishes a foundation for larger multi-omics investigations aimed at identifying microbiome-based biomarkers and therapeutic targets for precision oncology.

Background: Changes in the extracellular matrix (ECM) are a recognised feature of aggressive prostate cancer, but they are not exploited in clinical decision-making. We aimed to develop automated quantitative ECM parameters to facilitate risk stratification for localised prostate cancer. Methods: 378 quantitative ECM parameters were derived from picrosirius red-stained diagnostic prostate biopsies in a cohort of 422 patients, matched 1:1 for recurrence, recruited to the CHHiP (Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy in Prostate Cancer) trial of radiotherapy fractionation for localised prostate cancer. These ECM parameters comprehensively described fibre architecture, gaps and ECM texture. Machine learning models at the level of both individual image tiles and patients defined how ECM parameters related to tumour versus normal prostate, Gleason grade group and recurrence. Shapley analysis was used to interpret ECM feature importance and develop signatures associated with recurrence. Results: Specific ECM patterns identified tumour versus normal prostate, Gleason pattern 4 versus 3 and recurrence. ECM patterns associated with recurrence were enriched in Gleason 4+3 patients, versus Gleason 3+4 patients. Shapley analysis revealed that biopsies from patients with recurrence had smaller more elongated gaps between fibres, with finer grained ECM texture and lower ECM homogeneity than less recurrent regions. Interpretation: Quantitative automated analysis of ECM architecture can inform probability of prostate cancer recurrence after radiotherapy; Features relating to ECM gap size and texture are of particular relevance.

Background: KRAS mutation status is an important biomarker in rectal cancer, with implications for prognosis and treatment response. MRI-based radiomics has emerged as a non-invasive approach for predicting tumor genotypes. However, the diagnostic performance of MRI radiomics for predicting KRAS mutation status remains unclear. This study aimed to evaluate the diagnostic accuracy of MRI radiomics for predicting KRAS mutations in rectal cancer. Methods: A systematic search of PubMed, Cochrane Library, Scopus, and Web of Science was performed through July 2025. Diagnostic test accuracy studies evaluating MRI-based radiomics or artificial intelligence models for predicting KRAS mutation status in adult patients with rectal cancer were included, using molecular testing as the reference standard. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Results: Seven studies involving 1,224 patients were included. The pooled sensitivity was 0.736 (95% CI: 0.697-0.772) and the pooled specificity was 0.645 (95% CI: 0.586-0.701). The false positive rate was 0.355 (95% CI: 0.299-0.414). The area under the hierarchical summary receiver operating characteristic curve was 0.754, with a normalized partial AUC of 0.666. Between-study heterogeneity ranged from low to moderate depending on the estimation method (I2 = 8.4%-53.3%). Conclusion: MRI radiomics demonstrates moderate diagnostic accuracy for predicting KRAS mutation status in rectal cancer and may serve as a promising non-invasive biomarker for preoperative molecular stratification. Further large-scale studies with external validation are required to confirm its clinical utility.

ABSTRACT Background: Despite its high prevalence and established impact on women's health, the long-term biological effects of Intimate Partner Violence (IPV) remain poorly understood. In particular, its potential role in increasing cancer risk has received limited attention. This review examines whether IPV may be associated with elevated cancer risk in women. Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA and MOOSE guidelines to evaluate whether IPV may be associated with cancer risk. Eligible studies included adult women ([&ge;]18 years) with documented IPV exposure and cancer or precancerous outcomes. We searched PubMed, Web of Science, Scopus, and Google Scholar for articles published from 2000 to 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was performed on longitudinal studies reporting adjusted risk estimates. Results: Thirteen studies were included in the qualitative synthesis, but only two met criteria for meta-analysis, both reporting on cervical cancer. The pooled odds ratio was 3.00 (95% CI: 2.05 - 4.38; I2 = 0%). A separate pooled prevalence analysis of six retrospective studies showed that 32.2% of women with cancer reported a lifetime history of IPV. Study quality ranged from low to high. Conclusions: This review underscores the limited and heterogeneous nature of the existing evidence on IPV as a potential cancer risk factor. While preliminary findings suggest a possible association, particularly with cervical cancer, the scarcity of high-quality longitudinal studies and the methodological variability in the studies reviewed prevent definitive conclusions regarding causal linkage. Further research, particularly prospective and mechanistic studies, is needed to clarify the relationship between IPV and oncogenesis across different cancer types and to identify underlying biological pathways.

Background: Early detection of pancreatic ductal adenocarcinoma (PDAC) is critical. While faecal elastase-1 (FE-1) is a standard clinical marker for pancreatic function, its diagnostic accuracy for malignancy is limited. We sought to identify plasma metabolites that enhance FE-1 performance in symptomatic "at-risk" patients. Methods: Using the DEPEND cohort (CRUK C45617/A29908), plasma metabolomics was performed on patients with resectable PDAC (n=23) and healthy volunteers (n=24). Predictive modelling included feature selection and cross-validation, with further validation in an independent external cohort. Results: Citrulline was identified as significantly depleted in PDAC patients across discovery and validation cohorts. In isolation, Citrulline achieved an AUC of 0.86 (internal) and 0.88 (external validation). Standalone FE-1 demonstrated an AUC of 0.67. However, combining Citrulline and FE-1 significantly improved diagnostic performance, achieving a combined AUC of 0.96. Stratification revealed distinct metabolomic signatures associated with poorly differentiated tumours, suggesting a link to histological grade. Conclusions: Integrating Citrulline with FE-1 testing substantially improves PDAC detection in symptomatic patients. This non-invasive panel offers high diagnostic potential, though prospective validation is required to establish clinical cut-offs for routine practice.

Background: Glioblastoma remains resistant to most immune-based therapies. Surgery may create a perioperative window in which systemic immune activation and tumor antigen release intersect. We evaluated whether COVID-19 vaccination shortly before first glioblastoma surgery was associated with survival. Methods: We performed a retrospective single-center cohort study of adults with newly diagnosed glioblastoma undergoing initial biopsy or resection from 2021 to 2025. The primary exposure was documented COVID-19 vaccination within 100 days before first tumor surgery. Overall survival was analyzed from surgery using Kaplan-Meier and Cox models, with 1:1 propensity matching and sensitivity analyses addressing treatment completion, calendar time, surgical selection, steroid exposure, immune-cell variables, COVID severity, and negative-control vaccination. Results: The cohort included 187 patients: 64 perioperatively vaccinated and 123 non-perioperative comparators. Among vaccinated patients, 59/64 (92.2%) received mRNA vaccines; median vaccination-to-surgery interval was 81 days (IQR 71-90). Median overall survival was 743 days in vaccinated patients versus 318 days in comparators (unmatched HR 0.48, 95% CI 0.30-0.76; p=0.002). After 1:1 matching, median survival was 743 versus 349 days (HR 0.52, 95% CI 0.34-0.80). Sensitivity analyses accounting for adjuvant therapy, surgery year, extent of resection, steroid exposure, immune-cell measures, and COVID hospitalization were directionally consistent. Influenza vaccination was not associated with survival. Conclusions: COVID-19 vaccination within 100 days before first glioblastoma surgery was associated with longer overall survival. These findings identify perioperative vaccination timing as a potentially relevant and modifiable variable in glioblastoma outcomes.

Impaired cellular differentiation is a defining characteristic of myeloid malignancies and remains a major therapeutic challenge. The BRG1/Brahma-associated factor (BAF) chromatin remodeling complex, through the ATPases SMARCA4 and SMARCA2, maintains the stemness of leukemic blasts and thus represents a promising target for novel differentiation-based therapies. In a phase 1 study in advanced myeloid malignancies, the first-in-class dual SMARCA4/2 inhibitor FHD-286 combined with decitabine (DAC) was tolerated and produced an objective response rate of 12.8% (6/47) compared with no responses with FHD-286 monotherapy. To understand the basis of this activity, we integrated high-dimensional flow cytometry and single-cell genomic analyses of longitudinal bone marrow samples from responders and nonresponders. While FHD-286 monotherapy was predominantly associated with myeloid differentiation, responders to FHD-286+DAC combination therapy exhibited a range of myeloid and erythroid differentiation trajectories. FHD-286 potentiated the transcriptional impact of DAC, driving tumor clones to fully differentiate out of the immunophenotypically and transcriptionally defined blast compartment. Responders had a baseline transcriptional profile similar to that of CEBPA-mutant acute myeloid leukemia and showed further downregulation of CEBPA upon treatment. These findings reinforce tumor cell differentiation as a mechanism of response to pharmacologic SMARCA4/2 inhibition and support further evaluation of FHD-286+DAC in molecularly defined patient subsets.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.

Background: Children born prematurely (before 37 weeks) or admitted to the neonatal unit (NNU) are at increased risk of adverse long-term physical health outcomes. It is also recognised that there is an association with later academic performance and special educational needs, however it is not clear whether these broad risk factors could be used as stand-alone heuristics to identify children who may benefit from additional support in educational settings. We aimed to examine the associations between neonatal unit (NNU) admission and educational attainment in mid-childhood. Methods and Findings: Pregnancy data from a prospective birth cohort (Pregnancy Outcome Prediction Study, Cambridge, United Kingdom, 2008-2012) were linked to national educational outcomes (Department for Education, United Kingdom). Multivariable regression models adjusted for maternal, child, and socioeconomic factors were used to evaluate associations between (i) all NNU admissions, (ii) at term NNU admissions >48 hours, (iii) preterm birth without ongoing physical health needs, and educational outcomes at ages 5-11 years. Children who required any NNU care were more likely not to meet expected educational standards across multiple ages and domains in early and mid-childhood: age 5 early year foundation (aOR 1.64, 95% CI 1.19-2.27, p=0.003), phonics at age 6 (aOR 2.43, 95% CI 1.72-3.57, p<0.001), and at age 7 (here assessments were divided into multiple domains): reading (aOR 1.67, 95% CI 1.18-2.38, p=0.004), writing (aOR 1.72, 95% CI 1.25-2.38, p<0.001), mathematics (aOR 1.56, 95% CI 1.09-2.22, p=0.020), and science (aOR 1.85, 95% CI 1.22-2.78, p=0.003). Similar patterns were observed among both at term-born infants who stayed >48hrs in NNU (phonics assessment at age 6 aOR 2.26, 95% CI 1.51-3.36, p<0.001) and in children born preterm without long-term physical health sequelae (phonics assessment at age 6 aOR 3.07, 95% CI 1.96-4.81, p<0.001). These associations were robust to adjustment for demographic, perinatal, and socio-economic factors. By age 11, differences in academic attainment were attenuated and no longer clearly distinguishable across all exposure groups. However, there was an increased likelihood of special educational needs (SEN) at age 11 associated with any NNU admission (aOR 1.78, 95% CI 1.15-2.73, p=0.009), at term NNU admission for >48hrs (aOR 1.88, 95% CI 1.19-3.00, p=0.007), and children born preterm without long-term physical health sequelae (aOR 1.50, 95% CI 1.00-2.25, p=0.049). Predictive performance of any NNU admission for SEN at age 11 was moderate (AUC 0.70, 95% CI: 1.14-2.65, p=0.010), with balanced sensitivity and specificity and high negative predictive value. Conclusions: NNU admission, for both term and preterm infants, is associated with poorer educational outcomes and an increased likelihood of special educational needs in mid-childhood.

Background Post-infection conditions (PICs), such as Long Covid, are associated with heterogeneous, fluctuating symptoms that profoundly affect daily functioning. Despite moderate-certainty evidence from the NIHR-funded LISTEN trial (COV-LT2-0009) that personalised self management support improves outcomes and may reduce societal and economic impacts of Long Covid, many people living with PICs still receive condition-specific services, generic advice, or stand-alone digital tools that do not address their complex needs. Aim To map care approaches in general practice and synthesise UK evidence for PIC management. Design and setting Scoping review and online survey. Method A two-phase study was conducted: (1) a scoping review of UK evidence on PIC management in general practice; and (2) a supplementary online survey of practitioners working in UK general practice to provide contextual insights. Results The scoping review identified 32 studies focused on Long Covid. One study included a comparator group (ME/CFS). Study populations were predominantly white ethnicity and female. Evidence for non-Covid PICs in UK general practice was largely absent. The supplementary survey (n=46) provided preliminary practice-level insights. Healthcare practitioners reported varied PIC presentations, diagnostic uncertainty, limited referral pathways, inequitable access, and low confidence in managing PICs. Conclusion Evidence informing PIC management in UK general practice remains predominantly Long Covid-focused and may not reflect the range of PICs encountered in practice. While survey findings are preliminary and require confirmation in larger samples, they highlight uncertainty around PIC management. Further research is needed to evaluate whether existing Long Covid pathways should be expanded or complemented by broader PIC models. Keywords general practice; Long Covid; self-management; post-viral syndromes

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.