Traditional cell counting in clinical and research settings often relies on hemocytometry, a manual technique that is labor-intensive and prone to human error. These limitations in precision and throughput can hinder the development of effective diagnostic and therapeutic strategies, particularly in the context of prostate cancer. Recent advances in machine learning have shown considerable promise in enhancing the accuracy and efficiency of cell enumeration. In this study, we present a novel software system for the automated counting of prostate cancer cells, integrating image processing with deep learning methodologies. Unique to our approach, the system robustly utilizes images acquired from conventional mobile phone cameras, offering a highly accessible and scalable solution. It applies a convolutional neural network (CNN) in conjunction with a selective search algorithm to accurately identify regions of interest (ROIs), followed by robust image analysis algorithms for precise cell detection and quantification. This two-stage pipeline addresses the inherent variability and extraneous content in mobile-captured images, which is a significant advancement over methods reliant on controlled microscopic environments. Experimental evaluations demonstrate that the proposed method achieves superior accuracy compared to conventional manual counting approaches. This automated framework offers a practical, scalable solution that may significantly improve the reliability and efficiency of cell counting in both research and clinical diagnostics.

Ziftomenib, a potent, highly selective, oral menin inhibitor, has demonstrated significant clinical benefit and deep responses in relapsed and/or refractory (R/R) nucleophosmin 1-mutated (NPM1-m) acute myeloid leukemia (AML) patients. Here we present data from analyses conducted to characterize ziftomenib pharmacokinetics (PK) in healthy volunteers and R/R AML patients and exposure-response (ER) relationships in R/R NPM1-m AML patients. A sequential two-stage modeling approach was employed to develop the popPK model for ziftomenib and its metabolites. A 2-compartment model with first-order elimination and first-order absorption with a lag time adequately described the PK data for ziftomenib with good precision. Thorough covariate analysis showed that the mutational status (NPM1-m vs. KMT2A-r) of patients, body weight, sex, race, age, mild or moderate renal or hepatic impairment, and P-gp inhibition did not impact ziftomenib PK. ER analyses were conducted using logistic regression models in a total of six combinations of efficacy endpoints and 12 safety endpoints. No steady-state exposure parameters of ziftomenib were statistically related to any of the efficacy or safety endpoints in R/R NPM1-m AML patients. In fact, flat ER profiles were observed between all ziftomenib exposure parameters and efficacy or safety endpoints over a wide range of ziftomenib exposure. The modeling results demonstrated a wide therapeutic margin for ziftomenib in adult R/R NPM1-m AML patients and supported a dose of 600 mg once daily in this patient population. Additionally, ER analyses demonstrated that antifungal azoles had no clinically meaningful impact on efficacy or safety profiles and thus could be co-administered with ziftomenib.

A 68-year-old woman with cervical cancer and pulmonary metastases presented with limb paresthesia and pain one week after chemotherapy. Whole-body bone scintigraphy performed to evaluate for bone metastases revealed rib metastases, along with poor visualization of the right distal lower extremity, raising suspicion for possible arterial flow obstruction in the right lower limb. Further imaging confirmed extensive thrombosis in the right common iliac artery and its distal branches. This case suggests that decreased uptake in the skeletal structures and surrounding soft tissues on whole-body bone scintigraphy may indicate a risk of arterial thrombosis.

Early detection of potentially malignant and malignant lesions in the oral cavity is mandatory for reduction in oral cancer incidence, detection at an early stage and improving survival. The objective of this study was to catalogue the transcriptomic pattern across pre-cancerous stages, identify the major signalling processes, and infiltrating immune cell subtypes using the integrated, multi-dataset, meta-analysis approach. Following a search in the public databases, a total of five datasets were included in the study. The patient samples were stratified to identify the changes in high-grade dysplasia (HGD, moderate/severe dysplasia) as opposed to low-risk lesions (LRL, benign/OPMD) and low-grade dysplasia (LGD). Weighted gene co-expression network analysis (WGCNA; p < 0.05, |Correlation coefficient|:0.3) integrated with differential gene expression (DEG; p < 0.05, Fold change: 1.5) analysis revealed alterations in low-to-high-risk lesions included changes in DNA replication, loss of cell adhesions converging on a hub panel of tumor promoter/suppressors. The shift to high grade dysplasia was marked by enhanced immune/cytokine signalling with hub-gene network driving metabolic regulation, immune evasion, and ECM-stroma interaction. In transformed lesions, the immune-modulatory environment persisted, now accompanied by a notable downregulation of interferon (IFN) signalling. Interferon-inducible network and stemness induction were key hub gene networks. Digital cytometric analysis indicated specific enrichment of T regulatory cells and dendritic cells in differentiating the grades of dysplasia, while T helper cells were specific for malignant transformation. Collectively, these results indicate the role of immune surveillance during oral carcinogenesis. The distinct molecular/immunological shifts during dysplastic progression and malignant transformation represent critical milestones in oral potentially malignant disorder (OPMD) progression, offering actionable insights for prognosis, prevention, and therapeutic intervention.

Liver metastasis is the primary cause of mortality in colorectal cancer (CRC) patients. To decipher the underlying mechanisms, we performed single-cell RNA sequencing (scRNA-seq) on paired primary colorectal tumors, adjacent tissues and liver metastases from three CRC liver metastasis (CRLM) patients, alongside colorectal tumors and adjacent tissues from three non-metastatic CRC patients. Our analysis revealed a significant enrichment of Enolase 2-expressing (ENO2⁺) cancer cells in CRLM patients compared to their non-metastatic counterparts. Functional characterization, supported by bioinformatics and murine models, demonstrated that ENO2⁺ cancer cells exhibit enhanced epithelial-mesenchymal transition (EMT) and are critical drivers of CRLM. Mechanistically, the ENO2 protein directly binds to macrophage migration inhibitory factor (MIF) within cancer cells, stabilizing MIF by inhibiting its C-terminus of Hsc70-Interacting Protein (CHIP)-mediated ubiquitination and degradation. This ENO2-MIF interaction activates MIF signaling, fostering robust tumor cell-macrophage crosstalk that promotes M2 macrophage polarization, which is validated by spatial transcriptomics showing the colocalization of ENO2⁺ cancer cells and M2 macrophages. Crucially, both organoid and in vivo models confirmed that ENO2 in CRC cells is essential for inducing M2 macrophage polarization via the MIF pathway, thereby facilitating liver metastasis. Knockout of ENO2 significantly suppressed tumor growth and liver metastasis in mouse models. An inhibitor of the ENO2-MIF interaction, pyrithioxin, can effectively reduce the burden of liver metastasis in mice. Collectively, our findings identify ENO2 as a key driver of CRLM by stabilizing MIF to orchestrate M2 macrophage polarization, highlighting the ENO2-MIF axis as a promising therapeutic strategy for CRLM.

Pseudogenes have been regarded as nonfunctional byproducts of evolutionary processes. However, emerging evidence indicates that pseudogenes perform diverse biological roles in human physiology and pathology. We identified the BRCA1 pseudogene (BRCA1P1), a fusion pseudogene derived from the BRCA1 tumor suppressor and RPLP1 ribosomal protein genes, as an immunoregulatory RNA in breast cancer. In this study, we show that BRCA1P1 expression varies across multiple cancer cell types, with no significant association with BRCA1 or BRCA2 somatic mutations in breast and ovarian tumors. Interestingly, BRCA1P1 inhibition elicits antitumor effects in multiple cancer cell types and preclinical tumor models through an antiviral defense mechanism. Loss of BRCA1P1 induces antiviral gene expression, promotes apoptosis, and increases sensitivity to chemotherapy in various cancer cells, without inducing apoptosis in nonmalignant cells. This antiviral response also enhances macrophage-mediated phagocytosis of BRCA1P1-deficient cancer cells. Mechanistically, the majority of BRCA1P1 transcripts are circular RNAs and regulate NF-κB-driven antiviral gene expression. Furthermore, intratumoral expression of BRCA1P1 is elevated in tumor cells, compared to normal breast tissue, and its depletion significantly inhibits the growth of both primary and metastatic breast tumor organoids. Finally, in a humanized mouse model of breast cancer, BRCA1P1 loss stimulates antiviral gene expression and increases T cell infiltration into tumors. These findings support a critical role for BRCA1P1 in regulating innate immune defense and antitumor responses across cancer types, suggesting that targeting pseudogene-derived RNAs may offer innovative therapeutic strategies to enhance antitumor immunity in breast and other cancers.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer (BC) and constitutes approximately 15-20% of all BC cases. This subtype has the most aggressive behavior and the worst prognosis. Numerous studies have been conducted over the past several decades to address the lack of clinically available treatment options. In particular, potential markers targeting effective treatment options have been actively studied. However, these efforts were hindered by the complex mechanisms of TNBC, and no study has demonstrated a model with a predictive performance exceeding 0.85. This study developed TNBC prognosis predictive models with a predictive performance exceeding 0.94. Applying the nine selected markers to five independent datasets demonstrated their potential as TNBC-specific prognostic markers. Most of these genes (including GPR61, PZP, IGFL1, and AHCTF1) are associated with overall survival (OS) in patients with TNBC. Based on these results, these nine selected genes may serve as prognostic markers for OS in patients with TNBC.

Post-COVID condition (PCC) in children and young people (CYP, PCCcyp) remains a significant health burden. Early identification of patients at risk for severe disease, including myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), is crucial for timely and adequate care. This monocentric, observational registry study, performed at a tertiary pediatric hospital in Germany, included CYP aged 7-25 years with PCCcyp at diagnosis. Standardized clinical assessment tools and patient-reported outcome measures were applied, including the novel Munich Long COVID Symptom Questionnaire (MLCSQ). Severe PCC was defined by chronic symptom clusters, Fatigue Severity Scale (FSS), Total Composite Autonomic Symptom Score-31 (COMPASS-31), SF-36 composite scores, Bell Score, and confirmed ME/CFS diagnosis. Among 120 participants, severe PCCcyp was associated with a higher number of acute symptoms (OR_adj 1.22, P < 0.001), acute orthostatic intolerance (OR_adj 9.87, P = 0.002), acute trouble concentrating (OR_adj 11.8, P = 0.005), and female sex (OR 3.31, P = 0.031). Categorizing acute symptoms at a threshold of ≥ 12 yielded optimal model performance (AUC 0.857; sensitivity 65.6%; specificity 90.2%). ME/CFS was diagnosed in 24% of participants, all within the severe PCCcyp cluster, and was characterized by greater acute symptom complexity, more fatigue, more autonomic symptoms, and poorer function. Conclusions: The number and pattern of acute symptoms during SARS-CoV-2 infection may serve as early, specific predictors of severe PCCcyp. Patients with ≥ 12 acute symptoms should be closely monitored to enable early diagnosis of severe PCCcyp and ME/CFS. A distinct cluster of severely affected patients, frequently with ME/CFS, was identified.Trial registration: ClinicalTrials.gov: NCT05638724; Ethics approval (511/21, 2025-465-S-SB).

Most children and adolescents recover rapidly from SARS-CoV-2 infection, yet a subset develops paediatric long COVID (LC). How immune ontogeny shapes LC biology and heterogeneity remains unclear. We deeply phenotype a two-visit cohort with severe LC (n = 74) and controls (n = 27) spanning up to 3.2 years post index infection. Symptom burden remains high and neurofilament light chain (NfL) percentiles inversely associate with functional status (Bell score; r  = -0.3536, P = 0.0060). Cardiopulmonary assessment and serology are unremarkable. Conventional autoantibodies are not enriched, whereas anti-DFS70 supports subgrouping. Immune features are temporally structured; SARS-CoV-2-associated mediators decline within 1 year, while innate-weighted, Th2-skewed cytokines persist. Metabolomics (43 metabolites) recapitulate the identified subgroups and align with EBV serostatus, disease phase (<1 year versus years 1-3.2), and anti-DFS70 positivity. In EBV-naïve LC, higher haemoglobin concentration (MCHC) tracks worse function, whereas higher IL-12p40, thiamine and basophils track milder impairment (all P ≤ 0.0170). These data delineate immune-metabolic and haematological axes of paediatric LC heterogeneity and support biomarker-guided stratification.

Myocarditis is a potentially severe adverse event after COVID-19 messenger RNA (mRNA) vaccination. Validation of reported cases is essential. We aimed to determine the occurrence, clinical characteristics and short-term outcomes of vaccine-associated myocarditis (VAM) in Norway.

In this nationwide, population-based validation study, we used national health registry data and hospital electronic medical records from 27 December 2020 to 30 April 2022. We identified all Norwegian residents who received at least one dose of BNT162b2 or mRNA-1273. By cross-linking registries, we identified myocarditis within 90 days after vaccination. Diagnoses were validated through individual chart review using Brighton Collaboration criteria. VAM was defined as myocarditis without a more likely alternative cause.

Among 4.1 million vaccinated individuals who received 10.9 million doses, we identified 367 potential myocarditis cases. Of 349 cases reviewed, 177 (51%) were validated as VAM, corresponding to 4.5 cases per 100 000 vaccinated individuals. In total, 110 (62%) cases occurred after the second dose. Of validated cases, 139 (79%) occurred in men. Median age was 30 (IQR 24-50) years for men and 54 (IQR 32-65) years for women. Three (2%) cases were under 18 years. The median hospital stay was 4 (IQR 3-5) days, and the median ejection fraction was 55% (IQR 53%-60%). Seven (4%) patients required intensive care and two (1%) older patients died. No patient required mechanical circulatory support or heart transplantation.

VAM occurred in 4.5 per 100 000 vaccinated individuals, based on validation of about half of registry-identified myocarditis cases. The acute clinical course was generally mild. National surveillance and systematic validation are essential for reliable estimates of vaccine-associated adverse events.

NCT05610423.