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PURPOSE Integrating palliative care into oncology is essential, yet disparities in access and quality persist, particularly in low- and middle-income countries (LMICs). The ASCO guidelines advocate for early, routine, interdisciplinary palliative care for patients with advanced cancer. Barriers to implementing these recommendations include resource limitations, inadequate training, and cultural perceptions. Recognizing these challenges is essential for improving equitable access to palliative care worldwide. METHODS This prospective survey assessed adherence to ASCO recommendations for palliative care integration among LMIC health care providers (HCPs). Participants were recruited via e-mail, social media, and a list of members involved in the ASCO Palliative Care Communities of Practice from February to May 2024. The survey included sections on sociodemographic information, self-perceived adherence to ASCO guidelines on a 5-point Likert scale, and open-ended questions on implementation barriers. Data were collected using Research Electronic Data Capture system. Participants were grouped by WHO regions. Descriptive statistics were used to summarize characteristics and adherence scores, and chi-square tests were used to evaluate regional differences. Thematic analysis identified key themes from open-ended responses. RESULTS One hundred eighty HCPs participated; 62% was female, and 51.1% was age 35-44 years. Most were physicians (66%), and 50% lacked palliative care specialization. Adherence to ASCO guidelines varied, with early palliative care referrals ranging from 50% in the Americas region to 0% in the Western Pacific region. Key barriers included lack of policy support (25%), unmet educational needs (22%), and accessibility constraints (19%). CONCLUSION Addressing identified barriers through evidence-based advocacy, comprehensive policy changes, training, and continuing education programs is essential for integrating palliative care into oncology services across LMICs, promoting health equity for patients with cancer.

SUMMARY Antibody and antigen detection assays continue to play a significant role in the diagnosis of dimorphic fungal pathogens, including Blastomyces dermatitidis complex, Coccidioides immitis/posadasii , Paracoccidioides species, Histoplasma species , Sporothrix species, and Talaromyces marneffei . The performance characteristics of serologic and antigen detection assays for these pathogens are variable, however, influenced by multiple factors, including sample type, disease presentation, patient immunostatus, and timing of specimen collection relative to symptom onset. As a result, there is a need for a centralized document summarizing the accuracy of currently available antibody and antigen detection assays for each of these agents, including discussion of individual assay nuances and caveats that should be considered by clinicians and laboratorians alike. In addition, this review provides expert recommendations for the utilization and interpretation of serologic and antigen detection assays for these dimorphic pathogens.

Objective Raynaud phenomenon (RP) is often the initial clinical manifestation of systemic sclerosis (SSc), but some patients develop other manifestations first. To help elucidate the diversity of SSc presentation in its early stages, we describe the initial clinical manifestations and antinuclear antibody (ANA) profiles of patients in two early SSc cohorts. Methods All patient data in the Genetics vs Environment in Scleroderma Outcomes Study (GENISOS) and Collaborative National Quality and Efficacy Registry (CONQUER) cohorts were reviewed. Both studies enrolled patients within five years of the first non‐RP symptom. Results In GENISOS and CONQUER, respectively, 194 (44.2%) of 439 and 292 (31.1%) of 938 patients had a non‐RP initial symptom, most commonly puffy fingers/hands. Black patients had a non‐RP symptom before RP more commonly than patients in other race and ethnicity categories. Non‐RP first patients were more likely than RP first patients to have diffuse cutaneous involvement and joint contractures at enrollment and had a higher prevalence of RNA polymerase III antibody positivity. Conclusion In two large US cohorts, >30% of patients began to manifest SSc with puffy fingers/hands or other symptoms, without the “warning sign” of RP as their initial symptom. These patients presented with more severe skin and musculoskeletal disease on average, highlighting the importance of early recognition. The most common autoantibody associated with this presentation was RNA polymerase III. These results should be considered in efforts to recognize SSc in its earliest stages. Puffy fingers/hands, even in the absence of RP, should prompt consideration of early SSc and testing for ANA and SSc‐associated autoantibodies, including RNA polymerase III.

ABSTRACTBackgroundDeep learning (DL) models for accurate renal tumor characterization may benefit from multi‐center datasets for improved generalizability; however, data‐sharing constraints necessitate privacy‐preserving solutions like federated learning (FL).PurposeTo assess the performance and reliability of FL for renal tumor segmentation and classification in multi‐institutional MRI datasets.Study TypeRetrospective multi‐center study.PopulationA total of 987 patients (403 female) from six hospitals were included for analysis. 73% (723/987) had malignant renal tumors, primarily clear cell carcinoma (n = 509). Patients were split into training (n = 785), validation (n = 104), and test (n = 99) sets, stratified across three simulated institutions.Field Strength/SequenceMRI was performed at 1.5 T and 3 T using T2‐weighted imaging (T2WI) and contrast‐enhanced T1‐weighted imaging (CE‐T1WI) sequences.AssessmentFL and non‐FL approaches used nnU‐Net for tumor segmentation and ResNet for its classification. FL‐trained models across three simulated institutional clients with central weight aggregation, while the non‐FL approach used centralized training on the full dataset.Statistical TestsSegmentation was evaluated using Dice coefficients, and classification between malignant and benign lesions was assessed using accuracy, sensitivity, specificity, and area under the curves (AUCs). FL and non‐FL performance was compared using the Wilcoxon test for segmentation Dice and Delong's test for AUC (p < 0.05).ResultsNo significant difference was observed between FL and non‐FL models in segmentation (Dice: 0.43 vs. 0.45, p = 0.202) or classification (AUC: 0.69 vs. 0.64, p = 0.959) on the test set. For classification, no significant difference was observed between the models in accuracy (p = 0.912), sensitivity (p = 0.862), or specificity (p = 0.847) on the test set.Data ConclusionFL demonstrated comparable performance to non‐FL approaches in renal tumor segmentation and classification, supporting its potential as a privacy‐preserving alternative for multi‐institutional DL models.Evidence Level4.Technical EfficacyStage 2.

Abstract Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive lung tumor marked by significant molecular heterogeneity. In a study of 590 patients across two independent cohorts, we observed comparable overall survival across treatment regimens (chemotherapy, chemoimmunotherapy, immunotherapy) without unexpected adverse events. Genomic analysis identified distinct NSCLC-like (KEAP1, KRAS, STK11 mutations) and SCLC-like (RB1, TP53 mutations) LCNEC subtypes, with 80% aligning with SCLC transcriptional profiles. Serial sampling revealed stable mutational but shifting transcriptomic landscapes over time. NSCLC-like LCNECs showed elevated FGL-1 (a LAG-3 ligand) and SPINK1 expression, while SCLC-like subtypes expressed higher levels of DLL3. Immunofluorescence confirmed FGL-1 in NSCLC-like LCNECs, and H&E slide analyses indicated fewer tumor-infiltrating lymphocytes in LCNECs versus other lung cancers. These findings highlight LCNEC’s distinct immunogenomic profile, supporting future investigations into LAG-3, SPINK1, and DLL3-targeted therapies.

Abstract Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES ( I nferred cell S ensitivity O perating on the integration of S ingle- C ell E xpression and L 1000 E xpression S ignatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo , and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. Highlights Integration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. Cell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. In silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. In silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival.

Aims To determine the associations between measures of oestrogen exposure and Fuchs endothelial corneal dystrophy (FECD) severity. Methods Clinic-based cross-sectional study of 32 postmenopausal women and 11 men with mild or severe FECD, age>55 years. Participants completed questionnaires for data on demographics, anthropometric factors, medical history and potential risk factors for FECD. Women completed an additional reproductive history questionnaire used to calculate the months of lifetime oestrogen exposure. Slit-lamp biomicroscopy, specular microscopy, corneal Scheimpflug tomography and laboratory testing ( TCF4 repeat expansion quantification, total estradiol, free estradiol, sex hormone binding globulin (SHBG), calcaneal bone density) were performed. Logistic regression models were developed to predict FECD severity based on three-way interactions of each oestrogen exposure measure, sex and TCF4 genotype. Results 43 patients were enrolled in the study (mild FECD: 17 women, 3 men; severe FECD: 15 women, 8 men). Serum-free estradiol was higher in the severe compared with mild FECD group (0.21±0.2 vs 0.09±0.1 pg/mL; p=0.046). When stratified by sex, men showed no significant associations between oestrogen measures and FECD severity. However, in women, the odds of severe FECD were increased with more months of lifetime oestrogen exposure (all women; log OR (95% credible interval): 1.3 (0.14 to 4.3)), higher concentrations of free estradiol (all women; 2.1 (0.0049 to 10)), greater % free estradiol (only women without TCF4 repeat expansion; 1.3 (0.16 to 3.8)) and higher concentrations of SHBG (only women with TCF4 repeat expansion; 2.2 (0.45 to 9.1)). Conclusions While the application of these data is constrained by the limited number of participants, a clinic-based sample, small number of men compared with women and single-point measures of serum hormone measures, these data suggest that FECD severity is associated with higher oestrogen exposures in women but not men and may be dependent on TCF4 repeat expansion status.

Abstract Microbiome sequencing data are inherently sparse and compositional, with excessive zeros arising from biological absence or insufficient sampling. These zeros pose significant challenges for downstream analyses, particularly those that require log-transformation. We introduce BMDD (BiModal Dirichlet Distribution), a novel probabilistic modeling framework for accurate imputation of microbiome sequencing data. Unlike existing imputation approaches that assume unimodal abundance, BMDD captures the bimodal abundance distribution of the taxa via a mixture of Dirichlet priors. It uses variational inference and a scalable expectation-maximization algorithm for efficient imputation. Through simulations and real microbiome datasets, we demonstrate that BMDD outperforms competing methods in reconstructing true abundances and improves the performance of differential abundance analysis. Through multiple posterior samples, BMDD enables robust inference by accounting for uncertainty in zero imputation. Our method offers a principled and computationally efficient solution for analyzing high-dimensional, zero-inflated microbiome sequencing data and is broadly applicable in microbial biomarker discovery and host-microbiome interaction studies. BMDD is available at: https://github.com/zhouhj1994/BMDD . Author Summary Understanding the microbes living in and on our bodies—the microbiome—relies on analyzing complex sequencing data. However, these data often contain many zeros, either because a microbe is truly absent or simply missed due to insufficient sampling. These missing values make it hard to accurately analyze microbial patterns and identify important differences between groups, especially for methods that work on a log scale. To address this, we developed a new method called BMDD that uses a more realistic model to impute the zeros. Unlike existing tools that assume each microbe follows an unimodal abundance distribution, BMDD allows for microbes to follow a bimodal distribution, so they could behave differently in different conditions. It provides not just a single guess, but a range of possible values to better reflect the uncertainty. Our testing shows that BMDD more accurately recovers the true microbial profiles and improves the ability to detect meaningful differences between groups. This method can help researchers gain clearer insights into how the microbiome affects health and disease.