Tag: statistics

Cardiol Ther. 2026 Jul 31. doi: 10.1007/s40119-026-00461-9. Online ahead of print.

ABSTRACT

INTRODUCTION: Acoramidis, an oral transthyretin stabilizer that achieves near-complete stabilization, is approved for the treatment of transthyretin amyloid cardiomyopathy (ATTR-CM) based on the phase 3 study, ATTRibute-CM (NCT03860935). To complement trial data, ACO-REAL (NCT07235462) was designed to generate real-world evidence on acoramidis use in ATTR-CM. This article describes the rationale and design of ACO-REAL.

METHODS: ACO-REAL is an ongoing, prospective, non-interventional, multicenter study planned in approximately 2000 adults with ATTR-CM (wild-type or variant) initiating treatment with acoramidis in real-world settings across 21 European countries. The primary objective of ACO-REAL is to assess the characteristics and treatment patterns of participants with ATTR-CM receiving acoramidis during an observational period of around 12 months.

PLANNED OUTCOMES: The primary objective is to assess demographics (including age and sex), disease characteristics, and treatment patterns of participants treated with acoramidis. Disease characteristics include type, diagnosis, and manifestations of ATTR-CM, and ATTR-CM-relevant comorbidities. Treatment patterns include adherence, persistence, and reasons for treatment modifications, including switching from a different ATTR-CM medication to acoramidis. The secondary objective is to describe the safety profile of acoramidis. The clinical course of ATTR-CM in participants initiating acoramidis is also being evaluated through measuring cardiac function, functional capacity and health status (New York Heart Association classification and 6-min walk distance), and health-related quality of life (Kansas City Cardiomyopathy Questionnaire and EuroQol 5-Dimension 5-Level questionnaire). Healthcare resource utilization is also being evaluated. Statistical analyses will be exploratory and descriptive. Categorical variables will be presented as frequencies and continuous variables as sample statistics.

CONCLUSIONS: ACO-REAL is the first multinational observational study to collect regular, prospective, real-world data in participants with ATTR-CM treated with acoramidis. This study aims to provide insights into ATTR-CM treatment using real-world evidence from European healthcare systems and may help to guide decision-making in clinical practice. Graphical abstract available for this article.

TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT07235462.

PMID:42533192 | DOI:10.1007/s40119-026-00461-9

Arch Virol. 2026 Jul 30;171(8):240. doi: 10.1007/s00705-026-06697-z.

ABSTRACT

HDV has a high genetic diversity among isolates and a high capacity to accumulate mutations over time. The mechanisms involved in the clinical progression of HDV are not fully elucidated. Few studies relate mutations to clinical implications, which makes this variable extremely relevant for research into HDV-3, a genotype associated with worse clinical progression. The aim of this study was to analyze mutations in complete genome sequences of HDV-3 isolated in the Brazilian Western Amazon. During 2010 to 2023, 35 hepatitis Delta patients with mild or advanced fibrosis gave serum samples that were submitted to conventional PCR and sequencing. Synonymous and non-synonymous mutations were analyzed along the delta antigen sequence based on the reference strain NC_076103.1. N-terminal and C-terminal regions of Large HDAg, molecular modeling and docking analyses were performed. 114 non-synonymous substitutions were identified in 76 positions along the HDAg-L. To synonymous mutations, 46 substitutions were found in 44 nucleotide sites. S/L8P/Q/M was the most frequent mutation among the advanced fibrosis group. The D46E and A70T mutation was more observed among patients with increased AST. These associations showed statistically significant p-values. Mutations in the N-terminal region of Large HDAg led to minor conformational changes. The C-terminal region indicates moderate structural differences caused by the mutations. The binding conformation of Large HDAg with Mutant2C exhibiting the most stable interaction, as suggested by its docking score. This study shows some evidence of the influence of HDAg mutations in relation to clinical significance, providing insights for further, longitudinal research.

PMID:42533182 | DOI:10.1007/s00705-026-06697-z

J Artif Organs. 2026 Jul 30;29(3):49. doi: 10.1007/s10047-026-01577-4.

ABSTRACT

The novel lab-scale hollow fiber membrane module was fabricated to determine the apparent sieving coefficient ([Formula: see text]) of commercial hemodiafiltration membranes, MFX-SW eco (polyethersulfone, Sample A) and NVF-P (polysulfone, Sample B), using the aqueous solution of dextran with a broader molecular weight distribution. All the molecular weight cut-off curves plotted as [Formula: see text] versus filtration flow rate (Q_F) exhibited smooth profiles without intersecting each other, showing distinct variations depending on Q_F across a wide range of molecular weights. Field emission scanning electron microscopy (FE-SEM) observations revealed that the equivalent pore diameters of samples A and B were nearly identical at 23.0 ± 15.5 nm and 23.3 ± 15.5 nm, respectively. Although the surface porosity of sample A (14.5 ± 3.9%) was higher than that of sample B (10.2 ± 3.2%), the difference was not statistically significant. These numerical data were in excellent agreement with the qualitative morphological findings obtained from the FE-SEM images. Furthermore, it is suggested that the subtle structural differences between the two membranes are also reflected in the relationship between the real sieving coefficients ([Formula: see text]) calculated by using [Formula: see text] and the equivalent pore diameters. Since the lab-scale modules are fabricated relatively easily by hand, this methodology is extremely valuable to rigorously define membrane performance.

PMID:42533176 | DOI:10.1007/s10047-026-01577-4

Surg Endosc. 2026 Jul 30. doi: 10.1007/s00464-026-13215-4. Online ahead of print.

ABSTRACT

BACKGROUND: Barrett’s esophagus (BE) is associated with longstanding gastroesophageal reflux disease and can progress to esophageal adenocarcinoma. BE is primarily managed with proton pump inhibitors (PPIs), which are metabolized by cytochrome P450 2C19 (CYP2C19). CYP2C19 polymorphisms affect PPI plasma levels, which are categorized as poor (PM), intermediate (IM), normal (NM), rapid (RM), and ultra-rapid (UM) metabolizers. This study assessed the prevalence of CYP2C19 phenotypes in patients with BE and concomitant hiatal hernias (HH).

METHODS: This was a single-institution retrospective review of medically-managed adult BE patients with CYP2C19 genotyping. Patients were grouped by HH size (small, medium, or large). CYP2C19 phenotypes were stratified by genotype as: PM/IM, NM, and RM/UM based on anticipated need for PPI dose adjustment. Descriptive statistics and a multivariable analysis to determine factors associated with RM/UM were used.

RESULTS: A total of 97 patients (58% female, median age 61, mean BMI 27.5) were included, and CYP2C19 phenotypes were PM/IM (32%, 95% CI 0.27-0.41), NM (37%, 95% CI 0.27-0.47), and RM/UM (31%, 95% CI 0.22-0.40). HHs were present in 59 patients. Notably, 17% of RM/UM patients had concomitant severe erosive esophagitis, LA grade C/D. On sub-analysis, there was a trend toward higher prevalence of RM/UMs(53%) compared with PM/IM/NM (33%, p = 0.06) in patients who did not have a HH. The presence of HHs tended to be more frequent in the PM/IM/NMs (67%) compared with RM/UMs (47%, p = 0.06). On multivariable analysis, this trend persisted with HHs being less present with RM/UMs (OR 0.43, 95% CI 0.18-1.03).

CONCLUSION: There is a trend toward high prevalence of the RM/UM phenotype in patients with BE. Patients with RM/UM phenotype could benefit from PPI dose optimization. Given the poor survival associated with esophageal adenocarcinoma and its association with BE, assessing CYP2C19 phenotype to optimize PPI dosage and effectiveness could have significant public health implications, and additional prospective studies are needed.

PMID:42533164 | DOI:10.1007/s00464-026-13215-4

Nature. 2026 Jul 30. doi: 10.1038/d41586-026-02079-2. Online ahead of print.

NO ABSTRACT

PMID:42533145 | DOI:10.1038/d41586-026-02079-2

Theory Biosci. 2026 Jul 30;145(3):41. doi: 10.1007/s12064-026-00468-9.

ABSTRACT

The aim of the present study is to obtain the numerical solutions of the fractional double-strain HIV co-infection model with intercellular delays and stochastic effects (FDS-HIV-IDS) by employing a novel radial basis neural network with Levenberg-Marquardt backpropagation (RBNN-LMB). The nonlinear model accounts for multiple interacting populations, and logistic growth is introduced to describe the interaction between wild-type and drug-resistant HIV strains. The analysis of the model considers threshold criteria for both local and global stability of infection-free, dominant, and coexistence equilibria. The nonlinear model incorporates multiple interacting groups, and its numerical solutions are approximated through the stochastic RBNN-LMB framework. Consequently, double-strain dynamics transition from stability to instability (periodic oscillations to chaos) more frequently and at earlier stages. This also leads to a higher total viral load compared to single-strain scenarios, highlighting the greater risk of treatment failure when resistance emerges. A dataset is generated using the numerical predictor-corrector method, where the data are split into 75% for training and 10% for validation and 15% for testing, in order to minimize the mean square error (MSE). The solver architecture consists of fifteen hidden neurons, a single input structure, and a radial basis activation function to approximate the system dynamics effectively. The accuracy of the method is demonstrated through the overlapping of predicted and reference outputs, while the very small absolute error (AE) values confirm its precision. Furthermore, statistical evaluations using different operators support the reliability and robustness of the proposed approach.

PMID:42533137 | DOI:10.1007/s12064-026-00468-9

Nat Genet. 2026 Jul 30. doi: 10.1038/s41588-026-02665-0. Online ahead of print.

ABSTRACT

Evolution has used cell-cell communication as a strategy to coordinate organ development, enabling the reproducible generation of intricate structures. Classically, these interactions have been studied one at a time in model organisms, limiting our understanding of how cellular interplay coordinates human development. We investigated human kidney development using single-cell RNA sequencing and spatial transcriptomics, analyzing over 700,000 cells. By mapping gene expression and differentiation trajectories in space, we define the spatial organization of kidney development. Our analysis revealed unrecognized plasticity, showing that cell fate established during early patterning can be later revised. This plasticity provides a potential mechanism for how cell fate is robustly established in complex patterned tissues. Additionally, through a genome-wide, spatially aware cell-cell interaction analysis, we link localized ligand signals to cell fate decisions. We also define biologically meaningful cellular neighborhoods based on aggregated extracellular cues, providing a blueprint to understand the coordination of human development at scale.

PMID:42533102 | DOI:10.1038/s41588-026-02665-0

Nat Comput Sci. 2026 Jul 30. doi: 10.1038/s43588-026-01016-7. Online ahead of print.

ABSTRACT

Understanding gene spatial expression and the organization of multicellular systems is vital for disease diagnosis and studying biological processes. However, existing models often struggle to integrate gene expression data with cellular spatial information effectively. Here we introduce SpatialFormer, a hybrid framework combining convolutional networks and transformers to learn single-cell multimodal and multiscale information in the niche context, including expression data and subcellular gene spatial distribution. Pretrained on 700 million cell pairs from 17 million spatially resolved single cells across 71 Xenium slides, SpatialFormer merges gene spatial expression profiles with cell niche information via the pairwise training strategy. Our findings demonstrate that SpatialFormer distills biological signals across various tasks, including single-cell batch correction, cell-type annotation and co-localization detection. The perturbation analysis identified gene pairs essential for the immune cell-cell communication in pulmonary fibrosis, epithelial-myoepithelial co-localization and tumor transition signals in breast cancer. These advancements enhance our understanding of cellular dynamics and offer additional pathways for applications in biomedical research.

PMID:42533054 | DOI:10.1038/s43588-026-01016-7

Nutrition. 2026 Jun 30;151:113348. doi: 10.1016/j.nut.2026.113348. Online ahead of print.

ABSTRACT

OBJECTIVES: Nutritional care is essential for improving clinical outcomes in people with cancer, and communicating with healthcare professionals about clinical nutrition is fundamental for effective nutritional management. The aim of this study was to evaluate the perceptions of healthcare professionals and patients regarding nutritional care in oncology, including the role of nutritionists, professional practices, and patient experiences.

METHODS: This cross-sectional study included healthcare professionals and patients from public and private oncology institutions. Data were collected using semistructured questionnaires. Professionals were questioned regarding criteria related to identifying nutritional risk, conducting assessments, and recommending nutritional therapy. Patients reported their perceptions of nutritional care, prior nutritional counseling, adherence to recommendations, and willingness to pay for consultations. Quantitative variables were expressed as frequencies, percentages, means ± standard deviations, medians, and ranges. A P-value ≤ 0.05 was considered statistically significant.

RESULTS: The study included healthcare professionals (n = 130) and patients (n = 360). Most professionals acknowledged the need and relevance of nutritional care during cancer treatment, but reported assessing the nutritional status of patients based primarily on body weight. Although 81.4% of physicians could identify patients at nutritional risk, 31.4% relied primarily on weight loss and body mass index as the main criteria, suggesting potential limitations in the methods used. Patients in both systems recognized the importance of nutritional care, although 7.3% did not follow recommendations of dietitians, citing a lack of dietary novelty.

CONCLUSIONS: Professionals and patients recognize the importance of nutritional care and the role of dietitians in oncology; however, gaps remain in translating this recognition into practice.

PMID:42531663 | DOI:10.1016/j.nut.2026.113348

J Oral Biosci. 2026 Jul 30;68(5):100802. doi: 10.1016/j.job.2026.100802. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the diagnostic performance, feature-level agreement, and error patterns of a multimodal large language model (MLLM) across the oral dysplasia-malignancy continuum.

METHODS: In this retrospective diagnostic accuracy study, 300 histopathology images representing normal oral mucosa (n = 100), oral potentially malignant disorders (OPMD) (n = 100), and oral squamous cell carcinoma (OSCC) (n = 100) were evaluated using a standardized two-stage prompting framework consisting of morphological feature extraction followed by structured diagnostic classification. Reference diagnoses were established by three experienced oral pathologists. Interobserver agreement among pathologists was assessed using Fleiss’ κ statistics. Primary outcomes included overall accuracy, macro-F1 score, and balanced accuracy. Secondary outcomes included feature-level agreement, confidence-accuracy alignment, and clinically significant error patterns.

RESULTS: Interobserver agreement among the reference pathologists was substantial for the primary three-class diagnosis (Fleiss’ κ = 0.81). Overall accuracy was 0.667 (95% confidence interval [CI]: 0.620-0.727), with a macro-F1 score of 0.662 and balanced accuracy of 0.755. Performance varied substantially across diagnostic categories, with high sensitivity for normal mucosa (0.990; 95% CI: 0.968-1.000). Reduced sensitivities were observed for OPMD (0.430; 95% CI: 0.340-0.531) and OSCC (0.580; 95% CI: 0.495-0.688), indicating reduced discriminative ability relative to normal mucosa. Feature-level agreement was generally low, with the weakest agreement for architectural dysplasia features (κ = -0.116 to -0.068), slight agreement for invasion-related features (κ = 0.111-0.120), and the highest agreement was for nuclear hyperchromasia (κ = 0.281). Most misclassifications (84.8%) occurred at the dysplasia-malignancy interface. Proxy calibration analysis demonstrated systematic overconfidence in the histopathological predictions.

CONCLUSIONS: The MLLM demonstrated moderate performance in oral histopathology, with reliable recognition of normal tissue and reduced discriminative ability for OPMD and OSCC relative to normal mucosa, particularly at the dysplasia-malignancy interface. These findings support a potential assistive role under expert supervision while highlighting important limitations in clinically critical diagnostic settings.

PMID:42531661 | DOI:10.1016/j.job.2026.100802