Scientific research, especially in experimental disciplines, depends on laboratories (labs), which are among the most energy- and resource-intensive facilities within universities. Despite their significant environmental footprint, labs are often overlooked in sustainability strategies, even though their contribution is crucial to achieving institutional and global climate goals. This Utrecht university case study presents the development of Green Labs initiatives and the use of the Laboratory Efficiency Assessment Framework (LEAF) as a scalable approach towards structured sustainability practices. We conducted a partial assessment of the university-wide lab-related environmental impact and a detailed evaluation of environmental and social outcomes from LEAF-bronze (entry-level) implementation in the Department of Pharmaceutical Sciences. Results show that LEAF low-cost measures produced measurable environmental benefits while enhancing user motivation and satisfaction. Post-implementation monitoring of a 910 m2 lab-facility showed a 5% reduction in total electricity use and a 38% decrease in targeted equipment electricity use, saving 15 MWh, 2.9 t CO2e, and 1500 EUR annually. Waste segregation improved substantially, particularly in plastic and paper recycling, while hazardous waste volumes remained stable. Surveys of lab users (n = 99) and LEAF-team members (n = 18) indicated high satisfaction, increased motivation for sustainable practices, and perceived -benefits for lab organization, safety, and team cohesion. Beyond technical interventions, the findings underscore the importance of grassroots initiatives, community engagement, and institutional support in driving systemic, long-lasting change. This case study offers a replicable model for embedding sustainability in academic labs and advancing the transition toward climate-neutral, resource-efficient, and socially responsible research environments.
Surveillance of antimalarial drug efficacy is essential for drug policy in the fight against malaria. This includes clinical trials of drug efficacy, molecular marker typing and ex vivo/in vitro drug susceptibility tests such as 50% inhibitory concentration (IC50) assay. The goal of this review was to elaborate on the variance in the IC50 assay across labs in sub-Saharan Africa (sSA) where malaria is endemic. We systematically reviewed 71 articles, published between 2015 and 2025, evaluating IC50 in sSA where only 56 were performed in labs in Western (WA), Central (CA), East (EA) and Southern Africa (SA). The IC50 values of the major antimalarial drugs, including dihydroartemisinin (DHA), lumefantrine (LUM), mefloquine (MFQ), amodiaquine (AMD), chloroquine (CQ), piperaquine (PPQ), artesunate (ATS), artemisinin (ART) and quinine (QN), were reported. An F-test performed on the IC50 values from WA and EA, where ex vivo assays were conducted, revealed a statistically significant variation (P value <0.05) in the IC50 values of some major antimalarials (DHA, CQ, LUM and AMD). The geometric means for DHA, CQ, LUM and AMD in WA versus EA were 2.46 versus 2.21, 83.15 versus 34.34, 10.06 versus 10.47, and 10.63 versus 17.61 nM, respectively. The overall ex vivo IC50 values of CQ were generally below the known resistance thresholds. Differences in drug reconstitution solvents, the range of drug concentrations, period of drug exposure (48 or 72 h), and curve-fitting tools and assay methods were observed. These differences together could account for the variance in IC50 values across sSA. We therefore recommend regional harmonization of the IC50 protocol for antimalarial drug efficacy surveillance and formation of a regional quality assessment network of malaria IC50 labs, as a step towards reliable data sharing and integration into strategic plans for malaria elimination.
Antimicrobial resistance (AMR) is a rapidly expanding worldwide health concern, disproportionately affecting low- and middle-income countries. The Eastern Mediterranean Region (EMR) presents unique issues, including war, inadequate surveillance systems, and widespread antibiotic abuse. This study evaluates AMR burden, mortality changes between 2013 and 2023, and progress toward AMR reduction milestones in EMR nations using Global Burden of Disease (GBD) AMR-based indicators. This ecological analysis used publicly available IHME and GBD-AMR estimates to examine AMR mortality rates (per 100,000), burden categories, milestone achievements, and AMR ranking among the top 10 causes of death in 2013 and 2023 across EMR countries. AMR mortality in the EMR ranged from 4 to 75 deaths per 100,000, with Gulf countries reporting the lowest rates and Pakistan, Afghanistan, Somalia, Yemen, and Djibouti the highest. Twelve countries made no progress between 2019 and 2023, while nine achieved reductions in AMR mortality. In Pakistan, Afghanistan, Somalia, Yemen, and Sudan, AMR ranked among the top 10 causes of death in 2013 and/or 2023, unlike most other EMR countries. AMR burden in the EMR varies widely, rising in high-burden, conflict-affected countries with little improvement. Strengthening labs, stewardship, and surveillance is essential. This study provides regional baseline data to guide targeted interventions and national AMR plans.
Many genomic regions exhibit allele-specific expression. This effect is most pronounced in imprinted genes, where one copy of a gene is epigenetically silenced, and the inactive X chromosome of female cells, where almost the entire chromosome is silenced. Allele specific gene expression can have significant effects on human health and is implicated in a wide array of diseases. Research into allele specific expression is most often carried out in mouse models where cross breeding of mouse strains can yield progeny with well characterised haplotypes where parent of origin is known for a huge number of SNPs. The same approach cannot be taken with human data and haplotypes must be assembled using expensive and labour intensive long read sequencing and Hi-C based approaches. Although resolved haplotypes are available for a number of cell lines, allowing accurate measurement of allele-specific gene expression, this type of analysis is inaccessible for non-specialist labs. We demonstrate how to use previously published haplotypes to investigate X linked gene silencing and epigenetic changes. Additionally, in this paper we present a method to exploit the profound difference in expression levels between the two human X chromosomes to assign SNPs in expressed RNA to the active or inactive X chromosome using only short read DNA and RNA sequencing. We demonstrate this technique using sequencing libraries generated in house and sequencing data from publicly available databases including for a cell line with a complex karyotype. In each instance we identified genes that were silenced in each cell line opening them up to further research avenues. This X chromosome haplotyping technique can be applied to any clonally derived human cell line with 2 or more X chromosomes allowing researchers to investigate X linked gene silencing in cell lines already present in their lab rather than in the limited number of cell lines for which a haplotype is available.
Interlobular septal thickening (ILST) is a feature on chest computed tomography (CT) that is characterized by abnormal widening of the interlobular septa, which are thin connective tissues that separate secondary pulmonary lobules within the lung parenchyma. They contain pulmonary venules and lymphatic vessels. On CT, these septa are normally not visible in healthy patients. However, various pulmonary pathologic conditions can lead to ILST. If present, ILST can be categorized into 3 different patterns based on its morphologic appearance: smooth, nodular, and irregular/fibrotic. Each pattern occurs from different disease processes with little overlap. In this pictorial essay, we review common and rare etiologies for each type of ILST. For each disease, we discuss relevant pathophysiology, epidemiology, risk factors, symptoms, physical exam findings, labs, and management. Subsequently, we present the ILST features along with the distribution (eg, unilateral, bilateral, central, peripheral) and ancillary imaging findings that can support the diagnosis. Finally, characteristic and high-resolution figures are presented.
We evaluate an intervention designed to give lab-based research teams an opportunity to intentionally discuss project-related data management practices within their labs, examining how such communication might inform perceptions of the relationship between formal data management plans and lab members' day-to-day data management practices. In an earlier study, we developed a lab-based intervention that encouraged a deliberative approach to discussions among lab members regarding the practices of data management and authorship, and an exploration of the ethical dimensions of those practices. This present study builds on this prior work, both as partial replication and extension. Here we show the significant effects of the intervention across several dimensions, but importantly and specific to this project, this deliberative communication approach enhances the likelihood that lab members share an understanding of and a commitment to its data management practices, in part because they have been actively involved in shaping those practices. Fostering shared understanding and commitment is crucial for maintaining rigor and responsibility across all aspects of a lab's work, and is essential for cultivating legitimate, defensible, and ethical approaches to producing scientific knowledge.
Rectal angiodysplasia is a vascular malformation typically associated with the elderly. It is an exceedingly rare cause of acute lower gastrointestinal bleeding (LGIB) in young adults. We present a unique case of massive hematochezia in a 33-year-old, potentially accelerated by long-standing vascular stress. A 33-year-old male with a history of chronic alcohol use and poorly controlled hypertension presented with acute, massive hematochezia and symptomatic anemia. Physical examination and initial labs may indicate significant blood loss. Urgent colonoscopy revealed multiple punctate, actively bleeding angiodysplastic lesions localized strictly to the rectum. Given the focal nature of the lesions, hemostasis was achieved using mechanical endoscopic titanium clips. No recurrent bleeding occurred during 6 months of follow-up. This single-case observation suggests that angiodysplasia may be considered in differential diagnosis for LGIB even in younger patients, particularly those with systemic vascular risk factors like hypertension and chronic alcohol consumption. Furthermore, it may indicate that mechanical clipping is a highly effective, targeted alternative to thermal ablation for localized rectal vascular lesions.
Efforts to develop quantitative models must face a trade-off between interpretability and quantitative accuracy, which often disfavors interpretability. Here we adopt an operational definition of interpretability, specifically that a model is described by an arrow diagram wherein each arrow corresponds to a positive effect or negative effect of one component upon a process, and fewer arrows is more interpretable than more arrows. We then develop a method to add flexibility-and thus accuracy in fitting data-to mathematical models by relaxing functional form assumptions, while constrained by the same arrow diagram and thus the same interpretability. We apply this method to the T cell, where quantitative models are particularly needed, in part because of ongoing efforts to engineer T cells as therapeutics. One avenue of experiments exposes T cells to pulsatile inputs and measures their frequency response, finding several nonlinear frequency responses: high-pass, low-pass, band-pass, and band-stop. Using our modeling approach with enhanced flexibility, we show that a simple signaling model quantitatively captures the frequency response of CD69 surface expression, one of the correlates of T cells activation, with accuracy within the experimental inter-replicate standard error of the mean. Specific qualitative behaviors map to specific parts of the arrow diagram: Band-pass behavior can be explained by refractory de-sensitizing circuit (we refer to this as "first-aid icing a wound"). Band-stop behavior can be explained by removal-inhibition (we refer to this as "roommate interrupts my studying"). Apparent low-pass emerges naturally when total stimulation time is constant. We test the model on independent experimental datasets from multiple labs. Taken together, our results demonstrate the ability to achieve both quantitative prediction and interpretability in understanding cellular dynamics. Simple models may at first appear incapable of explaining complex data, but might indeed be able to by adding this modest flexibility.
Conventional diffusion models for physical system prediction rely on grid-based and snapshot-level representations, limiting their adaptability to irregular domains and geometric variability. This study introduces a novel point-wise conditional diffusion framework that enables efficient and generalizable prediction of complex physical systems with diverse and irregular geometries. The core idea enables the diffusion process to operate directly on query points defined over arbitrary geometries, in contrast to conventional diffusion models that apply denoising to an entire snapshot at once. Each query point is independently conditioned on its spatio-temporal coordinates and physical information, allowing point-wise modeling without relying on grid topology or temporal discretization. To address the spectral bias inherent in coordinate-based representations, positional encoding is incorporated to capture high-frequency physical details and localized geometric variations. The flexibility and scalability of the proposed framework enable it to generalize across three physical domains: two-dimensional spatio-temporal systems and a three-dimensional large-scale aerodynamic system, without requiring additional preprocessing. Experimental results demonstrate that the proposed method, employing denoising diffusion implicit model (DDIM) sampling with only 5-10 steps, enables near real-time inference while ensuring deterministic reproducibility essential for physical system prediction. Comparative analysis reveals that our point-wise approach outperforms conventional image-based diffusion methods, yielding 35.8% reduction in mean absolute error with 94.4% less training time and 89.0% fewer parameters. Performance evaluations across three distinct physical systems consistently demonstrate superior accuracy, with error reductions ranging from 53% to 94% compared to established data-flexible surrogate models including DeepONet and Meshgraphnet. Furthermore, the framework demonstrates remarkable computational scalability in large-scale automotive aerodynamic systems, achieving superior performance with only 50% of training points while significantly reducing training cost, and generalizes robustly to previously unseen geometric configurations.
Acyl-CoA-binding protein (ACBP, encoded by diazepam binding inhibitor, DBI) is an abundant intracellular regulator of lipid metabolism that also circulates systemically, yet the mechanisms governing its release and its relationship to organ injury remain unresolved. Herein, we combine human multi-omics, mechanistic mouse models and controlled cell death assays to identify cell death-driven liberation of intracellular ACBP/DBI as a unifying mechanism underlying its elevation in disease. In a cohort of 1198 hospitalized adults, among whom 75% were acutely infected by SARS-CoV-2, plasma ACBP/DBI tightly correlated with inflammatory markers and biochemical signatures of cardiac, hepatic, renal, metabolic and hematologic dysfunction. SomaScan proteomics further revealed that ACBP/DBI co-varies with organ-enriched proteins, particularly those originating from skeletal muscle and pancreas, implicating tissue injury as a major determinant of its circulating abundance. Multiple forms of acute organ damage in mice, including hepatic or renal ischemia-reperfusion, bile duct ligation, pancreatitis and rhabdomyolysis, triggered rapid and robust increases in plasma ACBP/DBI. Using defined in vitro paradigms, we demonstrate that apoptosis, ferroptosis and necroptosis each cause loss of intracellular ACBP/DBI and its release upon plasma membrane permeabilization, independent of the upstream lethal pathway. These mechanistic insights translated in vivo: hepatocyte apoptosis, ferroptosis and necroptosis each elevated circulating ACBP/DBI in a manner attenuated by pathway-specific inhibitors. Finally, meta-analysis of >100,000 individuals across diverse populations revealed that elevated plasma ACBP/DBI consistently associates with systemic and organ-specific disease and predicts future morbidity. Together, our findings identify cell death-driven ACBP/DBI release as a conserved mechanism linking organ injury to increased plasma ACBP/DBI, positioning this molecule as an integrative biomarker of tissue damage across species, organs, and cell death modalities.
Xylazine is an alpha-2 agonist found as an adulterant in illicitly-manufactured fentanyl. Little information is available about its pharmacokinetics in humans. We conducted a naturalistic study with thirteen adult patients presenting to two urban, tertiary care emergency departments for suspected opioid overdose and concomitant xylazine exposure. This study was conducted at two US emergency departments in Worcester, MA. Serial blood specimens were collected over 4h at 30-60min intervals for pharmacokinetic analyses. We developed a quantitative analytical method to measure the blood concentration of xylazine and its metabolites, fentanyl and its metabolite, and medetomidine and its metabolite. All thirteen participants had detectable concentrations of xylazine and fentanyl in their blood; five participants also had medetomidine present. Initial blood concentrations (mean±SD, ng/mL) were 47 ± 53 for xylazine, 14 ± 15 for fentanyl, 23 ± 19 for norfentanyl, and 25 ± 24 for medetomidine. Data from a subset of eight participants were used to calculate drug half-lives (t1/2) (mean±SD, min): 345 ± 145 for xylazine and 537 ± 451 for fentanyl. The elimination t1/2 of xylazine in these opioid overdose participants was considerably longer than predicted from veterinary data. The fentanyl t1/2 values were within the upper range reported in the literature. Understanding the pharmacokinetics of xylazine is vital for projecting outcomes of xylazine exposed patients and could allow for improved interpretation in forensic analyses. The quantitative assay we developed for xylazine and its metabolites is suitable for clinical research.
Immune responses elicited by natural infection of the coronavirus SARS-CoV-2 (COVID-19) show significant heterogeneity in the magnitude and quality of memory T and B cell responses. However, little is known about the contributing factors. In this study, we investigated the early immune factors that contribute to this variability using RNA-seq, targeted proteomics, and flow cytometry analyses. Specifically, we sought to investigate associations between early immune responses and SARS-CoV-2 memory immunity in a longitudinal cohort of 46 individuals hospitalized for COVID-19 from May 2020 to March 2021. These participants returned for follow-up visits up to one-year post-hospitalization where we characterized antibody titers, antibody neutralization, antibody durability, and cellular memory T and B cell responses with multiple assays. Additionally, using integration analysis of Omic measurements, we identified common genes, proteins, and cellular pathways associated with differential memory response outcomes. Our data suggests that high levels of inflammatory proteins, and co-stimulatory molecules during the early stages of COVID-19 lead to enhanced memory T and B cell responses and improved durability. Alternatively, molecules that have a negative effect on dendritic cell maturation including TNFSF11 and FLT3LG correlated with suboptimal memory immune responses. Importantly, we were able to identify early markers that are positively and negatively associated with durable antibody responses in infected participants. This study provides a unique and thorough examination of both innate and memory immunity in the same patients over time, offering valuable insights into the long-term durability of SARS-CoV-2 immunity.
Impaired memory function is a frequent yet understudied symptom in kidney transplant recipients. In part, this knowledge gap reflects the lack of scalable, sensitive, and low-burden tools for quantifying memory function in large clinical populations. The aim of this study is to evaluate a brief, remote memory assessment and to examine memory function and its clinical correlates in kidney transplant recipients compared with healthy controls. In this cross-sectional study, we demonstrate a remote, minimally burdensome memory screener, the Seattle-Groningen Memory Assessment, which estimates a patient's speed of forgetting from paired-associate learning. Participants aged 22-86 years from a large transplant cohort completed an eight-minute online memory test. Memory performance was compared between kidney transplant recipients (n = 556) and kidney donors (n = 408). Associations with demographic, clinical, and physiological variables were examined using regression analyses. Here, we show that the memory score derived from an eight-minute session is a reliable and accurate measure of an individual's ability for long-term retention. Kidney transplant recipients show more forgetting than donors. Memory scores are sensitive to demographic factors, including age and education level, and are associated with self-reported sleep quality, fatigue, and health-related quality of life. On the physiological level, more forgetting in recipients is linked to higher monocyte, neutrophil, reticulocyte, and white blood cell counts, as well as lower ferritin and greater iron deficiency. This work highlights the potential of computational memory assessment as a minimally burdensome and reliable tool for detecting cognitive impairment in complex clinical populations. Such tools may enable scalable monitoring of cognitive health and improve the detection of subtle cognitive changes relevant for disease progression and treatment evaluation. Memory problems are commonly reported by people who receive a kidney transplant, but they are often difficult to measure with traditional cognitive or neuropsychological tests. In this study, we evaluated a brief online memory task that can be completed at home in about eight minutes. The test measures how quickly newly learned information is forgotten over time. More than 500 kidney transplant recipients and about 400 kidney donors completed the assessment. We found that transplant recipients, on average, forgot information more quickly than donors. Memory performance was also related to factors such as age, education, sleep quality, fatigue, and several blood markers linked to inflammation and iron levels. Our findings suggest that short digital memory tests may offer a practical way to monitor cognitive health in people with complex medical conditions and could support future research and clinical care.
The large 2025 Mpox clade IIb outbreak in Sierra Leone underscores the urgent need for portable, low-cost diagnostics in decentralized settings. While CRISPR-based assays offer high sensitivity and flexibility, their deployment during active outbreaks remains limited. Here we show the rapid development and field evaluation of Mpox SHINE, a CRISPR-Cas13 assay that integrates lyophilized reagents, ambient-temperature lysis, and automated fluorescence detection on the portable DxHub device. The assay achieves analytical sensitivity down to 10 copies/µL. Clinical validation in Sierra Leone, using 56 clinical specimens, confirms complete concordance with qPCR, demonstrating 100% sensitivity and 100% specificity. Crucially, Mpox SHINE also detects the virus directly from unextracted lesion swabs while maintaining 100% sensitivity and specificity. The mean time-to-result is fast, averaging 11.4 minutes for extracted samples and 27.9 minutes for unextracted samples. These findings demonstrate that CRISPR-based diagnostics translate quickly from genomic sequence to clinically validated, deployable tools within a single outbreak window.
Cardiovascular disease increases risks of chronic kidney disease (CKD) progression and mortality in type 2 diabetes. The study sought to assess semaglutide effects on kidney and survival outcomes by baseline cardiovascular status in the FLOW trial. Participants with type 2 diabetes and CKD were randomized to once-weekly subcutaneous semaglutide 1.0 mg vs placebo. Baseline subgroups included atherosclerotic cardiovascular disease (ASCVD), heart failure, and high total cardiovascular disease risk without established cardiovascular disease (10-year PREVENT [Predicting Risk of cardiovascular disease EVENTs] score ≥20%). The primary outcome was ≥50% estimated glomerular filtration rate (eGFR) decline, eGFR <15 mL/min/1.73 m2, dialysis, transplantation, and kidney or cardiovascular death. All-cause death was a confirmatory secondary outcome. At baseline, 1,198 (33.9%) of 3,533, 678 (19.2%) of 3,532, and 1,329 (66.5%) of 2,000 participants had ASCVD, heart failure, or high total cardiovascular disease risk in those without established cardiovascular disease, respectively. Semaglutide reduced the primary outcome risk in subgroups with (119 of 593 vs 146 of 605) or without (212 of 1,174 vs 264 of 1,161) ASCVD (HR: 0.80; 95% CI: 0.63-1.02; and HR: 0.74; 95% CI: 0.62-0.89, respectively; P for interaction = 0.62), with (67 of 342 vs 88 of 336) or without (264 of 1,424 vs 322 of 1,430) heart failure (HR: 0.67; 95% CI: 0.49-0.93; and HR: 0.79; 95% CI: 0.67-0.93, respectively; P for interaction = 0.40), and with (134 of 675 vs 168 of 654) or without (44 of 331 vs 58 of 340) high total cardiovascular disease risk (HR: 0.73; 95% CI: 0.58-0.91; and HR: 0.73; 95% CI: 0.49-1.08, respectively; P for interaction = 0.99). Numbers needed to treat to prevent 1 primary kidney outcome at 3 years were 22, 13, and 17 in the ASCVD, heart failure, and PREVENT score ≥20% subgroups, respectively. Semaglutide also reduced risks of all-cause death with (99 of 593 vs 121 of 605) or without (128 of 1,174 vs 158 of 1,161) ASCVD (HR: 0.82; 95% CI: 0.63-1.07; and HR: 0.78; 95% CI: 0.62-0.99, respectively; P for interaction = 0.79), with (64 of 342 vs 79 of 336) or without (163 of 1,424 vs 200 of 1,430) heart failure (HR: 0.75; 95% CI: 0.54-1.05; and HR: 0.81; 95% CI: 0.66-0.99, respectively; P for interaction = 0.74), and with (73 of 675 vs 98 of 654) or without (23 of 331 vs 28 of 340) high total cardiovascular disease risk (HR: 0.71; 95% CI: 0.52-0.95; and HR: 0.82; 95% CI: 0.47-1.43, respectively; P for interaction = 0.63). Semaglutide improved kidney and survival outcomes in type 2 diabetes with CKD, irrespective of established ASCVD, heart failure, or high total cardiovascular disease risk. (Evaluate Renal Function with Semaglutide Once Weekly [FLOW]; NCT03819153).
Brain tumors are formed when abnormal cells grow within the brain or its surrounding tissues. Approximately 400 people in Ireland receive a primary brain tumor diagnosis each year. In the US, this number increases to almost 90,000 individuals diagnosed each year. Timely diagnosis of brain tumor is essential to saving lives and significantly reducing treatment costs. To automate this process, different Artificial Intelligence (AI) techniques have been adopted to identify brain tumors in humans. Specifically, various deep learning algorithms have been used to segment and classify brain tumors. In this paper, a systematic review is conducted based on Kitchenham & Charters methodology. We selected seven research questions to identify commonly used methods, datasets, features, metrics, and Explainable AI (XAI) approaches for AI-based analysis of brain tumors. This process starts by sourcing papers that address these techniques via the IEEE Xplore and ACM biblographic databases between January 2013 and December 2024. The papers are then filtered using specifically designed inclusion and exclusion criteria. Out of 3950 papers sourced from two electronic databases, only 101 papers were selected for this review. In summary, despite a focus on segmentation and classification, our findings indicate that no AI methods have been fully adopted in clinical practice. Furthermore, none of the reviewed papers address the specific problem of weakly-supervised brain tumor segmentation, highlighting a clear research gap in the existing literature that warrants further investigation. Also, only four articles on XAI were identified. Given the importance of transparency in network predictions for brain tumor analyses, this fact supports the need for more research in this domain.
Background With the widespread availability of whole-slide imaging, many studies have utilized digital images of hematoxylin and eosin (H&E)-stained breast cancer tissues and applied convolutional neural networks (CNNs) for pathological diagnosis. However, CNN-based diagnosis is largely a black box and may be limited in quantitative morphological research. In this study, we developed a simple algorithm for morphometric analysis of three nuclear atypia features on H&E-stained whole-slide images to predict nuclear grade, hormone receptor status, and Ki-67 levels in breast cancer.Materials and Methods Using 43,183 H&E-stained nuclear images larger than 20 µm2 from 131 invasive ductal breast carcinomas, we calculated the following features of nuclear atypia using a computer vision algorithm: anisonucleosis (variation in nuclear size), inhomogeneous chromatin density, and the average size of prominent nucleoli. Anisonucleosis was quantified as the percentage of nuclei larger than 47 μm². Inhomogeneous chromatin was defined as the percentage of blue-saturated structures with 0.92-fold luminance or less than the average nuclear luminance. Prominent nucleoli were identified based on blue-saturated structures with 0.87-fold luminance or less, circularity greater than 0.65, and size greater than 1.15 μm². Using these values of nuclear atypia features, the thresholds that were the most associated with grade and biomarkers were calculated using receiver operating characteristic curves by Youden index.Results The morphometric algorithm using these thresholds predicted nuclear grade (grade 1 and 3), Ki-67 index ≧ 20%, and hormone receptor negative status with sensitivities of 52.1 to 100% and specificities from 34.3 to 85.3%. Two multivariable logistic regression models combining these three thresholds predicted nuclear grade, Ki-67, and hormone receptor negative with much better accuracy, sensitivities ranging from 57.1 to 91.3%, specificities from 50.9 to 82.2%, and area under curve of 0.70-0.82. The algorithm was applied to an independent set of 42 tumors.Conclusion The present morphological algorithm of nuclear atypia might provide new insights into the computational grading of invasive breast cancer.
Despite frequent spillover of sarbecoviruses, most SARS-related viruses discovered in animals fail to engage human ACE2 (hACE2), limiting mechanistic insight and risk assessment. Here we developed antibody-based chimeric entry receptors (ABCERs) that reprogram antibody-antigen recognition into a synthetic, cell-anchored receptor interface. By replacing the extracellular protease domain of hACE2 with single-chain variable fragments (scFvs) from broadly neutralizing antibodies, ABCERs mimic viral receptor engagement while preserving the intracellular architecture required for cathepsin L-dependent endocytic fusion. This modular design converts antibody specificity into a programmable entry module, supporting efficient infection and replication of diverse sarbecoviruses from both clinical and animal sources. Among the tested scFvs, E7 exhibited exceptional breadth, recognizing conserved epitopes shared across representative sarbecoviruses from all clades. Sera from Pfizer-BioNTech mRNA-vaccinated individuals potently blocked E7 binding to SARS-CoV-2 but showed limited cross-inhibition of E7 interactions with RBDs from hACE2-independent sarbecoviruses, revealing a substantial gap in current vaccine-induced humoral immunity. Together, our findings establish E7-based ABCERs as a programmable synthetic receptor platform that bridges antibody recognition and viral propagation, offering a universal tool for isolating, studying, and surveying sarbecoviruses beyond the hACE2-dependent paradigm.
The World Health Organization and international consensus 2022 classifications have proposed lowering the absolute monocyte count threshold to ≥ 0.5 × 109/L for the diagnosis of chronic myelomonocytic leukemia (CMML). This was based on reports describing patients with oligomonocytic CMML (O-CMML) (0.5 × 109/L to 1.0 × 109/L) as a CMML variant. We identified patients among the myelodysplastic syndrome (MDS) database with O-CMML who meet the new criteria for CMML and compared them to MDS and CMML (≥1.0 × 109/L). Among 1861 patients with MDS, 468 (25%) were O-CMML meeting new CMML criteria. The genomic landscape of O-CMML was more comparable to MDS. Classical CMML somatic mutations (TET-2, ASXL-1 and SRSF2) frequencies were closer to MDS. Proliferative CMML (P-CMML) mutations were less common. DNMT3A and TP53 mutations were more commonly observed in MDS and O-CMML compared to CMML. SF3B1 SM was observed in 29% of O-CMML compared to 16% in MDS, 8% dysplastic CMML (D-CMML) and 5% P-CMML (P < .005). Thirteen patients (2.8%) progressed from O-CMML to CMML. The rate of acute myeloid leukemia transformation was less than MDS and P-CMML. The hazard ratio for overall survival was 1.2 (95% confidence interval [CI], 1.03-1.4; P = .018) for O-CMML, 1.3 (95% CI, 1.07-1.58; P = .008) for D-CMML and 1.87 for P-CMML (95% CI, 1.57-2.4; P = .005) compared to MDS after adjusting for molecular international prognostic scoring system. Although O-CMML may be a unique entity, the current classification does not enrich CMML-like variants by all clinical measures. A comprehensive analysis of clinical, molecular and immunophenotype is needed for better classification.
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