SLAS Technology

Recombinase polymerase amplification (RPA) enables rapid nucleic acid testing in low-resource environments, but poorly characterized byproducts can compromise assay specificity and cause false-positive results. Here, we amplified the thirteen original CODIS core loci and Amelogenin to characterize recurrent RPA artefacts and establish conditions that reduce their formation. First, RPA products were analyzed for two reference samples by Oxford Nanopore Technologies sequencing. This revealed two distinct classes of multimeric products: primer multimers and amplicon multimers, consisting of repeated primer or amplicon sequences, respectively. Individual artefacts contained up to 281 primer copies or 22 amplicon copies, demonstrating the extensive range of these products. Next, we performed an optimization study to evaluate the effects of reaction temperature and reagent concentrations at two representative loci, D3S1358 and D5S818. Among the conditions tested, temperature had the most pronounced effect. Reducing the temperature from 42{degrees}C to 34{degrees}C increased the relative target amplicon fraction from 15% to 83% for D3S1358 and from 84% to 98% for D5S818, while maintaining or increasing absolute target concentration. Lower primer concentrations and higher T4 UvsX concentrations also reduced multimer formation, although lower primer concentrations reduced target yield and caused allelic dropout. Finally, amplification at 34{degrees}C was evaluated across all fourteen loci by sequencing. Relative to 42{degrees}C, the target read fraction increased by more than 5 percentage points for 7/14 loci in one reference sample and 9/14 loci in the other, with the largest improvements at multimer-prone loci. These findings identify multimers as an important class of RPA artefacts and establish reaction temperature and T4 UvsX concentration as promising conditions to improve RPA specificity.

Direct experimental analysis of the mammalian oviduct is constrained by limited tissue access and the short lifespan of ex vivo preparations. Extracellular matrix-embedded three-dimensional epithelial organoids provide longer-term in vitro models. However, their inward-facing apical surface and the absence of supporting stromal cells limit physiological studies of the oviduct, including ciliary activity and maternal-embryonic interactions. Here, we provide a step-wise protocol detailing the generation of mouse and human oviductal assembloids in which epithelial cells form an outward-facing (apical-out) layer around a stromal core. Epithelial and stromal cells from adult mouse oviducts or human Fallopian tubes are isolated, expanded separately, and subsequently aggregated in a rotational culture system. The protocol also outlines morphological and immunostaining criteria for confirming cellular organization, whole-mount detection of external cilia, measurement of ciliary beat frequency, and co-culture of mouse assembloids with preimplantation embryos. Mouse and human assembloids retained epithelial and stromal identity and displayed cilia at the accessible outer surface. In a proof-of-concept experiment, embryos co-cultured with the assembloids developed to blastocysts at a rate similar to that of in vivo-derived blastocysts. This reductionist system provides a straightforward and tractable model to investigate oviduct physiology and embryo-maternal communication while allowing direct manipulation and observation of the epithelial interface. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/743297v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1917a7borg.highwire.dtl.DTLVardef@41d7org.highwire.dtl.DTLVardef@e2bf98org.highwire.dtl.DTLVardef@90c9f3_HPS_FORMAT_FIGEXP M_FIG C_FIG SummaryThe protocol for generating mouse and human oviductal assembloids by combining epithelial and stromal cells for studying oviductal function in an in vitro setting.

Electrical stimulation (ES) and transepithelial/transendothelial electrical resistance (TEER) measurements are essential techniques in cell biology and tissue engineering, yet commercial devices for these applications cost between USD 2,500-9,000 and typically offer only one functionality. We present LATEER (Low-cost Arduino-based TEER and Electrical stimulation device), an open-source hardware platform that combines both ES and TEER measurement capabilities at a total cost below USD 100. The device features four independent channels, configurable pulsatile signals (amplitude up to 8.2 V, frequency 0.1-500 Hz, pulse width [&ge;]0.1 ms), and a resistance measurement range of 300 {Omega} to 1 M{Omega}, with <5% error for R {gtrsim} 4.7 k{Omega}. LATEER uses commercially available graphite pencil leads as electrodes ([~]USD 2 vs. USD 350 for commercial Ag/AgCl electrodes), which demonstrated excellent biocompatibility in cell culture. The system includes 3D-printed electrode holders compatible with standard 12-well and 24-well plates, allowing microscope visualization without electrode removal, and a Python-based graphical user interface for parameter configuration and real-time data acquisition. Because the electrodes remain fixed in the plate lid and only a single cable enters the incubator, both stimulation and resistance measurement can run continuously under standard culture conditions (37 {degrees}C, 5% CO2) without removing the plate or repositioning the electrodes, avoiding the temperature excursions and placement variability inherent to manual chopstick measurements. Validation with human pluripotent stem cell-derived cardiomyocytes demonstrated reliable frequency capture (electrical pacing) of the contracting monolayer, with a capture threshold between 250 and 400 mV/mm and controlled pacing across the 0.5-5 Hz range. TEER functionality was verified with mesenchymal stem cells, where the device resolved cell-density-dependent differences in electrical resistance in real time. All design files, firmware, and software are freely available under the CERN-OHL-S v2 license, enabling replication and customization by research laboratories worldwide. HighlightsO_LIAn open-source device combines electrical stimulation and TEER measurement under $100 C_LIO_LIGraphite electrodes offer biocompatibility at 0.6% cost of commercial alternatives C_LIO_LIFour independent channels with configurable parameters and real-time data logging. C_LIO_LIContinuous run setup in-incubator; no electrode repositioning needed C_LIO_LIValidated with stem cell-derived cardiomyocytes, achieving frequency capture (threshold 250-400 mV/mm) C_LIO_LI3D-printed holders enable microscope visualization without electrode removal C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/743263v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@98f9deorg.highwire.dtl.DTLVardef@13c73aborg.highwire.dtl.DTLVardef@1cdf099org.highwire.dtl.DTLVardef@16ed0cf_HPS_FORMAT_FIGEXP M_FIG C_FIG Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@4ef802org.highwire.dtl.DTLVardef@7c651borg.highwire.dtl.DTLVardef@d20013org.highwire.dtl.DTLVardef@102fc89org.highwire.dtl.DTLVardef@111b927_HPS_FORMAT_FIGEXP M_TBL C_TBL

Three-dimensional (3D) spheroid models have become essential in cancer biology, drug screening, and tissue engineering. However, their small size, fragile structure, and tendency to disintegrate during routine histoprocessing present persistent technical challenges. Conventional paraffin embedding often results in tissue fragmentation, loss of spatial orientation, and poor section quality, whereas cryosectioning often compromises cellular morphology. Here, we present a robust, cost-effective protocol for preserving and sectioning fragile 3D spheroids, resulting in high-quality histological sections with intact architecture and excellent cellular detail. The method involves optimized handling and embedding procedures that stabilize spheroids during standard formalin fixation, paraffin infiltration, and microtomy, eliminating mechanical distortion and preserving spherical integrity for consistent sectioning. We demonstrate successful application across different cell line spheroids, with subsequent compatibility with hematoxylin and eosin (H&E) staining protocols. Compared to conventional methods, our approach significantly reduces sample loss, improves inter-section reproducibility, and preserves fine structural features such as necrotic cores, proliferative zones, and extracellular matrix components. This protocol provides a reliable, accessible solution for routine histological analysis of fragile 3D spheroids, facilitating more accurate morphological and molecular assessment in translational research settings. Key featuresO_LIMaintains spheroid integrity: Prevents mechanical distortion, fragmentation, and loss of spatial orientation during processing. C_LIO_LISignificantly reduces sample loss: Decreases failure rate compared to traditional methods, conserving valuable samples. C_LIO_LIBroad spheroid compatibility: Works effectively with primary tumor-derived, stem cell-derived, and co-culture spheroid models. C_LIO_LIEnables high-quality sectioning and staining: Delivers consistent, reproducible sections that are fully compatible with H&E, IHC, and IF. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/743094v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1670c4org.highwire.dtl.DTLVardef@145810aorg.highwire.dtl.DTLVardef@1accb1org.highwire.dtl.DTLVardef@17481c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

We present an active mixing toolhead for extrusion bioprinting. The tool enables the programmable fabrication of tumor microenvironment gradient arrays, through controlled deposition of mixed hydrogel precursor formulations into 384-well plates, pre-seeded with tumor cells. It operates on an open-source bioprinter and can actively mix arbitrary ratios of two hydrogel precursors prior to extrusion. These concentration gradient arrays are compatible with quantitative image analysis of cell viability and morphological responses to hydrogels conditioned with drug or extracellular matrix (ECM) proteins. The tools capacity to mix and print hydrogel precursor gradients was demonstrated using alginate and highly concentrated mCherry-conjugated mini-spidroin solutions. Hydrogel precursor stocks contained fluorescent reporters to facilitate quantifications of mixing efficiency, and as proxies for drug and ECM protein concentrations. The tool was applied to generate hydrogel-based gradients of the apoptosis-inducer staurosporine, from which concentration-dependent MDA-MB-231 breast cancer cell death responses were quantified. Gradient arrays of the ECM protein laminin-511, implicated in breast cancer tumorigenesis, were generated and revealed that increasing laminin-511 concentrations potentiated staurosporine-induced cell death. The study demonstrates the utility of this active mixing toolhead for producing hydrogel gradient arrays, and demonstrates the relevance of studying drug-responses in tumor microenvironment models that account for disease-specific ECM components.

Background and aims Model systems for bile duct disorders are needed for testing therapeutic interventions. Current models have poor human relevance or limited potential for recreating the complex bile duct microenvironment at scale. We aimed to generate a humanized microphysiological system to model and treat cholangiopathies. Methods An in vitro bile duct was created using 3D printed microfluidic chips containing a collagen-embedded canal seeded with patient-derived primary human cholangiocytes. Barrier permeability and compound transport across the epithelium was measured, and disruption of the barrier was performed with lipopolysaccharide treatment. The duct was challenged with the known hepatotoxicant Chlorpromazine. Biliatresone was used to model biliary-atresia and treated using N-acetyl-L-cysteine. Results Cholangiocytes in the bile duct chip established a tight, polarized epithelial barrier. Verapamil and Linerixibat inhibited transport of rhodamine 123 and cholyl-lys-fluorescein respectively with 66 % (p = 0.0004) and 57 % (p = 0.03) reduction. 10 g/mL lipopolysaccharide led to a loss of epithelial barrier integrity, measured by an increase of over 1000 % in leakage of both 3 kDa (p = 0.0002) and 10 kDa dextran (p = 0.0001) along with upregulation of cytokines. Chlorpromazine displayed dose-dependent toxicity with EC50 values of 84, 140 and 96 M for three patient lines. Biliatresone induced a dose-dependent abnormal phenotype with loss of viability. The induced phenotype could be treated with N-acetyl-L-cysteine, improving viability from 23 % to 59 % (p < 0.0001) with treatment of 2 g/mL Biliatresone. Conclusions Our novel platform allows complex studies of bile duct biology, testing of off-target effects from drugs and treatment of a disease phenotype.

Multi-stage continuous bioprocessing can increase volumetric productivity, operational consistency, and process throughput, but its design is complicated by coupling among dilution rate, reactor volume, feed allocation, and cellular physiology. Here, we present ContiDesigner, available at https://chemnettools.anc.univie.ac.at/ContiDesigner/, a mechanistic steady-state framework and interactive web tool for the system-level design of continuous fermentation cascades. Comparing one- and two-stage configurations at equal total reactor volume and outlet flow, ContiDesigner reveals how internal flow and reactor volume allocation shape space-time yield and identifies productivity-maximizing operating conditions. Compared with one-stage processes, two-stage cascades favor lower over-all dilution rates, thereby preserving residence time in the production stage. The first-stage dilution rate approaches the corresponding one-stage productivity optimum, but the cascade optimum occurs earlier, reflecting a system-level compromise between biomass generation and production-stage residence time. However, two-stage operation outperforms optimized one-stage operation only when non-growth-associated production in the second stage is sufficiently strong, whereas increasing growth coupling favors one-stage operation. Two case studies demonstrate both the potential and limits of process intensification. An optimized two-stage design is predicted to achieve a more than 1.5 fold increase in space-time yield for poly-R-3-hydroxybutyrate (PHB) production compared with a published experimental five-stage cascade, whereas the lactic acid case study identifies conditions under which staging offers no advantage. ContiDesigner translates these design principles into an accessible workflow to explore feasible operating regions and prioritize cascade designs for experimental evaluation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743657v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@ef58faorg.highwire.dtl.DTLVardef@1ba88a4org.highwire.dtl.DTLVardef@160edd3org.highwire.dtl.DTLVardef@9dda34_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIContiDesigner enables system-level design of continuous fermentation cascades C_LIO_LIHigh stage-one dilution supports biomass generation C_LIO_LILow stage-two dilution preserves productive residence time C_LIO_LIYet two-stage cascades favor lower overall dilution than one-stage systems C_LIO_LITwo-stage advantage requires strong non-growth-associated production in stage two C_LI

BackgroundContinuous glucose monitoring (CGM) systems are used not only for retrospective assessment of the glycemic profile but also for real-time decision-making, including automated insulin delivery. Accordingly, CGM performance characterization must capture not only the agreement of individual paired values but also the systems ability to reproduce the direction, rate, amplitude, and shape of glucose concentration change. Summary metrics, most notably MARD, cannot establish whether an observed deviation reflects an error in the formation of the test profile itself, a constant sensor offset, amplitude compression, a change in response rate, temporal misalignment, or hysteresis. ObjectiveTo adapt a programmable flow-based in vitro platform for the separate assessment of the experimentally delivered glucose profile and the dynamic response of CGM systems, and to propose a set of metrics that decomposes dynamic error into its components. MethodsGLU profiles were generated by programmable mixing of solutions at a constant total flow rate of 2 mL/min. Actual GLU concentration was independently measured with a SUPER GL2 glucose analyzer. Four static levels, three repeats of a 5.5[-&gt;]12.0[-&gt;]5.5 mmol/L profile, three repeats of a 6.0[-&gt;]3.0[-&gt;]6.0 mmol/L hypoglycemic profile, three 5.0[-&gt;]15.0[-&gt;]5.0 mmol/L profiles at different rates, one complex 4[-&gt;]18[-&gt;]3[-&gt;]12[-&gt;]5.5 mmol/L profile, and two proof-of-concept sensor experiments at 100- and 200-min transitions were investigated. Dynamic response was characterized by bias, MAE, RMSE, MARD, amplitude transfer coefficient K_A, rate transfer coefficients K_up and K_down, normalized shape RMSE, residual shift, and hysteresis loop area. ResultsAt the static levels, measured GLU exceeded the programmed value by 0.234-0.780 mmol/L. In the repeated 5.5[-&gt;]12.0[-&gt;]5.5 profiles, the ratio of actual to programmed rate was 0.978-1.083 on the rising phase and 0.987-1.157 on the falling phase, while the amplitude transfer coefficient was 0.967-1.066. In the hypoglycemic profile, minimum GLU was 2.55- 2.96 mmol/L, and time below 3.0 mmol/L was 15.2-72.6 min. The measured rates of 0.0519, 0.1045, and 0.2027 mmol/L/min preserved the intended ratio of approximately 1:2:4. In the complex profile, the programmed plateau of 18 mmol/L was not reached: mean measured GLU was 16.20 mmol/L. For CGM-A, K_A was 0.682 and 0.650, and K_up/K_down were 0.666/0.730 and 0.634/0.626; the corresponding values for CGM-B were 1.228 and 1.128, and 1.564/1.328 and 1.276/1.145. Hysteresis loop area differed 5- to 10-fold between the two sensor responses, exceeding an order of magnitude at the 100-min transition. ConclusionThe programmed concentration should be treated as a control setpoint, rather than as a reference measurement. The "programmed trajectory -- measured glucose -- CGM output" cascade first allows quantitative assessment of the agreement between the programmed and actually realized profile and only then separate characterization of sensor response. Decomposition of dynamic error into amplitude, rate, shape, and hysteresis components reveals differences that a single MARD value or correlation coefficient cannot capture.

Rapidly converting viral genome information into deployable molecular tests remains a major challenge in outbreak preparedness. We developed CONAN-SWIFT (Simple Workflow for Isothermal Field Testing), a sequence-to-test platform that integrates computational assay design, reverse-transcription loop-mediated isothermal amplification, CRISPR-Cas3 detection, reagent lyophilization and lateral-flow readout. Sequence-guided assays for Andes virus and Bundibugyo virus were established within approximately three weeks and extended to four additional filoviruses. A web-based designer supported crRNA selection, and systematic RT-LAMP primer optimization improved amplification performance. Recombinant Escherichia coli-expressed Cascade enabled standardized preparation of lyophilized Cas3-detection reagents, which were combined with a battery-operated isothermal device. The portable system detected as few as 10 input RNA copies per reaction within approximately 40 min. It also detected viral RNA and biologically contained, replication-incompetent Ebola virus in spiked human blood and concentrated wastewater. These findings establish the analytical feasibility of a rapidly adaptable CRISPR-Cas3 engineering framework for decentralized detection of emerging RNA viruses.

Background: Positron emission tomography (PET) myocardial perfusion imaging (MPI) provides complementary information on perfusion, myocardial blood flow and ventricular function. While these markers are often considered collectively during interpretation, their quantitative integration with imaging and clinical data into a unified predictive framework remains limited. We developed a multimodal artificial intelligence framework that combines PET polar maps with quantitative imaging and clinical features to improve obstructive coronary artery disease (CAD) detection. Methods: We retrospectively analyzed the multicenter REFINE PET registry. Among 38,682 PET MPI studies from 14 sites, 2,833 patients without known prior CAD underwent invasive coronary angiography within 180 days. Obstructive CAD was defined as >=50% left main stenosis or >=70% stenosis in other major epicardial coronary arteries. We developed a two-stage contrastive learning framework to learn multimodal PET representations from studies without angiographic labels and transfer them to supervised CAD prediction. In Stage 1, PET image and tabular encoders were pretrained on 12,225 PET MPI studies from eight development sites using 15-channel PET polar maps, quantitative PET perfusion, flow and gated functional measures, and clinical variables. In Stage 2, the pretrained encoders and a lightweight classification head were fine-tuned in 968 angiography-labeled patients, using lower encoder learning rates to limit overfitting. The model was externally validated for angiographically defined obstructive CAD detection in 1,865 patients from six independent sites and compared with standard PET MPI metrics. Results: The prevalence of obstructive CAD was 60% in the training cohort (66% male, median age of 70 years [63, 77]), and 55% in the external validation cohort (64% male, median age of 67 years [60-74]). In external validation, the AI model achieved an AUC of 0.85 (95% confidence interval (CI), 0.83-0.87) for obstructive CAD detection and outperformed conventional quantitative PET metrics (all P < 0.001). At a specificity matched to visual summed stress score, the AI model achieved higher sensitivity (89% [95% CI, 87-91] versus 85% [95% CI, 82-87]) and negative predictive value (81% [95% CI, 77-84] versus 73% [95% CI, 69-77]; both p<0.001). The overall net reclassification improvement was 8.9% (95% CI, 4.2-13.6%; p = 0.001). Conclusions: Multimodal contrastive pretraining improved obstructive CAD detection from PET imaging beyond conventional perfusion-based scoring in independent multisite external validation.

Two-photon polymerization (2PP) enables fabrication of hydrogel constructs with submicron, cell-scale resolution, but hydrogel-based bioinks are markedly more sensitive to process variability than conventional photoresists, and this sensitivity is further amplified when living cells are embedded in the resin. Post-processing evaluation, performed only after development, occurs too late to enable any corrective action. A full-factorial design of experiments across laser power and scan speed shows that fabrication outcome depends on both parameter choice and cell presence, with cells shifting and broadening the range of conditions yielding structurally sound constructs. However, substantial variability persists within each nominal condition and cannot be resolved by parameter refinement alone, indicating that outcome is governed by what occurs during each individual print rather than by the parameters set. To capture this, a layer-wise polymerization score is derived from pairwise comparisons of same-layer coaxial images, grounded in the psychophysics of relative judgment, and assembled into a Layer-wise Image Trajectory (LIT) for each print. Applied to both acellular and cell-laden formulations, LIT curves separate cleanly by post-processing outcome without any outcome label used in training, showing that fabrication quality can be predicted early in the build. Building on this signal, individual LIT curves are compared against statistical control limits derived from confirmed successful prints, enabling early detection of anomalous fabrication behavior at early-to-mid layers, well before development. To the best of the authors knowledge, this is the first application of in situ quality prediction and anomaly detection to cell-laden two-photon polymerization.

Poly(ethylene terephthalate) (PET) is one of the most widely produced plastics, and enzymatic depolymerization offers a promising route to closed-loop recycling under mild conditions. However, most known bacterial PET hydrolases belong to a conserved canonical-fold cutinase family, leaving much of alpha/beta-hydrolase diversity unexplored. Here, we mapped bacterial cutinase sequence space by combining bioinformatics-guided sequence selection with high-throughput secretion screening in Bacillus subtilis. A library of 1,120 genes encoding 954 unique bacterial cutinases, spanning canonical- and minimal-fold families, was screened for activity on Impranil DLN and semicrystalline PET. We identified 156 secreted cutinases with polyester activity, broadly distributed across sequence space, but only ten showed detectable PET hydrolysis, all from the canonical-fold family. These PET hydrolases were active at 40-50{degrees}C, preferred alkaline pH, and showed moderate thermostability. Our results demonstrate that PET activity is rare among bacterial cutinases and provide a scalable workflow for discovering diverse enzyme starting points.

Induced pluripotent stem cells (iPSCs) hold great promise for both allogeneic and autologous cellular therapies. However, broad application and clinical translation is hindered by fragmented, complex and time-intensive workflows, resulting in high manufacturing costs, poor standardization and increased risk of genomic aberrations in derived iPSCs. In this study we developed a standardizable, automatable and time- efficient process for the derivation of monoclonal iPSC lines straight from skin including a comprehensive and cascaded OC strategy. We generated monoclonal iPSC lines derived from human skin punch biopsies of ten donors (age 49-81) via mRNA-based reprogramming that subsequently underwent comprehensive and thorough characterization of phenotypic and genetic properties. The use of a combined mechanical and enzymatic fibroblast isolation protocol and a transient non-integrative reprogramming technology allowed us to obtain 78 monoclonal iPSC lines, ready for banking, molecular characterization and further differentiation within seven weeks from initial sample processing to passage four iPSC lines. The phenotypical characterization via flow cytometry-based pluripotency marker expression and 2D-directed differentiation into the three germ layers showed low intra- and inter-donor variability over all generated lines. A combination of SNP array based CNV analysis followed by whole exome sequencing proved to be the most efficient approach for assessment of genomic integrity. Proof-of-concept experiments for closed system processing revealed that a substantial part of the most error-prone and technically demanding steps can be transferred to semi- automated, closed systems. In conclusion, the described protocol allows for time- efficient, standardizable and automatable generation of high-quality monoclonal iPSC lines from human skin punch biopsies within seven weeks, thus moving the field of autologous iPSC manufacturing one step further towards cost-efficient clinical implementation.

Click-qPCR is a browser-based application for relative qPCR analysis that requires a tidy-format CSV file containing four columns: sample, group, gene, and Cq. Preparing this input from qPCR instrument output typically requires manual reformatting and calculation of mean Cq values for technical replicates. To simplify this process, we developed Click-Prep (https://kubo-azu.shinyapps.io/Click-Prep/), an interactive web-based application designed specifically to create Click-qPCR input files. Click-Prep imports CSV, TXT, TSV, and XLS/XLSX files and supports skipping of instrument-generated metadata rows, interactive column mapping, and manual assignment of experimental groups. Users can review technical-replicate measurements, exclude selected rows according to predefined quality-control criteria, and calculate mean Cq values for each sample-group-target combination. Missing or nonnumeric Cq values are flagged for review and must be resolved before the mean is calculated. Click-Prep can also combine compatible formatted CSV files, such as datasets obtained from separate qPCR plates. The resulting dataset is exported as a standardized CSV file containing the four fields required by Click-qPCR. By integrating these operations into a guided browser-based workflow, Click-Prep enables users to prepare Click-qPCR input files rapidly and consistently without programming.

Diffusion models achieve high reconstruction quality in low-dose computed tomography (LDCT), but their iterative sampling trajectories impose substantial computational costs. Unlike unconditional generation, paired LDCT reconstruction starts from an image that already contains the anatomy and spatial structure of the standard-dose CT (SDCT) target; reconstruction primarily requires correcting dose-related noise and artifacts. We therefore introduce Residual Endpoint Flow Matching (REFM), an LDCT reconstruction method that learns to transport an LDCT image directly toward its paired SDCT endpoint rather than defining a noise-to-image trajectory. REFM predicts the residual velocity along linear interpolations between both images and supports single-step and multi-step reconstruction using the same trained network. We evaluate five model capacities using 1 to 50 Euler steps against deterministic U-Net and diffusion-based baselines. Across all REFM variants, one-step inference consistently provides the highest reconstruction quality. On the TCIA validation set, REFM Base achieves 50.98 dB PSNR and 0.9865 SSIM at 94.54 fps, compared with 50.92 dB, 0.9847, and 9.26 fps for DDPM-10. REFM Small retains 50.71 dB while increasing throughput to 198.56 fps. Without fine-tuning, REFM Base also matches the 25-step DDPM baseline on the external Mayo Clinic dataset, although DDPM remains stronger on synthetically degraded CRLM images. Thus, our results show that exploiting paired anatomical correspondence enables diffusion-level LDCT reconstruction with a single step reconstruction.

Automated lesion segmentation is increasingly central to PSMA PET/CT interpretation, supporting staging, treatment planning, and response assessment at a scale that outpaces available nuclear-medicine expertise. However, automated PSMA-PET/CT whole-body lesion segmentation models are trained on images alone, with no knowledge of where in the body prostate metastases actually tend to occur. Radiologists use clinical domain knowledge of metastatic spread, but its absence in machine learning models produces false positives in anatomically implausible locations and missed lesions in high-risk sites such as the liver. In this work, we explore whether population-level spatial knowledge of metastatic spread can be used to augment deep learning segmentation predictions, and how such a prior should be fused with a network's output, without additional training. We build a data-driven metastasis atlas from 375 expert-annotated whole-body PSMA PET/CT scans and investigate its fusion with a trained segmentation network under a Bayesian framework, in which prediction probabilities from an nnU-Net-based lesion segmentation model serve as the likelihood and the data-driven atlas as the prior. Because metastases occupy only a small fraction of whole-body voxels, the atlas's peak probability is too low, and standard power-scaled or naive Bayesian pooling references lack the tools to deal with this shortcoming. This causes these standard fusion strategies to fail and, in the naive Bayesian case, to sharply degrade performance. We instead derive a calibrated, background-referenced log-odds fusion, one of many possible approaches to combine a population atlas with a deep learning model's predictions, distinct from classical multi-atlas label fusion in that it fuses a single population prior with a trained network's softmax rather than combining several registered atlases. Furthermore, this approach is neutral outside atlas support by construction, reduces exactly to the baseline network when unweighted, and requires no retraining. This atlas fusion significantly improved mean Dice over the baseline nnU-Net on a disjoint internal test set ($+0.011$, Holm-adjusted $p=0.021$) and on an independent external cohort ($+0.0129$, Holm-adjusted $p=3.8\times10^{-16}$), with lesion sensitivity improving from 0.849 to 0.861 internally and Dice improving over baseline in every stratified anatomic region, including the rare, high-risk sites motivating this work, while naive Bayesian pooling degrades performance sharply and power-scaled pooling underperforms it throughout. Our findings suggest that population-level spatial priors can meaningfully augment deep learning predictions in whole-body oncologic segmentation, provided the fusion rule is calibrated to where the prior actually carries signal.

Endothelial cells (ECs) derived from human induced pluripotent stem cells (hiPSCs) are increasingly used to model vascular diseases and test therapeutic strategies. However, the efficiency and reproducibility of differentiation can vary depending on the culture medium and its supplemented factors and stages. Here, we directly compared two defined media, APEL and BPEL, for iPSC-to-ECs differentiation. iPSCs were differentiated over 10 days with sequential growth factor induction, followed by magnetic-activated cell sorting or flow cytometry for CD31+ cells. Both media produced ECs with similar morphology and marker expression, including CD31 and VE-cadherin. Functional assays demonstrated comparable tube formation, indicating equivalent endothelial functionality. Cost analysis indicated that APEL had a higher total reagent cost but generated a higher total cell yield, resulting in a comparable cost per 10 total cells, whereas BPEL was more cost-efficient for producing CD31/VE-cadherin endothelial-specific cells. Our results suggest that APEL and BPEL media are equally effective for generating iPSC-derived ECs, providing flexibility in method selection for vascular disease modeling and drug discovery applications.

Background: Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods: We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings: During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation: An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies.

Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (~100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19-74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep-learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross-walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini-Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings - a sexually dimorphic axis (PC1), a slender-versus-stout body-habitus contrast (PC2), and a free-rib-size axis at ribs 11-12 (PC3). The sexes were nearly fully separated along PC1 (Cohen's d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

During development, endothelial cells (ECs) migrate into the brain and acquire blood-brain barrier (BBB) properties such as tight junctions, limited transcellular transport, and high electrical resistance. Although key signaling pathways that are active in vivo have been identified, factors critical in inducing brain EC differentiation in vitro remain unclear. Here, we describe conditions that promote brain EC-like gene expression in human pluripotent stem cell (hiPSC)-derived ECs. Activation of Wnt/{beta}-catenin signaling upregulates the brain EC marker GLUT1 (SLC2A1) while suppressing the peripheral EC marker PLVAP. Simultaneously, stimulation of STAT3 by CNTF together with TGF-{beta} inhibition increases CLDN5 expression. We further found that hiPSC-derived ECs secrete high levels of angiopoietin-2 (ANGPT2) and that razuprotafib (AKB-9778), a PTPRB (VE-PTP) inhibitor, inhibits ANGPT2 and improves monolayer integrity. These results suggest that combinatorial modulation of specific signaling pathways stimulates the differentiation of human brain ECs in vitro.