Frontiers in Bioengineering and Biotechnology

Emerging efforts to engineer multicellular kidney tissues for drug discovery, disease modeling, and therapeutic repair require scalable generation of kidney organoids derived from human induced pluripotent stem cells (hiPSCs). Here, we report a method for generating kidney ureteric bud and collecting duct epithelial organoids in stirred tank bioreactors. We demonstrate that extracellular matrix microcarriers facilitate organoid expansion and are compatible with suspension bioreactor methods. The combination of cell-coated matrix microcarriers with stirred bioreactors promotes maturation marker expression compared to static controls comprising matrix-embedded organoids. Our method offers promise for generating epithelial organoids that serve as multicellular models and organ building blocks for scalable biofabrication.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

Lymphatic endothelial cell (LEC) function is strongly tied to transitions between button- and zipper-like junctions that control fluid flow into vessels. Published methods quantifying junction morphology are limited to single cell analysis or use few quantitative metrics to describe junctions across LEC monolayers. Therefore, a novel 3-step image analysis pipeline was developed to identify new metrics that quantify differences in button- and zipper-like junctions in LECs. The pipeline used Cellpose, CellProfiler, and Fiji to analyze VE-cadherin expression across lymphatic vessels within a 3D microfluidic chip and quantify VE-cadherin radial intensity, number of objects, object size, branchpoints, end-points, and junction thickness. Tumor necrosis factor (TNF)- exposure produced but-ton-like junctions, and unexposed cells were zippered. Radial intensity was heightened at the cell membrane in zipper-like junctions while perinuclear intensity increased for but-ton-like junctions. Increased branchpoints, endpoints, objects per cell, and junction thick-ness were associated with junction buttoning, while increased object size was associated with junction zippering. Principle component analysis results reinforced these trends and identified metric combinations that represented each junctional state. Overall, this semi-automated pipeline quantified differences in VE-cadherin junction morphology across LEC monolayers and generated new quantitative metrics to expand the tools available for studying junction morphology in lymphatic vasculature.

Despite the use of technological countermeasures, studies show that the sensory conflict, and thus symptoms of motion sickness such as nausea, cannot be completely prevented while traveling as a car passenger. Accordingly, reliable prediction of motion sickness will represent a possible solution for intelligent vehicles to avoid the abortion of the drive, e.g., through adapted driving strategies or route planning. In previous works, the authors were able to demonstrate an application-oriented parameter set for the popular 6DOF-SVC model to estimate motion sickness incidence (MSI) in passengers with a lowered gaze on a non-driving-related task. In the current study, these parameters were validated and further optimized using five previous conducted motion sickness driving experiments. For the first time, it is also shown that the expected natural MSI fluctuations based on the driving dynamic profile, due to route characteristics or driving style, as well as the variation in experimental determined MSI is approximately 10%. Moreover, findings indicate that the participants appear to have an individual MSI threshold that correlates with their susceptibility to motion sickness, however, this threshold fluctuates too strongly across experiments to be suitable for practical application. As a result, the model can successfully estimate motion sickness risk across the test tracks, however, inaccuracies in the predicted MSI trajectories remain.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5 to 3 degrees across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7-7.8 degrees; manual: 8.3-24.2 degrees; neutral: 15.5-35.5 degrees). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.

Stereotactic needle-based delivery remains the standard for local administration of Advanced Therapy Medicinal Products (ATMPs) to the human brain. ATMP administration typically involves multiple trajectories, presenting cumulative risks and prolonging surgery. Reflux-prone, patchy therapy coverage compromises clinical results. We demonstrate a novel approach, deploying a helical delivery catheter via a single access trajectory per target, referred to as SpiraMed. Helix retraction is synchronised with therapy delivery, enabling comprehensive target coverage in seconds. Helix pitch and diameter can be precisely tailored to patient-specific target volume and vascular anatomy, as part of the preoperative stereotactic surgical planning process. Testing in agarose phantoms, live sheep and cadaveric human brain confirms enhanced therapy delivery volume, delivery speed and target coverage, with reductions in reflux and predicted risk of bleeding complication. SpiraMed represents a new paradigm, promising to help deliver on the transformative potential of cell and gene therapies across the spectrum of human CNS disease.

The human pancreas secretes digestive fluid that flows through an extensive ductal network before emptying into the duodenum. However, little is known about how hydrodynamic forces influence pancreatic function and development. We hypothesized that pancreatic cells respond to physiological levels of fluid shear stress. In this study, we developed a microfluidic system featuring reversible sealing between the device layer and the cell culture substrate, enabling (1) conventional open-well cell culture prior to fluid shear stress application and (2) selective patterning of proteins and cells for migration studies. Channel dimensions were chosen to support 24-hour perfusion using standard syringe pumps. Using this platform, we investigated effects of physiological shear stress levels representing basal and activated pancreatic function (2, 8 and 33 mPa) on human iPSC-derived pancreatic progenitor cells. Pancreatic progenitor cells exhibit graded sensitivity to shear stress at the transcriptomic level. Specifically, shear stress upregulated expression of cell cycle-related gene sets; endocrine-associated genes including NEUROG3; and Notch receptor ligands (DLL1, DLL3) in a magnitude-dependent manner. The exposure to unidirectional fluid flow also resulted in a reduction in YAP1 protein content, decreased WWTR1 gene expression, and downregulation of YAP/TAZ target genes. Furthermore, barrier-based migration assays under shear stress revealed that cell patches expanded their boundaries both along and against the flow, with greater distance in the direction of flow. Our system and findings may help lay the foundation for understanding biophysical regulation of developmental processes in a physiologically important, yet hard-to-access, epithelial model system.

Tissue expansion is an established technique for the reconstruction of skin defects, but the cost of conventional silicone expanders may limit its application in contexts with lower resource availability. This study evaluated, in an experimental model, whether a container for storing saline solution could act as a low-cost alternative device for tissue expansion. An experimental, prospective, controlled and randomized study was carried out with 10 Wistar rats, distributed in two groups: conventional silicone expander and saline storage container. Both underwent the same surgical and expansion protocol. Abdominal circumference gain, final expanded skin length, histological and histomorphometric aspects of the skin, including collagen types I and III, as well as exploratory cost analysis, were analyzed. The conventional expander produced a greater gain in abdominal circumference, but the final length of the expanded skin was similar between the groups. Microscopic evaluation showed preservation of the cutaneous architecture, intact epidermis, cellularized dermis, and collagen deposition in both groups. There was no significant difference in the proportion of type I or type III collagen. The saline solution container had a substantially lower cost per centimeter of expanded skin than the conventional expander. These findings suggest that the saline container can produce experimental skin expansion with final length and collagen composition similar to the conventional method, at a lower cost, although additional studies are needed before clinical application.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

Influenza A virus (IAV) defective interfering particles (DIPs) are regarded as promising broad-spectrum antivirals due to their high efficacy demonstrated in animal trials. Previously, an OP7 chimera DIP was generated that showed markedly enhanced antiviral efficacy compared to conventional IAV DIPs. In addition, a high-yield cell culture-based production process for OP7 chimera DIPs was established in perfusion mode. However, due to the technical complexity of this technology, we aimed to combine the advantages of perfusion in terms of yield with that of a simple and less complex batch process. In the present work, we established a fed-batch production process for OP7 chimera DIPs that allows a more straightforward implementation into existing production facilities. Using a feeding regime that was able to overcome the cell density effect, we achieved productive virus infection in stirred tank reactors until a cell density of 10E+6 cells/mL. Furthermore, we obtained a high virus titer of 3.8 log10(HAU/100 uL), corresponding to a 3.4-fold increase compared to a reference batch production. Moreover, the produced material demonstrated a significantly increased interfering bioactivity shown by an in vitro potency assay. Interestingly, this efficacy was similarly high as compared to OP7 chimera DIP material previously produced in perfusion mode, despite twofold lower virus titers. Finally, we demonstrate robustness of OP7 chimera DIP seed virus trains, which is significant step towards later pharmaceutical production. Further, corresponding GMP MDCK-PB2(sus) cell and OP7 chimera virus banks are now available for later biomanufacturing further use. In conclusion, the established fed-batch process offers a valid n additional option for pharmaceutical grade OP7 chimera DIP GMP manufacturing production under GMP conditions required for subsequent clinical testing and for later large-scale manufacturing.

Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.

Organ-on-chip (OoC) systems are increasingly used to generate human-relevant evidence in drug discovery and preclinical development, yet interpretation can be limited by the interaction of biological variability, device-to-device variability, and technical assay noise. Here, we describe the UstarFlowAI barrier-confidence framework, an in silico assay-readiness approach that quantifies whether a predefined permeability-related effect is resolvable under specified uncertainty. The proposed context of use is decision support for laminar, low-Reynolds-number airway epithelial monolayer barrier OoC assays in which device fabrication, pump performance, flow, wall shear stress, and biological fluctuation may affect permeability-related readouts. Monte Carlo simulations propagate defined manufacturing, biological, systemic, and well-level uncertainty and estimate the minimum detectable shift (MDS) under a prespecified decision rule. In the present proof-of-concept design, the reference workflow was parameterized at 9.4% technical coefficient of variation (CV), whereas the standardized workflow was parameterized at 5.0% CV, corresponding to an approximately 46.8% reduction in the modeled technical-noise component. At n = 48 replicate wells per group and a nominal total assay-noise operating point of 17% CV, a predefined 10% permeability shift exceeded the upper MDS uncertainty bound for the standardized workflow in both best- and worst-case scenarios, whereas it overlapped the 95% MDS interval for the reference workflow and therefore did not meet the prespecified PASS criterion. These findings demonstrate the behavior of the computational framework under the stated assumptions; they do not constitute experimental validation, regulatory qualification, or evidence that a 10% shift is universally meaningful across barrier OoC systems. We therefore propose a prospective wet-lab validation strategy in which model predictions are tested against airway barrier measurements generated under controlled sources of technical and biological variability. The framework is intended to complement, rather than replace, the underlying biological model by making assay uncertainty and decision thresholds explicit and auditable.

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability, affecting 50-60 million people annually. Cascading secondary injury mechanisms, including oxidative stress and neuroinflammation, impair motor and cognitive function, while current therapies often manage symptoms rather than restore lost neurological function. This study investigated an ultrasound-activated piezoelectric hydrogel scaffold composed of barium titanate nanoparticles (BTNPs), sodium alginate (SA), and polyethylene oxide (PEO). The hypothesis was that TBI flies receiving the scaffold and ultrasound would display a higher climbing assay pass rate than TBI flies receiving no treatment or BTNPs alone. TBI was induced in Drosophila melanogaster with a high-impact trauma (HIT) apparatus. Treatments were applied by opening and resealing the fly head cuticle, followed by ultrasound stimulation. Motor recovery was quantified with a climbing assay across four groups (N=10 flies per group), which measured the percentage of flies that could cross a 5 cm mark within 30 seconds. Assay validity was supported by a statistically significant difference between flies without TBI and those with TBI using Mann-Whitney U testing (p=0.0035). Although TBI treatment groups did not differ significantly (p>0.05), median pass rates increased from injury control (35%) to BTNPs (45%) to scaffold (50%). However, high inter-trial variation, a 45.2% procedural mortality rate, and small sample size limited statistical power. These results suggest that piezoelectric electrospun hydrogel scaffolds may be a pathway for safer and more biocompatible restoration after TBI, but provide insufficient evidence to conclude that the scaffold significantly improved motor recovery.

Synthetic endosymbiosis, the deliberate introduction of bacteria into eukaryotic host cells, provides a tractable model for how endosymbiotic relationships become established, and a route to cells with new metabolic capabilities that never evolved naturally in that lineage. Current methods for introducing bacteria into mammalian cells rely on microinjection, engineered invasion, listeriolysin-mediated phagosomal escape, or naturally permissive hosts. These methods are invasive, low-throughput, or costly, and most require the prospective endosymbiont to be genetically tractable, which excludes endosymbiotic bacteria which are of great interest as potential organelle precursors. Here we describe a protocol based on polyethylene glycol (PEG), long used to fuse mammalian cells, that delivers Escherichia coli into HeLa cells without genetic modification of either partner or the use of specialised equipment. We optimised the governing parameters in two stages: first, bacterial density, gentamicin concentration, and PEG molecular weight and concentration were optimised; then, medium composition and the timing of fusion and recovery. Across four parameters screened, 10% PEG-3350 applied for 4 min to HeLa cells 12 h after seeding, followed by 4 h in gentamicin-containing medium, gave the most consistent delivery. Under these conditions, confocal microscopy and z-stack reconstruction detected mCherry-tagged E. coli inside HeLa cells. Applying the same protocol to Candidatus Kinetoplastibacterium crithidii, a naturally occurring {beta}-proteobacterial endosymbiont of the trypanosomatid Angomonas deanei, we observed the endosymbiont inside the HeLa cells. This protocol offers an accessible entry point for constructing artificial endosymbioses.

Roseoloviruses, notably human herpesviruses 6A and 6B (HHV-6A and HHV-6B), are neurotropic viruses implicated as agents in neurological disorders, including: epilepsy, multiple sclerosis, and chronic fatigue syndrome. However, the effects of roseolovirus infection on neuronal signaling and network activity are not characterized. This is, in part, due to the complexities of monitoring electrical activity in individual neurons and across neuronal connections during viral infection. This protocol describes a human induced pluripotent stem cell (iPSC)-derived neuronal culture system that employs multi-electrode array (MEA) recordings to study neurophysiological changes during viral infection. Two culture platforms are described: (a) NGN2-induced forebrain neuronal cultures; and, (b) progenitor cell-derived neuron-astrocyte mixed cultures. Cell composition in cultures is validated by immunofluorescence staining using neuronal, glial, and viral markers. Functional activity is assessed using extracellular recordings detected via the MEA2100 system. Mean firing rate, inter-spike interval, single-electrode bursting, and network burst activity are characterized between roseolovirus-infected versus uninfected (control) states. Pharmacological treatment with bicuculline, gabazine, and nicotine in conjunction with immunofluorescence is used to confirm functional responsiveness of defined neuronal neurotransmitter chemotypes. Here, we show that HHV-6A infection changes neuronal firing compared to uninfected controls. The methods/workflows described provide a strategy to study how virus infection alters neuronal excitability and may be adapted to compare the effects of different viruses on nerve cell function, infection time courses, different multiplicities of infection (MOI), and therapeutic interventions.

Our prior works have demonstrated the therapeutic potential extracellular vesicles (EVs) derived from batch cultures of brain endothelial cells (BECs) owing to their ability to transfer mitochondria to injured recipient cells. However, conventional batch/flask cultures limit scalable and reproducible production of EVs. Here, we evaluated a CELLine AD1000 bioreactor as a platform for sustained production of EVs from mouse b.End3 BECs and compared bioreactor-derived (BR) EVs with EVs obtained conventional batch cultures (BT). Large EVs (lEVs) and small (sEVs) were isolated using a sequential centrifugation protocol and were characterized using dynamic light scattering, nanoparticle tracking analysis, microBCA assay, western blotting, proteomics analysis, and their ability to rescue bioenergetics in oxygen-glucose deprived (OGD) BECs. Bioreactor cultures maintained high cell viabilities (~90%) for up to eight weeks and produced approximately three- to four-fold greater EV protein yields than batch cultures. BR EVs showed reproducible particle characteristics consistent with EV subtypes derived from BT cultures. CD63, CD9 and Arf6 were detected in EV fractions, whereas the mitochondrial outer-membrane protein TOMM20 was enriched in lEVs. Proteomic analyses revealed that bioreactor-derived lEVs retained the mitochondrial cargo signature observed in batch-derived lEVs. Both bioreactor and batch EVs restored relative ATP levels in ODG-exposed BECs. These findings suggest that bioreactor BEC cultures are a scalable approach for generating functionally active BEC-EVs for downstream characterization and therapeutic development.

Functional protein micro/nanofibers integrate high specific surface areas with bioactive architectures but remain hampered low production throughput and severe processing instability. Focused rotary jet spinning (FRJS) shows promising to break these throughput constraints while enabling direct, conformal deposition onto complex, irregular substrates. However, navigating FRJS's narrow processing windows in proteins remains failure-prone without closed-loop experimental guidance. Here, we report FiberPro 1.0, a large language model driven multi agent framework that unites high-throughput spinning with real-time experimental feedback for autonomous design, execution, and optimization. Across three protein systems, FiberPro 1.0 converged on spinnable formulations within an average of two iterations. To demonstrate high-throughput conformal deposition in a translational setting, FiberPro designed a zein-based active packaging system applied directly onto diverse food matrices. The resulting conformal coating combined potent antibacterial activity with real-time freshness monitoring, markedly suppressing Escherichia coli and extending shelf life. This work connects autonomous AI reasoning with high-throughput processing, establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers.

Point-of-care (POC) molecular detection has enabled rapid and accessible testing at or near the site of need, facilitating timely decision-making in clinical, field, and industrial settings. Despite these advancements, molecular detection platforms remain limited in their ability to prepare DNA from complex tissue matrices, restricting their applicability to a narrow range of sample types. To address sample preparation inefficiencies, focused ultrasound extraction (FUSE) has been developed for the rapid release of DNA from robust tissue matrices. FUSE disintegrates target tissue and releases DNA by delivering high pressure ultrasound pulses that generate a cavitation bubble cloud. In prior work, FUSE was performed using large, laboratory-based equipment. Here, a miniaturized FUSE device was designed and tested to enable POC DNA sample preparation. Design constraints were defined based on prior FUSE experiments, and based on these metrics, a 750 kHz cylindrical transducer was fabricated. The pressure output was measured, and high-speed optical imaging demonstrated that the transducer generated sustained cavitation within the defined focal region. The feasibility of DNA extraction from piscine and timber tissues was evaluated, and results show that the device can release DNA with quality suitable for PCR-based detection from both tissue types. These results demonstrate the potential of this device to rapidly extract DNA from complex samples and expand the applicability of molecular detection platforms.

With the increase of recycled water to offset water demand, the potential possibility to spread contagious RNA viruses, such as Coxsackievirus B3, increases. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. Robust monitoring is needed with low cost and low infrastructure technologies to increase accessibility of monitoring technologies worldwide. Novel viral detection methods have been developed, based on synthetic riboswitches that bind to the target virus and trigger a reporter gene, thus amplifying the detection signals. Such monitoring technologies can leverage machine learning to optimize the candidate nucleic acid sequences used for detection. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features based on free energy calculations and parameters that represent single strandedness. This model uses a sparsely gated Mixture of Experts (MoE) architecture to route sequence and thermodynamic features to specialized experts, achieving strong generalization performance across cross-validation and held-out testing. When evaluated against baseline Decision Tree, Random Forest, Gaussian Naive Bayes, and Dense Neural Network classifiers, the MoE model demonstrated near-zero classification errors. Furthermore, post-hoc feature importance and k-mer analyzes reveal that near-perfect predictive performance in silico is strongly driven by sequence constructs in addition to generalizable folding mechanics. An ablation study of numerical feature and sequence inputs showed that while the Dense Neural Network also achieved high accuracy across all ablation conditions, the MoE architecture was retained as the primary model because its specialized subnetworks do not require all model parameters to be activated offering a potential computational advantage.

ObjectiveTo evaluate usability and workflow implications of Pheno+, an electronic health record- (EHR) embedded natural language processing tool that extracts patient phenotypes and maps them to Human Phenotype Ontology (HPO) terms. Materials and MethodsThis study performed a mixed-methods formative evaluation of Pheno+ in the context of rapid genome sequencing in the neonatal intensive care unit. Two rounds of think-aloud interviews and surveys were conducted with neonatologists, advanced practice providers, medical geneticists, genetic counselors, non-genetics pediatric subspecialists and laboratory molecular geneticists. Usability was assessed using the System Usability Scale (SUS). Structured data were analyzed descriptively. Qualitative data underwent hybrid deductive-inductive thematic analysis. ResultsTwenty-eight interviewees participated in two rounds of data collection. Users retained an average of 24.2 HPO terms from an average of 45.6 terms per patient. Mean time curating terms was 8:59 per patient. Final SUS score was 76 (above scale mean). Qualitative themes included: burden of curating terms; interpretation of phenotypes; adding information to phenotypes; role of genetics expertise; and tool uses beyond the original core use case for Pheno+. DiscussionPheno+ demonstrated positive usability but required curation effort to reduce erroneous terms. Participant responses indicate that effective use may depend on knowledge of HPO structure and laboratory workflows. Automated phenotyping seems likely to reduce net clinician workload, while also reshaping clinical responsibilities, expertise requirements, and communication in genetic testing. ConclusionTools like Pheno+ can support scalable genomic medicine. Design and implementation may benefit from addressing curation burden, phenotype contextualization, expertise requirements, and other workflow needs.