Interface Focus

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

The global coral reef crisis has prompted restoration initiatives worldwide. Targeting the coral larval stage is among the most scalable approaches as recruitment operates over large spatial scales. It thus represents one of the best levers for coral population recovery. Active coral larval seeding has shown considerable success, and passive substrate engineering has emerged as a promising complementary strategy. Coral settlement modules featuring helix recesses have increased settlement and survival by up to 80-fold on small experimental units, but whether these results translate to tools deployable at the scale of thousands of units, remains yet an open question. Here, we transferred structural features from successful experimental coral settlement designs into production-ready concrete modules to (i) evaluate coral recruitment on five designs at four reef sites differing in flow regime and coral cover over one year; (ii) compare production-scale performance against experimental clay modules and natural reef substrate; and (iii) identify key parameters for large-scale production. The helix recess geometry of coral settlement modules outperformed the featureless control design approximately 20-fold and exceeded natural reef recruitment at least 3- to 32-fold. The helix features were successfully transferred from experimental clay to production-scale concrete modules, yielding comparable settlement densities when standardized to crevice length, which proved to be the biologically relevant unit of available habitat. Production feasibility was demonstrated by producing 690 modules for deployment on a hybrid reef on the west side of Oahu, Hawaii. The passive coral larval recruitment approach presented here could substantially improve the logistical and economic feasibility of large-scale coral reef restoration. This approach requires neither coral larval rearing, handling, nor coral fragmenting, and is compatible with active larval seeding where genetic diversity or larvae supply are limiting factors. The coral settlement modules can be cast in standardized concrete molds at precast facilities. Modules have demonstrated consistent coral recruitment enhancement across reef environments with contrasting flow and coral cover. Deploying mixed arrays of helix-recess structures with designs offering multi-level complexity and three-dimensional rugosity maximizes outcomes for coral, fish, and invertebrate communities simultaneously. Site selection is the most critical deployment decision and should consider larval supply, hydrodynamics, and substrate stability which drive recruitment outcomes more than design choice alone. The modules offer a range of application potential, ranging from integration into existing coastal infrastructure over stand-alone reef restoration approaches, to substrate-consolidating interconnected arrangements.

BackgroundMethyltransferase-like 1 (METTL1) is highly expressed in organs like the pancreas but less so in the brain. The METTL1-WDR4 complex catalyzes N7-methylguanosine (m7G) methylation in tRNA, miRNA, mRNA, and rRNA, which impacts RNA stability and function. These modifications affect mRNA translation and tRNA functionality, influencing protein production and cellular activities. Such modifications can regulate tumor growth, invasion, and metabolism by selectively controlling protein expression. MethodGene expression data from public databases were analyzed to compare METTL1 expression in normal and tumor tissues. Western blot (WB) and immunohistochemistry (IHC) were used to quantify METTL1 levels in glioma samples and assess their prognostic significance. Cell viability, migration, invasion, and proliferation were evaluated using Cell Counting Kit-8 (CCK-8), wound healing, Transwell, cell cycle analysis, and colony formation assays. RNA immunoprecipitation PCR (RIP-PCR) identified m7G methylation sites on EPHA2 mRNA, and RNA stability was assessed with actinomycin D. ResultsBioinformatics analysis revealed that METTL1 is overexpressed in gliomas, correlating with poor prognosis. Knockdown of METTL1 significantly affected cell proliferation, migration, and invasion. RNA sequencing (RNA-seq) and m7G analysis identified EPHA2 as a downstream target, influencing the cell cycle via the AKT pathway. RIP and methylated RNA immunoprecipitation (MeRIP) confirmed two m7G sites on EPHA2 mRNA regulated by METTL1. Small interfering RNA (siRNA)-mediated METTL1 knockdown in EPHA2 mutants affected mRNA stability. Rescue experiments restored cell proliferation and AKT pathway gene expression. ConclusionMETTL1 methylates EPHA2 mRNA, enhancing its stability and expression, which activates the AKT signaling pathway and influences glioma cell proliferation. METTL1 could be a potential therapeutic target in glioma treatment.

The endothelial cells (ECs) that line blood vessels continuously sense and respond to the physical forces exerted by blood flow. In vivo, pulsatile arterial flow interacts with vessel curvature, branching and other anatomical features to generate complex local hemodynamic environments that dictate the magnitude, direction, pulsatility, and oscillatory nature of wall shear stress experienced by ECs. Currently, accessible and reproducible in vitro models of complex pulsatile flow that recapitulate in vivo vascular anatomy remain limited. Here, we combine a novel rotational-flow endothelial culture platform with detailed computational fluid dynamics (CFD) modeling to characterize four well geometries designed to generate distinct hemodynamic environments. CFD analyses demonstrate that these geometries intrinsically generate pulsatile flow and produce reproducible spatially distinct regions of wall shear stress magnitude, pulsatility, and oscillatory shear within a single culture well. Endothelial alignment mapping and functional assays reveal region-specific cellular responses to the predicted local flow conditions that closely corresponded to the predicted local hemodynamic environment, linking complex flow patterns to endothelial adaptation. The technical advancements of our modeling efforts should support a faster, cheaper, simpler, and--importantly--validated framework for future investigation into EC mechanobiology under complex flow conditions. HIGHLIGHTSO_LISimple engineered well geometries generate distinct hemodynamic microenvironments, mimicking in vivo vascular structures, using a conventional orbital shaker. C_LIO_LIComputational fluid dynamics (CFD) reveals spatially distinct patterns of wall shear stress, pulsatility, and oscillatory shear applied to ECs within individual culture wells. C_LIO_LIHigh average wall shear stress and elevated oscillatory shear index induces a unique perpendicular alignment of ECs to the dominant flow vector. C_LI

Emerin (EMD), an inner nuclear membrane protein essential for nuclear architecture integrity, gene expression, cellular signaling, and chromatin stability, interacts with the LINC complex and participates in cytoskeleton-nucleoskeleton communication by binding to nuclear actin filaments. EMD is implicated in migration, invasion, and metastasis in some tumors, but its role in glioblastoma (GBM) remains unclear. This study evaluated the effects of EMD knockdown and overexpression in GBM cell lines following genotoxic treatment with cisplatin. In both wild-type p53 (U87-MG) and mutant p53 (U138-MG) GBM cells, EMD expression is high, and cisplatin treatment did not affect these protein levels. EMD knockdown in U87-MG cells significantly increased cisplatin IC50, viability, and proliferation. Conversely, stable overexpression of EMD in U87-MG cells led to reduced cisplatin IC50, viability, proliferation, and migration. EMD knockdown or overexpression did not affect any U138-MG phenotypes, with or without cisplatin treatment. Modulation of EMD levels causes morphological changes in stress fiber cytoskeleton, whereas overexpression of EMD in U87-MG cells promotes an increase and a decrease in nuclear and cytoplasmic actin levels, respectively. These biological responses of U87-MG cells overexpressing EMD were coincidentally associated with alterations in the levels of pH2AX(Ser139), p-p53(Ser15), p53, and p21Kip1 proteins after cisplatin exposure. In sum, modulation of EMD levels affects the viability, migration, and proliferation of wild-type p53 GBM cells treated with cisplatin, suggesting unknown roles in the DNA damage response and repair. This work highlights EMD as a potential regulator of GBM chemoresistance and a target for therapeutic intervention.

The extracellular matrix (ECM) is a complex network of ubiquitously present acellular material that plays a critical role in cell proliferation, migration, invasion, and tissue morphogenesis. Non-enzymatic glycation of ECM modifies the structure and function of ECM proteins and can support a pro-inflammatory milieu in the tumor microenvironment. However, the impact of glycated ECM on cancer cell growth remains underexplored despite its importance in facilitating disease progression. Here, we investigate the effect of ECM glycation on cancer cell morphology and migration behavior. We used methylglyoxal (MG) as a glycation agent and collagen as our ECM model protein. For in vitro growth analysis, breast cancer cells were seeded on growth surfaces coated with both non-glycated and glycated collagen. Cell behavior was monitored for 24 hours using a real-time holographic imaging system. Holographic image analysis revealed significant differences between non-glycated and glycated growth substrates in cell spreading area, eccentricity, perimeter length, optical thickness, and optical volume, as well as cell migration and motility, which directly influence cell adhesion and proliferation. These changes were found to be cell line biased. Overall, our findings suggest that ECM glycation has a significant effect on cell morphology, migration and cell growth. Holographic live cell imaging was determined to be an excellent method to monitor cells without the need for any labeling and with minimal perturbations.

Background. Sentinel lymph node biopsy (SLNB) is an essential component of axillary staging in breast cancer. Fluorescence-guided mapping with indocyanine green (ICG) enables real-time visualization of lymphatic drainage; however, the parameters of fluorescence-signal recording require standardization. Objective. To assess the technical feasibility and clinical applicability of SLNB with ICG under near-infrared (NIR) imaging guidance in patients with breast cancer. Materials and Methods. This prospective single-center study included 30 patients who underwent ICG-guided SLNB between 2023 and 2025. In 24 patients, ICG mapping was combined with technetium-99m radioisotope navigation; in 6 patients, ICG guidance alone was used. The protocol comprised periareolar ICG injection, standardized imaging conditions, and fluorescence-index recording. Results. The protocol was completed in all 30 patients. Fluorescent visualization of the lymphatic pathway and/or the sentinel lymph node (SLN) was achieved in every case, and no ICG-related adverse reactions were recorded. The mean fluorescence index was 213.0 +/- 24.7, the median was 206.0 [192.5-237.2], and the min-max was 180-255. Conclusion. SLNB with ICG under NIR imaging guidance demonstrated technical feasibility in a prospective single-center cohort. Quantitative fluorescence-index recording may serve as a component of standardizing intraoperative fluorescence guidance. Keywords: breast cancer; sentinel lymph node biopsy; indocyanine green; near-infrared imaging; fluorescence lymphography; fluorescence index; axillary staging.

Gastric-type adenocarcinoma of the uterine cervix (GAS) is a rare, aggressive cervical cancer unrelated to HPV infection that is frequently misdiagnosed because it closely resembles both benign cervical glands and HPV-related cervical adenocarcinoma. This diagnostic confusion can lead to inappropriate treatment, highlighting the need for objective molecular markers. Here, we performed the systematic multi-center proteomic analysis of GAS, profiling 407 cervical tissue samples to map its molecular landscape. To overcome limited sample size and biological noise, we developed WEDGE. First, generative AI synthesizes realistic artificial proteomic profiles to augment the training data. Biologically informed network analysis then leverages known biological relationships to surface diagnostically meaningful patterns. WEDGE identified a two-protein signature, Pepsinogen C (PGC) and DNA Methyltransferase 1 (DNMT1), that distinguished GAS from HPV-related cervical cancer with 93% accuracy in the test cohort and 97% accuracy in an external proteomic cohort, outperforming existing biomarker-discovery methods. Tissue staining of an IHC validation cohort confirmed the expression patterns and reached a diagnostic accuracy of 87.9%. Beyond diagnosis, PGC independently predicted patient outcomes, and combining PGC with routine clinical features improved risk prediction (C-index 0.701). Together, these results establish an AI-driven framework for biomarker discovery and provide clinically relevant candidate tools for diagnosing and prognosticating for GAS.

Biosynthetic gene clusters (BGCs) encode enzymatic pathways for natural products with pharmaceutical potential, yet prioritizing candidates from fragmented environmental DNA (eDNA) assemblies remains computationally challenging. We present BGC-QDR (Biosynthetic Gene Cluster Quantum Discovery and Ranking), an open-source pipeline that integrates input quality control, Prodigal ORF prediction, Pfam HMM domain annotation, rule-based BGC classification, MiBIG 4.0 novelty assessment, and variational quantum classifier (VQC) ranking via PennyLane. BGC-QDR is designed as a quantum-assisted ranking framework for biologically informed BGC prioritization, not as a claim of quantum computational advantage over classical machine learning. We evaluate the pipeline on MiBIG 4.0 (2,636 annotated BGCs) using a 20-dimensional biosynthetic feature vector and stratified 10-fold cross-validation. The integrated VQC (6 qubits x 3 layers, 54 parameters) achieves accuracy of 0.789 {+/-} 0.076 and ROC-AUC of 0.835 {+/-} 0.057. Random Forest achieves the highest ROC-AUC (0.898 {+/-} 0.032), followed by Logistic Regression (0.874 {+/-} 0.020) and MLP (0.872 {+/-} 0.024). Wilcoxon signed-rank tests on per-fold AUC scores show that VQC ROC-AUC is significantly lower than Random Forest (p = 0.0098) and Logistic Regression (p = 0.037) at = 0.05, with no significant difference versus MLP (p = 0.064). Architecture ablation identifies 4 qubits x 3 layers as the best VQC configuration on hold-out validation (AUC = 0.737). Feature importance analysis highlights peptidyl carrier protein domains, cluster length, and module count as dominant predictors. BGC-QDR provides a reproducible, end-to-end workflow for eDNA-derived BGC discovery with integrated novelty scoring and quantum-assisted candidate ranking. The complete BGC-QDR source code, benchmark datasets, and reproduction instructions are publicly available at: Abhishekmishra2808/BGC-PIPELINE

Microglia are the resident immune cells of the central nervous system (CNS), including the brain, spinal cord, and retina, where they serve as the first line of defense against infection and inflammation. Dysregulated microglia activity has been implicated in vision-threatening diseases, highlighting the need to understand how retinal microglia respond to inflammatory stimuli. Importantly, acute inflammation induces substantial redistribution of microglia across retinal layers, yet the mechanisms governing this migration remain poorly understood. Here, we develop the first mathematical model of retinal microglia migration during inflammation to determine how inflammatory exposure, administration route, and species-specific pharmacokinetics shape redistribution dynamics across the retina. The model couples lipopolysaccharide (LPS) pharmacokinetics with microglia migration between the outer plexiform layer (OPL), inner plexiform layer (IPL), and ganglion cell layer/nerve fiber layer (GCL/NFL). Model parameters are calibrated to retinal microglia density measurements from mice following LPS (bacterial endotoxin) challenge, before extending the framework to rats and rhesus macaques to investigate species-specific responses. Simulations also compare how administration route, i.e. intravenous or intraperitoneal injections, alter retinal LPS exposure and subsequent microglia redistribution. Our results suggest that redistribution patterns are driven primarily by LPS delivery route and species-specific pharmacokinetics, rather than the initial microglia distribution across retinal layers. Together, these findings provide new insight into immune cell reorganization in the inflamed retina and demonstrate how mechanistic mathematical modeling can be adapted across experimental designs, administration routes, and animal species.

Background/ObjectivesGlioblastoma is an aggressive primary brain tumor that develops within a chronically low-oxygen microenvironment, yet most preclinical studies are performed under atmospheric oxygen conditions that poorly reflect in vivo physiology. This study investigated how sustained culture under physiological oxygen tension (physioxia; 5% O{square}) influences glioblastoma cell behavior, signaling, and therapeutic response. MethodsMultiple patient-derived glioblastoma models were cultured under normoxia (21% O{square}) or sustained physioxia (5% O{square}) for at least seven days before experimentation. Cell migration, proliferation, cell cycle distribution, expression of the epithelial-to-mesenchymal transition-associated transcription factor Slug (SNAI2), PDGFR{beta}-associated signaling, and sensitivity to 5-fluorouracil were evaluated using transwell migration assays, cell counting, flow cytometry, RT-qPCR, immunoblotting, and BrdU incorporation assays. Additional patient-derived cultures established and maintained continuously under physioxia were used to examine the effects of oxygen history. ResultsSustained physioxia consistently increased migration across all glioblastoma models while reducing proliferation in normoxia-adapted cell lines through increased G0/G1 cell cycle arrest. Physioxia significantly increased Slug expression in all models and enhanced PDGFR{beta}, AKT, and ERK phosphorylation in a cell line-dependent manner. Therapeutic sensitivity to 5-fluorouracil was also altered, with physioxia conferring increased resistance in selected glioblastoma models but not universally. Patient-derived cultures maintained continuously under physioxia retained enhanced migratory capacity and exhibited increased proliferation compared with normoxia, indicating that prior oxygen exposure influences proliferative responses while the pro-migratory phenotype remains conserved. ConclusionsPhysiological oxygen tension is a major regulator of glioblastoma cell behavior, influencing migration, proliferation, signaling, and therapeutic response. These findings demonstrate that conventional normoxic culture conditions can obscure biologically relevant phenotypes and support incorporating physioxia into experimental design to improve the physiological and translational relevance of preclinical glioblastoma research.

Angiogenesis drives the formation and remodeling of capillary networks throughout tissue repair, regulating the vascular environment that supports healing and tissue remodeling. Experimental characterization of these processes is commonly performed using CD31-stained histological tissue sections to quantify capillary surface density and morphology throughout healing. However, these measurements provide only two-dimensional characterization of an underlying three-dimensional (3D) vascular network, limiting direct estimation of volumetric capillary density and vascular architecture. To address this limitation, an experimentally informed framework was developed to generate representative 3D capillary networks, enabling estimation of volumetric capillary density from histologically quantified vascular measurements. CD31-stained histological sections obtained from a longitudinal porcine lumpectomy study were analyzed to quantify the percentage of CD31-positive area (%CD31+) and capillary morphology within healthy tissue and healing surgical cavities. Histologically quantified morphology distributions and literature-informed vascular branching characteristics were incorporated into a capillary network generation framework to construct representative 3D vascular networks. Capillary branches were iteratively generated within representative tissue volumes until virtual histological sections reproduced experimental %CD31+ measurements, enabling estimation of volumetric capillary density. Generated capillary networks demonstrated good agreement with experimentally characterized 3D vascular architecture, while simulated histological sections accurately reproduced experimentally quantified capillary counts and vascularization measurements. Application of the framework to the porcine lumpectomy dataset captured temporal changes in vascular remodeling throughout healing, revealing progressive increases in volumetric capillary density and vascular maturation. Collectively, this framework provides an experimentally informed methodology for relating histological vascular measurements to volumetric capillary density estimates, supporting future computational studies of angiogenesis and tissue repair.

Background: For patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearance of metastases as a summary of their molecular makeup) to predict whether a patient is resistant to standard chemotherapy. Methods: The retrospective observational feasibility study included 35 adult patients who underwent staging laparoscopy for non-colon gastrointestinal adenocarcinoma with biopsy-confirmed peritoneal surface metastases and who underwent chemotherapy as their only treatment modality. Chemotherapy resistance was determined based on each patient's observed cancer-specific survival after controlling for confounders. Results: Of 35 patients, 17 were assigned to the chemotherapy sensitive group and 18 to the chemotherapy resistant group. The study cohort provided 1010 laparoscopy image patches of 101 biopsy-confirmed metastases. A densely connected convolutional neural network with cross-validation provided the best results for correctly predicting chemotherapy resistance at the patient level (accuracy 0.80 (95%CI 0.63-0.92), sensitivity 0.72, specificity 0.88, AUC-ROC 0.78). Saliency maps demonstrated the system's trustworthiness. Conclusion: In this study, a prototype surgical computer vision system designed to determine chemotherapy resistance from operative images of peritoneal surface metastases demonstrated its technical feasibility. Further development and validation in a multi-institutional clinical study are pending.

Spatial transcriptomics characterizes tissue-scale gene expression patterns, yet its observations are sparse discrete samples of an underlying continuous molecular field, leading to spatial aliasing and sub-resolution information loss. Existing methods usually formulate this task as spot-level point regression, making it difficult to capture both expression continuity and the regional nature of observation. Here, we propose HiFi-ST, a conditional neural field framework for continuous spatial transcriptomics modeling. HiFi-ST formulates spatial gene expression prediction as continuous expression field learning, models each spot as a regional observation over a finite support domain, approximates local integration through Monte Carlo sampling, and integrates multiscale tissue feature extraction with FiLM-based conditional modulation to improve modeling of complex spatial heterogeneity and consistency with the underlying measurement process. Systematic evaluation on three independent datasets (HER2+, cSCC, and Alex_NatGen) showed that HiFi-ST outperformed mclSTExp, BLEEP, THItoGene, His2ST, and HisToGene on key metrics. On HER2+, HiFi-ST achieved an average PCC improvement of 65.1% and an average MSE reduction of 40.9%; on cSCC, PCC improved by 10.2% and MSE decreased by 51.2%; on Alex_NatGen, PCC improved by 80.0% and MSE decreased by 16.3%. In addition, the learned multiscale tissue representations supported downstream spatial immunoanalysis, including assisted identification of candidate TLS regions. Overall, HiFi-ST provides a unified framework bridging discrete measurements and continuous expression field reconstruction for tumor microenvironment analysis and spatial immune structure characterization.

BackgroundThe physical properties of leukocytes, such as cell size and stiffness, are critical for their circulation in microcapillary networks where rapid shape changes are required to squeeze through vascular constrictions. Alterations in the cells physical phenotype can promote venous thromboembolism (VTE), a common cause of death in cancer patients receiving chemotherapy. While biochemical VTE predictors are well studied, physical properties of blood cells receive less attention. MethodsUsing real-time deformability cytometry (RT-DC), we monitored for the first time the physical phenotype of leukocytes in a longitudinal study of a breast cancer patient treated with epirubicin/cyclophosphamide (EC) and paclitaxel (Pax). ResultsThe leukocyte counts extracted from RT-DC were in good agreement with standard clinical leukograms and EC had no immediate effect on leukocyte properties. However, Pax caused a significant softening of granulo/monocytes and a stiffening of lymphocytes immediately after administration. Leukocyte size was constant throughout the therapy, but we observed an overall increase in leukocyte stiffness, which was restored to normal values 45 weeks post treatment. ConclusionTaken together, our data reveal chemotherapy-induced specific alterations of leukocyte stiffness potentially critical for microcirculation. Thus, RT-DC measurements can add important, yet currently not available information to VTE prediction in cancer patients.

Objective: To develop and evaluate a deep learning model for five-year breast cancer risk prediction from screening breast ultrasound (BUS) examinations. Methods: This retrospective study included 295,298 breast ultrasound examinations from 122,072 women imaged between 2012 and 2020. Patients were split into training, validation, and test sets; the test set included screening examinations only. BUS-Risk-Net aggregated image features using attention-based multiple instance learning and combined them with age and ultrasound-estimated breast density to predict 2- to 5-year risk. Performance was compared with the full Tyrer-Cuzick model in a matched case-control cohort and with a reduced Tyrer-Cuzick model in the held-out test set. Risk stratification was evaluated within BI-RADS density categories. Results: In the matched case-control cohort (n = 240 women), BUS-Risk-Net achieved a 5-year AUC of 0.632 (95% CI, 0.562-0.702), versus 0.514 for the full Tyrer-Cuzick model (95% CI, 0.440-0.588; p = 0.04). Among 19,548 examinations from 9,015 women eligible for 5-year evaluation in the test set, BUS-Risk-Net achieved an AUC of 0.679 (95% CI, 0.653-0.706), versus 0.594 for the reduced Tyrer-Cuzick model (95% CI, 0.564-0.623; P < .001). Observed 5-year cancer incidence increased across AI-defined risk tiers within each BI-RADS density category, ranging from 0.0% to 5.8% after AI stratification, compared with 2.1% to 3.6% across density categories alone. Discussion: Deep learning models applied to screening breast ultrasound could enable long-term breast cancer risk prediction and stratify risk beyond breast density alone. External and prospective validation is needed before clinical use.

Cells adjust their internal circuits in response to changes in their environment. Hence, exposing cells to changing conditions provides a way to probe the intrinsic dynamics of cellular internal circuits. Metabolic networks are examples of such circuits since metabolic fluxes dynamically adjust when environmental conditions are transiently altered. Most existing theoretical frameworks focus on cellular metabolic steady states and do not consider the dynamics of changes in metabolic fluxes. In this work, we applied transfer function analysis from control theory to analyze the changes of NADH oxidative fluxes in the mitochondria and cytoplasm in mouse oocytes in response to dynamical perturbations of oxygen depletion and recovery. We observed an overshoot of NADH oxidative flux in the cytoplasm upon oxygen recovery which is absent in the mitochondrial NADH oxidative flux. Metabolic perturbation experiments and transfer function analysis indicate that this cytoplasmic NADH overshoot results from the coupling of the mitochondrial and cytoplasmic NADH cycles. The degree of overshoot is determined by competing timescales associated with the exchange rates of lactate and pyruvate with the media and their interconversion rates catalyzed by lactate dehydrogenase. Applying control theory to the data enables the inference of the exchange and conversion rates of pyruvate and lactate, allowing predictions of the contribution of lactate to mitochondrial respiration. Our work indicates that the oocytes maintain a homeostatic respiration rate across nutrient conditions by modulating the contribution of lactate to mitochondrial respiration.

Objective To develop and validate a risk model for predicting postoperative bleeding in patients with thyroid cancer. Methods A total of 2800 consecutive patients diagnosed with thyroid cancer in the Department of Thyroid and Breast Surgery of the Affiliated Hospital of Xuzhou Medical University between January 2020 and December 2023 were retrospectively analyzed. Patients were categorized into two groups based on postoperative bleeding occurrence: bleeding and non-bleeding groups. Univariate and multivariate logistic regression analyses were utilized to screen independent risk factors. Meanwhile, risk prediction models were developed and nomogram . Subgroup analysis was performed to identify independent risk factors. The predictive effects of the models were assessed using the Hosmer-Lemeshow test and receiver operating characteristic (ROC) curves. Results Of the 2800 recruited patients, 50 had postoperative bleeding, with an incidence rate of 1.7%. Multivariate logistic regression analysis showed that age, hypertension, total thyroidectomy, tumor size [&ge;]4 cm, and operation time [&ge;]90 min were the risk factors for postoperative bleeding in thyroid cancer patients (P<0.05). A risk prediction model was established based on the above factors, and the area under the ROC curve was 0.881, with a sensitivity of 94.0%, a specificity of 67.3%, and an accuracy of 74.0%. Decision curve analysis revealed that the model had good predictive ability. Conclusions The constructed risk prediction model has good predictive power and can provide a reference for healthcare professionals to predict the risk of bleeding in patients after thyroid cancer surgery.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Background: High ambient temperature in early pregnancy has been linked to an increased risk of childhood acute lymphoblastic leukemia (ALL). To better understand biological mechanisms, the current study evaluated potential interaction between temperature and genetic characteristics. Methods: We used data from California birth records (1982-2008) and California Cancer Registry (1988-2011) to identify ALL cases (n=3,353) diagnosed <=14 years of age and non-cancer controls (n=3,530) matched 1:1 on sex, race, ethnicity, and birth year and month. Weekly ambient temperatures throughout pregnancy were assessed on a 1-km grid around the birth address, while genetic data were available from a genome-wide association study using neonatal blood spots. We evaluated the association between ambient temperature and ALL risk by quartiles of established genetic risk score for ALL. Next, we formally tested gene-temperature interactions in the association with ALL, correcting for multiple testing, for genes previously identified with epigenetic changes due to both temperature and ALL. All analyses were adjusted for potential confounders. Results: The elevated risk of ALL per 5 degrees C increase of weekly mean ambient temperature, confined to early pregnancy, was more pronounced among children with the lowest genetic susceptibility to ALL, especially among Latino children (first quartile: odds ratio [OR] = 1.50, 95% confidence interval [CI]: 1.14-1.97); fourth quartile: OR=1.03, 95% CI: 0.83-1.28). There were significant interactions (p<0.002) between ambient temperature and polymorphisms in BNC1 among non-Latino White children, and suggestive interactions (p<0.05) with TBPL2 and NRXN1 in the full population. Conclusions: Our findings suggest that there may be interactions between ambient temperature in early pregnancy and offspring genotype in the risk of childhood ALL. Impact: If replicated, these findings could help elucidate the biological mechanisms linking high ambient temperature in early pregnancy and the risk of childhood ALL.