The growing recognition of the human microbiome as a key regulator of immune homeostasis has accelerated the application of computational intelligence for microbiome-driven disease understanding and therapeutic design. However, existing microbiome studies largely rely on classical machine learning or shallow deep learning models that fail to capture higher-order microbial interactions, multimodal functional dependencies, and immune feasibility constraints simultaneously. Moreover, most approaches lack biological constraint enforcement, leading to predictions that may be statistically accurate but immunologically implausible. To address these limitations, this study introduces SIMT, the Synthetic Immune Modulation Transformer, a novel immune-aware deep learning framework for microbial interaction modelling and synthetic microbiota design. SIMT integrates a graph transformer for microbe-microbe interaction learning, a multimodal transformer for immune-associated functional inference, and a newly proposed Immune-Aware Constraint Layer (IACL) that enforces immune feasibility and homeostasis during optimization. The framework operates by learning weighted microbial interaction networks, integrating taxonomic abundance with inferred functional pathways, and constraining latent representations to physiologically meaningful immune ranges. The entire pipeline was implemented using Python-based deep learning libraries for scalable and reproducible analysis. Experimental evaluation demonstrated that the proposed approach achieved an F1-score of 96.41% and an AUC of 95.12%, outperforming existing microbiome-based models, including Random Forest, explainable RF frameworks, convolutional neural networks, fine-tuned language models, and regularized logistic regression reported in prior studies. Beyond predictive performance, SIMT enables immune-stable synthetic consortium optimization, offering interpretable and biologically grounded insights. Overall, the results confirm that immune-aware transformer modelling significantly advances microbiome analytics, supporting reliable in silico design of immune-compatible microbial communities for translational biomedical applications.
PURPOSE:This study aimed to identify the signature genes that mediate the effects of type 2 diabetes (T2D) on coronary artery bypass grafting (CABG), and to elucidate their molecular regulatory mechanisms and potential clinical therapeutic value for improving the clinical outcomes of T2D patients undergoing CABG. METHODS:An integrated approach of bioinformatics analysis, machine learning, clinical validation and in vitro experiments was employed. Multi-cohort T2D and CABG data were analyzed via differential expression profiling, functional enrichment, PPI network construction, and LASSO regression/Boruta algorithms to screen core signature genes mediating the T2D-CABG interaction. Nomogram models were built and validated to assess the diagnostic value of these genes. LncRNA-miRNA-mRNA regulatory networks were constructed to explore their molecular mechanisms. Potential targeted drugs were predicted via DSigDB mining and molecular docking. Clinical validation was performed in CABG patients, and human aortic smooth muscle cells were used for in vitro experiments to verify the functional role of the key signature gene SLCO5A1. RESULTS:MINPP1, HES4 and SLCO5A1 were identified as core signature genes mediating the adverse effects of T2D on CABG, with consistent abnormal expression in T2D and CABG datasets and all AUC values > 0.625. Nomogram models based on these genes showed excellent calibration and clinical net benefit for evaluating T2D-associated pathological risks in CABG patients. JUN and GATA2 were found to be key transcription factors regulating these genes, and a novel complex lncRNA-miRNA-mRNA regulatory network was constructed. Clinical detection revealed significantly up-regulated SLCO5A1 in T2D patients undergoing CABG, with its expression positively correlated with systemic inflammatory burden. In vitro experiments confirmed that SLCO5A1 knockdown markedly inhibited T2D-related metabolic stress (high glucose/Ox-LDL)-induced vascular inflammation, endothelial dysfunction, oxidative stress and cytoskeletal remodeling via the NF-κB pathway. Molecular docking demonstrated high binding affinity of CHEMBL1182312 to SLCO5A1 (docking score = -6.9 kcal/mol) and stable binding of valproic acid to MINPP1 (docking score = -5.0 kcal/mol). CONCLUSION:MINPP1, HES4 and SLCO5A1 were signature genes mediating the effects of T2D on CABG.
Nanofiber production technology has always been interesting as there are a variety of flexible ways to produce one-dimensional organic, inorganic, and constructed nanomaterials with controllable dimensions. Electrospinning is a simple and affordable method for the production of nanofibers, providing large specific surfaces and highly porous structures with diameters ranging from nanometers to micrometers. This process is based on electrostatic fields and precisely controls the dimensions and morphology of the fibers through parameter optimization and the use of special spinning and collectors. This paper covers Electrospinning processes and parameters in detail and illuminates the factors that influence Electrospinning. It deals with the morphological and structural aspects of Electrospinning fibers used in different applications. Additionally, this paper examines the various polymers and non-polymeric materials used in Electrospinning, and the properties and applications of nanofibers produced by the electrospinning method are investigated. Nanofibers have a variety of applications, including defense industries, tissue engineering, filtration, wearable biosensors, cosmetics, etc. Additionally, we will examine the inclusion of fillers in the Electrospinning to improve properties and functionality using the electrical field. This check ends with signs of knowledge in upper grade Electrospinning production.
BACKGROUND:Prostate cancer (PCa) bone metastases cause significant morbidity and mortality in advanced disease. The tumor microenvironment (TME) of bone metastases drives disease progression and therapeutic resistance, yet comprehensive characterization of its cellular heterogeneity remains limited. This study aims to characterize cellular populations and molecular signatures of PCa bone metastases using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. METHODS:scRNA-seq data from PCa bone metastasis samples were obtained from GEO. Quality control, normalization, dimensionality reduction, and cell type identification were performed using Seurat. Differential expression, pseudotime trajectory, pathway enrichment, gene regulatory network, and cell-cell communication analyses were conducted to investigate molecular mechanisms of bone metastasis progression. RESULTS:Single-cell analysis identified distinct cellular populations within the bone metastatic TME, including malignant epithelial cells, fibroblasts, endothelial cells, osteoblasts, osteoclasts, and immune cells. Clustering revealed heterogeneous transcriptional signatures, while pseudotime analysis uncovered developmental transitions between cell states. Key transcription factors, enriched pathways related to bone remodeling, angiogenesis, and immune regulation, and critical signaling interactions between cancer and stromal cells were identified. CONCLUSION:This study provides comprehensive insights into the cellular composition and molecular architecture of the PCa bone metastatic TME, revealing distinct cell populations, type-specific gene signatures, and cell-cell communication networks driving bone metastasis progression.
BACKGROUND:Age-related macular degeneration (AMD) is accompanied by inflammatory changes in the retinal pigment epithelium/choroid complex, but the cellular sources of pyroptosis-related transcriptional programs in human AMD tissue remain unclear. This study profiled these programs at single-cell resolution and explored candidate regulatory molecules. METHODS:We analyzed the human retinal pigment epithelium (RPE)/choroid single-cell RNA-sequencing dataset GSE135922 to define cell clusters, pyroptosis-related genes, and regulons. AUCell was applied to estimate pyroptosis-related signature activity in individual cell types. The macrophage cluster with the highest score was examined by pathway enrichment, subclustering, and Monocle 2 pseudo-time analysis, and SCENIC-based regulon analysis was used to infer candidate transcriptional regulators. Pyroptosis-related genes and transcription factors were also evaluated in T-cell, endothelial-cell, and fibroblast subclusters. Bulk RNA sequencing and immunofluorescence in a laser-induced choroidal neovascularization (CNV) mouse model were used for supportive evidence. RESULTS:Across human RPE/choroid cell clusters, 60 cluster-specific pyroptosis-related marker genes were detected. At the cell-type ranking level, macrophages, T cells, endothelial cells, and fibroblasts showed relatively higher pyroptosis-related signature activity across clusters, with the highest signal in Macrophages-2 and lower activity in RPE cells. Marker genes of Macrophages-2 were enriched in immune and inflammatory pathways, including complement and coagulation cascades, NOD-like receptor signaling, and NF-κB signaling. Pseudo-time analysis resolved Macrophages-2 into divergent trajectories, and NLRP3 was enriched in one post-branch state, consistent with macrophage state heterogeneity rather than uniform activation. IRF1, STAT3, and NEAT1 recurred in cell-type-specific analyses, and Irf1/Stat3 protein signals were higher in CNV lesions. CONCLUSION:These findings indicate a macrophage-centered, cell-state-specific pattern of pyroptosis-related inflammatory remodeling in AMD. Branch-associated NLRP3 inflammasome signatures, together with IRF1, STAT3, and NEAT1, define candidate molecular features that warrant further mechanistic evaluation.
Accurate consumer-oriented diagnostic devices for cancer screening at an early stage are limited despite the rapid evolution of consumer electronics technology, owing to a high error rate and lack of predictive accuracy. In our study, we propose an equivalent \circuit modeling methodology to improve the predictive accuracy of DNA/RNA-based impedimetric biosensors in the context of transcriptomics. Our methodology has two key objectives: one is to minimize errors to achieve analytical accuracy, and the other is to achieve a label-free biosensor to make it user-friendly. In our study, a biosensor was developed by immobilizing probe DNA on gold nanoparticle-modified screen-printed electrodes. Circuit parameters were estimated by simulation and curve-fitting techniques based on a conventional equivalent circuit known as the Randles circuit, resulting in an error rate of ∼6.5%. To accurately simulate multilayer structures consisting of gold nanoparticles, probe DNA, and target miRNA, our model was extended by adding resistive elements and a finite diffusion element, resulting in a significantly low error rate of ∼1.8% to ∼2.2%. However, this setting was not as appropriate for the case of magnetite and magnetite nanocomposite-based systems, in which errors were found to be around 5-6%. In the case of magnetic nanoparticle-based biosensors, a modified Randles circuit containing double-layer capacitance (Cdl), Cole-Cole (CC), and inductive elements (L) was found to have higher fitting accuracy, with errors as low as 1-2%. Moreover, the proposed biosensor has shown promising results in terms of ultra-low detection limits, i.e., 0.5 ag/mL, which makes this biosensor suitable for the detection of transcriptomic biomarkers such as miRNA. Therefore, this study has shown the potential of equivalent circuit modeling in improving the predictive accuracy of nucleotide-based biosensors, thereby promoting their use in scalable, label-free, and user-centric applications in the field of transcriptomics-based diagnostics and RNA-based interventions, including decentralized cancer screening in rural and urban areas.
BACKGROUND:Metabolic reprogramming represents a hallmark feature of hepatocellular carcinoma (HCC). As a crucial branch of the polyol pathway, the biological functions and clinical implications of sorbitol metabolism in HCC progression remain to be fully elucidated. METHODS:This study integrated single-cell transcriptomic data from 67 HCC patients to establish a sorbitol metabolism scoring system. Pseudotime trajectory analysis was employed to investigate the differentiation patterns of cells with aberrant sorbitol metabolism, while spatial transcriptomics was utilized to characterize their spatial distribution. Using machine learning approaches on HCC cohort transcriptomic data, we developed a prognostic prediction model, complemented by CIBERSORT-based immune infiltration analysis and CellMiner-derived drug sensitivity predictions. In addition, in vitro gain- and loss-of-function experiments were conducted to validate the biological role of SQSTM1 in HCC cells. RESULTS:Our findings demonstrate that ALDH3A1+ malignant cells with high sorbitol metabolism scores exhibit dysregulated cell adhesion, enhanced immune evasion capacity, and activated hypoxia signaling pathways. These cells were predominantly localized at the tumor invasive front. The ALDH3A1+-based predictive model identified a high-risk group with significantly poorer prognosis, characterized by increased TP53 mutation frequency and a distinct immunosuppressive microenvironment. Drug sensitivity analysis suggested Irofulven as a potential therapeutic agent targeting sorbitol metabolism-active tumor cells. Furthermore, in vitro experiments demonstrated that SQSTM1 promoted HCC cell proliferation and migration, supporting its functional involvement in HCC progression. CONCLUSION:This study uncovers the mechanistic role of sorbitol metabolism in driving HCC progression through shaping an immunosuppressive microenvironment. The sorbitol metabolism scoring system may serve as a novel prognostic biomarker and provides a theoretical foundation for precision treatment strategies, such as Irofulven-targeted therapy, in HCC management. Moreover, SQSTM1 may act as an important downstream functional mediator and represents a potential therapeutic target in hepatocellular carcinoma.
Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations in gut microbiota. Variability in clinical phenotypes and lack of biomarker specificity hinder the efficiency of current traditional diagnostic and treatment approaches. This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in CD. Current studies employ integration of multi-omics like genomics, proteomics, transcriptomics, metabolomics, and microbiome analysis in CD with AI and ML for significant advancement of biomarker discovery and clinical applications. Emerging evidence reveals that CD is a multi-factorial disorder involving host genetics, immune dysfunction, and microbiome shifts. Integration of advanced AI/ML models with multi-omics data can predict disease-specific biomarkers for easy diagnosis and facilitate precision medicine to enhance therapies. For a successful clinical implementation of an AI/ML model with multi-omics in CD, a standardized data framework and large-scale validation are needed. Additionally, future research should focus on developing interpretable AI models, real-time monitoring systems, and theranostic platforms to enhance precision healthcare delivery.
High-throughput automated parallel amide synthesis is essential in early-phase medicinal chemistry; yet, purification of milligram-scale reaction mixtures remains to be a major operational bottleneck. Conventional approaches, including miniaturized preparative HPLC, provide challenges in scaling for extensive libraries and are limited by sample dilution, analyte-dependent method development, off-plate transfers, and high solvent consumption. Herein, we report a microplate-integrated purification workflow that seamlessly interfaces with automated synthesis platforms using readily available laboratory components. The method employs two consecutively applied, custom slurry-packed hydrophilic PVDF filter plates containing mixed-bed ion-exchange resins and normal-phase silica, facilitating vacuum-assisted cleanup directly from reaction wells. Independent optimization and benchmarking against a recently commercialized single-step alternative demonstrated superior purification efficiency at identical sorbent loadings. Validation across a 24-member amide matrix delivered product purities above 90% in the majority of cases without reaction-specific adjustment of purification conditions. By decoupling ionic scavenging and chromatographic polishing into orthogonal stages, the platform establishes a robust, scalable, and automation-ready purification strategy. This instrument-light approach reduces reliance on chromatographic systems and provides a broadly applicable option for accelerating high-throughput amide synthesis in early drug discovery workflows.
Portable analytical platforms are rapidly transforming point-of-care chemical analysis by enabling fast, on-site measurements with minimal instrumentation, reduced cost, and simplified operation. Among emerging approaches, smartphone-assisted sensing systems have attracted considerable attention due to their accessibility, integrated imaging capabilities, and potential for quantitative analysis without sophisticated laboratory equipment. In this study, a smartphone-based colorimetric sensing platform is developed for sensitive and selective glucose determination using bimetallic cobalt-nickel co-modified graphitic carbon nitride (Co-Ni/g-C3N4) nanoparticles as efficient peroxidase-mimicking nanozymes. The Co-Ni/g-C3N4 nanocomposite was synthesized through a hydrothermal-assisted reduction method and characterized by SEM, HRTEM, EDX, FTIR, and XRD, confirming successful incorporation of Co and Ni within the g-C3N4 structure. The sensing strategy relies on an enzyme-nanozyme cascade reaction in which glucose oxidase converts glucose to gluconic acid, producing hydrogen peroxide that is subsequently utilized by the Co-Ni/g-C3N4 nanozyme to catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine, generating a blue color signal proportional to glucose concentration. The color intensity was recorded using a smartphone camera and quantitatively analyzed through RGB extraction with ImageJ software. Under optimized conditions, the platform exhibited linear detection ranges of 10–500 µM (UV–Vis) and 10–300 µM (smartphone analysis) along with low detection limits, high sensitivity, and excellent selectivity toward glucose against common interferents. The smartphone-based measurements showed strong agreement with conventional UV–Vis results, demonstrating the reliability of the portable approach. This simple and cost-effective sensing system highlights the potential of integrating bimetallic g-C3N4 nanozymes with smartphone colorimetry for decentralized glucose monitoring and point-of-care biochemical analysis.
We developed EasyPip, a proprietary software application, to easily configure and efficiently process common plate-based pipetting tasks on various automated liquid handlers. EasyPip can handle tasks such as reagent addition, replication, hit picking and intra-plate transfers in 96-well microtiter plates, all configurable on demand and at the single well level. It allows the ad-hoc design of complex workflows, like serial dilutions or sample plate preparation, without requiring programming knowledge by simply combining multiple tasks. EasyPip uses a consistent front-end across different high-end automated liquid handlers, including those from Tecan, Hamilton and Beckman-Coulter, and operates independently of other workflows, which enables its use by lab associates without additional training. The software application generates worklists based on user input, which are then processed automatically by the connected liquid handler through a dedicated method script. EasyPip enhances the efficient use of lab automation equipment by enabling immediate application of routine tasks, thus reducing idle times and addressing major barriers to the flexible use of lab automation in research and development settings.
BACKGROUND:Osteoarthritis is the predominant joint ailment. Multiple studies demonstrate that the dysregulation of catalytic regulators of ubiquitination and deubiquitination disrupts cartilage imbalance, consequently facilitating the advancement of osteoarthritis. METHODS:Ubiquitination-related biomarkers for osteoarthritis were found by differential expression, weighted gene co-expression network analysis, Mendelian randomization, Receiver Operating Characteristic curves, and expression analyses. Subsequently, an examination of immune infiltration was conducted to evaluate the contrasting immunological circumstances between osteoarthritis and controls. Additionally, single-cell analysis was employed to screen key cell types, and analyze the expression of biomarkers during their differentiation. The expression of biomarkers was subsequently validated using real time quantitative polymerase chain reaction. RESULTS:CBLB and NQO2 were determined as biomarkers, having risk effects on osteoarthritis (odd ratio > 1). Analysis of immune infiltration indicated a significant disparity in the number of 15 immune cell types between osteoarthritis and control groups, such as type 2 T helper cells and macrophages, and two biomarkers showed opposite associations with these immune cells. Single-cell analysis annotated seven cell types, with prehypertrophic chondrocytes as the key cells. Notably, two biomarkers had expression early and late stages during prehypertrophic chondrocytes differentiation. Finally, experiments analysis indicated that CBLB decreased and NQO2 increased in osteoarthritis samples. CONCLUSION:CBLB and NQO2 were biomarkers associated with ubiquitination that exert causal effects on osteoarthritis. These findings provide potential therapeutic targets for clinical intervention and help to personalize treatment for osteoarthritis patients.
Cell-based assays that report endogenous signaling events are attractive for high-throughput screening (HTS), but many formats rely on engineered reporters or fluorescence detection that can be limited by background and compound interference. We adapted the bioluminescent Lumit p-ERK1 (T202) immunoassay to 384- and 1536-well plate formats suitable for HTS. Using MCF-7 cells stimulated with EGF or PMA, we optimized lysis conditions and antibody concentrations to maximize signal-to-background while preserving detection of basal p-ERK. In 384-well plates, a manually run screen of a 190-compound FDA-approved Tocris library yielded an average Z’ factor of 0.69 and a ∼32-fold signal window and enriched for known MAPK/ERK pathway inhibitors. In 1536-well format, a single-concentration screen of ∼7000 chemogenetically annotated small molecules produced a Z’ factor of 0.60, an ∼18-fold signal window, and hit rates of 3.4% for inhibitors and 1.3% for activators. Concentration–response testing confirmed 95% and 57% of these, respectively. Orthogonal p-ERK HTRF assays showed high concordance with the Lumit assay, and target annotation analysis revealed enrichment for MEK, ERK, BRAF, PKC. To support plate-based HTS campaigns such as the 1536-well screen of a chemogenetic compound library, we used an alternative substrate dilution buffer that markedly improves on-deck substrate stability for at least 18 h without altering EC₅₀/IC₅₀ values or assay window, enabling reliable integration of the Lumit p-ERK assay into automated HTS workflows. Together, these data support the use of the Lumit p-ERK assay as a scalable, automation-compatible bioluminescent format for cell-based assays.
Continuous bioprocessing has shown promise for efficiently scaling production of monoclonal antibodies for biotherapeutic production. Integrating process analytical technologies at the lab-scale stage improves the reliability of results during development and the translation of the system during scale-up. Smart Manufacturing provides a framework for implementing process analytical technologies at all process scales by leveraging modern hardware, software, and techniques. Demonstration of scaling and retrofitting a legacy system into a smart manufacturing system for the continuous downstream processing of monoclonal antibodies at the lab-scale was achieved through modular and flexible controller hardware, Industrial Internet-of-Things architecture, interoperable OPC-UA servers, and cloud computing on the Smart Manufacturing Interoperability Platform. The system consisted of lab-scale equipment connected to a legacy control device with limited operation and I/O. A new system of hardware was implemented that interfaced with the legacy equipment while also providing scalability, reconfigurability, and controllability. Peristaltic pumps, weigh-scales, single-use pressure sensors, and optical sensors were connected to edge devices which bridged the hardware and software used for process monitoring and control. Lab-scale implementation and operation challenges such as connecting multiple vendor systems and inconsistent pump flowrates were addressed. Water-based pump flowrate control tests were performed on the legacy equipment with readiness for deployment onto the smart manufacturing system. The techniques used include a Kalman Filter, steady-state data reconciliation, and closed-loop control based on scale measurements.
Accurate microliter-level dispensing is crucial for generating reliable and reproducible results in biomedical and diagnostic assays. While automated liquid handling systems (ALHS) improve throughput and reduce operator variability, volumetric accuracy (trueness and precision) can drift with liquid properties, tip geometry, and instrument settings, necessitating routine verification. This study utilized ALHS from Agilent Technologies, Bravo automated liquid handling platform (Bravo) with a 96-barrel head and evaluated its dispensing performance against a calibrated manual pipetting reference. We tested four settings of the Bravo, combining two tip capacities (30 µL and 70 µL) with two aspiration modes (with or without post-aspirate air gap volume, PAV) to determine optimal accuracy. Volumetric accuracy was checked every 10 days over 30 days, comparing performance before and after preventive maintenance of the 96-barrel head. Bravo pipetting demonstrated excellent agreement with manual pipetting (r² = 0.998; p < 0.0001), validating the reliability of the spectrophotometric approach. Although the volumetric accuracy was generally stable with Bravo, a slight tendency toward under-dispensing was noticed compared to manual pipetting. Among all the tested conditions, the 30 µL tip with PAV achieved the highest trueness (-2.22% bias) and precision (CV ≤ 1%) for 4 µL target volumes. Furthermore, longitudinal assessments revealed no significant change in mean dispensed volumes before or after preventive maintenance. In this study, the overall performance of the Bravo remained within acceptable operational limits. We believe the Orange-G protocol described here provides a fit-for-purpose alternative to commercial verification systems, enabling regular, plate-level checks aligned with concepts of volumetric performance in automated systems.
The accumulation of pathological bronchial secretions compromises ventilation and oxygenation in critically ill patients and may lead to atelectasis or secondary infection in severe cases, making timely identification and removal of pathological secretions essential during intensive care and surgical anesthesia. Conventional manual bronchoscopic assessment depends heavily on operator experience, lacks real-time reliability, and fails to meet clinical requirements for efficient and precise intervention. To address this limitation, this study proposes an Edge-Aware Dual-Scale Transformer (EADST) for intelligent and automated bronchial secretion recognition based on Canny edge features. Bronchoscopic image data from 50 critically ill pneumonia patients, including both normal physiological and pathological secretions, were preprocessed by grayscale enhancement and Canny edge detection to generate structural representations of secretion regions, which were subsequently processed through a patch embedding module for edge-aware feature mapping, a dual-scale attention module for capturing both global semantic and local structural dependencies, and an edge-aware feed-forward network to adaptively enhance critical channels, followed by a back-end classification head for real-time pathological secretion discrimination. All experiments were conducted under a unified Canny feature representation, and the proposed framework was evaluated against several classic state-of-the-art (SOTA) models, including VGG-16, ResNet-50, EfficientNet, MobileNet, and Vision Transformer (ViT). Experimental results demonstrate that EADST achieves superior accuracy (89.2%) and robustness in pathological secretion recognition on edge-derived features, indicating that attention-driven and edge-adaptive feature modeling effectively enhances bronchoscopic visual perception and providing a promising foundation for intelligent, real-time bronchoscopic pathological secretion aspiration decision support systems.
The high-throughput screening (HTS) technologies have enabled unprecedented scalability and efficiency in drug discovery, driving advancements in acoustic liquid handling systems like the Echo series. These systems allow contactless nanoliter transfers and have become pivotal in modern laboratories for reducing reagent consumption, minimizing cross-contamination, and simplifying dose-response experiments. Despite these advancements, a persistent challenge in HTS lies in the lack of accessible, open-source tools, that enable easy transfer layout generation. To address this, we present MOLD, a novel KNIME-based application that bridges the gap between experimental design and execution by customizable experimental plate layout generation. MOLD integrates seamlessly with Echo systems, calculating custom dilution series and optimizing transfer volumes while ensuring compatibility with high-throughput pipelines. With its open-source foundation, intuitive interface, and robust functionality, MOLD can be used in academic or industrial laboratories as a powerful tool to enhance reproducibility, scalability, and time efficiency in high-throughput experiments.
