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Industrial emissions of large amounts of CO₂ have seriously affected human health, making it imperative to reduce atmospheric CO₂ concentrations. However, carbon capture technologies such as chemical absorption and membrane separation are still limited by high regenerative energy costs, corrosion, and low efficiency in diluting flue gas. Within this technological landscape, physical adsorption separation technology, due to its advantages such as a wide operating temperature range, low equipment corrosivity, and low regeneration energy consumption, has gradually become a research hotspot in carbon capture technology. The core of physical adsorption lies in finding high-quality adsorbents. Metal–organic frameworks (MOFs), with their ultra-high specific surface area, tunable pore structure, and abundant functionalization sites, are considered highly promising next-generation CO₂ adsorbent materials. This review summarizes strategies for modifying MOFs to improve CO₂ adsorption performance, focusing on aperture adjustment, doped metal ions, functional group doping, and computational screening. Performance enhancements are mechanism-dependent rather than simply additive. Moderate aperture adjustment and defect engineering can improve gas selectivity and CO₂ capture capacity, while excessively narrow pores sacrifice available pore volume and gas diffusion. Doped metal ions, particularly in MOF-74 and related materials, can enhance CO₂ capture capacity while controlling framework integrity and dopant composition. Functional group Doping remains an effective method for capturing low-partial-pressure CO₂. Computational screening is shifting from ranking based on single adsorption capacity to a comprehensive consideration that includes humidity tolerance, stability, and regenerability. Overall, under industrial conditions, modified MOFs should be evaluated by balancing affinity, selectivity, capacity, stability, and energy efficiency. This review provides guidance for the rational design of MOF-based carbon capture adsorbents. Full article

(This article belongs to the Section Environmental Nanoscience and Nanotechnology)

30 pages, 8598 KB

Open AccessArticle

Synergistic Virus Neutralizing Activities of European Black Elderberry Fruit Extract and Iota-Carrageenan Against SARS-CoV-2, Influenza A Virus and Respiratory Syncytial Virus

by Christian Setz, Melanie Setz, Pia Rauch, Oskar Schleicher, Stephan Plattner, Andreas Grassauer and Ulrich Schubert

Nutrients 2026, 18(8), 1205; https://doi.org/10.3390/nu18081205 - 10 Apr 2026

Abstract

Background/Objectives: Seasonal waves of respiratory viruses—including SARS-CoV-2, influenza A virus (IAV), and respiratory syncytial virus (RSV)—continue to pose a global health burden and highlight the need for antiviral agents that are effective, safe, broadly active, affordable, and widely accessible. Current interventions are limited by the need for their early administration, the risk of resistance, their costs, and the restricted availability in large parts of the world. For certain natural products, such as European black elderberry (Sambucus nigra L.) fruit extract (ElderCraft^®; EC) and the seaweed-derived sulfated polymer iota-carrageenan (IC), antiviral activities against respiratory viruses, particularly IAV and SARS-CoV-2, have previously been shown. Here, we assessed the antiviral activity of IC and an anthocyanin-standardized EC extract against SARS-CoV-2, IAV, and RSV, either as monotherapy or in multiple-dose combinations. Methods: MDCKII cells were infected with IAV_PR8, human Calu-3 lung epithelial cells with the SARS-CoV-2 Omicron variant, and HEp-2 cells with RSV (A2 strain). Inhibitors were administered either by pre-incubation of cell-free virions prior to infection or, in separate time-of-addition experiments, during or post-infection. Viral replication was quantified by qRT-PCR or intracellular immunostaining. Cytotoxicity was evaluated using a neutral red uptake assay. Results: Most intriguingly, both EC and IC are able to neutralize virions derived from SARS-CoV-2, IAV, or RSV extracellularly in a dose-dependent manner. Notably, EC and IC alone exhibited strong anti-RSV activity, which was not reported previously. Most importantly, combined treatment with IC and EC caused a pronounced synergistic antiviral effect against the tested viruses, as confirmed by the Bliss independence model, without any detectable impact on cell viability. Finally, solutions prepared from matrix-standardized mono- or combi-lozenges, containing IC and/or EC in high or low doses, reproduced the antiviral and synergistic combination effects observed with the pure compounds. Conclusions: In summary, these findings support further development of EC and IC as a topically accessible, virion-neutralizing combination (e.g., lozenges) to provide additional protection against major respiratory viruses and potentially strengthen pandemic preparedness. Full article

(This article belongs to the Section Phytochemicals and Human Health)

20 pages, 4549 KB

Open AccessArticle

Online Track Anomaly Detection: Comparison of Different Machine Learning Techniques Through Injection of Synthetic Defects on Experimental Datasets

by Giovanni Bellacci, Luca Di Carlo, Marco Fiaschi, Luca Bocciolini, Carmine Zappacosta and Luca Pugi

Machines 2026, 14(4), 424; https://doi.org/10.3390/machines14040424 - 10 Apr 2026

Abstract

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)

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18 pages, 606 KB

Open AccessArticle

Information-Preserving Spiking for Accurate Time-Series Forecasting in Spiking Neural Networks

by Jiwoo Lee and Eun-Kyu Lee

Electronics 2026, 15(8), 1597; https://doi.org/10.3390/electronics15081597 - 10 Apr 2026

Abstract

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)

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21 pages, 425 KB

Open AccessArticle

Microgrid Planning by Stochastic Multi-Objective Multi-Year Optimization with Capacity Expansion and Non-Linear Asset Degradation

by Davide Fioriti, Marina Petrelli, Alberto Berizzi and Davide Poli

Sustainability 2026, 18(8), 3785; https://doi.org/10.3390/su18083785 - 10 Apr 2026

Abstract

Decentralized microgrids have been proven to enable socioeconomic growth in developing countries. However, they are long-lasting investments whose profitability is highly uncertain due to unstable local socioeconomic contexts, which may delay the breakeven point, if ever reachable. Over the long term, capacity expansion and non-linear degradation of components also arise. Moreover, policymakers and developers are increasingly focusing on environmental and social considerations, raising the complexity of project development. Accordingly, multi-year planning has been simplified by addressing single challenges independently. In this paper, we propose a comprehensive procedure to efficiently solve stochastic multi-year problems for off-grid microgrids in developing countries, including capacity expansion and the non-linear degradation of battery and renewable assets. The novel procedure combines the efficient A-AUGMECON2 methodology for multi-objective formulation, the iterative decomposition of the non-linearities of the battery, and the inclusion of a two-step capacity expansion. A case study developed for Soroti, Uganda shows that the proposed model is suitable for planning purposes, with savings even beyond 20%. The Pareto frontier highlights the trade-offs among the net present cost, total emissions, and land use, which can support policy and business decision-making under uncertainty. The methodology renders these complex modeling challenges solvable and is scalable to energy system applications. Full article

(This article belongs to the Special Issue Power System Transformation and Smart Electricity Sustainable Development)

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34 pages, 2126 KB

Open AccessReview

A Critical Review of Mycotoxin Contamination in Food and Feed in the Democratic Republic of the Congo and Neighboring Countries: Challenges and Future Directions

by Michel Kawayidiko Kasongo, Arthur Mpanzu Duki, Christophe Tsobo Masiala, Sarah De Saeger and José Diana Di Mavungu

Toxins 2026, 18(4), 182; https://doi.org/10.3390/toxins18040182 - 10 Apr 2026

Abstract

Mycotoxin contamination remains a persistent threat to food safety in the Democratic Republic of the Congo (DRC) and neighboring countries, driven by conducive tropical agroecological conditions, inadequate post-harvest practices, and limited regulatory governance. This critical narrative review (2009–2024) synthesizes the occurrence data for major staple foods (maize, peanuts, cassava, sorghum, millet, and beans) and dairy products compiled from Google Scholar, ScienceDirect, MDPI and institutional sources. It examines the co-occurrence patterns, exposure pathways, and analytical and regulatory gaps. Warm, humid lowland environments favor Aspergillus and aflatoxins, whereas cooler, humid highland zones promote Fusarium, fumonisins, and deoxynivalenol. Across commodities, contamination intensifies along food value chains through inadequate drying, non-hermetic storage, insect damage, and prolonged handling, with processed products generally exhibiting the highest levels of mycotoxins. Regulated mycotoxins, including aflatoxins, fumonisins, trichothecenes, ochratoxins, and zearalenone, frequently exceed European Union (EU), East African Community (EAC), and Codex Alimentarius Commission (CAC) limits in staple foods. Their co-occurrence is widespread, including emerging mycotoxins such as beauvericin and enniatins, particularly in maize- and peanut-based products, raising concerns about potential additive or synergistic effects. Aflatoxin M1 in milk highlights plant–feed–animal–human transfer within a One Health framework. Despite increasing evidence, the available data remain fragmented and heterogeneous; rapid tests dominate, while few studies employ multi-mycotoxin LC-MS/MS methods. Cross-border trade between countries, such as Uganda, Tanzania, Zambia and Angola, facilitates the circulation of contaminated commodities in the absence of harmonized standards and risk-based controls. Priorities include harmonized regional surveillance, biomarker-based co-exposure assessment, cost-effectiveness evaluation of mitigation strategies, and regulatory alignment at borders. Coordinated, multisectoral action is essential to reduce chronic dietary exposure and improve food safety across the region. Full article

(This article belongs to the Special Issue Advances in Contamination, Detection and Risk Assessment of Mycotoxins)

18 pages, 7647 KB

Open AccessArticle

A Machine Learning Model to Predict Post-Operative Intensive Care Unit Admission in Patients with Cancer Based on Clinical Characteristics and Hematologic Parameters Data

by Jiaxin Cao, Zengfei Xia, Qun Chen, Chaozhuo Lin, Ting Yang and Fan Luo

J. Clin. Med. 2026, 15(8), 2898; https://doi.org/10.3390/jcm15082898 - 10 Apr 2026

Abstract

Background and Objectives: The prioritization of intensive care unit (ICU) admission following surgery for cancer is controversial. There is an urgent need to develop an appropriate clinical predictive model to aid in making ICU admission decisions after surgery. Materials and Methods: Four model strategies were used to build post−operative ICU admission predictive models: SVM, Catboost, ANN, and KNN. Internal verification was used for model evaluation at a ratio of 7:3. The area under the curve (AUC) value, calibration plots, and decision curve analysis were employed to assess the performance and clinical usefulness of the model. Results: The ICU group of patients with cancer who underwent surgery showed better prognosis for disease−free survival (DFS, p = 0.0008) and overall survival (OS, p < 0.0001). Cox multivariate analyses validated that lower baseline RBC, LDH, and CRP; higher baseline ALB, LCR, and lower post−operative LDH; higher post−operative HCT and ApoA−I; and higher fluctuating MCH independently predicted better DFS and OS (all p < 0.05). The AUC of the Catboost model was superior to that of the other models in the training cohort and internal validation cohort. Calibration plot and decision curve analysis indicated that the Catboost model possessed the best performance, with higher clinical utility, compared with other models. Conclusions: ICU admission after surgery was associated with superior survival in patients with cancer. The cost−effective Catboost model has promising clinical application but requires further clinical evaluation. Unravelling the cellular and molecular foundation of ICU admission might enable the development of more practical life−support strategies. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Clinical Medicine)

33 pages, 3032 KB

Open AccessArticle

Carbons from Pistachio Nutshells Activated with Phosphoric Acid and Microwave Treatments: Towards Sustainable Sorbents for Treating Water

by Magdalena Sobiesiak, Monika Parcheta and Rosa Busquets

C 2026, 12(2), 32; https://doi.org/10.3390/c12020032 - 10 Apr 2026

Abstract

Activated carbons are usually prepared from natural precursors (e.g., fruit stones or nutshells) by carbonization and activation processes carried out at 400–1000 °C. They exhibit well-developed porosity, and chemical activation introduces hydrophilic functional groups on their surface, providing excellent sorption properties. However, the high temperatures required during thermal treatment increase production costs. In this work, cost-reducing methods for preparing carbon sorbents are proposed. Carbonization of H₃PO₄ activated waste pistachio nutshells was performed using classical pyrolysis (500 or 550 °C, 30 min, N₂ atmosphere) and microwave treatment (power 1000 W, 20 min). The properties of the synthesized carbons were characterized using thermogravimetry and spectroscopic techniques including infrared (ATR), Raman, photoelectron (XPS) spectroscopies, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). Porous structure parameters were determined using nitrogen adsorption experiments. The efficiency of Pb²⁺ removal from spiked ultrapure, tap and river water was evaluated by batch sorption experiments and inductively coupled plasma–mass spectrometry. The most porous carbons were those prepared at 500 and 550 °C, with specific surface areas of 910 and 256 m²/g, respectively. Surface phosphates increased the Pb²⁺ sorption efficiency to 99% from ultrapure water, at an initial concentration of 300 µg Pb²⁺/L. The material obtained with the microwave method was not fully carbonized and remained nonporous, but it also exhibited 99% Pb²⁺ uptake from ultrapure water due to the presence of oxygen-containing surface groups. The Pb²⁺ removal from spiked tap and river water reached up to 84% and 94%, respectively, at the spiking level of 300 µg Pb²⁺/L. Full article

(This article belongs to the Section Carbon Materials and Carbon Allotropes)

23 pages, 3790 KB

Open AccessArticle

CrystalCells: An Open-Source Modular Bioprinting Platform with Automated Tool Exchange, High-Performance Extruding, Thermal Control, and Microscopic Imaging

by Shuang Liang, Silas Habimana and Feiyang Zheng

Appl. Sci. 2026, 16(8), 3727; https://doi.org/10.3390/app16083727 - 10 Apr 2026

Abstract

Open-source bioprinting can broaden access to biofabrication, enabling existing systems to perform high-resolution tissue manufacturing. However, most of these focus on low cost, easy assembly, or specific biomaterial ink rather than making a robust standardized and modularized multifunction platform. In this study, we present CrystalCells, a user-friendly modular open-source bioprinting system centered on the TridentExtruder, a high-performance syringe extruder with extrusion/retraction capability and tool-free automated syringe coupling. The system enables the automated exchange of syringe, temperature-controlling, microscope, and pipette modules. Repeated syringe return-and-pickup cycles showed repositioning errors within ±20 μm, while the extruder generated pressures above 950 kPa and exhibited lower elastic deformation than the Replistruder 4 under the same pressure conditions. CrystalCells supported the extrusion of pre-crosslinked alginate, FRESH printing, and dual-biomaterial inks printing with automated exchange. A microscope module resolved stained HeLa cells and enabled layer-by-layer imaging for defect detection during printing. A thermoelectric module maintained the syringe barrel below 6 °C during the printing of an alginate–collagen biomaterial ink at 23 °C (room temperature), and a pipette module transferred 2–10 μL volumes with errors within ±0.5 μL. These results show that CrystalCells is an open-source modular biofabrication platform integrating printing, imaging, temperature control, and liquid handling within a single workflow. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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12 pages, 1200 KB

Open AccessArticle

Optimizing Abbreviated Breast MRI for Surveillance in Women with Personal History of Breast Cancer

by Han Song Mun, Sung Hun Kim, Bong Joo Kang and Ga Eun Park

Diagnostics 2026, 16(8), 1138; https://doi.org/10.3390/diagnostics16081138 - 10 Apr 2026

Abstract

Background/Objectives: Breast MRI surveillance for women with a personal history of breast cancer (PHBC) is often limited by costs and acquisition times. This study aims to identify the optimal abbreviated breast MR (ABMR) protocol for this population by assessing the diagnostic performance of different sequence additions. Methods: This retrospective study included 1002 women with PHBC who underwent postoperative breast MRI with ultrafast sequences. Propensity score matching using 12 variables yielded recurrence (n = 21) and nonrecurrence (n = 42) groups with balanced characteristics. Four ABMR protocols were simulated by sequentially combining sequences: Step 1 (FAST protocol) included precontrast T1-weighted imaging (T1WI), early-phase T1WI, and subtracted maximal intensity projection (MIP). Step 2 added ultrafast MIP; Step 3 incorporated delayed-phase T1WI; and Step 4 included T2WI and diffusion weighted imaging (DWI). Three expert breast radiologists independently reviewed MRIs. Sensitivity, specificity, accuracy, and area under the curve (AUC) were assessed. Results: Sensitivity, specificity, and accuracy for ABMR protocols ranged from 76.2% to 90.5%, 88.1% to 92.9%, and 85.7% to 90.5%, respectively. The FAST protocol alone provided reliable performance (sensitivity: 81%; specificity: 88.1–90.5%; accuracy: 85.7–87.3%). Additional sequences yielded modest improvements, but no statistically significant differences were observed across all 3 readers (p > 0.05). ABMR protocols demonstrated equivalent diagnostic performance for PHBC surveillance. Conclusions: The FAST protocol alone provided reliable results, indicating its potential as a primary ABMR protocol. While additional sequences slightly improved specificity, they did not significantly enhance diagnostic accuracy. Full article

(This article belongs to the Special Issue Frontline of Breast Imaging)

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23 pages, 1053 KB

Open AccessArticle

Sustainable Food and Feed Flours for Formaldehyde Reduction in Resins and Particleboards

by Mirel Glevitzky, Ciprian Răzvan Rațiu and Mihai-Teopent Corcheş

Sustainability 2026, 18(8), 3782; https://doi.org/10.3390/su18083782 - 10 Apr 2026

Abstract

Formaldehyde emissions from urea–formaldehyde (UF)-bonded particleboards remain a significant environmental and health concern. This study evaluates the effectiveness of flours as bio-based formaldehyde scavengers in particleboard production. Food-based flours (soy, wheat, green pea) and feed flours (hemp, maize DDGS, feather meal) were incorporated into UF resin at concentrations of 0.3–2.0%. Resin characterization included pH, viscosity, gelation time, solid content, and free formaldehyde, while rheological behavior was monitored at 70 °C and 90 °C. The addition of flour decreased pH from 9.1 to 7.9 and increased viscosity from 414 to up to 1600 cP, depending on flour type and dosage. Free-formaldehyde content was reduced from 0.17% to as low as 0.08%, with the most effective reduction observed for hemp flour. At industrial scale, particleboards produced with 0.5% soy and hemp flours significantly reduced free formaldehyde, with emission values of 3.26 mg/m² and 3.05 mg/m², corresponding to reductions of 66–70% compared to the reference (3.97 mg/m²). Mechanical properties, including density (652–665 kg·m⁻³), bending strength (13.2–14.1 N·mm⁻²), and internal bond (0.42–0.45 N·mm⁻²), were maintained within acceptable limits. While feed flours such as feather meal showed strong scavenging potential, they caused significant viscosity increases (up to 1800 cP), limiting processability. These findings demonstrate that adding low levels of flour, particularly soy or hemp, is an effective, renewable, and low-cost strategy to reduce formaldehyde emissions in UF-bonded particleboards, supporting the production of safer and more sustainable wood-based composites. Full article

(This article belongs to the Special Issue Advancements in Sustainable and Smart Materials)

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15 pages, 1827 KB

Open AccessArticle

C16-Functionalized Diatomaceous Earth: A Sustainable Approach for the Selective Encapsulation and Remediation of Hydrocarbons from Water

by Rosalia Maria Cigala, Mario Samperi, Paola Cardiano, Alessandro Tripodo, Giuseppe Sabatino, Catia Cannilla, Giuseppina La Ganga and Ileana Ielo

Materials 2026, 19(8), 1529; https://doi.org/10.3390/ma19081529 - 10 Apr 2026

Abstract

The primary objective of this research is to engineer a high-performance, sustainable material for aquatic remediation by repurposing low-cost biogenic silica into a selective hydrophobic adsorbent. By integrating the natural hierarchical porosity of Diatomaceous Earth (DE) with a tailored silanization strategy, this work aims to provide a scalable and eco-friendly solution for the efficient encapsulation and mechanical recovery of hydrocarbons from contaminated water. To overcome the inherent hydrophilicity of DE, a two-step functionalization process was developed, involving alkaline activation followed by the covalent grafting of hexadecyltrimethoxysilane (C16) in different concentrations. The resulting C16@DE hybrid materials underwent a dramatic surface energy transformation, shifting from hydrophilic behavior to robust hydrophobicity, with static contact angles reaching up to 134.8°. Optical analysis revealed a unique remediation mechanism: while pristine DE disperses homogeneously in the aqueous phase, functionalized C16@DE spontaneously organizes into discrete pellets upon contact with diesel, effectively encapsulating the fuel. Quantitative UV/vis spectrophotometry confirmed that these composites sequester approximately 55–56% of the diesel phase. Together, these results demonstrate that C16@DE materials couple intrinsic biosilica porosity with tailored hydrophobicity to achieve efficient hydrocarbon capture. By combining the natural hierarchical porosity of diatoms with engineered surface selectivity, this research positions functionalized DE as a scalable, low-cost, and eco-friendly promising solution for marine oil spill recovery and industrial wastewater treatment. Full article

(This article belongs to the Section Green Materials)

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21 pages, 3065 KB

Open AccessArticle

STF-Net: A Robust Fine-Grained Land Classification Method Fusing Images and Spatiotemporal Metadata

by Hengzhou Ye, Peikang Tang, Xiang Zhuang, Yongmei Tan, Gong Chen and Haoxiang Wen

Electronics 2026, 15(8), 1592; https://doi.org/10.3390/electronics15081592 - 10 Apr 2026

Abstract

Fine-grained land classification provides low-cost and efficient data support for land resource monitoring, agricultural assessment, and ecological protection. However, classification methods based on field-captured images often suffer from performance limitations due to challenges such as the complexity of land categories, variations in illumination and viewing angles, seasonal changes, and the absence of spatiotemporal metadata. Addressing the characteristics of significant seasonal variations in crops and strong correlations between adjacent land parcel categories, this paper proposes a robust Spatiotemporal Fusion Network (STF-Net) for fine-grained land classification by fusing images with spatiotemporal metadata. The main components of STF-Net include a visual backbone network, a spatiotemporal metadata encoder, a cross-modal multi-head attention fusion module, and a fallback branch designed for cases where metadata is missing. The model is robust to missing metadata and adaptable to different visual backbone networks such as the Swin Transformer and EfficientNet. Experiments on a dataset containing 91 categories of land use photos show that STF-Net achieves an overall accuracy of 93.54% and an F1-score of 0.92, significantly outperforming baseline models. Ablation studies further validate the necessity of fusing spatiotemporal metadata. Full article

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19 pages, 1737 KB

Open AccessArticle

Mixing is Dispensable for Optical Density-Based High-Throughput Growth Screening Assay in Fission Yeast

by Kim Kiat Lim, Jiunn Jye Chung, Sha Ma, Ching-Chiuan Yen, Louxin Zhang and Ee Sin Chen

Int. J. Mol. Sci. 2026, 27(8), 3410; https://doi.org/10.3390/ijms27083410 - 10 Apr 2026

Abstract

Optical density (OD)-based cell growth measurement is commonly used in high-throughput screening (HTS) during drug discovery or when deciphering the pharmaceutical mechanism of action. While resuspending the cells via a mixing step is often assumed to be necessary prior to OD measurement, its essentiality in HTS workflows has not been systematically verified. Here, through the measurement of the growth of several strains of the microbial yeast Schizosaccharomyces pombe cells, we compared the overall growth dynamics between samples that have been mixed and not mixed. Using statistical quantification by a two-tailed paired t-test followed by multiple comparison corrections, we concluded from the comparison of the doubling time of cells growing in the exponential phase that mixing did not significantly affect the biological interpretation compared to unmixed samples. Doubling time quantification between mixed and unmixed samples showed a difference of approximately 10% on average based on the assessment of the growth of eight strains. As such, if the experimental outcome can accommodate this level of variability, incorporating a mixing step before OD determination would not be necessary. These observations support the simplification of HTS processes, improving the cost efficacy and process efficiency of readouts, yet maintaining the accuracy of data acquisition. Full article

(This article belongs to the Special Issue Advances in Yeast Engineering and Stress Responses)

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