SLAS Technology

Automated platforms such as Chi.Bio enable simultaneous monitoring of optical density and fluorescent reporter expression in 20 ml reactor cultures with controllable pump systems. As such, they provide an appealing option for contemporary gene expression quantification, quantitative physiology, and laboratory evolution and ecology experiments. While optical density calibration for this device is well established, no equivalent calibration framework exists for fluorescence, making quantitative comparison with reference instruments unreliable. Here, we characterize Chi.Bio fluorescence capabilities using fluorescent calibration microspheres and fixed GFP-expressing S. cerevisiae and E. coli cells, compared with orthogonal plate-reader measurements. We show that microsphere fluorescence is detectable and scales linearly with concentration, whereas the GFP signal from both species falls below the device detection limit. Comparison of background-correction strategies indicates that direct subtraction of a non-fluorescent control measured within the same device yields more reliable fluorescence estimates than the commonly used on-line normalization method. Knowledge of these sensitivity boundaries of the device provides practical guidelines for experimental design of future studies.

Isothermal nucleic acid amplification tests enable rapid and decentralized molecular diagnostics but often lack robust quantitative readouts compared to quantitative PCR. Here, we present a semi-quantitative nucleic acid measurement approach using machine learning to extract spatiotemporal features from real-time fluorescence imaging of rapid isothermal amplification reactions in microfluidic chips. A convolutional neural network was trained on multiple images sampled throughout a chip-based recombinase polymerase amplification reaction to classify samples into clinically relevant or logarithmically spaced concentration ranges spanning five orders of magnitude. The clinical classification model achieved 94.6% accuracy, and the logarithmic model achieved 92.7% accuracy, with most errors occurring between adjacent concentration categories. By learning spatiotemporal patterns of fluorescence development rather than relying on explicit feature extraction, the model remained accurate at both high and low nucleic acid concentration regimes where other quantitative isothermal molecular tests struggle. This approach enables automated interpretation of amplification reactions and extends the usable dynamic range of the assay. These results demonstrate that integrating machine learning with image-based amplification methods can support rapid semi-quantitative molecular testing and may facilitate broader deployment of nucleic acid diagnostics outside centralized laboratory settings. Author summaryMany rapid nucleic acid testing methods for infectious diseases are simple to run but struggle to measure how much genetic material is present, which limits their usefulness in clinical decision-making. In our work, we study a technique that produces visible fluorescent patterns during nucleic acid amplification reactions. Traditionally, the amount of nucleic acids present are measured by counting individual bright spots, but this becomes difficult when the target nucleic acid concentration is high and the spots merge together. We developed a machine learning approach that models how the fluorescence pattern changes over time. By analyzing a sequence of images from each reaction, our model can assign samples to concentration ranges across a wide span. This allows us to extract meaningful information even when traditional analysis methods break down. Because this approach works with simple imaging systems and does not require complex equipment, it could help support more informative and accessible diagnostic testing in point-of-care and low-resource settings.

Luminescence is a powerful method for detecting trace analytes and monitoring biological processes. However, most bioluminescence reagents, including luciferase and its substrates, are sensitive to temperature, limiting their useable shelf lives, and resulting in inconsistent performance. Enhancing the stability of these reagents could improve data quality, simplify workflows, and address cold chain storage issues. In this study, we demonstrate the application of the platform stabilization technology, capillary-assisted vitrification (CAV), as a tool to stabilize different luciferases and their substrates, and the application of the stabilized reagents in both in vitro and in vivo bioluminescent assays. We demonstrate that CAV-stabilized reagents can be stored and shipped ambiently, maintain consistent performance over time, and are suitable for use in cell viability quantification, tumor monitoring, in vivo imaging, microbial detection, and immunoassays. Additionally, different reagents can be co-formulated to make ready-to-use assay kits that can also be shipped and stored ambiently. Our results demonstrate that CAV stabilization is a viable alternative to traditional storage methods, with broad potential to improve bioluminescence workflows.

Intestinal organoids are powerful in vitro models, but their use in large-scale analyses remains constrained by the low throughput, labor-intensive handling, and high reagent consumption of conventional Matrigel dome culture. Here, we present Organoid-in-Bead (OrB), a vortex-based compartmentalization workflow that partitions organoid fragments into thousands of discrete Matrigel microbeads, enabling scalable, high-density culture from a single batch preparation. OrB maintains dome-comparable organoid growth and epithelial polarity, supports passaging-based culture expansion, yields more than 5,000 organoids in the final 10 cm dish format, and reduces Matrigel and medium consumption by approximately 70% on a per-organoid basis. OrB therefore provides a practical and scalable upstream workflow for generating screening-scale intestinal organoids. HighlightsO_LIOrB generates Matrigel microcompartments by vortexing without microfluidics C_LIO_LIOrB enables scalable, high-density intestinal organoid culture in one batch C_LIO_LIOrB maintains dome-comparable growth and epithelial polarity and supports passaging C_LIO_LIOrB yields >5,000 organoids per batch with [~]70% less Matrigel/medium per organoid C_LI

Conventional extrusion-based 3D bioprinting encounters limitations in fabricating intricate tissue architectures due to fixed nozzle diameters and fixed deposition orientations. These constraints restrict conformal printing on curved or non-planar surfaces and often necessitate support-intensive fabrication strategies. This work introduces a mechanically simplified extrusion platform inspired by the swivel jet nozzle, featuring a free-degree-of-orientation extrusion head termed the universal extrusion head (Univ-Ex head), coupled with a modular nozzle architecture. The Univ-Ex head employs a swivel-like mechanical design that enables orientation freedom without external actuation in its current implementation, thereby minimizing mechanical complexity while supporting deposition on physiologically relevant, non-planar geometries. Multiple nozzle concepts were developed through comparative CAD iterations, with two representative geometries--a flat nozzle and a conical nozzle--selected for experimental validation. The platform is evaluated through parametric CAD design, stereolithography-printed prototypes, proof-of-concept extrusion experiments, and fluid dynamics simulations performed using FLOW-3D software. Numerical and experimental results demonstrate stable filament formation and clear diameter-dependent extrusion behavior, while simulations further confirm the feasibility of angled and non-planar deposition. A variable-diameter nozzle concept is proposed as a forward design direction to enable real-time adjustment of bioink flow rate and deposition resolution in principle; however, the present study intentionally validates the system using fixed-diameter nozzle variants to maintain stable numerical and experimental boundary conditions. A gear-integrated Univ-Ex head is also presented as a forward upgrade and demonstrated as a single-piece prototype. Collectively, this work establishes a scalable, hardware-focused pathway toward conformal bio-additive manufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/734010v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@8833caorg.highwire.dtl.DTLVardef@33dforg.highwire.dtl.DTLVardef@14d8d11org.highwire.dtl.DTLVardef@685ef0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Animal cell-cultured ingredients are entering the EU and UK pet food markets under frameworks that do not require pre-market, ingredient-level safety assessments, creating an ethical need for transparent safety disclosure. We present the first public safety dossier for this sector, describing the proprietary mouse embryonic stem cell line PE25 and its derived, non-viable cellular and conditioned media ingredient produced in food and feed-grade media. PE25 characterization confirmed Mus musculus identity, sterility, absence of mycoplasma and replication-competent retroviruses, and stable growth. Doxorubicin-induced p53 stress testing, CD44/BMI1 profiling, and soft agar assays showed no cancer-like traits and a non-tumorigenic profile; the final ingredient contains no viable cells. Independent OECD TG 471 and 487 assays confirmed non-genotoxicity. Heavy metals, biogenic amines, solvents, and chemical residues were below regulatory limits. Given process variability, we recommend case-by-case safety evaluation and propose this dossier as a model for responsible commercialization.

Mechanical forces have a major effect on cell behavior. Most cells in vitro are grown under static conditions on hard tissue culture plastic, conditions that do not accurately reflect living tissues. The ability of cells to sense and respond to mechanical forces is essential for key biological processes, including development, proliferation, and migration. Disruption of the ability to respond to mechanical forces are known to be a critical factor in many diseases, including cardiovascular disease, progeria, and cancer. Here, we present the design, fabrication, and biological testing of a custom-built cell-stretching device that applies controlled biaxial strain to cells cultured on a polydimethylsiloxane (PDMS) membrane. We then used this device to examine how cells respond to strain. In response to biaxial strain, MCF-7 cells activated the mechanosensitive immediate early gene (IEX-1), with its expression increasing significantly after 1 and 3 hours of stretching. Cells exposed to mechanical strain also remodeled their cytoskeleton in a direction-dependent manner. Under uniaxial strain, actin filaments reoriented perpendicular to the stretch direction, whereas biaxially stretched cells do not promote directional reorientation, but instead appear to reinforce actin at the cell periphery. Similarly, cells under uniaxial strain exhibited changes in nuclear orientation and shape that were not observed under biaxial strain. Nuclear area remained unchanged in either strain condition. These results highlight that the biaxial stretcher can be used to apply strain to cells, and that cells respond differently to biaxial strain compared to what has been reported for uniaxial strain.

Sensitive and low-cost protein biomarker detection is critical for disease diagnosis. Advanced microfluidic systems can generate miniature reaction compartments for a high-sensitivity assay. However, these platforms often require external instruments, skilled operators, and complex setups. Here, we develop a Lab on a Capillary (LabCap) platform that integrates photopatterned hydrogel rings within a glass capillary using a reconfigurable stop-flow lithography system. During sample loading and unloading steps, nanoliter-scale aqueous droplets (torodrops) are spontaneously formed around the hydrogel rings, creating isolated reaction compartments without the need for external instruments or an immiscible oil phase. The LabCap platform enables quantitative detection of clinically relevant biomarkers, including C-reactive protein (CRP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP). By adjusting the incubation protocol, assay speed and sensitivity can be tuned to meet different analytical requirements. A periodic medium exchange protocol enables biomarker detection at concentrations as low as 1 ng/mL, whereas prolonged static incubation extends detection to 0.1 ng/mL. In addition, LabCap offers practical advantages, including low fabrication cost (< EUR 1 per device), low reagent consumption (<100 microlitres per assay step), and minimal wash-buffer usage (1 mL). These results demonstrate that LabCap is a simple, cost-effective, and versatile platform for biomarker detection.

Optogenetic control enables light-actuated regulation of gene expression and provides a programmable interface between living cells and electronic systems. However, routine prototyping of optogenetic constructs remains limited by infrastructure. Existing closed-loop platforms often require chemostats, microfluidics, robotic handling, or custom optical sensors, which can increase cost, reduce accessibility, or constrain measurement performance. Here, we present LEMOS 2.0, an updated LED-Embedded Microplate for Optogenetic Studies, a low-cost device for optogenetic stimulation and gene-circuit characterization inside standard off-the-shelf microplate readers. LEMOS 2.0 builds on the original LEMOS platform by increasing throughput from 16 to 32 microwells and reducing light leakage between adjacent microwells, allowing dark conditions to be used as an additional illumination state. The device consists of a 3D-printed frame, individually addressable LEDs positioned next to each microwell, a rechargeable battery, and an onboard microcontroller for Bluetooth-based wireless communication. Biocompatible polydimethylsiloxane microwells are cast directly into the device by replica molding, allowing bacterial cultures to be stimulated while optical density and fluorescence are measured by the microplate reader. This protocol describes the full LEMOS 2.0 workflow, including device fabrication, circuit assembly, Arduino programming, PDMS microwell casting, plate-reader setup, strain and culture preparation, automated experiment execution, device cleanup, and fluorescence/OD600 data analysis. As a demonstration, the protocol uses the CcaSR optogenetic system, in which sfGFP expression is activated by green light and repressed by red light. LEMOS 2.0 is intended to make optogenetic perturbation and gene-expression characterization more accessible to wet-lab users, enabling faster design-build-test-learn cycles without requiring specialized bioreactor or microfluidic infrastructure.

High content imaging is being applied to achieve quantitative fluorescence readouts in increasingly complex 3-dimensional (3D) cell culture models such as spheroids and organoids. Compared to conventional 2D assays, 3D assays better represent biological heterogeneity but require more complex sample preparation, 3D imaging and 3D image analysis that can affect the accuracy and precision of such assays. We used spheroids formed from the NRAS-activated melanoma cell line 19161 modified to express an ERK kinase translocation reporter (KTR) as an exemplar 3D phenotypic assay carried out in 96-well plates. The spheroids were treated with the ERK activator TPA and a range of concentrations of the MEK inhibitor Binimetinib. 3D live-cell imaging with sub-cellular spatial resolution was performed using a dual-view oblique plane microscope (dOPM) - a form of single-objective light-sheet microscope - and the experiment was performed separately at 4 different institutes. The results were analysed using an identical 3D analysis pipeline and parameters. We assessed the variation in assay readout using a linear mixed effects model. Random variance at the well level was negligible (SD = 0.0048 relative to range of KTR biosensor readout at reference site of 0.17), indicating low technical noise. Treatment effects were dose-dependent and highly statistically significant compared to DMSO control across all sites (Dunnett-corrected p < 0.001). The range in KTR readout between the minimum (3.5 M Binimetinib) and maximum (100 nM TPA) treatments varied between 59 to 96% relative to the reference site. Measured bias in KTR readout between sites was between 6 and 12% of the range of the reference site. This study quantifies the reproducibility of a 3D live spheroid-based assay employing a fluorescence biosensor requiring readout out at the per-cell level using the dOPM platform and discusses areas where experimental protocol could be improved in the future to further improve reproducibility.

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 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.

CT perfusion (CTP) is central to acute-stroke and oncologic imaging, yet quantitative outputs vary substantially across vendor software, undermining reproducibility. We present an open, transparent core (ctp-core) that fits first-pass time-attenuation curves with a gamma-variate model, derives perfusion indices (peak enhancement, time-to-peak, bolus-arrival time, and area under the curve) analytically from the fitted parameters, and renders parametric maps with the ASIST-Japan standardized lookup table (a-LUT) so that visualization is comparable across sites. Every parameter, bound, and processing step is exposed. The method is validated on Monte-Carlo synthetic curves with known ground truth; no confidential or patient data are used. Across signal-to-noise ratio (SNR) levels 5 to 100 (200 independent runs per level) the pipeline recovers peak time to within 0.03-0.52 s and peak amplitude to within 0.4-8.1% (mean absolute error), degrading monotonically with noise; at a representative SNR of 20 it recovers peak time within 0.13 s, peak amplitude within 2.0%, and bolus-arrival time within 0.51 s, with fit quality R-squared = 0.98. The reproducibility demonstration is deterministic (fixed seed) and re-runs to bit-stable metrics. All code, the synthetic-data generator, the standardized-visualization module, evaluation scripts, and a 34-test suite are released openly for independent verification. The contribution is a fully open, parameter-transparent gamma-variate plus standardized-visualization pipeline with reproducible synthetic benchmarks: a reference others can audit, reuse, and build on.

Deep learning models deployed in clinical imaging frequently encounter distribution shifts, yet most out-of-distribution (OOD) detection methods are evaluated only on controlled research datasets. As a result, it is unclear whether existing approaches can reliably identify segmentation failures that arise in real-world clinical practice. We evaluated six OOD detection methods on a deployed liver CT segmentation model (3D nnU-Net) using internal data from 400 patients and external data from 100 patients collected across nearly 70 sites in 7 countries. One method was Pairwise Surface DSC, a surface-based extension of Pairwise DSC, that we introduced. OOD performance was measured using sensitivity, AUROC, and balanced accuracy, with thresholds determined on an independent cohort of 400 patients using the Youden J statistic. Statistical significance was assessed using McNemar tests and stratified bootstraps ( = 0.05) with Benjamini-Hochberg correction. Pairwise Surface DSC was the top-performing method, with perfect sensitivities (1.00), near-perfect AUROCs (0.97 internal; 1.00 external), and the highest balanced accuracies (0.94 internal; 0.88 external; p<0.001). These results show that automated failure detection for liver CT segmentation is clinically feasible and that Pairwise Surface DSC is a promising candidate for deployment. Our code is available at https://github.com/mckellwoodland/liver_ct_ood_translation.

Artificial intelligence (AI) applications in computed tomography (CT) imaging require objective and continuous testing, yet standardised methods for this purpose have not been established. Here, we present a framework using physical phantoms for standardised testing and monitoring of AI, demonstrated in liver lesion detection. We begin by designing phantoms tailored to the anatomical input domain expected by AI algorithms, and then systematically assess how AI performance is affected by variations in scanner technology and operation across two clinical CT systems. Next, we perform longitudinal monitoring, yielding consistent results over fifteen months on both systems. Finally, we validate clinical relevance by demonstrating that AI models trained on phantom data generalize effectively to patients and exhibit no evidence of phantom-specific adaptation. Our findings show that anatomically realistic phantoms enable standardised, site-specific testing and monitoring of AI, providing a proactive method for local and cross-institutional quality assurance.

The development of cost-effective, serum-free media is critical for scalable cultivated meat production. This study used high-throughput screening through a Design of Experiments (DoE) approach to develop an animal-free, serum-free medium (MMM1) specifically for the C2C12 murine myoblasts model cell line with applicability in cultivated meat research including for pet food. Low cost, food-grade inputs such as methylcellulose and spirulina extract resulted in significant cell growth improvements. The optimised MMM1 formulation containing low cost, food-grade inputs, achieved cumulative population doublings comparable to 10% (v/v) fetal bovine serum over four consecutive passages. Furthermore, MMM1 supported scalable cell expansion on commercially available dextran-based microcarriers (Cytodex-3) in both static and agitated conditions in spinner flasks, matching growth rates of serum-based controls. Finally, transitioning to a food-grade DMEM/F12 basal medium maintained cell proliferation equivalent to the pharmaceutical-grade DMEM/F12, but at a significantly lower cost, thus offering a viable strategy to substantially reduce biomanufacturing costs which is a critical challenge in cultivated meat production.

The tomato leafminer, Tuta absoluta, poses a severe global agricultural threat due to its rapid leaf-mining behavior and swift development of resistance to conventional chemical pesticides. While microbial chitinases are potent biopesticides, their field efficacy is limited by environmental degradation and the short exposure window before larvae penetrate leaf tissues. This study evaluates a stimuli-responsive, controlled-release nanobiopesticide system utilizing a novel chitinase from newly characterized Serratia marcescens GBS19. A 61.1 kDa chitinase (GBS19_ChiA) was heterologously expressed in Escherichia coli and purified to a specific activity of 215.01 U/mg. The enzyme was immobilized onto starch-coated silica nanoparticles designed for target-triggered release via host alpha-amylase. Genomic profiling and R-based kinetic modeling were integrated to evaluate the efficacy of purified and immobilized forms against T. absoluta. Immobilization enhanced thermal and pH stability, with the nanocarrier maintaining 85% activity over 10 weeks. In larval bioassays, immobilization increased mortality from 21.9% to 59.4% (5000 U/mL) by day 3, reaching 62.5% by day 6. Genomic analysis identified an expansive secretome and a Type VI Secretion System (T6SS), characterizing GBS19 as a multi-pronged pathogen. Kinetic modeling established that while immobilized enzymes are effective, the 2.5-hour exposure time on T. absoluta requires the synergistic action of chitinases (ChiA/B/C) to reach the lethal desiccation threshold before larvae establish protective mines. Starch-coated silica nanoparticles significantly improve chitinase stability and delivery. However, overcoming the rapid penetration of T. absoluta necessitates a whole-cell or multi-enzyme synergistic approach to outpace larval behavioural defences.

Background: Coronary artery calcium (CAC) is an established measure of coronary atherosclerosis from computed tomography (CT). While deep learning (DL) can quantify CAC from non-dedicated CT, the accuracy is limited by image quality. Purpose: We derived and validated a novel method for DL CAC segmentation on ultra-low dose CT attenuation correction (CTAC) scans that is trained with synthetic low-dose, ungated images. Materials and Methods: Models were trained using one center and externally tested in two other centers. Synthetic, ungated CT scans were generated so that expert segmentations from dedicated CAC scans could be used as ground truth for perfectly registered synthetic images through knowledge adaptation (KAD-CAC). We evaluated agreement between CAC scoring methods vs expert readers on a per-patient and per-vessel basis, as well as associations with the primary outcome of death or myocardial infarction (MI). Results: The DL models were externally tested on 5969 patients with a median age of 64 (IQR 56 - 73), of whom 50.2% were male. The KAD-CAC model had higher Cohens kappa K (0.86, 95% CI 0.85 - 0.87) compared to previous convolutional LSTM model (K 0.78, 95% CI 0.76 - 0.80, p<0.01), or models trained with only gated images (K 0.81, 95% CI 0.80 - 0.82, p<0.01). Net reclassification improvement for CAC stratified risk of death or MI, was greatest for the KAD-CAC model over baseline including age, sex, hypertension, diabetes, dyslipidemia, family history, smoking, stress total perfusion deficit, and left ventricular ejection fraction. Conclusion: We use paired synthetic ungated scans to transfer expert gated CAC annotations into the ungated domain, resulting in substantially better vessel-level CAC scoring and improved risk stratification.

The purification of industrial enzymes typically relies on costly, multi-step chromatographic protocols. To address this, we developed a novel platform termed Coated Bacterial Enzymes (CBEs), which enables one-step purification and immobilization of recombinant proteins fused to the SlpA cell wall binding domain. As a proof of concept, we used a {beta}-galactosidase from Bifidobacterium bifidum of dairy relevance. The chimeric enzyme BbgII-SlpA was expressed in Escherichia coli and captured from crude lysate onto glutaraldehyde-inactivated Bacillus subtilis cells via SlpA domain. Binding was characterized by a dissociation constant (Kd) of 16.2 {micro}M and maximum binding capacity (Bmax) of 144 {micro}mol/g. The resulting CBE biocatalyst exhibited optimal activity at pH 6.0 for ONPG and lactose, with a broader pH profile than the free enzyme. Optimal temperatures were 60 {degrees}C for ONPG and 50 {degrees}C for lactose, and CBE retained >80% activity after 390 min at 45 {degrees}C, compared to 20% for the free enzyme. Catalytic efficiencies (kcat/Km) were 2.62 x106 M-1{middle dot}s-1 for ONPG and 4.40 x102 M-1{middle dot}s-1 for lactose. Moreover, CBE showed improved tolerance to cations such as Ca2+ and Fe2+. These results suggest that the CBE platform offers a cost-effective alternative for producing high-purity, immobilized enzymes for diverse industrial bioprocesses.

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.