Search Results (389)

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

This article discusses the transformation of a continuous-time model of the fractional system into a discrete-time model of the fractional system. For the continuous-time model, the exact solution of the nonlinear equation with fractional derivatives (FDs) that has the form of the damped rotator type with power non-locality in time is obtained.This equation with two FDs and periodic kicks is solved in the general case for the arbitrary orders of FDs without any approximations. A three-stage method for solving a nonlinear equation with two FDs and deriving discrete maps with memory (DMMs) is proposed. The exact solutions of the nonlinear equation with two FDs are obtained for arbitrary values of the orders of these derivatives. In this article, the orders of two FDs are not related to each other, unlike in previous works. The exact solution of nonlinear equation with two FDs of different orders and periodic kicks are proposed. Using this exact solution, we derive DMMs that describe a kicked damped rotator with power-law non-localities in time. For the discrete-time model, these damped DMMs are described by the exact solution of nonlinear equations with FDs at discrete time points as the functions of all past discrete moments of time. An example of the application, the exact solution and DMMs are proposed for the economic growth model with two-parameter power-law memory and price kicks. It should be emphasized that the manuscript proposes exact analytical solutions to nonlinear equations with FDs, which are derived without any approximations. Therefore, it does not require any numerical proofs, justifications, or numerical validation. The proposed method gives exact analytical solutions, where approximations are not used at all. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Background/Objectives: Ibrutinib has revolutionized the treatment of chronic lymphocytic leukemia but has off-target side effects, most notably cardiac. In order to evaluate the efficacy and toxicity of ibrutinib treatment, risk factors for adverse outcomes and the influence of pretreatment cardiologic evaluation, KroHem collected data on Croatian patients with chronic lymphocytic leukemia treated with this drug. Methods: This is a retrospective survey performed in order to analyze the efficacy and toxicity of ibrutinib in a real-life setting. Patients starting therapy with ibrutinib for chronic lymphocytic leukemia between the time the drug became reimbursable in 2015 and 31 December 2021 were included, irrespective of treatment line. Results: We identified 436 patients fulfilling entry criteria; 404 (92.7%) responded to treatment. Cardiovascular side effects occurred in 25.0% of patients and hemorrhagic in 15.6%. The dose of ibrutinib was permanently reduced in 22.2% of patients. Median follow-up of the cohort was 29 months (IQR 18–41 months), estimated median overall survival 75 months (IQR 36 months–not reached), progression-free survival 54 months (IQR 24–81 months) and time on ibrutinib treatment 44 months (IQR 14–78 months). Factors significantly related to overall survival in multivariate analysis were stage, treatment line and age. Factors significantly related to progression-free survival in multivariate analysis were treatment line, age and pretreatment history or ECG finding of cardiac arrhythmia. Factors significantly related to time on ibrutinib treatment in multivariate analysis were age, pretreatment history or ECG finding of cardiac arrhythmia, and permanent dose reduction for toxicity. Sex, FISH and the presence of arterial hypertension were not independently significantly related to any of these outcomes. Pretreatment cardiologic consultation did not improve time on ibrutinib therapy, progression-free survival, overall survival, risk of stopping treatment due to cardiovascular side effects or risk of cardiovascular or sudden death, neither in the whole cohort nor in the subgroup of patients with and without pretreatment cardiac arrhythmia. Conclusions: Our analysis confirms the efficacy and tolerability of ibrutinib for the treatment of chronic lymphocytic leukemia. Patients older than 75 do significantly less well. Routine pretreatment cardiologic consultation does not improve outcomes and should not be considered part of standard pretreatment assessment without additional proof of its usefulness. Future investigations should aim at identifying predictive factors, mechanisms, and preventive strategies for reducing cardiotoxicity in chronic lymphocytic leukemia patients taking Bruton tyrosine kinase inhibitors. Full article

In the context of climate change, it becomes of utmost importance to limit the negative impact of industrial activities on carbon emissions, water stress, biodiversity loss, and natural resources depletion. Whether we consider the situation from a product-centered perspective (life cycle, R&D&I process, tools, methods, design, production, etc.) or from a human-centered perspective (habits, practices, fixation, strategic orientations, emotional sensitivity, etc.), coming years will represent a formidable upheaval for companies. To support this transition, various tools assessing products’ impact have been developed over the past decade. They aim at guiding decision makers, integrating new criteria to assess project success, and promoting the development and industrialization of solutions answering pressing environmental issues. If assessment is a key factor of success, it has become clear that processes and practices also need to evolve for practitioners to properly integrate sustainable requirements from the initial stages of their project. In that context, biomimicry, the approach aimed at taking nature as a model to support the design of more sustainable solutions, has been the center of growing interest. However, no integrated methods exist in the cosmetics sector to assess if a product is properly developed through biomimicry. This missing framework led to difficulties for cosmetic companies to support eco-design through biomimicry. In this article, we present a method called Biomim’Index developed by L’Oréal research and innovation sustainable development team to address three objectives: (i) to characterize cosmetic technologies according to whether they are based on bioinspiration, biomimetics or biomimicry; (ii) to guide the project’s leaders to identify key steps to improve existing cosmetic technologies through biomimicry; and (iii) to support the integration of biomimicry as an operational approach towards the development of new sustainable cosmetic technologies. This method, focusing on the problem-driven biomimetic approach is based on a combination of procedural requirements from the biomimetics TC288 18458:2015 ISO norm and environmental design requirements from L’Oréal for the Future (L4TF) commitments. Results present a proof of concept to outline the method’s efficiency and limits to support innovative eco-designed projects and value cosmetic technologies designed through biomimicry. Full article

Background/Objectives: Radiolabeled biomolecules specifically targeting overexpressed structures on tumor cells hold great potential for prostate cancer (PCa) imaging and therapy. Due to heterogeneous target expression, single radiopharmaceuticals may not detect or treat all lesions, while simultaneously applying two or more radiotracers potentially improves staging, stratification, and therapy of cancer patients. This study explores a dual-tracer SPECT approach using [¹¹¹In]In-RM2 (targeting the gastrin-releasing peptide receptor, GRPR) and [^99mTc]Tc-PSMA-I&S (targeting the prostate-specific membrane antigen, PSMA) as a proof of concept. To mimic heterogeneous tumor lesions in the same individual, we aimed to establish a dual xenograft mouse model for preclinical evaluation. Methods: CHO-K1 cells underwent lentiviral transduction for human GRPR or human PSMA overexpression. Six-to-eight-week-old female immunodeficient mice (NOD SCID) were subsequently inoculated with transduced CHO-K1 cells in both flanks, enabling a dual xenograft with similar target density and growth of both xenografts. Respective dual-isotope imaging and γ-counting protocols were established. Target expression was analyzed ex vivo by Western blotting. Results: In vitro studies showed similar target-specific binding and internalization of [¹¹¹In]In-RM2 and [^99mTc]Tc-PSMA-I&S in transduced CHO-K1 cells compared to reference lines PC-3 and LNCaP. However, in vivo imaging showed negligible tumor uptake in xenografts of the transduced cell lines. Ex vivo analysis indicated a loss of the respective biomarkers in the xenografts. Conclusions: Although the technical feasibility of a dual-tracer SPECT imaging approach using ¹¹¹In and ^99mTc has been demonstrated, the potential of [^99mTc]Tc-PSMA-I&S and [¹¹¹In]In-RM2 in a dual-tracer cocktail to improve PCa diagnosis could not be verified. The animal model, and in particular the transduced cell lines developed exclusively for this project, proved to be unsuitable for this purpose. The in/ex vivo experiments indicated that results from an in vitro model may not necessarily be successfully transferred to an in vivo setting. To assess the potential of this dual-tracer concept to improve PCa diagnosis, optimized in vivo models are needed. Nevertheless, our strategies address key challenges in dual-tracer applications, aiming to optimize future SPECT imaging approaches. Full article

Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. The system recorded the full vertical profile of tomato plants grown under two deleafing strategies: modifying leaf height (LH) and altering leaf density (LD). Vegetative and leaf areas were extracted using color-based masking and semantic segmentation with the Segment Anything Model (SAM), a general-purpose deep learning tool. Regression models based on leaf or all vegetative pixel counts showed strong correlations with destructively measured LAI, particularly under LH conditions (R² > 0.85; mean absolute percentage error ≈ 16%). Under LD conditions, accuracy was slightly lower due to occlusion and leaf orientation. Compared with prior 3D-based methods, the proposed 2D approach achieved comparable accuracy while maintaining low cost and a labor-efficient design. However, the system has not been tested in real production, and its generalizability across cultivars, environments, and growth stages remains unverified. This proof-of-concept study highlights the potential of side-view imaging for LAI monitoring and calls for further validation and integration of leaf count estimation. Full article

Background and Objectives: To evaluate the mRNA vaccine platform for blood-stage Plasmodium parasites, we completed a proof-of-concept study using the P. yoelii mouse model of malaria and two mRNA-based vaccines. Both encoded PyMSP1₁₉ fused to PyMSP8 (PyMSP1/8). One was designed for secretion of the encoded protein (PyMSP1/8-sec); the other encoded membrane-bound antigen (PyMSP1/8-mem). Methods: Secretion of PyMSP1/8-sec and membrane localization of PyMSP1/8-mem were verified in mRNA-transfected cells. As recombinant PyMSP1/8 (rPyMSP1/8) is known to protect mice against lethal P. yoelii 17XL infection, we first compared immunogenicity and efficacy of the PyMSP1/8-sec mRNA vaccine versus the recombinant formulation in outbred mice. Animals were immunized three times followed by challenge with a lethal dose of P. yoelii 17XL-parasitized RBCs (pRBCs). Similar immunization and challenge experiments were conducted to compare PyMSP1/8-sec versus PyMSP1/8-mem mRNA vaccines. Results: Immunogenicity of the PyMSP1/8-sec mRNA vaccine was superior to the recombinant formulation, inducing higher antibody titers against both vaccine components. Following challenge with P. yoelii 17XL pRBCs, all PyMSP1/8-sec-immunized animals survived, with 50% of these showing no detectible pRBCs in circulation (<0.01%). In addition, mean peak parasitemia in PyMSP1/8-sec mRNA-immunized mice was significantly lower than that in the rPyMSP1/8 vaccine group. Both PyMSP1/8-sec and PyMSP1/8-mem were protective against P. yoelii 17XL challenge, with PyMSP1/8-mem immunization providing a significantly higher level of protection than PyMSP1/8-sec immunization considering the number of animals with no detectable pRBCs in circulation and the mean peak parasitemia in animals with detectable parasitemia. Conclusions: mRNA vaccines were highly immunogenic and potently protective against blood-stage malaria, outperforming a similar recombinant-based vaccine. The membrane-bound antigen was more effective at inducing protective antibody responses, highlighting the need to consider antigen localization for mRNA vaccine design. Full article

Background: Colorectal cancer (CRC) remains a significant global health burden, with early detection and intervention crucial for improving patient outcomes. This study aims to develop and evaluate a novel proof-of-concept ensemble framework combining transformer-based language models and decision tree-based models for early-stage CRC screening, diagnosis, and prognosis. Methods: The ensemble framework consists of four key components: (1) GastroGPT, a transformer-based language model for extracting relevant data points from patient histories; (2) a decision tree-based model for assessing CRC risk and recommending colonoscopy; (3) GastroGPT for extracting data points from early CRC patients’ histories; and (4) a suite of decision tree-based models for predicting survival outcomes in early-stage CRC patients. The study employed a retrospective, observational, methodological design using simulated patient cases. Results: GastroGPT demonstrated high accuracy in extracting relevant data points from patient histories. The decision tree-based model for CRC risk assessment achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.85 (95% CI: 0.78–0.92) in predicting the need for colonoscopy. The decision tree-based models for survival prediction showed strong performance, with C-indices ranging from 0.71 to 0.75 for overall survival and disease-free survival at 24, 36, and 48 months. Conclusions: The novel ensemble framework demonstrates promising performance in early-stage CRC screening, diagnosis, and prognosis. Further research is needed to validate the models using larger, real-world datasets and to assess their clinical utility in prospective studies. Full article

Influenza is still a chronic global health threat, inducing a sustained search for effective antiviral therapeutics. Computational methods have played a pivotal role in developing small molecule therapeutics. In this study, we applied a combined in silico and in vitro approach to explore the potential anti-influenza activity of cyproheptadine, a clinically used histamine H1 receptor antagonist. Virtual screening based on the average quasivalence number (AQVN) and electron–ion interaction potential (EIIP) descriptors suggests similarities between cyproheptadine and several established anti-influenza agents. The subsequent ligand-based pharmacophore screening of a focused H1 antagonist library was aligned with the bioinformatics prediction, and further experimental in vitro evaluation of cyproheptadine demonstrated its anti-influenza activity. These findings provide proof of concept for cyproheptadine’s in silico-predicted antiviral potential and underscore the value of integrating computational predictions with experimental validation. The results of the current study provide a preliminary proof of concept for the predicted anti-influenza potential based on computational analysis and emphasize the utility of integrating in silico screening with experimental validation in the early stages of drug repurposing efforts. Full article

With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. Due to the freight pooling matching problem involving the combination and allocation of multiple vehicle owners and cargo owners, which is highly complex, few scholars have conducted research on such issues. The lack of coordinated optimization in matching models may result in inefficiencies, and the limited consideration of individual user preferences can lead to low user satisfaction. Therefore, this paper focuses on the freight pooling matching problem in vehicle–cargo matching platforms. To improve matching efficiency and fully consider user preferences, the theory of stable matching is introduced into the freight pooling matching problem. It defines the concepts of combination preferences and group stability based on combination preferences, establishes a group stable matching model for the freight pooling business of vehicle–cargo matching platforms, and verifies the stability of the model through theoretical proof. Since this model is a mixed-integer linear programming model with relatively few decision variables but a large number of constraints, this paper introduces the cutting-plane algorithm. Based on the characteristics of the problem, the algorithm is improved by removing ineffective constraints and only using key constraints, significantly reducing computational complexity, optimizing the solving process, and greatly improving the model’s solution efficiency. This approach aligns well with the characteristics of the vehicle–cargo freight-pooling matching model. The research results indicate that the group stable matching model significantly improves platform revenue, vehicle owners’ profits, and cargo owners’ satisfaction across various supply and demand scenarios. Additionally, the cutting-plane algorithm reduces computation time by 97% and decreases the number of constraints during the solving process by 99%. The stable matching theory and solution algorithm proposed in this paper can provide users with precise matching schemes, significantly improving matching efficiency, user satisfaction, platform revenue and platform competitiveness. It demonstrates high innovation and practical application value. Full article

Rapid and accurate identification of pathogenic bacteria phenotypic traits, including virulence, drug resistance, and metabolic activity, is essential for clinical diagnosis and infectious disease control. Traditional methods are time-consuming, highlighting the need for more efficient approaches. This study develops a single-cell Raman spectroscopy approach to detect multiple phenotypic traits of Staphylococcus aureus (S. aureus) as a proof of concept. We constructed a single-cell Raman spectral database encompassing 6240 spectra from 10 strains of S. aureus with diverse phenotypic traits and developed a convolutional neural network (CNN) to predict these phenotypes from the Raman spectra. The CNN model achieved 93.90%, 98.73%, and 98.66% accuracy in identifying enterotoxin-producing strains, methicillin-resistant S. aureus (MRSA), and growth stages, respectively. Characteristic Raman peaks for enterotoxin producers mainly appeared at 781, 939, 1161, 1337, 1451, and 1524 cm⁻¹, whereas MRSA primarily exhibited peaks at 723, 780, 939, 1095, 1162, 1340, 1451, 1523, and 1660 cm⁻¹. During culture, nucleic acid-related peaks weakened, lipid peaks increased, and protein peaks initially increased and subsequently decreased. This integration of Raman spectroscopy and machine learning demonstrates considerable potential for rapid bacterial phenotyping. Future research should expand to a wider range of bacterial species and phenotypes to enhance the diagnosis, prevention, and management of infectious diseases. Full article

(1) Background: Globally, lung cancer is the leading cause of cancer death, but immunotherapy has impressively improved the outcomes, generating longer progression-free survival and overall survival. Endocrine immune-related adverse events (irAEs) are mostly irreversible and need life-long hormonal substitution therapy. The evaluation of the quality of life of lung cancer patients experiencing endocrine toxicities during immune checkpoint inhibitor (ICI) treatment is relevant for both patients and healthcare providers. (2) Methods: This was a prospective cohort study of lung cancer patients treated with immune checkpoint inhibitors in a tertiary-level hospital in Romania from 1 December 2021 to 30 September 2024. Quality of life was assessed using versions of the EORTC QLQ-C30 and EORTC QLQ-LC-13 validated and translated into the Romanian language. We investigated several clinical variables to evaluate their impact on QoL. (3) Results: Fifty-nine lung cancer patients were evaluated for their QoL before ICI initiation and at the end of the study. Endocrine-irAEs occurred in 17 lung cancer patients (28.8%). Quality of life as assessed using the EORTC questionnaires was statistically significantly improved, even in patients experiencing endocrine-irAEs. (4) Conclusions: Our prospective cohort study succeeded in delivering the proof of concept of an increased QoL in lung cancer patients who had developed endocrine-irAEs under immunotherapy. Despite toxicities, especially rather frequent endocrine-irAEs, ICIs enabled durable disease control and symptom relief, improving the QoL of the overall trial population. As more lung cancer patients undergo immunotherapy in metastatic or early stages, we draw attention to this particular patient population with autoimmune endocrinopathies, as they will live longer and require life-long hormonal therapy. Full article

Clinical documentation, particularly the hospital discharge report (HDR), is essential for ensuring continuity of care, yet its preparation is time-consuming and places a considerable clinical and administrative burden on healthcare professionals. Recent advancements in Generative Artificial Intelligence (GenAI) and the use of prompt engineering in large language models (LLMs) offer opportunities to automate parts of this process, improving efficiency and documentation quality while reducing administrative workload. This study aims to design a digital system based on LLMs capable of automatically generating HDRs using information from clinical course notes and emergency care reports. The system was developed through iterative cycles, integrating various instruction flows and evaluating five different LLMs combined with prompt engineering strategies and agent-based architectures. Throughout the development, more than 60 discharge reports were generated and assessed, leading to continuous system refinement. In the production phase, 40 pneumology discharge reports were produced, receiving positive feedback from physicians, with an average score of 2.9 out of 4, indicating the system’s usefulness, with only minor edits needed in most cases. The ongoing expansion of the system to additional services and its integration within a hospital electronic system highlights the potential of LLMs, when combined with effective prompt engineering and agent-based architectures, to generate high-quality medical content and provide meaningful support to healthcare professionals. Hospital discharge reports (HDRs) are pivotal for continuity of care but consume substantial clinician time. Generative AI systems based on large language models (LLMs) could streamline this process, provided they deliver accurate, multilingual, and workflow-compatible outputs. We pursued a three-stage, design-science approach. Proof-of-concept: five state-of-the-art LLMs were benchmarked with multi-agent prompting to produce sample HDRs and define the optimal agent structure. Prototype: 60 HDRs spanning six specialties were generated and compared with clinician originals using ROUGE with average scores compatible with specialized news summarizing models in Spanish and Catalan (lower scores). A qualitative audit of 27 HDR pairs showed recurrent divergences in medication dose (56%) and social context (52%). Pilot deployment: The AI-HDR service was embedded in the hospital’s electronic health record. In the pilot, 47 HDRs were autogenerated in real-world settings and reviewed by attending physicians. Missing information and factual errors were flagged in 53% and 47% of drafts, respectively, while written assessments diminished the importance of these errors. An LLM-driven, agent-orchestrated pipeline can safely draft real-world HDRs, cutting administrative overhead while achieving clinician-acceptable quality, not without errors that require human supervision. Future work should refine specialty-specific prompts to curb omissions, add temporal consistency checks to prevent outdated data propagation, and validate time savings and clinical impact in multi-center trials. Full article

Albuminuria is a sensitive biomarker of kidney dysfunction, and the albumin/creatinine ratio (ACR) is an essential measure for monitoring diabetic kidney disease (DKD). Abnormal levels can indicate a propensity for progressive renal failure and other complications such as cardiovascular diseases. This study employed UV/Visible spectroscopy to analyze aqueous urine samples spiked with bovine serum albumin (BSA) and creatinine at clinically relevant concentrations (0–30 mg/L for albumin and 600–1800 mg/L for creatinine) using a multivariate method. UV/Visible spectra of co-spiked samples recorded in triplicate revealed distinct bands at 229 nm and 249 nm, corresponding to BSA and creatinine, respectively, alongside other amino acid bands. Partial Least Squares Regression (PLS-R) analysis for BSA yielded a Root Mean Square Error of Calibration (RMSEC) and Cross-Validation (RMSECV) values of 66.93 and 73.92 mg/L, respectively. For creatinine, RMSEC and RMSECV values were 244.32 and 275.65 mg/L, respectively. Prediction models for both BSA and creatinine compared to ELISA demonstrated a robust performance with R²_PRED values of 0.96 and 0.95, respectively, indicating strong model reliability. The Limit of Detection (LOD) for co-spiked samples was 19.82 mg/L for BSA and 58.43 mg/L for creatinine. The significance of the achieved Limit of Detection (LOD) lies in its ability to measure concentrations well below the normal physiological ranges of 0–30 mg/L for albumin and 600–1800 mg/L for creatinine. These results demonstrate the proof of concept of applying an UV/Visible-spectroscopy-based method as a rapid, cost-effective point-of-care (PoC) tool for ACR measurements, offering promising applications in the early diagnosis, monitoring, and prognosis of diabetic kidney disease and associated cardiovascular complications. The next stage will involve a pilot trial to evaluate the technology’s potential using clinical patients. Full article