Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

BACKGROUND: The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS: In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS: We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS: This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.

Monitoring Live Mycobacteria in Real-Time Using a Microfluidic Acoustic-Raman Platform.

Tuberculosis (TB) is the most common cause of death from an infectious disease. Although treatment has been available for more than 70 years, it still takes too long and many patients default risking relapse and the emergence of resistance. It is known that lipid-rich, phenotypically antibiotic-tolerant, bacteria are more resistant to antibiotics and may be responsible for relapse necessitating extended therapy. Using a microfluidic system that acoustically traps live mycobacteria, M. smegmatis, a model organism for M. tuberculosis we can perform optical analysis in the form of wavelength-modulated Raman spectroscopy (WMRS) on the trapped organisms. This system can allow observations of the mycobacteria for up to 8 h. By adding antibiotics, it is possible to study the effect of antibiotics in real-time by comparing the Raman fingerprints in comparison to the unstressed condition. This microfluidic platform may be used to study any microorganism and to dynamically monitor its response to many conditions including antibiotic stress, and changes in the growth media. This opens the possibility of understanding better the stimuli that trigger the lipid-rich downregulated and phenotypically antibiotic-resistant cell state.

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Chick sexing based on the blood analysis using Raman spectroscopy.

Efforts are underway to develop technology for automatically determining the sex of chick embryos, aimed at establishing a stable and efficient poultry farming system while also addressing animal welfare concerns. This study investigated the possibility of chick sexing through blood analysis using Raman spectroscopy. Raman spectra were obtained from whole blood and its constituents, such as red blood cells (RBCs) and blood plasma, collected from chicks aged 1-2 days, using a 785-nm excitation wavelength. Principal component analysis (PCA) revealed statistically significant sex-dependent spectral variations in whole blood and RBCs, whereas blood plasma showed less clear dependency. These spectral differences between male and female chicks were attributed to differences in the proportion of spectral components from oxygenated (oxy-) and deoxygenated (deoxy-) RBCs, with males exhibiting a slightly stronger contribution of oxy-RBCs compared to females. This reflects the higher oxygen affinity of hemoglobin (Hb) in males compared to females. A model for discriminating chick sex was built using the ratios of certain Raman band characteristics of oxy-RBCs and deoxy-RBCs, achieving a sensitivity of 100%. This spectroscopic method holds promise for developing technology to discriminate the sex of early chicken embryos in ovo by detecting differences in oxygen saturation of RBCs based on sex.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

Plasmonic trimers designed as SERS-active chemical traps for subtyping of lung tumors.

Plasmonic materials can generate strong electromagnetic fields to boost the Raman scattering of surrounding molecules, known as surface-enhanced Raman scattering. However, these electromagnetic fields are heterogeneous, with only molecules located at the 'hotspots', which account for ≈ 1% of the surface area, experiencing efficient enhancement. Herein, we propose patterned plasmonic trimers, consisting of a pair of plasmonic dimers at the bilateral sides and a trap particle positioned in between, to address this challenge. The trimer configuration selectively directs probe molecules to the central traps where 'hotspots' are located through chemical affinity, ensuring a precise spatial overlap between the probes and the location of maximum field enhancement. We investigate the Raman enhancement of the Au@Al2O3-Au-Au@Al2O3 trimers, achieving a detection limit of 10-14 M of 4-methylbenzenethiol, 4-mercaptopyridine, and 4-aminothiophenol. Moreover, single-molecule SERS sensitivity is demonstrated by a bi-analyte method. Benefiting from this sensitivity, our approach is employed for the early detection of lung tumors using fresh tissues. Our findings suggest that this approach is sensitive to adenocarcinoma but not to squamous carcinoma or benign cases, offering insights into the differentiation between lung tumor subtypes.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning.

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy.

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Multimodal Imaging Using Raman Spectroscopy and FTIR in a Single Analytical Instrument with a Microscope (Infrared Raman Microscopy AIRsight, Shimadzu): Opportunities and Applications.

Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy are powerful analytical techniques widely used separately in different fields of study. Integrating these two powerful spectroscopic techniques into one device represents a groundbreaking advance in multimodal imaging. This new combination which merges the molecular vibrational information from Raman spectroscopy with the ability of FTIR to study polar bonds, creates a unique and complete analytical tool. Through a detailed examination of the microscope's operation and case studies, this article illustrates how this integrated analytical instrument can provide more thorough and accurate analysis than traditional methods, potentially revolutionising analytical sample characterisation. This article aims to present the features and possible uses of a unified instrument merging FTIR and Raman spectroscopy for multimodal imaging. It particularly focuses on the technological progress and collaborative benefits of these two spectroscopic techniques within the microscope system. By emphasising this approach's unique benefits and improved analytical capabilities, the authors aim to illustrate its applicability in diverse scientific and industrial sectors.

Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms.

Raman microspectroscopy has become an effective method for analyzing the molecular appearance of biomarkers in skin tissue. For the first time, we acquired in vitro Raman spectra of healthy and malignant skin tissues, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), at 532 and 785 nm laser excitation wavelengths in the wavenumber ranges of 900-1800 cm-1 and 2800-3100 cm-1 and analyzed them to find spectral features for differentiation between the three classes of the samples. The intensity ratios of the bands at 1268, 1336, and 1445 cm-1 appeared to be the most reliable criteria for the three-class differentiation at 532 nm excitation, whereas the bands from the higher wavenumber region (2850, 2880, and 2930 cm-1) were a robust measure of the increased protein/lipid ratio in the tumors at both excitation wavelengths. Selecting ratios of the three bands from the merged (532 + 785) dataset made it possible to increase the accuracy to 87% for the three classes and reach the specificities for BCC + SCC equal to 87% and 81% for the sensitivities of 95% and 99%, respectively. Development of multi-wavelength excitation Raman spectroscopic techniques provides a versatile non-invasive tool for research of the processes in malignant skin tumors, as well as other forms of cancer.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction.

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Improvement of a low-cost protocol for a simultaneous comparative evaluation of hydrolytic activity between sessile and planktonic cells: Candida albicans as a study model.

Candida albicans is often implicated in nosocomial infections with fatal consequences. Its virulence is contributed to hydrolytic enzymes and biofilm formation. Previous research focused on studying these virulence factors individually. Therefore, this study aimed to investigate the impact of biofilm formation on the hydrolytic activity using an adapted low-cost method. Eleven strains of C. albicans were used. The biofilms were formed on pre-treated silicone discs using 24-well plates and then deposited on the appropriate agar to test each enzyme, while the planktonic cells were conventionally seeded. Biofilms were analysed using Raman spectroscopy, fluorescent and scanning electron microscopy. The adapted method provided an evaluation of hydrolytic enzymes activity in C. albicans biofilm and showed that sessile cells had a higher phospholipase and proteinase activities compared with planktonic cells. These findings were supported by spectroscopic and microscopic analyses, which provided valuable insights into the virulence mechanisms of C. albicans during biofilm formation.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography.

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Vibrational imaging of metabolites for improved microbial cell strains.

Significance: Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing. Aim: This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism. Approach: We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity. Results: Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level. Conclusions: Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy.

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

Nasopharyngeal carcinoma cell screening based on nuclear targeting Surface-Enhanced Raman Scattering (SERS) detection.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a malignant epithelial carcinoma arising from the nasopharyngeal mucosal lining. Diagnosis of NPC at early stage can improve the outcome of patients and facilitate reduction in cancer mortality. The most significant change between cancer cells and normal cells is the variation of cell nucleus. Therefore, accurately detecting the biochemical changes in nucleus between cancer cells and normal cells has great potential to explore diagnostic molecular markers for NPC. Highly sensitive surface-enhanced Raman scattering (SERS) could reflect the biochemical changes in the process of cell cancerization at the molecular level. However, rapid nuclear targeting SERS detection remains a challenge. RESULTS: A novel and accurate nuclear-targeting SERS detection method based on electroporation was proposed. With the assistance of electric pulses, nuclear-targeting nanoprobes were rapidly introduced into different NPC cells (including CNE1, CNE2, C666 cell lines) and normal nasopharyngeal epithelial cells (NP69 cell line), respectively. Under the action of nuclear localization signaling peptides (NLS), the nanoprobes entering cells were located to the nucleus, providing high-quality nuclear SERS signals. Hematoxylin and eosin (H&E) staining and in situ cell SERS imaging confirmed the excellent nuclear targeting performance of the nanoprobes developed in this study. The comparison of SERS signals indicated that there were subtle differences in the biochemical components between NPC cells and normal nasopharyngeal cells. Furthermore, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples, and high sensitivity, specificity, and accuracy were obtained in the screening of NPC cells from normal nasopharyngeal epithelial cells. SIGNIFICANCE: To the best of our knowledge, this is the first study that employing nuclear-targeting SERS testing to screen nasopharyngeal carcinoma cells. Based on the electroporation technology, nanoprobes can be rapidly introduced into living cells for intracellular biochemical detection. Nuclear-targeting SERS detection can analyze the biochemical changes in the nucleus of cancer cells at the molecular level, which has great potential for early cancer screening and cytotoxicity analysis of anticancer drugs.

Bead-based spontaneous Raman codes for multiplex immunoassay.

In the immunoassay process, for fulfilling the need to identify multiple analytes in a small amount of complex sample matrix, it is desirable to develop highly efficient and specific multiplex suspension array technology. Raman coding strategy offers an attractive solution to code the suspension arrays by simply combing narrow spectral bands with stable signal intensities through solid-phase synthesis on the resin beads. Based on this strategy, we report the bead-based spontaneous Raman codes for multiplex immunoassay. The study resulted in superior selectivity of the Raman-encoded beads for binding with single and multiple analytes, respectively. With the use of mixed types of Raman-encoded immunoassay beads, multiple targets in small amounts of samples were identified rapidly and accurately. By confirming the feasibility of bead-based spontaneous Raman codes for multiplex immunoassay, we anticipate this novel technology to be widely applied in the near future.

A ratiometric SERS aptasensor based on catalytic hairpin self-assembly mediated cyclic signal amplification strategy for the reliable determination of E. coli O157:H7.

A ratiometric SERS aptasensor based on catalytic hairpin self-assembly (CHA) mediated cyclic signal amplification strategy was developed for the rapid and reliable determination of Escherichia coli O157:H7. The recognition probe was synthesized by modifying magnetic beads with blocked aptamers, and the SERS probe was constructed by functionalizing gold nanoparticles (Au NPs) with hairpin structured DNA and 4-mercaptobenzonitrile (4-MBN). The recognition probe captured E. coli O157:H7 specifically and released the blocker DNA, which activated the CHA reaction on the SERS probe and turned on the SERS signal of 6-carboxyl-x-rhodamine (ROX). Meanwhile, 4-MBN was used as an internal reference to calibrate the matrix interference. Thus, sensitive and reliable determination and quantification of E. coli O157:H7 was established using the ratio of the SERS signal intensities of ROX to 4-MBN. This aptasensor enabled detection of 2.44 × 102 CFU/mL of E. coli O157:H7 in approximately 3 h without pre-culture and DNA extraction. In addition, good reliability and excellent reproducibility were observed for the determination of E. coli O157:H7 in spiked water and milk samples. This study offered a new solution for the design of rapid, sensitive, and reliable SERS aptasensors.

A double boronic acid affinity "sandwich" SERS biosensor based on magnetic boronic acid controllable-oriented imprinting for high-affinity biomimetic specific recognition and rapid detection of target glycoproteins.

Transferrin (TRF), recognized as a glycoprotein clinical biomarker and therapeutic target, has its concentration applicable for disease diagnosis and treatment monitoring. Consequently, this study developed boronic acid affinity magnetic surface molecularly imprinted polymers (B-MMIPs) with pH-responsitivity as the "capture probe" for TRF, which have high affinity similar to antibodies, with a dissociation constant of (3.82 ± 0.24) × 10-8 M, showing 7 times of reusability. The self-copolymerized imprinted layer synthesized with dopamine (DA) and 3-Aminophenylboronic acid (APBA) as double monomers avoided nonspecific binding sites and produced excellent adsorption properties. Taking the gold nanostar (AuNS) with a branch tip "hot spot" structure as the core, the silver-coated AuNS functionalized with the biorecognition element 4-mercaptophenylboronic acid (MPBA) was employed as a surface-enhanced Raman scattering (SERS) nanotag (AuNS@Ag-MPBA) to label TRF, thereby constructing a double boronic acid affinity "sandwich" SERS biosensor (B-MMIPs-TRF-SERS nanotag) for the highly sensitive detection of TRF. The SERS biosensor exhibited a detection limit for TRF of 0.004 ng/mL, and its application to spiked serum samples confirmed its reliability and feasibility, demonstrating significant potential for clinical TRF detection. Moreover, the SERS biosensor designed in this study offers advantages in stability, detection speed (40 min), and cost efficiency. The portable Raman instrument for SERS detection fulfills the requirements for point-of-care testing.