Quantification Of Parameters Research Articles

Correlation analysis between the mDIXON-quant fat quantification parameters of the infrapatellar fat pad and the severity of knee osteoarthritis

PurposeThis study aimed to explore the correlation between the proton density fat fraction (PDFF) of the infrapatellar fat pad and the severity of knee osteoarthritis (KOA), with a focus on identifying potential imaging biomarkers for KOA progression.MethodsSeventy patients with KOA and forty-four healthy controls (HCs) were prospectively recruited for conventional MR and mDIXON-Quant sequence scanning. The mDIXON-Quant technique was selected for its precision in fat quantification and ability to provide three-dimensional water-fat separation, offering superior accuracy in assessing fat content compared to traditional methods.The severity of KOA was assessed via the whole-organ magnetic resonance imaging score (WORMS). Intraclass correlation coefficients (ICCs) were calculated to assess interobserver agreement for the PDFF measurements. Differences in the PDFF between KOA patients and HCs were compared. Additionally, the correlations between the PDFF of the infrapatellar fat pad in KOA patients and scores of the characteristic areas included in the knee WORMS were analyzed.ResultsThe PDFF of the infrapatellar fat pad in KOA patients was significantly lower than in HCs. PDFF in KOA patients was negatively correlated with multiple aspects of total knee WORMS scores (e.g., articular cartilage integrity, etc.; r from − 0.94 to -0.25; P < 0.05), except for medial or lateral collateral ligament integrity (r = 0.27, P ≥ 0.05). Interobserver agreement was excellent (ICC = 0.793, P < 0.001).ConclusionsThe PDFF of the infrapatellar fat pad is significantly associated with KOA severity, demonstrating a progressive decrease as the disease advances. These findings suggest that PDFF holds promise as a potential objective biomarker for evaluating KOA severity. However, further validation in larger and more diverse cohorts is required to confirm its clinical applicability. This study is a prospective investigation that adhered to the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of Hebei Medical University (Approval No. [2024] Research Review No. 056).

Comparison of EGF Methods for Ridgecrest Sequence: Can EGFs Help Resolve Ambiguity in Isolating Source Spectra?

ABSTRACT The spectral stress drop is a popular parameter for the simple quantification and characterization of an earthquake source and its expected seismic radiation, enabling investigation of earthquake spatial and temporal variability for larger numbers of events. In addition, spectral measurements are one of the few possible for earthquake characterization and hazard prediction in regions of low seismicity. However, spectral stress-drop estimates are uncertain, especially as recorded earthquakes may be too complex to characterize ideally with a single parameter. Empirical Green’s function (EGF) approaches to isolate the earthquake source are widely regarded as one of the best for individual analysis of well-recorded earthquakes. However, analysis decisions related to the selection of stations, EGFs, time windows, frequency bandwidth, and source models can cause discrepancies in resulting estimates of the source spectrum, source time function, and source parameters. We present results following one well-developed EGF approach, and compare it with those from three other independent methods applied to earthquakes in the 2019 Ridgecrest, California, earthquake, sequence selected for the Southern California Earthquake Center /U.S. Geological Survey Community Stress Drop Validation Study. The common data set consists of two weeks of earthquakes from the 2019 Ridgecrest earthquake sequence, including nearly 13,000 events of M 1 and greater, recorded on stations within 100 km. We obtain estimates of corner frequency and spectral stress drop for 75 earthquakes (M 2.2–4.6) and find varying degrees of similarity among studies. We investigate four events in detail (M 2.7–4.1) and find that we obtain consistent results when the sources are relatively simple. Multiple EGFs produce good ratios and source time functions at stations with good azimuthal distribution. This suggests that there is a role for such approaches to resolve the inherent ambiguity in larger scale inversions between source scaling and attenuation and site effects.

Atomic Force Microscopy: A Versatile Tool in Cancer Research.

The implementation of novel analytic methodologies in cancer and biomedical research has enabled the quantification of parameters that were previously disregarded only a few decades ago. A notable example of this paradigm shift is the widespread integration of atomic force microscopy (AFM) into biomedical laboratories, significantly advancing our understanding of cancer cell biology and treatment response. AFM allows for the meticulous monitoring of different parameters at the molecular and nanoscale levels, encompassing critical aspects such as cell morphology, roughness, adhesion, stiffness, and elasticity. These parameters can be systematically investigated in correlation with specific cell treatment, providing important insights into morpho-mechanical properties during normal and treated conditions. The resolution of this system holds the potential for its systematic adoption in clinics; its application could produce useful diagnostic information regarding the aggressiveness of cancer and the efficacy of treatment. This review endeavors to analyze the current literature, underscoring the pivotal role of AFM in biomedical research, especially in cancer cases, while also contemplating its prospective application in a clinical context.

Planning transcatheter pulmonary valve implantation in the dysfunctional native RVOT: A semi-automated pipeline for dynamic analysis based on 4D-CT imaging.

Planning transcatheter pulmonary valve implantation in the dysfunctional native RVOT: A semi-automated pipeline for dynamic analysis based on 4D-CT imaging.

An automated and high-throughput approach for enhanced precision of adenoviral titering.

An automated and high-throughput approach for enhanced precision of adenoviral titering.

Longitudinal assessment of structural and locomotor deficits as a prediction of severity in the collagenase-induced mouse model of osteoarthritis

BackgroundThe aim of this study was to provide an in-depth longitudinal locomotor and structural characterisation of the collagenase-induced osteoarthritis (CIOA) mouse model, using the most relevant and up-to-date non-invasive locomotor phenotyping and imaging methods. The ultimate goal of this study was to predict histological scores, the gold standard parameter in osteoarthritis (OA), based on locomotor or structural deficits.MethodsThe CIOA model was induced in C57BL/6 male mice, which were then maintained in their home cage with or without a running wheel for 6 weeks. Both global and fine locomotor effects were measured using the open field and Catwalk™ tests. Imaging of bone and cartilage was performed using either µCT, contrast-enhanced µCT or confocal laser scanning microscopy (CLSM) at different time points. Correlations between functional or structural changes and histological scores were sought in order to provide tools for predicting histological degradation.ResultsLocomotor deficits were observed at early time points (days 3 to 9) but did not persist to the end of the experiment. Signs of inflammation appeared as early as day 9. They worsened on day 28 as the disease progressed and meniscal calcifications were observed by µCT. The early functional and structural changes correlated with the histological scores measured post mortem and some specific locomotor or structural parameters were identified as predictors of histological changes. Free exercise (voluntary running wheel activity) did not seem to influence the severity of the observed changes.ConclusionsOpen-field quantification of kinetic parameters is a simple and timely relevant test to detect early locomotor changes and predict histological changes. Meniscal calcifications and osteophyte formation, which can be observed by µCT at early time points, are also highly predictive of OA severity. These two non-invasive techniques are very useful for longitudinal monitoring of mice and OA score prediction.

Artificial Intelligence-Based Uveitis Diagnosis Through Retinal Vasculature Analysis: A Paradigm Shift in Ocular Tuberculosis.

Diagnosis of uveitis is complex and often requires a series of investigations. Here, we utilize artificial intelligence (AI) for the quantitative analysis of retinal vasculature parameters from fundus photographs to differentiate confirmed ocular tuberculosis (TB) from QuantiFERON (QFT)-positive uveitis without another identifiable cause and ocular toxoplasmosis. Medical records and stored fundus images of patients with uveitis from a cohort at the Department of Ophthalmology, University of Indonesia, were analyzed. Three groups of patients were included: confirmed ocular TB (group A), QFT-positive uveitis (group B), and ocular toxoplasmosis (group C). Fundus images were processed using the Retinalysis models package for segmentation and quantification of retinal vasculature parameters. The study included nine patients (13 eyes) in group A, 38 patients (48 eyes) in group B, and 26 patients (39 eyes) in group C. Significant differences were found in vein tortuosity parameter, in the eyes within group A showing lower tortuosity score compared to eyes within group B (p = 0.030) and group C (p = 0.013). The area under the curve (AUC) of vein tortuosity for group A compared to group B was 0.749 (95% confidence interval (CI): 0.606-0.892), with a sensitivity of 67.3% and specificity of 76.9%. The AUC of vein tortuosity for group A against group C was 0.803 (95% CI: 0.658-0.948), with a sensitivity of 74.4% and a specificity of 84.6%. In group A, uveitis resolution and vein tortuosity tended to be normalized upon complete antitubercular treatment. AI-based quantification of retinal vasculature parameters, particularly vein tortuosity, can differentiate confirmed ocular TB from QFT-positive uveitis and ocular toxoplasmosis. This approach shows promise for more precise diagnostic and therapeutic accuracy in ocular TB.

Investigations into the Nucleation Dynamics of the Stable Na-Metal Anode: Revealing the Role of a Tin-Infused Carbon Nanofiber Interlayer.

Fundamental understanding and controlling of sodium nucleation are essential for enhancing the performance, safety, and longevity of sodium metal batteries, which is not yet clearly understood in the case of sodium metal batteries. The present study showcases how a modification in the host material influences nucleation kinetics. Current-time transient studies on copper, carbon nanofiber, and tin-embedded carbon nanofiber interlayers employing the Scharifker-Hills model elucidate the mode of nucleation. This work tries to delve deep and presents a case study on how a tin-based interlayer can not only minimize the barrier for sodium nucleation but also direct the sequential progressive and instantaneous nucleation of sodium metal while reducing the overpotential substantially, resulting in crystalline, uniform Na-metal deposition. Further, to account for the complex dynamics of solid electrolyte interphase (SEI) formation distinctly associated with alkali metal deposition, the SEI-fracture model has been included, and the quantification of electrochemical nucleation parameters is obtained. The results provide important insights into the sodium nucleation mechanism, paving the way to counter dendrite formation and SEI dissolution issues of the Na-metal anode.

Impact of radiation dose to immune cells on survival in patients with esophageal cancer receiving neoadjuvant chemoradiotherapy: a retrospective analysis

BackgroundNeoadjuvant chemoradiotherapy (NCRT) is the standard treatment for locally advanced esophageal squamous cell carcinoma (ESCC). This study aims to explore the effect of the estimated dose of radiation to immune cells (EDRIC) on lymphocytes in patients with locally advanced ESCC undergoing NCRT and evaluate the impact of EDRIC on patient prognosis.MethodsThis retrospective study included patients with locally advanced ESCC who received NCRT followed by radical esophagectomy at our institution between March 2016 and December 2022. EDRIC was calculated as a function of the number of fractions and the average doses to the lungs, heart, and remaining body. The optimal cut-off value of EDRIC was determined based on disease-free survival (DFS), and patients were divided into two groups according to this cut-off value. Kaplan–Meier and Cox proportional hazards regression analyses were used to estimate overall survival (OS) and DFS.ResultsThe 182 patients included in the study had a mean EDRIC of 5.41 ± 1.70 Gy and an EDRIC cut-off value of 6.86 Gy. Patients with an EDRIC ≤ 6.86 Gy had a significant advantage in DFS (hazard ratio [HR]: 0.37, 95% confidence interval [CI]: 0.20–0.68, P = 0.001) and OS (HR: 0.22, 95% CI: 0.10–0.48, P = 0.001). EDRIC was closely associated with the lymphocyte nadir (P < 0.001) and lymphocytopenia values (P < 0.001). Patients with a lymphocyte nadir ≤ 0.16 × 109/L had poorer DFS (HR: 2.04, 95% CI: 1.07–3.88, P = 0.025) and OS (HR: 2.52, 95% CI: 1.14–5.56, P = 0.018).ConclusionEDRIC is an independent prognostic factor for patients with locally advanced ESCC undergoing NCRT. A higher EDRIC is associated with poorer DFS and OS and is closely related to reduced lymphocyte counts. As a quantifiable parameter, future research should explore methods of lowering EDRIC during NCRT while ensuring adequate tumor coverage and sufficient dosage.

4D Dynamic contrast-enhanced breast CT: Phantom-based reconstruction parameter optimization for iodine quantification.

Four-dimensional dynamic contrast-enhanced breast CT (4D DCE-bCT) offers promising high-resolution spatial and temporal imaging capabilities for the characterization and monitoring of breast tumors. However, the optimal combination of parameters for iodine quantification in image space remains to bedetermined. This study aims to optimize a dedicated bCT system to perform long dynamic contrast-enhanced scans with high spatio-temporal resolution while maintaining a reasonable radiation dose. Our protocol includes the acquisition of a high-quality prior image that is reconstructed with a polychromatic iterative algorithm (IMPACT). The acquisition of the post-contrast sequence is continuous but sparse and these images are reconstructed using prior image constrained compressed sensing (PICCS). A four-step optimization process is performed using images of a physical phantom. First, the optimal tube current is selected by taking the noise level into account. Second, the optimal number of angles is selected based on the absence of streak artifacts. Third, the number of iterations in IMPACT is specified at the lowest value that achieves the highest spatial resolution. Finally, the number of iterations in PICCS is determined based on the quantitative accuracy of a range of iodine concentrations. When a high-quality prior image is available, the imaging of post-contrast images can be performed using just 40 projection angles with a tube current of 32 mA. The noise level in the post-contrast images is inherited from the prior image and no streak artifacts are visible. Mean difference between the linear attenuation coefficients of samples containing iodine reconstructed with IMPACT using all 360 projections and PICCS using 40 projections is 0.0004 at most. The spatial resolution of images reconstructed with PICCS is lower than the one of IMPACT images and is concentration dependent. The cut-off frequency at 10% modulation transfer function drops from 1.55 in the prior image to 0.9 when the target with the largest concentration is evaluated. The total mean glandular dose of the protocol does not exceed 22.5mGy. This study found the optimal acquisition and reconstruction parameters for a low-dose dynamic contrast-enhanced bCT protocol. The numerical accuracy of the proposed protocol was ensured by performing a physical phantomstudy.

Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete

The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 concrete sample data, including compressive strength values and eleven other physico-mechanical properties, collected from the Cua Luc bridge project in Vietnam. This dataset was partitioned into a training set (70%) and a testing set (30%) for model training and validation. Various validation metrics, including R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the models. The findings of this study demonstrated that both models GP-RBF and GP-POL exhibited strong performance in predicting CSC, with GP-POL demonstrating marginal superiority over GP-RBF. Consequently, it can be concluded that POL is more efficacious than RBF in training the GP model for CSC prediction.

Value of Advanced Cardiac CTA in Clinical Assessment of Hypertrophic Cardiomyopathy: A Literature Review and Practical Implications.

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiac anomaly with a potentially unfavorable clinical outcome. The essential role of multimodality imaging in HCM is well recognized by major professional cardiac imaging societies and has been incorporated into the HCM clinical practice guidelines. Appropriate utilization of cardiac imaging tools is cardinal for accurate diagnosis and appropriate management for HCM patients to mitigate their lifelong risk of adverse events. Echocardiography is the imaging modality of choice for clinical diagnosis of HCM. Cardiac magnetic resonance (CMR) and coronary computed tomography angiogram (CCTA) offer complementary practical information for an inclusive evaluation in such patients. CCTA provides a thorough analysis of the cardiac anatomy and function that is paramount in HCM clinical decision-making. This review summarizes the utility of CCTA in the clinical assessment of patients with HCM. It outlines the multi-role of CCTA in HCM, including the quantification of cardiac parameters, myocardial tissue characterization, left ventricular (LV) functional analysis, the definition of cardiac and coronary arteries (CA) anatomy, and the provision of a roadmap for septal reduction therapies (SRT), mitral valve (MV) intervention, and atrial fibrillation (AF) ablation.

Real-time monitoring of fermentation processes in wine production with benchtop 1H NMR spectroscopy.

Real-time monitoring of fermentation processes in wine production with benchtop 1H NMR spectroscopy.

What influences the distribution of microplastics in the marine environment? An interdisciplinary study reveals key factors driving microplastic in the North Sea.

What influences the distribution of microplastics in the marine environment? An interdisciplinary study reveals key factors driving microplastic in the North Sea.

Prognostic significance of tricuspid regurgitation grading using different echocardiographic approaches

Abstract Introduction Tricuspid regurgitation (TR) is routinely graded using quantitative and qualitative echocardiographic parameters. The current grading scheme consists of five TR grades and primarily incorporates vena contracta (VC) and effective regurgitation orifice area (EROA) assessed by the proximal isovelocity surface area (PISA) method. Additionally, an angle-corrected PISA method was proposed as the standard approach seem to underestimate especially ventricular TR. Purpose Since different measurements may lead to different TR severity grades, the present study aimed to evaluate the prognostic significance of TR grading using various approaches. Methods Hospitalized patients with heart failure (HF) according to current guidelines of the European Society of Cardiology (ESC) were enrolled from 2020 to 2022 (NCT04570098). Inclusion criteria comprised an echocardiographic examination up to three months prior to study participation. TR was graded into the categories moderate to torrential according to the recommended standard, including all quantitative and qualitative parameters as proposed by the guidelines, biplane VC, PISA EROA, and angle-corrected PISA EROA. Survival status was assessed during twelve-month follow-up. Cox regression analysis and log-rank test were used to evaluate the prognostic value of TR grades according to the different echocardiographic methods with the reference category none or mild TR. Results A total of 1003 HF patients were enrolled, including 66% with none or mild TR. The prevalence of moderate to torrential TR depending on the use of the different methods was as follows: 18% moderate, 10% severe, 2% massive, and 3% torrential TR according to quantitative and qualitative parameters; 12% moderate TR, 6% severe TR, 4% massive TR, and 4% torrential TR assessed by PISA EROA; 9% moderate TR, 6% severe TR, 3% massive TR, and 6% torrential TR according to angel-corrected PISA EROA; as well as 13% moderate TR, 14% severe TR, 4% massive TR, and 1% torrential TR measured by VC. During the study period, the overall-mortality rate reached 15%. A more severe TR was associated with an increase in mortality regardless of the TR grading approach (log-rank test p&amp;lt;0.001). As shown in Table 1, only TR grading based on VC consistently demonstrated an increased mortality risk across all severity grades. Conclusion There are various parameters for TR quantification used in clinical routine. Overall, a more severe TR was associated with an increase in all-cause mortality in HF patients. VC may be superior to PISA EROA and angle-corrected PISA EROA to differentiate the mortality risk of moderate to torrential TR.

Cyclical Physiology of Elite Female Athletes: Longitudinal Quantification of Wellness Parameters Considering Menstrual, Weekly, and Seasonal Variations

The purpose of the study is to identify variations in wellness parameters among elite female athletes, considering simultaneously the influence of natural menstrual cycle (MC) or hormonal contraception (HC) phases and weekly and seasonal variations. A total of 54 elite female athletes were followed up with through a 6-month daily monitoring of their reported fitness, sleep quality, mood, and menstrual symptoms. Estimated cycle phases, days of the week, and seasons were all integrated into a single model to study cyclicities concomitantly. A total of 263 cycles were analyzed, totaling 7370 pieces of daily self-reported data. We observed more frequent symptoms during the menstruation and the pause phase. Reported fitness was greater in the mid-cycle and mood was lower at the end of the cycle for the MC group. Wellness parameters were rather stable for HC users. For both groups, symptoms were more frequent in summer. Reported fitness levels were lower on Monday, while mood scores were highest on Saturday and Sunday. In summer and autumn, fitness, sleep quality, and mood were greater. A cyclical wellness rhythm was identified in the menstrual cycle and persists even when considering weekdays and seasons cyclicities. This comprehensive approach helps to identify physiological influence on human behaviors, considering the specificities of women.

Lyophilized human platelet lysate: manufacturing, quality control, and application.

A significant number of platelet concentrates (PCs) is discarded daily in blood banks due to limited shelf life. Human platelet lysate (HPL), derived from expired PCs, has gained attention as an ethical and sustainable cell culture media supplement in biomedical research and cell therapy production. However, HPL is subject to decisive disadvantages such as batch differences and lack of storage stability. To overcome these limitations and to enhance the applicability of HPL, we developed an HPL manufacturing protocol including a lyophilization process. The aim of this study was to investigate the influence of HPL lyophilization on parameters of quality control, including growth factor concentrations and the culture of human mesenchymal stromal cells (hMSCs). We performed a paired comparison of six batches of HPL and lyophilized HPL (L-HPL) regarding the quality parameters pH, total protein, osmolality, sodium, potassium and chloride concentration. Concentrations of 11 growth factors and cytokines were compared between HPL and L-HPL. Additionally, we determined cell yield, proliferation capacity, viability and trilineage differentiation potential of hMSCs following expansion in HPL- and L-HPL-supplemented cell culture media. Quantification of the quality parameters revealed non-altered pH, osmolality and potassium concentrations and slightly lower total protein, sodium and chloride concentrations of L-HPL compared to HPL. Growth factor and cytokine concentrations did not differ between HPL and L-HPL. Cell yield, division cycles and viability of hMSCs cultured in either HPL- or L-HPL-containing media were comparable. Cells differentiated in medium containing L-HPL showed a slightly higher capacity for osteogenic differentiation, while adipogenic differentiation and chondrogenic differentiation potentials remained unchanged. We successfully developed a method to produce well-applicable L-HPL. The comparison of L-HPL with HPL did not reveal any relevant differences regarding quality control parameters of routine testing, growth factor concentrations and hMSC functionality, demonstrating the suitability of L-HPL as a cell culture supplement. These results emphasize the potential of L-HPL as a sustainable and ethical alternative to animal-derived serum products in biomedical research and drug development.

Optimization of Two Methods for the Rapid and Effective Extraction of Quinine from Cinchona officinalis.

This study successfully optimized two advanced extraction methods, microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), for the efficient and rapid recovery of quinine from Cinchona officinalis. Among the evaluated parts of the plant, the bark consistently yielded the highest quinine concentration, highlighting its significance as the primary source for alkaloid extraction. The optimized conditions for MAE (65% ethanol, 130 °C, 34 min) achieved a maximum yield of 3.93 ± 0.11 mg/g, while UAE (61% ethanol, 25 °C, 15 min) provided a faster but slightly lower yield of 2.81 ± 0.04 mg/g. These findings confirm the superiority of MAE and UAE over conventional methods like Soxhlet extraction in terms of time efficiency and sustainability. The quantification of quinine using high-performance liquid chromatography (HPLC) coupled with advanced detection methods further validated the reliability and reproducibility of the results. While this study focused on optimizing extraction and quantification parameters, it sets the groundwork for future research into the sustainable utilization and potential valorization of C. officinalis byproducts. These findings not only provide a standardized protocol for extracting quinine but also contribute to the broader application of green chemistry principles in pharmaceutical production.

Biocompatible EDOT-Pyrrole Conjugated Conductive Polymer Coating for Augmenting Cell Attachment, Activity, and Differentiation.

Developing scaffolds supporting functional cell attachment and tissue growth is critical in basic cell research, tissue engineering, and regenerative medicine approaches. Though poly(ethylene glycol) (PEG) and its derivatives are attractive for hydrogels and scaffold fabrication, they often require bioactive modifications due to their bioinert nature. In this work, biomimetic synthesized conductive polypyrrole-poly(3,4-ethylenedioxythiophene) copolymer doped with poly(styrenesulfonate) (PPy-PEDOT:PSS) was used as a biocompatible coating for poly(ethylene glycol) diacrylate (PEGDA) hydrogel to support neuronal and muscle cells' attachment, activity, and differentiation. The synthesized copolymer was characterized by Raman spectroscopy and dynamic light scattering. Its electrochemical properties were studied using galvanostatic charge-discharge (GCD) and voltammetry. PPy-PEDOT:PSS-coated hydrogels were characterized by Raman spectroscopy and atomic force microscopy, and protein adsorption was assessed using a quartz crystal microbalance with dissipation monitoring. Attachment and differentiation of the ND7/23 neuron hybrid cell line and C2C12 myoblasts were evaluated by cell cytoskeleton staining and quantification of morphological parameters. Viability was assessed by live/dead staining using flow cytometry. Cortex neural activity was studied by calcium ion influx that could be detected through the dynamic fluorescence changes of Fluo-4. The PPy-PEDOT:PSS coating supported cell attachment and differentiation and was nontoxic to cells. Primary neurons attached and remained responsive to electrical stimulation. Altogether, the biocompatible copolymer PPy-PEDOT:PSS is a simple yet effective alternative for hydrogel coating and presents great potential as an interface for nervous and other electrically excitable tissues.

Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing. Replacing optical probes with contactless short-wave infrared (SWIR) hyperspectral cameras allows efficient collection of thousands of absorption signals in a handful of images. This high repetition allows for effective denoising of each spectrum, so interpretable linear models can quantify metabolites. To illustrate, an interpretable linear model called L-SLR is trained using small datasets obtained with a SWIR HSI camera to quantify fructose, viable cell density (VCD), glucose, and lactate. The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). Further, these models are also able to handle quantification of a metabolite like fructose in the presence of high background concentration of similar metabolite with almost identical chemical interactions in water like glucose. The model achieves reasonable quantification performance for large fructose level (100–1000 mg/dL) quantification (r2 = 0.92, MAE = 25.1 mg/dL) and small fructose level (< 60 mg/dL) concentrations (r2 = 0.85, MAE = 4.97 mg/dL) in complex media like Fetal Bovine Serum (FBS). Finally, the model provides sparse interpretable weight matrices that hint at the underlying solution changes that correlate to each cell parameter prediction.