A skin-interfaced microfluidic platform supports dynamic sweat biochemical analysis during human exercise.

Blood lactate concentration is an established circulating biomarker for measuring muscle acidity and can be evaluated for monitoring endurance, training routines, or athletic performance. Sweat is an alternative biofluid that may serve similar purposes and offers the advantage of noninvasive collection and continuous monitoring. The relationship between blood lactate and dynamic sweat biochemistry for wearable engineering applications in physiological fitness remains poorly defined. Here, we developed a microfluidic wearable band with an integrated colorimetric timer and biochemical assays that temporally captures sweat and measures pH and lactate concentration. A colorimetric silver nanoplasmonic assay was used to measure the concentration of lactate, and dye-conjugated SiO2 nanoparticle-agarose composite materials supported dynamic pH analysis. We evaluated these sweat biomarkers in relation to blood lactate in human participant studies during cycling exercise of varying intensity. Iontophoresis-generated sweat pH from regions of actively working muscles decreased with increasing heart rate during exercise and was negatively correlated with blood lactate concentration. In contrast, sweat pH from nonworking muscles did not correlate with blood lactate concentration. Changes in sweat pH and blood lactate were observed in participants who did not regularly exercise but not in individuals who regularly exercised, suggesting a relationship to physical fitness and supporting further development for noninvasive, biochemical fitness evaluations.

Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach.

Recent advances in remote sensing techniques provide a new horizon for monitoring the spatiotemporal variations of harmful algal blooms (HABs) using hyperspectral data in inland water. In this study, a hierarchical concatenated variational autoencoder (HCVAE) is proposed as an efficient and accurate deep learning (DL) based bio-optical model. To demonstrate its usefulness in retrieving algal pigments, the HCVAE is applied to bloom-prone regions in Daecheong Lake, South Korea. By abstracting the similarity between highly related features using layer-wise clique-based latent-feature extraction, HCVAE reduces the computational loads in deriving outputs while preventing performance degradation. Graph-based clique-detection uses information theory-based criteria to group the related reflectance spectra. Consequently, six latent features were extracted from 79 spectral bands to consist of a multilevel hierarchy of HCVAE that can simultaneously estimate concentrations of chlorophyll-a (Chl-a) and phycocyanin (PC). Despite the parsimonious model architecture, the Chl-a and PC concentrations estimated by HCVAE closely agree with the measured concentrations, with test R2 values of 0.76 and 0.82, respectively. In addition, spatial distribution maps of algal pigments obtained from HCVAE using drone-borne reflectance successfully capture the blooming spots. Based on its multilevel hierarchical architecture, HCVAE can provide the importance of latent features along with their individual wavelengths using Shapley additive explanations. The most important latent features covered the spectral regions associated with both Chl-a and PC. The lightweight neural network DNNsel, which uses only the spectral bands of highest importance in latent-feature extraction, performed comparably to HCVAE. The study results demonstrate the utility of the multilevel hierarchical architecture as a comprehensive assessment model for near-real-time drone-borne sensing of HABs. Moreover, HCVAE is applicable to a wide range of environmental big data, as it can handle numerous sets of features.

HSP47 levels determine the degree of body adiposity.

Adiposity varies among individuals with the influence of diverse physiological, pathological, environmental, hormonal, and genetic factors, but a unified molecular basis remains elusive. Here, we identify HSP47, a collagen-specific chaperone, as a key determinant of body adiposity. HSP47 expression is abundant in adipose tissue; increased with feeding, overeating, and obesity; decreased with fasting, exercise, calorie restriction, bariatric surgery, and cachexia; and correlated with fat mass, BMI, waist, and hip circumferences. Insulin and glucocorticoids, respectively, up- and down-regulate HSP47 expression. In humans, the increase of HSP47 gene expression by its intron or synonymous variants is associated with higher body adiposity traits. In mice, the adipose-specific knockout or pharmacological inhibition of HSP47 leads to lower body adiposity compared to the control. Mechanistically, HSP47 promotes collagen dynamics in the folding, secretion, and interaction with integrin, which activates FAK signaling and preserves PPARγ protein from proteasomal degradation, partly related to MDM2. The study highlights the significance of HSP47 in determining the amount of body fat individually and under various circumstances.

COVID-19, Obesity, and GRP78: Unraveling the Pathological Link.

The coronavirus disease 2019 (COVID-19) pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to an unprecedented global surge in infections and fatalities. Notably, obesity has emerged as an important susceptibility factor for COVID-19; however, the pathological mechanisms for this remain poorly understood. Recent studies proposed a role for glucose-regulated protein 78 (GRP78), a protein implicated in both obesity and metabolic syndrome, which may function as a binding partner and/or co-receptor for SARS-CoV-2. Given its crucial involvement in diverse biological processes, GRP78 likely plays a major role in multiple facets of the viral life cycle and the pathology of COVID-19. This perspective review discusses the potential contributions of GRP78 to the dynamics of SARS-CoV-2 infection and pathology, particularly in the context of obesity. The primary objective is to facilitate a deeper understanding of the pathogenesis of COVID-19. Through this exploration, we aim to illuminate the complex interactions underpinning the nexus of COVID-19, obesity, and GRP78, ultimately paving the way for informed therapeutic strategies and preventive measures.

Hybrid model for daily streamflow and phosphorus load prediction.

Environmental factors, such as climate change and land use changes, affect water quality drastically. To consider these, various predictive models, both process-based and data-driven, have been used. However, each model has distinct limitations. In this study, a hybrid model combining the soil and water assessment tool and the reverse time attention mechanism (SWAT-RETAIN) was proposed for predicting daily streamflow and total phosphorus (TP) load of a watershed. SWAT-RETAIN was applied to Hwangryong River, South Korea. The hybrid model uses the SWAT output as input data for the RETAIN. Spatial, meteorological, and hydrological data were collected to develop the SWAT to generate high temporal resolution data. RETAIN facilitated effective simultaneous prediction. The SWAT-RETAIN exhibited high accuracy in predicting streamflow (Nash-Sutcliffe efficiency (NSE): 0.45, root mean square error (RMSE): 27.74, percent bias (PBIAS): 22.63 for test sets), and TP load (NSE: 0.50, RMSE: 423.93, PBIAS: 22.09 for test sets). This result was evident in the performance evaluation using flow duration and load duration curves. The SWAT-RETAIN provides enhanced temporal resolution and performance, enabling the simultaneous prediction of multiple variables. It can be applied to predict various water quality variables in larger watersheds.

Water-redispersible and high-yield α-chitin nanocrystals isolated using electron-beam irradiation as adsorbents to remove heavy metals and dye.

An innovative approach to fabricate transparent and redispersible α-chitin nanocrystals (ChNCs) with high overall yields was developed in this work for eventual commercial use. The nanomanufacturing process included electron-beam irradiation (EBI) of chitin for oxidation and degradation in the dried state, high-pressure nanoscale homogenization via swelling, CO2 absorption, and spray-drying to obtain dehydrated products. The resulting EBI-disassociated chitins contained increased amounts of carboxylate (0.19-0.27 mmol g-1) and a negligible fraction of D-glucosamine moiety (from ca. 6.5 to <1.0%) with an intrinsic structure identical to α-chitin derived from shrimp shell prior to purification by conventional methods, such as deproteination. The resulting EBI-induced ChNC series exhibited nano-sized and rod-like morphology with tunable lengths averaging 608-259 nm, uniform widths of ca. 16-12 nm, a high isolation yield of max. 81%, and sufficient anionic surface charges, as zeta potentials of -32 to -34 mV indicate that it is homogenously water-dispersible and stable with background transparency. Unlike ChNC prepared by HCl-hydrolysis, the dehydrated particles of EBI-induced ChNCs were clearly redispersible in water and retained the characteristics of the original nanomaterials. We also tested the redispersible EBI-induced ChNCs as effective adsorbents. Their anionic groups interacted with cationic heavy metals (Cu2+ and Fe3+) and organic blue dye via electrostatic attraction, forming robust hydrogels, which were self-supporting after centrifugation. The EBI-induced ChNCs produced with low environmental impact in this work offer a promising choice of adsorbents for the removal of undesirable chemicals during wastewater treatment.

Machine learning-guided prediction of potential engineering targets for microbial production of lycopene.

The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.

High reliability and accuracy of dynamic magnetic resonance imaging in the diagnosis of cervical Spondylotic myelopathy: a multicenter study.

BACKGROUND: Cervical spondylotic myelopathy (CSM) is a critical condition that results in significant neurologic deterioration. An accurate diagnosis is essential for determining its outcome and prognosis. The pathology is strongly associated with dynamic factors; therefore, dynamic magnetic resonance (MR) image could be crucial to accurately detect CSM. However, very few studies have evaluated the reliability and accuracy of dynamic MR in CSM. In this study, we aimed to compare intra- and interobserver reliabilities and accuracy of dynamic MR in detecting CSM using sagittal MR scans of the neck in the flexed, neutral, and extended position. METHODS: Out of 131 patients who underwent surgical treatments for CSM, 107 were enrolled in this study. The patient underwent three-types of sagittal MR scans that were obtained separately in different neck positions (neutral, flexion, and extension postures). The MR scans of the cervical spine were evaluated independently by three spine professionals, on the basis of tabled questionnaires. For accuracy, we performed a receiver operator characteristic analysis, and the overall discriminating ability of each method was measured by calculating the area under the ROC curve. The Cohen's kappa coefficient and the Fleiss-generalized kappa coefficient was used to the inter- and intra-observer reliabilities. RESULTS: The intraobserver reliability (using the Cohen's kappa coefficient) and interobserver reliability (using the Fless kappa coefficient) were respectively 0.64 and 0.52 for the neutral sagittal MR. The accuracy of neutral sagittal MR in detecting CSM was 0.735 (95% CI, 0.720 to 0.741) while that of extension sagittal MRI was 0.932 (96% CI, 0.921 to 0.948). CONCLUSIONS: Dynamic MR significantly showed better diagnostic reliability and accuracy in detecting CSM compared to conventional MR. In particular, extension MR scans could provide a more accurate diagnosis than other images.

GRP78, a Novel Host Factor for SARS-CoV-2: The Emerging Roles in COVID-19 Related to Metabolic Risk Factors.

The outbreak of coronavirus disease 19 (COVID-19), caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in an unprecedented amount of infection cases and deaths, leading to the global health crisis. Despite many research efforts, our understanding of COVID-19 remains elusive. Recent studies have suggested that cell surface glucose-regulated protein 78 (GRP78) acts as a host co-receptor for SARS-CoV-2 infection and is related to COVID-19 risks, such as older age, obesity, and diabetes. Given its significance in a wide range of biological processes, such as protein homeostasis and cellular signaling, GRP78 might also play an important role in various stages of the viral life cycle and pathology of SARS-CoV-2. In this perspective, we explore the emerging and potential roles of GRP78 in SARS-CoV-2 infection. Additionally, we discuss the association with COVID-19 risks and symptoms. We hope this review article will be helpful to understand COVID-19 pathology and promote attention and study of GRP78 from many clinical and basic research fields.

A Nanojunction pH Sensor within a Nanowire.

pH sensors that are nanoscopic in all three dimensions are fabricated within a single gold nanowire. Fabrication involves the formation of a nanogap within the nanowire via electromigration, followed by electropolymerization of pH-responsive poly(aniline) (PANI) that fills the nanogap forming the nanojunction. All fabrication steps are performed using wet chemical methods that do not require a clean room. The measured electrical impedance of the PANI nanojunction is correlated with pH from 2.0 to 9.0 with a response time of 30 s. Larger, micrometer-scale PANI junctions exhibit a slower response. The measured pH is weakly influenced by the salt concentration of the contacting aqueous solution. An impedance measurement at two frequencies (300 kHz and 1.0 Hz) enables estimation of the salt concentration and correction of the measured pH value, preserving the accuracy of the pH measurement across the entire calibration curve for salt concentrations up to 1.0 M. The result is a nanoscopic pH sensor with pH sensing performance approaching that of a conventional, macroscopic pH glass-membrane electrode.

Ketone body 3-hydroxybutyrate enhances adipocyte function.

Ketone bodies, including 3HBA, are endogenous products of fatty acid oxidation, and Hmgcs2 is the first rate-limiting enzyme of ketogenesis. From database analysis and in vivo and in vitro experiments, we found that adipose tissue and adipocytes express Hmgcs2, and that adipocytes produce and secrete 3HBA. Treatment with 3HBA enhanced the gene expression levels of the antioxidative stress factors, PPARγ, and lipogenic factors in adipose tissue in vivo and in adipocytes in vitro, accompanied by reduced ROS levels. Knockdown of endogenous Hmgcs2 in adipocytes markedly decreased 3HBA levels in adipocytes and decreased the gene expression levels of the antioxidative stress factors, PPARγ, and lipogenic factors with increased ROS levels. Conversely, overexpression of Hmgcs2 in adipocytes increased 3HBA secretion from adipocytes and enhanced the gene expression levels of the antioxidative stress factors, PPARγ, and lipogenic factors. These results demonstrate that 3HBA plays significant roles in enhancing the physiological function of adipocytes.

SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the lung, liver, adipose tissue, and pancreatic cells via IRF1.

BACKGROUND: COVID-19 can cause multiple organ damages as well as metabolic abnormalities such as hyperglycemia, insulin resistance, and new onset of diabetes. The insulin/IGF signaling pathway plays an important role in regulating energy metabolism and cell survival, but little is known about the impact of SARS-CoV-2 infection. The aim of this work was to investigate whether SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the host cell/tissue, and if so, the potential mechanism and association with COVID-19 pathology. METHODS: To determine the impact of SARS-CoV-2 on insulin/IGF signaling pathway, we utilized transcriptome datasets of SARS-CoV-2 infected cells and tissues from public repositories for a wide range of high-throughput gene expression data: autopsy lungs from COVID-19 patients compared to the control from non-COVID-19 patients; lungs from a human ACE2 transgenic mouse infected with SARS-CoV-2 compared to the control infected with mock; human pluripotent stem cell (hPSC)-derived liver organoids infected with SARS-CoV-2; adipose tissues from a mouse model of COVID-19 overexpressing human ACE2 via adeno-associated virus serotype 9 (AAV9) compared to the control GFP after SARS-CoV-2 infection; iPS-derived human pancreatic cells infected with SARS-CoV-2 compared to the mock control. Gain and loss of IRF1 function models were established in HEK293T and/or Calu3 cells to evaluate the impact on insulin signaling. To understand the mechanistic regulation and relevance with COVID-19 risk factors, such as older age, male sex, obesity, and diabetes, several transcriptomes of human respiratory, metabolic, and endocrine cells and tissue were analyzed. To estimate the association with COVID-19 severity, whole blood transcriptomes of critical patients with COVID-19 compared to those of hospitalized noncritical patients with COVID-19. RESULTS: We found that SARS-CoV-2 infection impaired insulin/IGF signaling pathway genes, such as IRS, PI3K, AKT, mTOR, and MAPK, in the host lung, liver, adipose tissue, and pancreatic cells. The impairments were attributed to interferon regulatory factor 1 (IRF1), and its gene expression was highly relevant to risk factors for severe COVID-19; increased with aging in the lung, specifically in men; augmented by obese and diabetic conditions in liver, adipose tissue, and pancreatic islets. IRF1 activation was significantly associated with the impaired insulin signaling in human cells. IRF1 intron variant rs17622656-A, which was previously reported to be associated with COVID-19 prevalence, increased the IRF1 gene expression in human tissue and was frequently found in American and European population. Critical patients with COVID-19 exhibited higher IRF1 and lower insulin/IGF signaling pathway genes in the whole blood compared to hospitalized noncritical patients. Hormonal interventions, such as dihydrotestosterone and dexamethasone, ameliorated the pathological traits in SARS-CoV-2 infectable cells and tissues. CONCLUSIONS: The present study provides the first scientific evidence that SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in respiratory, metabolic, and endocrine cells and tissues. This feature likely contributes to COVID-19 severity with cell/tissue damage and metabolic abnormalities, which may be exacerbated in older, male, obese, or diabetic patients.

Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach.

Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal frequency (e.g., weekly to monthly) that is unsuitable for capturing highly dynamic variations in cyanobacteria abundance. Therefore, we developed a model incorporating reverse time attention with a decay mechanism (RETAIN-D) to forecast HABs with simultaneous improvements in temporal resolution, forecasting performance, and interpretability. The usefulness of RETAIN-D in forecasting HABs was illustrated by its application to two sites located in the lower sections of the Nakdong and Yeongsan rivers, South Korea, where HABs pose a critical water quality issue. Three variations of recurrent neural network models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and reverse time attention (RETAIN), were adopted for comparisons of performance with RETAIN-D. Input features encompassing meteorological, hydrological, environmental, and biological factors were used to forecast cyanobacteria abundance (total cyanobacteria cell counts and cell counts of dominant cyanobacteria taxa). Incorporation of a decay mechanism into the deep learning structure in RETAIN-D allowed forecasts of HABs on a high temporal resolution (daily) without manual feature engineering, increasing the usefulness of resulting forecasts for water quality and resources management. RETAIN-D yielded a high degree of accuracy (RMSE = 0.29-1.67, R2 = 0.76-0.98, MAE = 0.18-1.14, SMAPE = 9.77-87.94% for test sets; on natural log scales) across model outputs and sites, successfully capturing high variability and irregularities in the time series. RETAIN-D showed higher accuracy than RETAIN (except for comparable accuracy in forecasting Microcystis abundance at the Nakdong River site) and outperformed LSTM and GRU across all model outputs and sites. Ambient temperature had high importance in forecasting cyanobacteria abundance across all model outputs and sites, whereas the relative importance of other input features varied by the output and site. Increases in contributions with increasing irradiance, decreasing flow rates, and increasing residence time were more pronounced in summer than other seasons. Differences in the contributions of input features among different time steps (1 to 7 days prior to forecasting) were larger in the Yeongsan River site. RETAIN-D is applicable to a wide range of forecasting models that can benefit from improved temporal resolution, performance, and interpretability.

Transforming growth factor ß1 signaling links extracellular matrix remodeling to intracellular lipogenesis upon physiological feeding events.

Adipose tissue dynamically changes its mass in response to external nutritional status, which plays an important role in maintaining the lipid homeostasis. Physiologically, feeding events are associated with the expansion of adipose tissue, but little is known about the detailed molecular mechanisms of this expansion. Here, using comprehensive transcriptome analysis, we found that levels of transforming growth factor ß1 (TGF-ß1), a key regulator of extracellular matrix (ECM) remodeling, were increased in adipose tissue under feeding conditions and associated with the lipogenic pathway. In addition, TGF-ß receptors are highly expressed in adipose tissue, and pharmacological inhibition of TGF-ß1 reduced adipose tissue mass and caused ectopic lipid accumulation in the liver. This reduced fat mass was associated with decreased gene expression in ECM remodeling and lipogenesis. Furthermore, similar results were observed in the adipose tissue of SMAD family member 3 knockout mice or upon systemic TGF-ß neutralization, with significant reductions in both ECM remodeling and lipogenesis-related genes. Mechanistically, we found that insulin-induced TGF-ß1 and cell-autonomous action remodels the ECM of adipocytes, which controls the downstream focal adhesion kinase-AKT signaling cascades and enhances the lipogenic pathway. Of note, destruction of collagens or matrix metalloproteinase/a disintegrin and metalloprotease activities, critical components of ECM remodeling, blocked TGF-ß1-mediated focal adhesion kinase-AKT signaling and the lipogenic pathway. Taken together, this study identifies a previously unknown lipogenic role of TGF-ß1 by which adipocytes can expand to adapt to physiological feeding events.

Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams.

The dynamics of fecal indicator bacteria, such as fecal coliforms (FC) in streams, are influenced by the interactions of a myriad of factors. To predict complex spatiotemporal patterns of FC in streams and assess the relative importance of numerous controlling factors, the adoption of a hierarchical Bayesian network (HBN) was proposed in this study. By introducing latent variables correlated to the observed variables into a Bayesian network, the HBN can represent causal relationships among a large set of variables with a multilevel hierarchy. The study area encompasses 215 sites across the watersheds of the four major rivers in South Korea. The monitoring data collected during the 2012-2019 period included 32 input variables pertaining to meteorology, geography, soil characteristics, land cover, urbanization index, livestock density, and point sources. As model endpoints, the exceedance probability of the FC standard concentration as well as two pollution characteristics (i.e., pollution degree and type), derived from FC load duration curves were used. The probability of exceeding an FC threshold value (200 CFU/100 mL) showed spatiotemporal variations, whereas pollution degree and type showed spatial variations that represent long-term severity and relative dominance of nonpoint and point source fecal pollution, respectively. The conceptual model was validated using structural equation modeling to develop the HBN. The results demonstrate that the HBN effectively simplified the model structure, while showing strong model performance (AUC = 0.81, accuracy = 0.74). The results of the sensitivity analysis indicate that land cover is the most important factor in predicting the probability of exceedance and pollution degree, whereas the urbanization index explains most of the variability in pollution type. Furthermore, the results of the scenario analysis suggest that the HBN provides an interpretable framework in which the interaction of controlling factors has causal relationships at different levels that can be identified and visualized.

Swelling-based preparation of polypropylene nanocomposite with non-functionalized cellulose nanofibrils.

Dispersion of nanofillers in a polymer matrix is one of the most important steps in preparing polymer nanocomposites. However, hydrophobic polymers and hydrophilic nanofillers are intrinsically incompatible, making it difficult to mix them homogeneously. Here, we propose the swelling-based particle adsorption method (SPA) providing a simple route to disperse cellulose nanofibrils (CNFs) within incompatible polypropylene (PP) matrix without surface functionalization of CNFs. The SPA enables CNFs to adsorb onto the surface of PP particles using a small amount of solvent. PP/CNFs composite films fabricated from the SPA showed increased Young's modulus by 80%, which agrees well with a theoretical prediction proving nano-dispersed. Furthermore, simply mixing a bit of polypropylene-graft-maleic anhydride can improve the tensile strength by 30% and the elongation at break by 10-fold than that of PP/CNFs composites. The SPA can be universally applied to any incompatible polymer-nanofiller pairs for the fabrication of nanocomposite materials.

Serum midkine levels for the diagnosis and assessment of response to interventional therapy in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the clinical significance of serum midkine (MDK) levels for the diagnosis of hepatocellular carcinoma (HCC) and evaluate the efficacy of interventional therapy. METHODS: Eighty-four patients with HCC were enrolled in this retrospective study. They received an interventional treatment. A follow-up was performed every 2 months, using magnetic resonance imaging, to determine whether the treatment should be continued. Serum alpha-fetoprotein (AFP) and MDK levels were measured at the first diagnosis and during the follow-ups, and the HCC detection rates based on the cutoff values of these two measurements were compared. The relationships between AFP and MDK and the clinical tumor characteristics and changes in APK and MDK before and after treatment were also compared using a rank sum test and χ2 test, respectively. The prognostic significance of MDK for HCC was determined through regression analysis. A two-sided P â€‹< â€‹0.05 was considered statistically significant. RESULTS: MDK expression was detected in 95.24% of the cases. Subgroup analysis revealed MDK expression in 95.35%, 95.12%, 85.19%, 86.67%, and 83.33% of the AFP-positive, AFP-negative, stage A Barcelona clinic liver cancer (BCLC-A), BCLC-A/AFP-positive, and BCLC-A/AFP-negative cases, respectively. MDK expression after the interventional treatment (66.7%) was significantly lower than that before the treatment (95.2%). The mean post-treatment MDK level was 0.67 â€‹ng/mL in patients with a positive response to therapy as compared with 3.66 â€‹ng/mL in those with no positive response. All patients were followed up for 18 months, and those positive for MDK expression before the intervention were more likely to relapse than patients without MDK expression. Subgroup analysis revealed the highest recurrence rate for patients who were positive for MDK expression before and after treatment. CONCLUSIONS: Serum MDK may serve as a powerful complement to AFP in the diagnosis of HCC. MDK measurement may improve the detection rate of BCLC-A and AFP-negative HCC. Serum MDK may help to determine the vascular invasion and poor clinical staging of HCC tumors. Patients with MDK-positive HCC before treatment may be more prone to postoperative tumor progression.

Facile and Quantitative Method for Estimating the Isolation Degree of Cellulose Nanocrystals (CNCs) Suspensions.

The isolation degree of cellulose nanocrystals (CNCs) suspensions calculated from the amount of sediments obtained with the centrifugation method can be estimated with turbidimetry, surface charge and dispersion analysis of the CNCs suspension. Three different types of raw cellulosic materials were used and carried out with an acid hydrolysis and mechanical disintegration. As the number of high-pressure homogenizer treatments increased, the isolation degree of CNCs from microcrystalline cellulose (MCC) increased from 2.3 to 99.6%, while the absorbencies from turbidimetric measurement of the CNCs suspension decreased, from 2.6 to 0.1 Abs units. Furthermore, the surface charges based on zeta potential measurements of the CNCs suspensions increased from -34.6 to -98.7 mV, but the heights of sediments from the CNCs suspensions were reduced, from 4.01 to 0.07 mm. Similar results were obtained for CNCs from softwood pulp (SWP) and cotton pulp (CP). These results show a direct correlation between yield, turbidity, surface charge and sedimentation of CNCs suspensions. Their correlation indices (0.9) were close to a maximal value of 1. This approach can be suggested as a facile and rapid estimation method for CNCs manufacturing process.

Possible Involvement of Adipose Tissue in Patients With Older Age, Obesity, and Diabetes With SARS-CoV-2 Infection (COVID-19) via GRP78 (BIP/HSPA5): Significance of Hyperinsulinemia Management in COVID-19.

Aging, obesity, and diabetes are major risk factors for the severe progression and outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019 [COVID-19]), but the underlying mechanism is not yet fully understood. In this study, we found that the SARS-CoV-2 spike protein physically interacts with cell surface GRP78, which promotes the binding to and accumulation in ACE2-expressing cells. GRP78 was highly expressed in adipose tissue and increased in humans and mice with older age, obesity, and diabetes. The overexpression of GRP78 was attributed to hyperinsulinemia in adipocytes, which was in part mediated by the stress-responsive transcription factor XBP-1s. Management of hyperinsulinemia by pharmacological approaches, including metformin, sodium-glucose cotransporter 2 inhibitor, or ß3-adrenergic receptor agonist, decreased GRP78 gene expression in adipose tissue. Environmental interventions, including exercise, calorie restriction, fasting, or cold exposure, reduced the gene expression of GRP78 in adipose tissue. This study provides scientific evidence for the role of GRP78 as a binding partner of the SARS-CoV-2 spike protein and ACE2, which might be related to the severe progression and outcome of COVID-19 in patients with older age, obesity, and diabetes. The management of hyperinsulinemia and the related GRP78 expression could be a therapeutic or preventative target.

Enhancing the Sensitivity of the Virus BioResistor by Overoxidation: Detecting IgG Antibodies.

The Virus BioResistor (VBR) is a biosensor capable of rapid and sensitive detection of small protein disease markers using a simple dip-and-read modality. For example, the bladder cancer-associated protein DJ-1 (22 kDa) can be detected in human urine within 1.0 min with a limit of detection (LOD) of 10 pM. The VBR uses engineered virus particles as receptors to recognize and selectively bind the protein of interest. These virus particles are entrained in a conductive poly(3,4-ethylenedioxythiophene) or PEDOT channel. The electrical impedance of the channel increases when the target protein is bound by the virus particles. But VBRs exhibit a sensitivity that is inversely related to the molecular weight of the protein target. Thus, large proteins, such as IgG antibodies (150 kDa), can be undetectable even at high concentrations. We demonstrate that the electrochemical overoxidation of the VBR's PEDOT channel increases its electrical impedance, conferring enhanced sensitivity for both small and large proteins. Overoxidation makes possible the detection of two antibodies, undetectable at a normal VBR, with a limit of detection of 40 ng/mL (250 pM), and a dynamic range for quantitation extending to 600 ng/mL.