Hierarchically porous and single Zn atom-embedded carbon molecular sieves for H2 separations.

Hierarchically porous materials containing sub-nm ultramicropores with molecular sieving abilities and microcavities with high gas diffusivity may realize energy-efficient membranes for gas separations. However, rationally designing and constructing such pores into large-area membranes enabling efficient H2 separations remains challenging. Here, we report the synthesis and utilization of hybrid carbon molecular sieve membranes with well-controlled nano- and micro-pores and single zinc atoms and clusters well-dispersed inside the nanopores via the carbonization of supramolecular mixed matrix materials containing amorphous and crystalline zeolitic imidazolate frameworks. Carbonization temperature is used to fine-tune pore sizes, achieving ultrahigh selectivity for H2/CO2 (130), H2/CH4 (2900), H2/N2 (880), and H2/C2H6 (7900) with stability against water vapor and physical aging during a continuous 120-h test.

Self-supervised learning for denoising of multidimensional MRI data.

PURPOSE: To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. THEORY AND METHODS: Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. RESULTS: The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. CONCLUSION: The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

A noise robust image reconstruction using slice aware cycle interpolator network for parallel imaging in MRI.

BACKGROUND: Reducing Magnetic resonance imaging (MRI) scan time has been an important issue for clinical applications. In order to reduce MRI scan time, imaging acceleration was made possible by undersampling k-space data. This is achieved by leveraging additional spatial information from multiple, independent receiver coils, thereby reducing the number of sampled k-space lines. PURPOSE: The aim of this study is to develop a deep-learning method for parallel imaging with a reduced number of auto-calibration signals (ACS) lines in noisy environments. METHODS: A cycle interpolator network is developed for robust reconstruction of parallel MRI with a small number of ACS lines in noisy environments. The network estimates missing (unsampled) lines of each coil data, and these estimated missing lines are then utilized to re-estimate the sampled k-space lines. In addition, a slice aware reconstruction technique is developed for noise-robust reconstruction while reducing the number of ACS lines. We conducted an evaluation study using retrospectively subsampled data obtained from three healthy volunteers at 3T MRI, involving three different slice thicknesses (1.5, 3.0, and 4.5 mm) and three different image contrasts (T1w, T2w, and FLAIR). RESULTS: Despite the challenges posed by substantial noise in cases with a limited number of ACS lines and thinner slices, the slice aware cycle interpolator network reconstructs the enhanced parallel images. It outperforms RAKI, effectively eliminating aliasing artifacts. Moreover, the proposed network outperforms GRAPPA and demonstrates the ability to successfully reconstruct brain images even under severe noisy conditions. CONCLUSIONS: The slice aware cycle interpolator network has the potential to improve reconstruction accuracy for a reduced number of ACS lines in noisy environments.

Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this paper, an unsupervised deep learning-based motion artifact correction method for turbo-spin echo MRI is proposed using the deep image prior framework. THEORY AND METHODS: The proposed approach takes advantage of the high impedance to motion artifacts offered by the neural network parameterization to remove motion artifacts in MR images. The framework consists of parameterization of MR image, automatic spatial transformation, and motion simulation model. The proposed method synthesizes motion-corrupted images from the motion-corrected images generated by the convolutional neural network, where an optimization process minimizes the objective function between the synthesized images and the acquired images. RESULTS: In the simulation study of 280 slices from 14 subjects, the proposed method showed a significant increase in the averaged structural similarity index measure by 0.2737 in individual coil images and by 0.4550 in the root-sum-of-square images. In addition, the ablation study demonstrated the effectiveness of each proposed component in correcting motion artifacts compared to the corrected images produced by the baseline method. The experiments on real motion dataset has shown its clinical potential. CONCLUSION: The proposed method exhibited significant quantitative and qualitative improvements in correcting rigid and in-plane motion artifacts in MR images acquired using turbo spin-echo sequence.

Physiological responses, trunk posture, and work pace in commercial building cleaning in Washington State: An observational field study.

INTRODUCTION: This study aimed to investigate whether work pace is a critical indicator for predicting a janitor's risk of work-related musculoskeletal disorders (WMSDs). METHOD: Field measurements were obtained from commercial building janitors as well as the determination of work pace. Physiological responses collected were heart rate, energy expenditure (calories), activity level (METs), steps, trunk posture. Data were obtained using direct measurements, along with a time study, which was performed by shadowing 13 janitors in Washington State. The measured values were summarized descriptively, and five of the most common janitorial tasks were compared. The relationships between work pace and the physiological response variables were determined by calculating the Pearson product-moment correlation coefficients. RESULTS: The highest average percent heart rate reserve (47.4%) was reported during restroom cleaning, while the highest activity and energy expenditure levels (3.6 METs and 217.1 calories/h) were reported for mopping. The top 90% of trunk flexion angles and the highest percentage of time in trunk flexion from 20° to 60° were recorded during restroom cleaning. Restroom cleaning showed the highest correlation between all the physiological response variables and work pace. In most of the tasks, a high work pace may have increased the degree and duration of severe trunk flexion. CONCLUSION: Overall, when several tasks were considered, the extent of physiological responses, trunk joint angles, and exposure time to awkward postures tended to increase with an increase in work pace. PRACTICAL APPLICATIONS: This study showed the feasibility of using the work pace measured from time studies as a predictive indicator of WMSDs risks. Using this information, managers may compose a schedule that can minimize WMSDs risks while considering actual work pace deviations that may impact a janitor's ability to complete assigned tasks properly within a shift.

Palladium-Percolated Networks Enabled by Low Loadings of Branched Nanorods for Enhanced H2 Separations.

Nanoparticles (NPs) at high loadings are often used in mixed matrix membranes (MMMs) to improve gas separation properties, but they can lead to defects and poor processability that impede membrane fabrication. Herein, it is demonstrated that branched nanorods (NRs) with controlled aspect ratios can significantly reduce the required loading to achieve superior gas separation properties while maintaining excellent processability, as demonstrated by the dispersion of palladium (Pd) NRs in polybenzimidazole for H2 /CO2 separation. Increasing the aspect ratio from 1 for NPs to 40 for NRs decreases the percolation threshold volume fraction by a factor of 30, from 0.35 to 0.011. An MMM with percolated networks formed by Pd NRs at a volume fraction of 0.039 exhibits H2 permeability of 110 Barrer and H2 /CO2 selectivity of 31 when challenged with simulated syngas at 200 °C, surpassing Robeson's upper bound. This work highlights the advantage of NRs over NPs and nanowires and shows that right-sizing nanofillers in MMMs is critical to construct highly sieving pathways at minimal loadings. This work paves the way for this general feature to be applied across materials systems for a variety of chemical separations.

Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI.

PURPOSE: A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously. METHODS: We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used. RESULTS: Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods. CONCLUSION: The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.

Cleaning in the 21st Century: The musculoskeletal disorders associated with the centuries-old occupation - A literature review.

Workers performing cleaning duties experience higher injury rates, especially in the form of musculoskeletal disorders (MSDs), than other industries. It is essential to understand the inherent risks associated with the nature of this occupation. Based on the Balance Theory (Smith & Carayon-Sainfort, 1989), this review surveys the current literature, especially those published since the previous review paper (Kumar & Kumar, 2008), and identifies which elements contributing to MSD risks were examined: task, technology, organization, environment, individual, and their interactions. Thirty-nine research papers published between 2005 and 2021 are identified and summarized. Among these papers, task and individual elements received the most attention, at 42 and 34 occurrences, respectively. The interaction elements of technology-organization, technology-environment, and organization-environment received less than three mentions. The goal of this literature review is to update the knowledge base and identify current trends for the cleaning occupation. Possible interventions for risk reduction and future research directions are suggested.

The job demand-control-support model and work-related musculoskeletal complaints in daytime and nighttime janitors: The mediating effect of burnout.

Janitors' jobs require repetitive work with low control (skill discretion, decision authority) and social support. Previous studies have found this constellation of work conditions leads to high stress levels. This study investigated the relationships among job demand-control-support, burnout, and musculoskeletal symptoms for commercial janitors in Washington State. Structural equation modeling was performed using data from 208 participants with analyses comparing models of daytime and nighttime janitors. Burnout fully mediated the relationship between job demands and musculoskeletal complaints among daytime janitors. Among nighttime janitors, burnout mediated between job demands, job control, and social support, and musculoskeletal complaints. The nighttime janitors' model was more fully supported compared to the daytime model. This study is one of a small number that examine and bring attention to the importance of janitors' burnout. Recommendations to improve the psychosocial work environment toward mitigating burnout and reducing musculoskeletal complaints are provided.

Potentiometric Biosensors Based on Molecular-Imprinted Self-Assembled Monolayer Films for Rapid Detection of Influenza A Virus and SARS-CoV-2 Spike Protein.

Rapid, yet accurate and sensitive testing has been shown to be critical in the control of spreading pandemic diseases such as COVID-19. Current methods which are highly sensitive and can differentiate different strains are slow and cannot be conveniently applied at the point of care. Rapid tests, meanwhile, require a high titer and are not sufficiently sensitive to discriminate between strains. Here, we report a rapid and facile potentiometric detection method based on nanoscale, three-dimensional molecular imprints of analytes on a self-assembled monolayer (SAM), which can deliver analyte-specific detection of both whole virions and isolated proteins in microliter amounts of bodily fluids within minutes. The detection substrate with nanoscale inverse surface patterns of analytes formed by a SAM identifies a target analyte by recognizing its surface nano- and molecular structures, which can be monitored by temporal measurement of the change in substrate open-circuit potential. The sensor unambiguously detected and differentiated H1N1 and H3N2 influenza A virions as well as the spike proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle-East respiratory syndrome (MERS) coronavirus in human saliva with limits of detection reaching 200 PFU/mL and 100 pg/mL for the viral particles and spike proteins, respectively. The demonstrated speed and specificity of detection, combined with a low required sample volume, high sensitivity, ease of potentiometric measurement, and simple sample collection and preparation, suggest that the technique can be used as a highly effective point-of-care diagnostic platform for a fast, accurate, and specific detection of various viral pathogens and their variants.

Quantification of intravoxel incoherent motion with optimized b-values using deep neural network.

PURPOSE: To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized. METHOD: A deep neural network (DNN) method is proposed for accurate quantification of IVIM parameters from multiple diffusion-weighted images. In addition, optimal b-values are selected to acquire the multiple diffusion-weighted images. The proposed framework consists of an MRI signal generation part and an IVIM parameter quantification part. Monte-Carlo (MC) simulations were performed to evaluate the accuracy of the IVIM parameter quantification and the efficacy of b-value optimization. In order to analyze the effect of noise on the optimized b-values, simulations were performed with five different noise levels. For in vivo data, diffusion images were acquired with the b-values from four b-values selection methods for five healthy volunteers at 3T MRI system. RESULTS: Experiment results showed that both the optimization of b-values and the training of DNN were simultaneously performed to quantify IVIM parameters. We found that the accuracies of the perfusion coefficient (Dp ) and perfusion fraction (f) were more sensitive to b-values than the diffusion coefficient (D) was. Furthermore, when the noise level changed, the optimized b-values also changed. Therefore, noise level has to be considered when optimizing b-values for IVIM quantification. CONCLUSION: The proposed scheme can simultaneously optimize b-values and train DNN to minimize quantification errors of IVIM parameters. The trained DNN can quantify IVIM parameters from the diffusion-weighted images obtained with the optimized b-values.

A fully integrated bacterial pathogen detection system based on count-on-a-cartridge platform for rapid, ultrasensitive, highly accurate and culture-free assay.

Rapid, sensitive and accurate point-of-care-testing (POCT) of bacterial load from a variety of samples can help prevent human infections caused by pathogenic bacteria and mitigate their spreading. However, there is an unmet demand for a POCT device that can detect extremely low concentrations of bacteria in raw samples. Herein, we introduce the 'count-on-a-cartridge' (COC) platform for quantitation of the food-borne pathogenic bacteria Staphylococcus aureus. The system comprised of magnetic concentrator, sensing cartridge and fluorescent image reader with a built-in counting algorithm facilitated fluorescent microscopic bacterial enumeration in user-convenient manner with high sensitivity and accuracy within a couple of hours. The analytical performance of this assay is comparable to that of a standard plate count. The COC assay shows a sensitivity of 92.9% and specificity of 100% performed according to global microbiological criteria for S. aureus which is acceptable below 100 CFU/g in the food matrix. This culture-independent, rapid, ultrasensitive and highly accurate COC assay has great potential for places where prompt bacteria surveillance is in high demand.

A smartphone fluorescence imaging-based mobile biosensing system integrated with a passive fluidic control cartridge for minimal user intervention and high accuracy.

A key challenge for realizing mobile device-based on-the-spot environmental biodetection is that a biosensor integrated with a fluid handling sensor cartridge must have acceptable accuracy comparable to that of conventional standard analytical methods. Furthermore, the user interface must be easy to operate, technologically plausible, and concise. Herein, we introduced an advanced smartphone imaging-based fluorescence microscope designed for Hg2+ monitoring by utilizing a biosensor cartridge that reduced user intervention via time-sequenced passive fluid handling. The cartridge also employed a metal-nanostructured plastic substrate for complementing the fluorescence signal output; this helped the realization of high-accuracy detection, in which a ratiometric dual-wavelength detection method was applied. Using 30 samples of Hg2+-spiked wastewater, we showed that our device, which has a detection limit of ∼1 pM, can perform analytical assays accurately. The detection results from our method were in good linearity and agreement with those of conventional standard methods. We conclude that the integration of a simple-to-use biosensor cartridge, fluorescence signal-enhancing substrate, dual-wavelength detection, and quantitative image data processing on a smartphone has great potential to make any population accessible to small-molecule detection, which has been performed in centralized laboratories for environmental monitoring.

Ephedra sinica Stapf and Gypsum Attenuates Heat-Induced Hypothalamic Inflammation in Mice.

Ephedra sinica Stapf (EH) exert toxic effects, such as excitability, cardiac arrhythmia, and others. On the contrary, in traditional herbal medicine, EH and gypsum (GF) are used most often to treat symptoms caused by external stressors. The hypothalamus plays a crucial role in thermal homeostasis. Inflammatory response in the hypothalamus by thermal stressors may affect thermal and energy homeostasis. This study investigates the effect of EH and GF against heat-induced mouse model. Mice were divided into four groups: saline, saline plus heat, EH plus heat, and GF plus heat treated groups. Heat stress was fixed at 43 °C for 15 min once daily for 3 days. Weight and ear and rectal temperature measurements were made after terminating heat stress. Hypothalamus tissue was collected to evaluate the HSP70, nuclear factor kappa-Β (NF-kB), and interleukin (IL)-1ß protein expression levels. EH and GF treatment suppressed the increased body temperature. EH significantly ameliorated heat-induced body weight loss, compared to gypsum. Regulatory effects of EH and GF for body temperature and weight against heat stress were mediated by IL-1ß reduction. EH showed significant HSP70 and NF-kB inhibition against heat stress. EH and GF contribute to the inhibition of heat-induced proinflammatory factors and the promotion of hypothalamic homeostasis.

An Aqueous Extract of Herbal Medicine ALWPs Enhances Cognitive Performance and Inhibits LPS-Induced Neuroinflammation via FAK/NF-κB Signaling Pathways.

Recent studies have shown that Liuwei Dihuang pills (LWPs) can positively affect learning, memory and neurogenesis. However, the underlying molecular mechanisms are not understood. In the present study, we developed ALWPs, a mixture of Antler and LWPs, and investigated whether ALWPs can affect neuroinflammatory responses. We found that ALWPs (500 mg/ml) inhibited lipopolysaccharide (LPS)-induced proinflammatory cytokine IL-1ß mRNA levels in BV2 microglial cells but not primary astrocytes. ALWPs significantly reduced LPS-induced cell-surface levels of TLR4 to alter neuroinflammation. An examination of the molecular mechanisms by which ALWPs regulate the LPS-induced proinflammatory response revealed that ALWPs significantly downregulated LPS-induced levels of FAK phosphorylation, suggesting that ALWPs modulate FAK signaling to alter LPS-induced IL-1ß levels. In addition, treatment with ALWPs followed by LPS resulted in decreased levels of the transcription factor NF-κB in the nucleus compared with LPS alone. Moreover, ALWPs significantly suppressed LPS-induced BV2 microglial cell migration. To examine whether ALWPs modulate learning and memory in vivo, wild-type C57BL/6J mice were orally administered ALWPs (200 mg/kg) or PBS daily for 3 days, intraperitoneally injected (i.p.) with LPS (250 µg/kg) or PBS, and assessed in Y maze and NOR tests. We observed that oral administration of ALWPs to LPS-injected wild-type C57BL/6J mice significantly rescued short- and long-term memory. More importantly, oral administration of ALWPs to LPS-injected wild-type C57BL/6J mice significantly reduced microglial activation in the hippocampus and cortex. Taken together, our results suggest that ALWPs can suppress neuroinflammation-associated cognitive deficits and that ALWPs have potential as a drug for neuroinflammation/neurodegeneration-related diseases, including Alzheimer's disease (AD).

Culture-free, highly sensitive, quantitative detection of bacteria from minimally processed samples using fluorescence imaging by smartphone.

A critical unmet need in the diagnosis of bacterial infections, which remain a major cause of human morbidity and mortality, is the detection of scarce bacterial pathogens in a variety of samples in a rapid and quantitative manner. Herein, we demonstrate smartphone-based detection of Staphylococcus aureus in a culture-free, rapid, quantitative manner from minimally processed liquid samples using aptamer-functionalized fluorescent magnetic nanoparticles. The tagged S. aureus cells were magnetically captured in a detection cassette, and then fluorescence was imaged using a smartphone camera with a light-emitting diode as the excitation source. Our results showed quantitative detection capability with a minimum detectable concentration as low as 10 cfu/ml by counting individual bacteria cells, efficiently capturing S. aureus cells directly from a peanut milk sample within 10 min. When the selectivity of detection was investigated using samples spiked with other pathogenic bacteria, no significant non-specific detection occurred. Furthermore, strains of S. aureus from various origins showed comparable results, ensuring that the approach can be widely adopted. Therefore, the quantitative fluorescence imaging platform on a smartphone could allow on-site detection of bacteria, providing great potential assistance during major infectious disease outbreaks in remote and resource-limited settings.

Inhibitory effects of Aconiti Lateralis Radix Preparata on chronic intermittent cold-induced inflammation in the mouse hypothalamus.

ETHNOPHARMACOLOGICAL RELEVANCE: Aconiti Lateralis Radix Preparata (AR) is the most frequently used herb to generate heat and treat symptoms associated with coldness in Asia. AIMS OF THE STUDY: The hypothalamus is one of the master regulators to maintain constant core body temperature. Chronic exposure to cold stress disturbs homeostatic regulation, gradually resulting in hypothalamic inflammation. This study investigate the effects of AR, on the chronic intermittent cold (CIC)-induced release of pro-inflammatory signaling molecules in the mouse hypothalamus. MATERIALS AND METHODS: Aconiti Lateralis Radix Preparata extract (ARE) were solubilized in distilled water and diluted with saline before administration. Male ICR mice (7 weeks old, 30-32g) were divided randomly into 6 groups: (1) control, (2) cold stress, (3) ARE 30, (4) ARE 100, (5) ARE 300, and (6) ARE 1000mg/kg groups. Groups (2)-(6) were exposed to CIC stress once a day for 14 days. CIC stress was achieved by exposing the mice to 4°C and 60 ± 10% humidity for 120min once a day. Rectal temperature was measured after terminating cold stress. Cortisol levels were measured from serum. Hypothalamus tissue was used for western blot analysis, and IL-9, IL-13, PGE1, and PGE2 levels were assessed. RESULTS: ARE treatment prevented the CIC-induced decrease in rectal temperature and increase in serum cortisol level. ARE-treated CIC-exposed mice demonstrated decrease in nuclear c-Fos levels dose-dependently compared to CIC-exposed mice. Nuclear NF-kB expression showed significant increase in CIC-exposed mice. ARE treatment significantly blunted the increase in nuclear NF-kB expression. CIC-exposed mice had significantly increased levels of both IL-9 and IL-13. Treatment with ARE suppressed the elevated IL-9 and IL-13 levels. Between control and CIC-exposed mice PGE1 levels showed no difference. However ARE (1000mg/kg)-treated CIC-exposed mice had a significant increase in PGE1 level compared to CIC-exposed mice. PGE2 levels were significantly higher in CIC-exposed mice compared to control mice. ARE treatment significantly attenuated the increase in PGE2 levels. CONCLUSIONS: Our findings suggest CIC stress disturbs the anti-inflammatory effect of cortisol and maintenance of the body temperature. Thus AR contributes to suppress the activated proinflammatory factors, IL-9, IL-13, and PGE-2, and to increase the heat production.

Epimedium koreanum Nakai inhibits PMA-induced cancer cell migration and invasion by modulating NF-κB/MMP-9 signaling in monomorphic malignant human glioma cells.

Previously, we showed that the herbal extract EYK (Epimedium koreanum Nakai) can regulate the immune response. Other studies showed that EYK has beneficial effects in human lung cancer, angiogenesis and Alzheimer's disease (AD). However, it remains unknown whether EYK can affect cancer cell migration and invasion in human brain cancer cell lines. In the present study, we found that pre- or post-treatment with EYK inhibited phorbol 12-myristate 13-acetate (PMA)-induced cancer cell migration and invasion in A172 cells, but not in U373MG or T98G cells. Additionally, pre- or post-treatment with PMA followed by EYK decreased MMP-9 activity in A172 cells. Moreover, treatment with a NF-κB inhibitor significantly decreased cell migration in A172 cells pre- or post-treated with EYK and PMA, suggesting that EYK requires NF-κB to alter cancer cell migration. Either pre- or post-treatment with EYK significantly decreased NF-κB nuclear translocation in comparison with PMA treatment. Taken together, our results suggest that EYK suppresses PMA-induced cancer cell migration in monomorphic malignant human glioma cells by downregulating the NF-κB pathway and decreasing MMP-9 activity.

Coptidis Rhizoma Prevents Heat Stress-Induced Brain Damage and Cognitive Impairment in Mice.

Heat stress conditions lead to neuroinflammation, neuronal death, and memory loss in animals. Coptidis Rhizoma (CR) exhibits potent fever-reducing effects and has been used as an important traditional medicinal herb for treating fever. However, to date, the effects of antipyretic CR on heat-induced brain damages have not been investigated. In this study, CR significantly reduced the elevation of ear and rectal temperatures after exposure to heat in mice. Additionally, CR attenuated hyperthermia-induced stress responses, such as release of cortisol into the blood, and upregulation of heat shock protein and c-Fos in the hypothalamus and hippocampus of mice. The administration of CR inhibited gliosis and neuronal loss induced by thermal stress in the hippocampal CA3 region. Treatment with CR also reduced the heat stress-induced expression of nuclear factor kappa ß, tumor necrosis factor-α, and interleukin-1ß (IL-1ß) in the hippocampus. Moreover, CR significantly decreased proinflammatory mediators such as IL-9 and IL-13 in the heat-stressed hypothalamus. Furthermore, CR attenuated cognitive dysfunction triggered by thermal stress. These results indicate that CR protects the brain against heat stress-mediated brain damage via amelioration of hyperthermia and neuroinflammation in mice, suggesting that fever-reducing CR can attenuate thermal stress-induced neuropathology.

A smartphone imaging-based label-free and dual-wavelength fluorescent biosensor with high sensitivity and accuracy.

The accuracy of a bioassay based on smartphone-integrated fluorescent biosensors has been limited due to the occurrence of false signals from non-specific reactions as well as a high background and low signal-to-noise ratios for complementary metal oxide semiconductor image sensors. To overcome this problem, we demonstrate dual-wavelength fluorescent detection of biomolecules with high accuracy. Fluorescent intensity can be quantified using dual wavelengths simultaneously, where one decreases and the other increases, as the target analytes bind to the split capture and detection aptamer probes. To do this, we performed smartphone imaging-based fluorescence microscopy using a microarray platform on a substrate with metal-enhanced fluorescence (MEF) using Ag film and Al2O3 nano-spacer. The results showed that the sensitivity and specificity of the dual-wavelength fluorescent quantitative assay for the target biomolecule 17-ß-estradiol in water were significantly increased through the elimination of false signals. The detection limit was 1pg/mL and the area under the receiver operating characteristic curve of the proposed assay (0.922) was comparable to that of an enzyme-linked immunosorbent assay (0.956) from statistical accuracy tests using spiked wastewater samples. This novel method has great potential as an accurate point-of-care testing technology based on mobile platforms for clinical diagnostics and environmental monitoring.