Oridonin ameliorates renal fibrosis in diabetic nephropathy by inhibiting the Wnt/ß-catenin signaling pathway.

Diabetic nephropathy (DN) is one of the most serious and frequent complications among diabetes patients and presently constitutes vast the cases of end-stage renal disease worldwide. Tubulointerstitial fibrosis is a crucial factor related to the occurrence and progression of DN. Oridonin (Ori) is a diterpenoid derived from rubescens that has diverse pharmacological properties. Our previous study showed that Ori can protect against DN by decreasing the inflammatory response. However, whether Ori can alleviate renal fibrosis in DN remains unknown. Here, we investigated the mechanism through which Ori affects the Wnt/ß-catenin signaling pathway in diabetic rats and human proximal tubular epithelial cells (HK-2) exposed to high glucose (HG) levels. Our results revealed that Ori treatment markedly decreased urinary protein excretion levels, improved renal function and alleviated renal fibrosis in diabetic rats. In vitro, HG treatment increased the migration of HK-2 cells while reducing their viability and proliferation rate, and treatment with Ori reversed these changes. Additionally, the knockdown of ß-catenin arrested cell migration and reduced the expression levels of Wnt/ß-catenin signaling-related molecules (Wnt4, p-GSK3ß and ß-catenin) and fibrosis-related molecules (α-smooth muscle actin, collagen I and fibronectin), and Ori treatment exerted an effect similar to that observed after the knockdown of ß-catenin. Furthermore, the combination of Ori treatment and ß-catenin downregulation exerted more pronounced biological effects than treatment alone. These findings may provide the first line of evidence showing that Ori alleviates fibrosis in DN by inhibiting the Wnt/ß-catenin signaling pathway and thereby reveal a novel therapeutic avenue for treating tubulointerstitial fibrosis.

Brain abnormalities in survivors of COVID-19 after 2-year recovery: a functional MRI study.

Background: A variety of symptoms, particularly cognitive, psychiatric and neurological symptoms, may persist for a long time among individuals recovering from COVID-19. However, the underlying mechanism of these brain abnormalities remains unclear. This study aimed to investigate the long-term neuroimaging effects of COVID-19 infection on brain functional activities using resting-state functional magnetic resonance imaging (rs-fMRI). Methods: Fifty-two survivors 27 months after infection (mild-moderate group: 25 participants, severe-critical: 27 participants), from our previous community participants, along with 35 healthy controls, were recruited to undergo fMRI scans and comprehensive cognitive function measurements. Participants were evaluated by subjective assessment of Cognitive Failures Questionnaire-14 (CFQ-14) and Fatigue Scale-14 (FS-14), and objective assessment of Montreal Cognitive Assessment (MoCA), N-back, and Simple Reaction Time (SRT). Each had rs-fMRI at 3T. Measures such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Findings: Compared with healthy controls, survivors of mild-moderate acute symptoms group and severe-critical group had a significantly higher score of cognitive complains involving cognitive failure and mental fatigue. However, there was no difference of cognitive complaints between two groups of COVID-19 survivors. The performance of three groups was similar on the score of MoCA, N-back and SRT. The rs-fMRI results showed that COVID-19 survivors exhibited significantly increased ALFF values in the left putamen (PUT.L), right inferior temporal gyrus (ITG.R) and right pallidum (PAL.R), while decreased ALFF values were observed in the right superior parietal gyrus (SPG.R) and left superior temporal gyrus (STG.L). Additionally, decreased ReHo values in the right precentral gyrus (PreCG.R), left postcentral gyrus (PoCG.L), left calcarine fissure and surrounding cortex (CAL.L) and left superior temporal gyrus (STG.L). Furthermore, significant negative correlations between the ReHo values in the STG.L, and CFQ-14 and mental fatigue were found. Interpretation: This long-term study suggests that individuals recovering from COVID-19 continue to experience cognitive complaints, psychiatric and neurological symptoms, and brain functional alteration. The rs-fMRI results indicated that the changes in brain function in regions such as the putamen, temporal lobe, and superior parietal gyrus may contribute to cognitive complaints in individuals with long COVID even after 2-year infection. Funding: The National Programs for Brain Science and Brain-like Intelligence Technology of China, the National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality of China, and the National Key Research and Development Program of China.

Schistosoma-related molecules as a new strategy to combat type 1 diabetes through immune regulation.

The study of immune regulation mechanisms induced by parasites may help develop new treatment methods for inflammatory diseases including type 1 diabetes, which is related to type 1 immune responses. The negative correlation between schistosomiasis infection and type 1 diabetes has been confirmed, and the mechanism of Schistosoma-mediated prevention of type 1 diabetes may be related to the adaptive and innate immune systems. Schistosoma-related molecules affect immune cell composition and macrophage polarization and stimulate an increase in natural killer T cells. Furthermore, Schistosoma-related molecules can regulate the adaptive immune responses related to the prevention of type 1 diabetes and change the Th1/Th2 and Th17/Treg axis. Our previous review showed the role of regulatory T cells in the protective of type 1 diabetes mediated by Schistosoma. Here, we aim to review the other mechanisms of schistosomiasis infection and Schistosoma-related products in regulating the immune response associated with the treatment of type 1 diabetes.

A Review on Pulsed Laser Fabrication of Nanomaterials in Liquids for (Photo)catalytic Degradation of Organic Pollutants in the Water System.

The water pollution caused by the release of organic pollutants has attracted remarkable attention, and solutions for wastewater treatment are being developed. In particular, the photocatalytic removal of organic pollutants in water systems is a promising strategy to realize the self-cleaning of ecosystems under solar light irradiation. However, at present the semiconductor-based nanocatalysts can barely satisfy the industrial requirements because their wide bandgaps restrict the effective absorption of solar light, which needs an energy band modification to boost the visible light harvesting via surface engineering. As an innovative approach, pulsed laser heating in liquids has been utilized to fabricate the nanomaterials in catalysis; it demonstrates multi-controllable features, such as size, morphology, crystal structure, and even optical or electrical properties, with which photocatalytic performances can be precisely optimized. In this review, focusing on the powerful heating effect of pulsed laser irradiation in liquids, the functional nanomaterials fabricated by laser technology and their applications in the catalytic degradation of various organic pollutants are summarized. This review not only highlights the innovative works of pulsed laser-prepared nanomaterials for organic pollutant removal in water systems, such as the photocatalytic degradation of organic dyes and the catalytic reduction of toxic nitrophenol and nitrobenzene, it also critically discusses the specific challenges and outlooks of this field, including the weakness of the produced yields and the relevant automatic strategies for massive production.

Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning.

Laser-induced breakdown spectroscopy (LIBS) has been applied in coal analysis for advantages such as real-time online analysis. Fine-tuning is a transfer learning method that has been utilized in LIBS to improve accuracy in the target domain with a limited training set by introducing a model trained on a different but related source domain. This research proposed a hybrid transfer learning method (HTr-LIBS) to further enhance the performance of LIBS coal analysis by combining fine-tuning with sample reweighting. A neural network was pre-trained on the source domain and target domain training set. The sample weights of the source domain were iteratively adjusted according to the prediction errors. The pre-trained neural network with optimal sample weights was then fine-tuned using the target domain training set. The proposed method significantly improved the analytical accuracy compared to direct modeling using small training sets. When the training set size increased to 19, the R2P of direct modeling for ash content and volatile matter content were 0.8105 and 0.9440, respectively. HTr-LIBS increased the R2P for ash content and volatile matter content to 0.9029 and 0.9627, respectively. The improvements were more significant and stable than fine-tuning of the source domain model without sample reweighting. The introduction of target domain data during pre-training and the iterative adjustment of sample weights both contributed to the improvements.

Selective laser welding in liquid: A strategy for preparation of high-antibacterial activity nanozyme against Staphylococcus aureus.

Nanozyme was considered as one of the most promising substitutes for antibiotics, due to the selective catalysis for pathogens. In this work, a high-antibacterial activity SOD-like nanozyme based on hybrid Ag/CeO2 nanocomposite was facilely prepared by using an innovative approach of selective laser welding in liquid. This prepared nanozyme displayed a high antimicrobial effect against Staphylococcus aureus under visible light illumination, the sterilization rate as high as 82.4%, which was 2.93 and 2.99 times higher than those of pure Ag and pure CeO2, respectively. The enhanced antibacterial activity was attributed to the anchoring of Ag nanospheres on the surface of CeO2 nanosheets, which induced the reduction of CeO2 bandgap and boosted the visible light harvesting. Therefore, the charge carriers can be effectively stimulated to produce abundant reactive oxygen species on the Ag/CeO2 nanocomposite via a SOD-like route. This work demonstrated a facile strategy for the preparation of high-antibacterial activity nanozyme, giving it great potential for scalable application in the biomedical and pharmaceutical industry.

The phenotype and prediction of long-term physical, mental and cognitive COVID-19 sequelae 20 months after recovery, a community-based cohort study in China.

Long-term sequelae clustering phenotypes are important for precise health care management in COVID-19 survivors. We reported findings for 1000 survivors 20 months after diagnosis of COVID-19 in a community-based cohort in China. Sequelae symptoms were collected from a validated questionnaire covering 27 symptoms involved in five organ systems including self-reported physical condition, dyspnea, cognitive function and mental health. The generalized symptoms were reported with the highest rate (60.7%), followed by the mental (48.3%), cardiopulmonary (39.8%), neurological (37.1%; cognitive impairment, 15.6%), and digestive symptoms (19.1%). Four clusters were identified by latent class analysis: 44.9% no or mild group (cluster 1), 29.2% moderate group with mainly physical impairment (cluster 2), 9.6% moderate group with mainly cognitive and mental health impairment (cluster 3), and 16.3% severe group (cluster 4). Physical comorbidities or history of mental disorders, longer hospitalization periods and severe acute illness predicted severe group. For moderate group, adults less than 60 years, with physical comorbidities and severe acute illness were more likely to have physical symptoms, while adult women with longer hospitalization stays had increased risk of cognitive and mental health impairment. Overall, among more than half of community COVID-19 survivors who presented moderate or severe sequelae 20 months after recovery, three-tenth had physical vulnerability that may require physical therapy aiming to improve functioning, one-tenth mental or cognitive vulnerable cases need psychotherapy and cognitive rehabilitation, and one-sixth severe group needs multidisciplinary clinical management. The remaining half is free to clinical intervention. Our findings introduced an important framework to map numerous symptoms to precise classification of the clinical sequelae phenotype and provide information to guide future stratified recovery interventions.

Rapid determination of lead (Pb) in the soil-plant system by laser-induced breakdown spectroscopy (LIBS): case study of Pb-pollution from perovskite solar cells.

With the widespread usage of lead (Pb)-containing perovskite solar cells (PSCs), it is critical to monitor Pb pollution from PSCs in the environment. Among different analytical techniques, laser-induced breakdown spectroscopy (LIBS) has demonstrated good performance in the fast quantification of many elements in solid samples, without using toxic and expensive chemical reagents. Therefore, LIBS offers significant potential for detecting and quantifying Pb in the environment. In this study, a Pb migration model in the PSCs-soil-Houttuynia plants system was assessed based on the LIBS data. The Pb transfer rates and the Pb uptake coefficients were calculated to evaluate Pb migration from PSCs to plants. The results showed that the R2 of quantitative results were all greater than 0.98, with the root-mean-square error of cross-validation (RMSECV) being less than 1.53 wt.%. Furthermore, above 49% Pb from PSCs was swiftly diffused into soil under watering conditions, while Houttuynia plants absorbed over 10% Pb from polluted soil. This study revealed that Pb leakage from PSCs should not be underestimated and that LIBS is a viable and fast analytical method for monitoring Pb in the environment.

Activation of AMPK-PGC-1α pathway ameliorates peritoneal dialysis related peritoneal fibrosis in mice by enhancing mitochondrial biogenesis.

BACKGROUND: The pathogenesis of peritoneal dialysis (PD)-related peritoneal fibrosis (PF) is not clearly understood, and current treatment options are limited. METHODS: In this study, the effect of PD-related PF on mitochondrial biogenesis was investigated, and the effect of activation of the adenosine monophosphate-activated protein kinase (AMPK)-PGC-1α (peroxisome proliferator-activated receptor γ coactivator-1α) pathway on PF was evaluated in mice. RESULTS: In a mouse model of PD-related PF, AMPK-PGC-1α signaling (phospho-AMPK, PGC-1α, NRF-1, NRF-2 and TFAM expression) was downregulated, mitochondrial DNA (mtDNA) levels were reduced, and mitochondrial structure was damaged in the peritoneum. In addition, TdT-mediated dUTP nick-end labeling (TUNEL) staining showed typical apoptosis characteristics in peritoneal mesothelial cells (PMCs). Activation of the AMPK-PGC-1α pathway (PGC-1α overexpression or metformin, which is an agonist of AMPK) upregulated phospho-AMPK, PGC-1α, nuclear respiratory factors 1 (NRF-1) and 2 (NRF-2), and mitochondrial transcription factor A (TFAM) expression and mtDNA content, improved mitochondrial morphological manifestations, inhibited apoptosis of PMCs and alleviated PF. CONCLUSION: Our study may suggest that activation of the AMPK-PGC-1α pathway ameliorates PD-related PF by enhancing mitochondrial biogenesis.

Glyceraldehyde-3-phosphate dehydrogenase affects the growth of Schistosoma japonicum schistosomula.

This study was conducted to evaluate the effect of glyceraldehyde-3-phosphate dehydrogenase from Schistosoma japonicum (SjGAPDH) on the growth of schistosomula. Quantitative reverse transcription PCR and immunohistochemical analysis were performed to analyze the mRNA levels and immune localization of SjGAPDH. RNA interference experiments were conducted to further examine the role of SjGAPDH in the schistosomula growth of S. japonicum. The results demonstrated that SjGAPDH mRNA was expressed during all stages of S. japonicum development, with its expression gradually increasing over time. SjGAPDH was mainly distributed on the surface and in some parenchymal cells of S. japonicum. Double-stranded RNA-mediated GAPDH knockdown reduced SjGAPDH expression by approximately 59%. Light microscopic observations revealed that the size, length, width, volume, and area of schistosomula in the SjGAPDH interference group were significantly lower than those in the enhanced green fluorescent protein control group. These findings indicate that SjGAPDH may affect the growth of S. japonicum schistosomula and could be a useful target for treating schistosomiasis.

A Review of Spatter in Laser Powder Bed Fusion Additive Manufacturing: In Situ Detection, Generation, Effects, and Countermeasures.

Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the forming quality in L-PBF because it leads to metallurgical defects and the degradation of mechanical properties. This impact becomes more severe in the fabrication of large-sized parts during the multi-laser L-PBF process. Therefore, investigations of spatter generation and countermeasures have become more urgent. Although much research has provided insights into the melt pool, microstructure, and mechanical property, reviews of spatter in L-PBF are still limited. This work reviews the literature on the in situ detection, generation, effects, and countermeasures of spatter in L-PBF. It is expected to pave the way towards a novel generation of highly efficient and intelligent L-PBF systems.

Analysis of Key Genes Responsible for Low Urea Production in Saccharomyces cerevisiae JH301.

There is a potential safety risk with ethyl carbamate (EC) in Hongqu Huangjiu production; 90% of the EC in rice wine is produced by the reaction of the urea with the alcohol of Saccharomyces cerevisiae. In our previous experiments, we screened and obtained a S. cerevisiae strain JH301 that offered low urea production. However, the key genes responsible for low urea production of strain JH301 remain unclear. Here, the whole genome sequencing of S. cerevisiae strain JH301 was accomplished via a next-generation high-throughput sequencing and long-read sequencing technology. There are six main pathways related to the urea metabolism of strain JH301 based on KEGG pathway mapping. Three species-specific genes are related to the urea metabolism pathways and were found in comparative genome analysis between strains JH301 and S288c during Hongqu Huangjiu production for the first time. Finally, the ARG80 gene was found to be likely a key gene responsible for low urea production of S. cerevisiae strain JH301, as determined by PCR and qRT-PCR check analyses from DNA and RNA levers. In conclusion, the results are useful for a scientific understanding of the mechanism of low urea production by Saccharomyces cerevisiae during Hongqu Huangjiu fermentation. It also is important to control the urea and EC contents in Hongqu Huangjiu production.

High-sensitivity determination of available cobalt in soil using laser-induced breakdown spectroscopy assisted with laser-induced fluorescence.

Conventional laser-induced breakdown spectroscopy could not conduct high-sensitivity determination of available cobalt due to spectral interference and weak spectral intensity. To improve the poor detection sensitivity of available cobalt in soil, available cobalt was extracted from soil and prepared. Laser-induced breakdown spectroscopy assisted with laser-induced fluorescence was introduced to excite and detect the cobalt element. The results showed that coefficients of the calibration curve for the available cobalt element could reach 0.9991, and the limits of detection could reach 0.005 mg/kg in soil under optimized conditions, which were all much better than conventional LIBS and reach the international minimum detection standards. This work provides a possible approach for detecting available trace elements in soil.

Determination of fluorine content in rocks using laser-induced breakdown spectroscopy assisted with radical synthesis.

In the atmosphere, fluorine element in rocks is hard to detect using fluorine atomic emission spectrum in laser-induced breakdown spectroscopy. In this study, a novel radical synthesis method based on laser ablation was proposed, by which strontium-fluorine (SrF) radical spectrum was collected to quantify fluorine element in rocks instead of fluorine atom spectrum. A pure strontium carbonate was placed orthogonally to the sample, and ablated by an additional laser to provide sufficient strontium atoms for promoting SrF radical formation. The fluorine content in rocks was sensitively and accurately determined by SrF radical emission signal. The coefficient of determination, average relative standard deviation, root mean square error, limit of detection, and limit of qualification were 0.996, 4.68%, 0.0068 wt%, 6.36 µg g-1, and 21.2 µg g-1, respectively. This work proved that this novel method provides a new way to promote radical synthesis and has considerable potential for detecting fluorine in rocks in geological exploration.

The distribution of high-quality internal standard lines and their selection method based on the Q-value in portable laser-induced breakdown spectroscopy.

Internal standard lines play a crucial role in the univariate quantitative analysis in portable laser-induced breakdown spectroscopy (LIBS) technology. To overcome the uncertainty of the conventional internal standard method, the distribution principles of high-quality internal standard lines were studied and revealed at the macro and micro levels, and an automatic internal standard line selection method based on the Q-value was proposed. Using this method, in the quantitative analysis of Si in low-alloy steel samples, the average relative error of cross-validation (ARECV), root mean squared error of cross-validation (RMSECV), and the limit of detection (LoD) were decreased significantly from 27.42%, 0.041 wt%, and 1060 µg g-1 to 18.65%, 0.026 wt%, and 680 µg g-1, respectively. The quantitative analysis results of Cr, Cu and Ni showed that it has excellent generalization ability. The results indicate that this method can screen out the optimal internal standard lines efficiently and accurately, which provides a new approach to improve the performance of univariate quantitative analysis in portable LIBS.

Determination of nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid model: erratum.

We provide corrected funding number for the previous publication [Opt. Express28, 23037 (2020)10.1364/OE.399909].

Laser fabricated carbon quantum dots in anti-solvent for highly efficient carbon-based perovskite solar cells.

Additive passivation can be an effective strategy to regulate and control the properties of organic-inorganic halide perovskite film. In this article, carbon quantum dots (CQDs), fabricated by non-focused laser irradiation of carbon nanomaterial diluted in anti-solvent ethyl acetate, denoted as EACQDs, were adopted for perovskite film defect passivation and modification of carbon-based CH3NH3PbI3 perovskite solar cells (PSCs). The size of EACQDs can be tuned by manipulating the laser fluence. The morphology of perovskite film was uncovered through scanning electron microscopy and atomic force microscopy. After embedding of EACQDs, the defect in perovskite crystal was reduced, resulting in the decreased carrier recombination and accelerated carrier transportation, which were demonstrated by electrochemical impedance spectroscopy, photoluminescence and time-resolved photoluminescence. As a consequence, with the optimization of 0.01 mg/mL EACQDs (1064 nm-300 mJ·pulse-1·cm-2-10 min), the power conversion efficiency (PCE) of carbon-based PSCs achieved a maximum value of 16.43%, which improved 23.81% when compared with the pristine PSCs of 13.27%. Furthermore, the EACQDs optimized PSCs also exhibited an excellent stability and still retained 86% of its initial PCE after 50-day storage at the room atmosphere with a humidity of 30-50%.

Sensitive analysis of fluorine and chlorine elements in water solution using laser-induced breakdown spectroscopy assisted with molecular synthesis.

Fluorine and chlorine are key elements to affecting water quality, but they are hard to be determined by conventional laser-induced breakdown spectroscopy (LIBS). To achieve high sensitivity detection of them, the CaF and CaCl molecules were synthesized by combining calcium in calcite and F and Cl in sample. The temporal characteristics of CaF and CaCl molecular emissions were investigated. It shows that molecular emission of CaF and CaCl has a longer lifetime and high spectral intensity than that of their atomic emissions. Such unique feature of molecular emission inspired us to use it for high sensitivity analysis of Cl and F elements in water. The results show that these two elements can be sensitively and accurately detected using LIBS assisted with molecular emission. The limits of detections (LoDs) were 0.38 mg/L and 1.03 mg/L for F and Cl elements, respectively, and the limit of quantitation (LoQ) was 3.404 mg/L to 20.569 mg/L for fluorine elements and 9.986 mg/L to 39.757 mg/L for fluorine. These detection limits can meet the World Health Organization's detection requirements for F and Cl elements in water. The results show that LIBS assisted with molecular synthesis has a huge potential in water quality monitoring.

Determination of the nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models.

Nutrient profile determination for plant materials is an important task to determine the quality and safety of the human diet. Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectrometry of the material component analytical technique. However, quantitative analysis of plant materials using LIBS usually suffers from matrix effects and nonlinear self-absorption. To overcome this problem, a hybrid quantitative analysis model of the partial least squares-artificial neural network (PLS-ANN) was used to detect the compositions of plant materials in the air. Specifically, fifty-eight plant materials were prepared to split into calibration, validation and prediction sets. Nine nutrient composition profiles of Mg, Fe, N, Al, B, Ca, K, Mn, and P were employed as the target elements for quantitative analysis. It demonstrated that the prediction ability can be significantly improved by the use of the PLS-ANN hybrid model compared to the method of standard calibration. Take Mg and K as examples, the root-mean-square errors of calibration (RMSEC) of Mg and K were decreased from 0.0295 to 0.0028 wt.% and 0.2884 to 0.0539 wt.%, and the mean percent prediction errors (MPE) were decreased from 5.82 to 4.22% and 8.82 to 4.12%, respectively. This research provides a new way to improve the accuracy of LIBS for quantitative analysis of plant materials.

Quantitative analysis of coal quality by laser-induced breakdown spectroscopy assisted with different chemometric methods.

Rapid and accurate measurement of coal quality has great significance for efficient use of coal at thermal power plants. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods has many unique advantages in coal analysis. In this study, four calibration models, based on partial least squares regression (PLSR), support vector regression (SVR), artificial neural network (ANN), and principal component regression (PCR), were applied assisted by the LIBS technique for the quantitative analysis of coal quality. In order to find the optimal calibration method with LIBS for coal analysis, the spectral data of 40 standard coal samples with pressed-pellet pretreatment were acquired through a LIBS experimental setup, and the modeling efficiency and prediction accuracy of the four chemometric methods were compared based on these spectral data. As a result, the modeling efficiency of PLSR was found to be the highest, that of SVR was the lowest, and that of ANN ranked third. In terms of prediction performance, ANN was found to work better than the other three chemometric methods, and the average absolute error (AAE) of prediction of ash content, volatile matter content and calorific value were 0.69%, 0.87%, and 0.56 MJ kg-1, respectively. ANN can seek the best compromise of modeling efficiency and prediction accuracy and is demonstrated to be an optimal multivariate calibration method with LIBS for online measurement of coal quality at thermal power plants.