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.

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.

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.

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].

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 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.

Self-absorption reduction in laser-induced breakdown spectroscopy using laser-stimulated absorption: publisher's note.

This publisher's note contains corrections to Opt. Lett.40, 5224 (2015).OPLEDP0146-959210.1364/OL.40.005224.

Up-regulation of miR-135b expression induced by oxidative stress promotes the apoptosis of renal tubular epithelial cells under high glucose condition.

This study aimed to investigate the role and underlying mechanism of miR-135b in high glucose-induced oxidative stress of renal tubular epithelial cells. Here, in vivo experiments found that compared to the control group, miR-135b expression was significantly up-regulated in the diabetes group, whereas BMP7 mRNA and protein levels were down-regulated. In high glucose-treated renal tubular epithelial cells (HK-2) in vitro, oxidative stress was induced, which up-regulated miR-135b expression. In addition, the regulation of miR-135b on BMP7 expression was confirmed in HK-2 cells. Under high glucose conditions, oxidative stress promoted the apoptosis of HK-2 cells through the up-regulation of miR-135b expression. In vivo experiments indicated that interference with miR-135b improved renal function in mice with diabetic nephropathy. In conclusion, these results indicated that the up-regulation of miR-135b expression induced by oxidative stress promotes the apoptosis of HK-2 cells under high glucose conditions.

Experimental investigation of laser-induced breakdown spectroscopy assisted with laser-induced fluorescence for trace aluminum detection in steatite ceramics.

Laser-induced breakdown spectroscopy (LIBS) assisted with laser-induced fluorescence (LIF) was introduced to detect trace aluminum in steatite ceramics in this work. The mechanism and transition process of laser-induced aluminum atomic fluorescence in laser-induced plasma was described and discussed. Selective enhancement of LIF and temporal synchronicity between radiation laser and fluorescence were studied. The influences of ablation laser energy, power density of the radiation laser, and interpulse delay were experimentally investigated. The results showed that 60 mJ in ablation laser energy and 4 µs in interpulse delay were the optimal choice for fluorescent intensity. The fluorescence was increased to the saturation level over 4 MW/cm2. Spectral stability improvement of LIBS-LIF was also discovered in this work. The results proved that LIBS-LIF is a feasible and effective modification of LIBS for ceramics analysis.

Determination of Trace Available Heavy Metals in Soil Using Laser-Induced Breakdown Spectroscopy Assisted with Phase Transformation Method.

To detect available heavy metals in soil using laser-induced breakdown spectroscopy (LIBS) and improve its poor detection sensitivity, a simple and low cost sample pretreatment method named solid-liquid-solid transformation was proposed. By this method, available heavy metals were extracted from soil through ultrasonic vibration and centrifuging and then deposited on a glass slide. Utilization of this solid-liquid-solid transformation method, available Cd and Pb elements in soil were detected successfully. The results show that the regression coefficients of calibration curves for soil analyses reach to more than 0.98. The limits of detection could reach to 0.067 and 0.94 ppm for available Cd and Pb elements in soil under optimized conditions, respectively, which are much better than those obtained by conventional LIBS.

Spreading a water droplet through filter paper on the metal substrate for surface-enhanced laser-induced breakdown spectroscopy.

To improve the quantitative analysis accuracy of an aqueous solution using surface-enhanced laser-induced breakdown spectroscopy (SENLIBS), the filter paper was used as a transmission medium by placing it onto the surface of a metallic substrate to make the microdroplet spreading more uniform in a fixed region of the substrate surface. The trace elements (Cu, Pb, Cd, and Cr) in an aqueous solution were detected successfully using this method. The results showed that the sample preparation repeatability of SENLIBS was noticeably improved with the aid of filter paper. Moreover, the limit of detection (LoD) values was similar to those without filter paper. Furthermore, the R2 values were improved from 0.6192~0.9321 to 0.9481~0.9766, the RMSECV values were decreased from 0.53~1.95 µg/mL to 0.33~1.06 µg/mL, and the average relative error (ARE) values were decreased from 8.96~22.31% to 4.28~14.37% with the aid of filter paper. This demonstrated that the use of filter paper could improve the quantitative analysis accuracy of SENLIBS by increasing the sample preparation repeatability.

Classification accuracy improvement of laser-induced breakdown spectroscopy based on histogram of oriented gradients features of spectral images.

To improve the classification accuracy of laser-induced breakdown spectroscopy (LIBS), image histogram of oriented gradients (HOG) features method (IHFM) for materials analysis was proposed in this work. 24 rice (Oryza sativa L.) samples were carried out to verify the proposed method. The results showed that the classification accuracy of rice samples by the full-spectra intensities method (FSIM) and IHFM were 60.25% and 81.00% respectively. The classification accuracy was obviously improved by 20.75%. Universality test results showed that this method also achieved good results in the plastics, steel, rock and minerals classification. This study provides an effective method to improve the classification performance of LIBS.

Multielemental self-absorption reduction in laser-induced breakdown spectroscopy by using microwave-assisted excitation.

The self-absorption effect seriously affects the accuracy of determination in laser-induced breakdown spectroscopy (LIBS). In this work, we proposed to reduce multielemental self-absorption within a wide spectral range (200-900 nm) by using microwave-assisted excitation in LIBS (MAE-LIBS). Self-absorption reduction of sodium (Na), potassium (K), aluminum (Al), silicon (Si), and calcium (Ca) in potassium feldspar using MAE-LIBS was investigated. The mechanisms of self-absorption reduction in MAE-LIBS were also investigated. The results show that the serious self-absorption of spectral lines (Na and K) was reduced. The full widths at half maximum (FWHMs) of Na I 589.0 nm, Na I 589.6 nm, K I 766.5 nm, and K I 769.9 nm in potassium feldspar were reduced by 43%, 43%, 53%, and 47%, respectively. MAE-LIBS also has a little FWHM reduction for spectral lines with weak self-absorption. The results demonstrate that MAE-LIBS can simultaneously reduce multielemental self-absorption.

New spectral reduction algorithm for echelle spectrometer in laser-induced breakdown spectroscopy.

In this work, a new spectral reduction algorithm for the echelle spectrometer was proposed. Unlike conventional approaches, the key concept in this algorithm is to model the spectrogram rather the spectrometer, which makes the algorithm more adaptive to different designs. This algorithm also introduces a dynamic adjusting procedure for generating optimized spectra from laser-induced plasmas. This additional step improved the spectrum stability and absolute line intensity of the spectrum and yielded better quantification performance. Experimental results demonstrated that the quantification results of analyzing aluminum alloy samples were improved using this new algorithm.

Analytical-performance improvement of aqueous solution by chemical replacement combined with surface-enhanced laser-induced breakdown spectroscopy.

In this study, chemical replacement combined with surface-enhanced laser-induced breakdown spectroscopy (CR-SENLIBS) was introduced for detecting the trace chromium (Cr) element in an aqueous solution, which could use chemical replacement to change the sample from liquid to solid. In order to illustrate the analytical-performance of the CR-SENLIBS, the direct analysis of the liquid surface by LIBS (LSLIBS) was investigated for comparison. The results show that the spectral intensity of Cr I 357.86 nm, plasma lifetime, and spectral stability were improved. Moreover, the limit of detection of Cr I 357.86 nm was 0.018 µg/mL, which was lower than 1.814 µg/mL for LSLIBS. Furthermore, the accuracy and precision were improved more than 60% and 80%, respectively; for example, the root-mean-square error of cross-validation was improved from 1.09 to 0.75 µg/mL, and the average relative standard deviation of the predicted concentration of Cr was reduced from 22.89% to 4.53% by using CR-SENLIBS. These results suggest that CR-SENLIBS has good analytical-performance, which exhibits great potential in water quality monitoring.

Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Determination of antimony in soil using laser-induced breakdown spectroscopy assisted with laser-induced fluorescence.

Antimony (Sb) in soil is attracting attention in the research community due to its potential toxicity and carcinogenicity. Traditional methods of detecting Sb lack the ability of rapid and nondigestion analysis, which hinders their development and application. Moreover, it is still a challenge for laser-induced breakdown spectroscopy (LIBS) to detect Sb in soil due to the weak intensities and intense interference of spectral lines. Here, LIBS, assisted with laser-induced fluorescence (LIBS-LIF), was used to selectively enhance the Sb's characteristic spectral lines under optimal parameters. The quantitative analysis performance was notably improved with a determination coefficient (R2) of 0.991, the limit of detection of 0.221 µg/g, and root mean square error of cross validation of 3.592 µg/g. These results demonstrate that LIBS-LIF has the potential to realize the rapid and accurate analysis of Sb in soil.

Analytical-performance improvement of laser-induced breakdown spectroscopy for the processing degree of wheat flour using a continuous wavelet transform.

Quality and safety of food are two of the most important matters in our lives. Wheat is one of the most important products in the modern agricultural processing industry. Issues of mislabeling and adulteration are of increasingly serious concern in the grain market. They threaten the credibility of producers and traders and the rights of the consumers. Therefore, it is very significant to guarantee the processing degree of wheat flour. In this work, two different spectral peak recognition methods, i.e., artificial spectral peak recognition and automatic spectral peak recognition, are carried out to study the adulteration problem in the food industry. Three grades of the processing degree of wheat flour from northern China are classified by laser-induced breakdown spectroscopy (LIBS). To search for an automatic classification model, continuous wavelet transform is used for the automatic recognition of the LIBS spectrum peak. Principal component analysis is used to reduce the collinearity of LIBS spectra data. First, 20 principal components were selected to represent the spectral data for the following discrimination analysis by a support vector machine. The results showed that the classification accuracies of automatic spectral peak recognition are better than those of artificial spectral peak recognition. The classification accuracies of artificial spectral peak recognition and automatic spectral peak recognition are 95.33% and 98.67%; the fivefold cross-validation classification accuracies are 94.67% and 96.67%; and the operation times were 240 min and 2 min, respectively. It can be concluded that LIBS can provide simpler and faster classification without the use of any chemical reagent, which represents a decisive advantage for applications dedicated to rapidly detecting the processing degree of wheat flour and other cereals.

Identification of cervical cancer using laser-induced breakdown spectroscopy coupled with principal component analysis and support vector machine.

Cervical cancer is one of the most widespread diseases in women. Traditional cancer diagnosis is extremely complicated and relies on subjective interpretation of biopsy material. In this work, laser-induced breakdown spectroscopy (LIBS) was used in cervical cancer recognition. In order to improve identification accuracy of cervical cancer by LIBS, the chemometric methods of principal component analysis (PCA) and support vector machine (SVM) were combined. The results show that the content of trace elements in normal tissues and cervical cancer tissues was significantly different. Normalized peak intensities of Na, Mg, and K in the cervical cancer tissues were significantly higher than normal tissues, and the normalized peak intensities of Ca in the normal tissues were higher than cervical cancer tissues. The identification accuracies of PCA-SVM are better than SVM, with the achieved accuracies of 94.44% and 93.06%, respectively. It can be concluded that LIBS techniques coupled with chemometric method is a potential in cancer tissue identification, which provides a preliminary research basis for real-time diagnosis of cancer tissues using LIBS.

Spectral Interference Elimination in Soil Analysis Using Laser-Induced Breakdown Spectroscopy Assisted by Laser-Induced Fluorescence.

The complex and serious spectral interference makes it difficult to detect trace elements in soil using laser-induced breakdown spectroscopy (LIBS). To address it, LIBS-assisted by laser-induced fluorescence (LIBS-LIF) was applied to selectively enhance the spectral intensities of the interfered lines. Utilizing this selective enhancement effect, all the interference lines could be eliminated. As an example, the Pb I 405.78 nm line was enhanced selectively. The results showed that the determination coefficient (R2) of calibration curve (Pb concentration range = 14-94 ppm), the relative standard deviation (RSD) of spectral intensities, and the limit of detection (LOD) for Pb element were improved from 0.6235 to 0.9802, 10.18% to 4.77%, and 24 ppm to 0.6 ppm using LIBS-LIF, respectively. These demonstrate that LIBS-LIF can eliminate spectral interference effectively and improve the ability of LIBS to detect trace heavy metals in soil.