Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs.Approach.We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine.Main results.Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively.Significance.By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.
Brain-Computer Interfaces , Imagination , Transfer, Psychology , Humans , Imagination/physiology , Transfer, Psychology/physiology , Support Vector Machine , Electroencephalography/methods , Movement/physiology , Algorithms , Machine Learning , Databases, Factual , Male
PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists. MATERIALS AND METHODS: This multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods. RESULTS: The findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors. CONCLUSION: The fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model's potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.
Renowned for their nutritional benefits, citrus fruits are harvested at various stages in China for functional food production. This study introduces an innovative analytical method, DART-MS, enabling direct qualitative analysis of citrus samples without the need for preprocessing. Simultaneously, the combination of chemometrics can be applied to distinguish between three different citrus samples: Citri Reticulatae Pericarpium, Citri Reticulatae Pericarpium Viride, and Citri Reticulatae "Chachi". Notably, given the international regulatory concerns surrounding synephrine, a precise quantitative analysis method for synephrine was developed. The limit of detection (LOD) and the limit of quantification (LOQ) were 39 ng mL-1 and 156 ng mL-1, respectively. The recovery rates obtained varied from 98.46% to 100.71%. Furthermore, the intra-day and inter-day precision demonstrated robust consistency, with values spanning 5.0-6.1% and 5.03-6.08%, respectively, offering quicker results compared to those from HPLC-MS, promising a safer assessment of herbal and food products.
Guided nanowires grown on polymer surfaces facilitate their seamless integration as flexible devices without post-growth processing steps. However, this is challenging due to the inability of polymer films to provide the required lattice-matching effect. In this work, this challenge is addressed by replicating highly aligned nanogrooves from a compact disc (CD) onto a casted flexible polydimethylsiloxane (PDMS) surface. Leveraging the replicated nanogrooves, copper hexadecafluorophthalocyanine (F16CuPc) and various metal phthalocyanines are guided into large-area, self-aligned nanowires. Subsequently, by employing specifically designed shadow masks during electrode deposition, these nanowires are seamlessly integrated as either a monolithic flexible photodetector with a large sensing area or on-chip flexible photodetector arrays. The resulting flexible photodetectors exhibit millisecond and long-term stable response to UV-vis-NIR light. Notably, they demonstrate exceptional bending stability, retaining stable and sensitive photoresponse even at a curvature radius as low as 0.5 cm and after enduring 1000 bending cycles. Furthermore, the photodetector array showcases consistent sensitivity and response speed across the entire array. This work not only proves the viability of guided nanowire growth on flexible polymer surfaces by replicating CD nanogrooves but also underscores the potential for large-scale monolithic integration of guided nanowires as flexible devices.
OBJECTIVE: Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs. APPROACH: This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signed R-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling. MAIN RESULTS: We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity. SIGNIFICANCE: These results indicate that AWDSNet has great potential for applications in ERP decoding.
Maize, a salt-sensitive crop, frequently suffers severe yield losses due to soil salinization. Enhancing salt tolerance in maize is crucial for maintaining yield stability. To address this, we developed an introgression line (IL76) through introgressive hybridization between maize wild relatives Zea perennis, Tripsacum dactyloides, and inbred Zheng58, utilizing the tri-species hybrid MTP as a genetic bridge. Previously, genetic variation analysis identified a polymorphic marker on Zm00001eb244520 (designated as ZmSC), which encodes a vesicle-sorting protein described as a salt-tolerant protein in the NCBI database. To characterize the identified polymorphic marker, we employed gene cloning and homologous cloning techniques. Gene cloning analysis revealed a non-synonymous mutation at the 1847th base of ZmSCIL76 , where a guanine-to-cytosine substitution resulted in the mutation of serine to threonine at the 119th amino acid sequence (using ZmSCZ58 as the reference sequence). Moreover, homologous cloning demonstrated that the variation site derived from Z. perennis. Functional analyses showed that transgenic Arabidopsis lines overexpressing ZmSCZ58 exhibited significant reductions in leaf number, root length, and pod number, alongside suppression of the expression of genes in the SOS and CDPK pathways associated with Ca2+ signaling. Similarly, fission yeast strains expressing ZmSCZ58 displayed inhibited growth. In contrast, the ZmSCIL76 allele from Z. perennis alleviated these negative effects in both Arabidopsis and yeast, with the lines overexpressing ZmSCIL76 exhibiting significantly higher abscisic acid (ABA) content compared to those overexpressing ZmSCZ58 . Our findings suggest that ZmSC negatively regulates salt tolerance in maize by suppressing downstream gene expression associated with Ca2+ signaling in the CDPK and SOS pathways. The ZmSCIL76 allele from Z. perennis, however, can mitigate this negative regulatory effect. These results provide valuable insights and genetic resources for future maize salt tolerance breeding programs.
BACKGROUND: Natural medicines present a considerable analytical challenge due to their diverse botanical origins and complex multi-species composition. This inherent complexity complicates their rapid identification and analysis. Tangerine peel, a product of the Citrus species from the Rutaceae family, is widely used both as a culinary ingredient and in traditional Chinese medicine. It is classified into two primary types in China: Citri Reticulatae Pericarpium (CP) and Citri Reticulatae Pericarpium Viride (QP), differentiated by harvest time. A notable price disparity exists between CP and another variety, Citri reticulatae "Chachi" (GCP), with differences being based on the original variety. METHODS: This study introduces an innovative method using portable miniature mass spectrometry for swift on-site analysis of QP, CP, and GCP, requiring less than a minute per sample. And combined with machine learning to differentiate the three types on site, the method was used to try to distinguish GCP from different storage years. RESULTS: This novel method using portable miniature mass spectrometry for swift on-site analysis of tangerine peels enabled the characterization of 22 compounds in less than one minute per sample. The method simplifies sample processing and integrates machine learning to distinguish between the CP, QP, and GCP varieties. Moreover, a multiple-perceptron neural network model is further employed to specifically differentiate between CP and GCP, addressing the significant price gap between them. CONCLUSIONS: The entire analytical time of the method is about 1 minute, and samples can be analyzed on site, greatly reducing the cost of testing. Besides, this approach is versatile, operates independently of location and environmental conditions, and offers a valuable tool for assessing the quality of natural medicines.
Citrus , Machine Learning , Mass Spectrometry , Citrus/chemistry , Citrus/classification , Mass Spectrometry/methods
BACKGROUND: Stroke is a leading global cause of death and disability. Daily tea/coffee intake is consumed by >50% of populations and may represent an important population-level exposure. Therefore, it is first essential that we better understand the associations between the tea/coffee intake and stroke. AIMS: This research aims to generate hypotheses about the global associations between tea and coffee intake and stroke. These insights will identify interventions for stroke prevention that can be further explored using alternative study designs. METHODS: INTERSTROKE is a large international matched case-control study of first stroke from 32 countries. Participants were asked "how many cups do you drink each day?" of coffee, green tea, black tea and other tea. Multivariable conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between intake and stroke. RESULTS: We included 13,462 cases and 13,488 controls from INTERSTROKE; mean age was 61.7 (13.4) years and 59.6% (n=16,010) were male. Overall, 19.4% (n=5,239) did not consume tea/coffee, 47.0% (n=12,666) consumed tea only, 14.9% (n=4,024) consumed coffee alone and 18.6% (n=5,021) consumed both, with significant regional variations. After multivariable adjustment, there was no association between low/moderate coffee intake and stroke, but high consumption (>4/day) was associated with higher odds of all stroke (OR 1.37 (95%CI 1.06-1.77)) or ischaemic stroke (OR 1.32 (95% CI 1.00-1.74)). Tea consumption was associated with lower odds of all (OR 0.81 (95% CI 0.69-0.94) for highest intake) or ischaemic stroke (OR 0.81 (95% CI 0.68-0.98) for highest intake). CONCLUSIONS: High coffee consumption was associated with higher odds of all or ischaemic stroke; low-moderate coffee had no association with stroke. In contrast, tea consumption was associated with lower odds of stroke. These associations suggest that individuals consider avoiding high coffee consumption (>=5cups/day) to impact future stroke risk. DATA ACCESS STATEMENT: The design and rationale of INTERSTROKE was published previously. Individual participant data, or other documents are not available.
Tiller angle is a key agricultural trait that establishes plant architecture, which in turn strongly affects grain yield by influencing planting density in rice. The shoot gravity response plays a crucial role in the regulation of tiller angle in rice, but the underlying molecular mechanism is largely unknown. Here, we report the identification of the BIG TILLER ANGLE2 (BTA2), which regulates tiller angle by controlling the shoot gravity response in rice. Loss-of-function mutation of BTA2 dramatically reduced auxin content and affected auxin distribution in rice shoot base, leading to impaired gravitropism and therefore a big tiller angle. BTA2 interacted with AUXIN RESPONSE FACTOR7 (ARF7) to modulate rice tiller angle through the gravity signaling pathway. The BTA2 protein was highly conserved during evolution. Sequence variation in the BTA2 promoter of indica cultivars harboring a less expressed BTA2 allele caused lower BTA2 expression in shoot base and thus wide tiller angle during rice domestication. Overexpression of BTA2 significantly increased grain yield in the elite rice cultivar Huanghuazhan under appropriate dense planting conditions. Our findings thus uncovered the BTA2-ARF7 module that regulates tiller angle by mediating the shoot gravity response. Our work offers a target for genetic manipulation of plant architecture and valuable information for crop improvement by producing the ideal plant type.
Realizing the common wealth of all people is the essential requirement of socialism with Chinese characteristics. Measuring the process of realizing common wealth and the differences between groups is one of the important issues that need to be addressed urgently. In order to reasonably measure the process of realizing common wealth in China, on the premise of horizontal comparability and vertical consistency, the principles of comparability and consistency are introduced, and a comparative method of opportunity advantage based on income distribution is proposed from the perspective of opportunity equity. Using the 2012-2020 CFPS data to measure and test the opportunity advantages and their differences across regions and groups in China. The study found, firstly, that the opportunity advantage persists but tends to diminish across groups, with the more educated group having a more pronounced opportunity advantage, but that this advantage is diminishing over time. Secondly, the doctoral degree group has a greater probability of earning higher incomes, followed by the master's and bachelor's degree groups, but this opportunity advantage, i.e., the probability of earning higher incomes, is diminishing, i.e., the education dividend is diminishing. Third, the difference in opportunity advantage between urban and rural areas still exists, as evidenced by the greater probability of higher incomes in towns than in rural areas, but this advantage has narrowed further over time, with a clear process of urban-rural integration. Fourthly, in terms of gender, men have a certain opportunity advantage over women, but this difference is not significant. Fifthly, in the context of education levels, gender and urban/rural subgroups, under the framework proposed in this paper, China has achieved some success in the process of realizing the common wealth, and is showing a steady upward trend.
Income , Rural Population , China , Humans , Female , Male , Socioeconomic Factors , Urban Population , Educational Status
BACKGROUND AND PURPOSE: Blood pressure variability, in acute stroke, may be an important modifiable determinant of functional outcome after stroke. In a large international cohort of participants with acute stroke, it was sought to determine the association of blood pressure variability (in the early period of admission) and functional outcomes, and to explore risk factors for increased blood pressure variability. PATIENTS AND METHODS: INTERSTROKE is an international case-control study of risk factors for first acute stroke. Blood pressure was recorded at the time of admission, the morning after admission and the time of interview in cases (median time from admission 36.7 h). Multivariable ordinal regression analysis was employed to determine the association of blood pressure variability (standard deviation [SD] and coefficient of variance) with modified Rankin score at 1-month follow-up, and logistic regression was used to identify risk factors for blood pressure variability. RESULTS: Amongst 13,206 participants, the mean age was 62.19 ± 13.58 years. When measured by SD, both systolic blood pressure variability (odds ratio 1.13; 95% confidence interval 1.03-1.24 for SD ≥20 mmHg) and diastolic blood pressure variability (odds ratio 1.15; 95% confidence interval 1.04-1.26 for SD ≥10 mmHg) were associated with a significant increase in the odds of poor functional outcome. The highest coefficient of variance category was not associated with a significant increase in risk of higher modified Rankin score at 1 month. Increasing age, female sex, high body mass index, history of hypertension, alcohol use, and high urinary potassium and low urinary sodium excretion were associated with increased blood pressure variability. CONCLUSION: Increased blood pressure variability in acute stroke, measured by SD, is associated with an increased risk of poor functional outcome at 1 month. Potentially modifiable risk factors for increased blood pressure variability include low urinary sodium excretion.
Fragment growing is an important ligand design strategy in drug discovery. In this study, we present FragGrow, a web server that facilitates structure-based drug design by fragment growing. FragGrow offers two working modes: one for growing molecules through the direct replacement of hydrogen atoms or substructures and the other for growing via virtual synthesis. FragGrow works by searching for suitable fragments that meet a set of constraints from an indexed 3D fragment database and using them to create new compounds in 3D space. The users can set a range of constraints when searching for their desired fragment, including the fragment's ability to interact with specific protein sites; its size, topology, and physicochemical properties; and the presence of particular heteroatoms and functional groups within the fragment. We hope that FragGrow will serve as a useful tool for medicinal chemists in ligand design. The FragGrow server is freely available to researchers and can be accessed at https://fraggrow.xundrug.cn.
Drug Design , Internet , Software , Ligands , Models, Molecular , User-Computer Interface
Adipose tissue plays a crucial role in maintaining energy balance, regulating hormones, and promoting metabolic health. To address disorders related to obesity and develop effective therapies, it is essential to have a deep understanding of adipose tissue biology. In recent years, RNA methylation has emerged as a significant epigenetic modification involved in various cellular functions and metabolic pathways. Particularly in the realm of adipogenesis and lipid metabolism, extensive research is ongoing to uncover the mechanisms and functional importance of RNA methylation. Increasing evidence suggests that RNA methylation plays a regulatory role in adipocyte development, metabolism, and lipid utilization across different organs. This comprehensive review aims to provide an overview of common RNA methylation modifications, their occurrences, and regulatory mechanisms, focusing specifically on their intricate connections to fat metabolism. Additionally, we discuss the research methodologies used in studying RNA methylation and highlight relevant databases that can aid researchers in this rapidly advancing field.
Epigenesis, Genetic , Lipid Metabolism , RNA , Lipid Metabolism/genetics , Humans , Methylation , Animals , RNA/metabolism , RNA/genetics , Adipogenesis/genetics , Adipose Tissue/metabolism , RNA Methylation
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium-and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid-and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.
STATEMENT OF PROBLEM: Transcutaneous electrical nerve stimulation (TENS) has been used in several clinical areas. However, the effect of TENS on the masticatory muscles of young individuals with normal occlusion remains unclear. PURPOSE: The purpose of the study was to assess the effect of TENS on the surface electromyographic (sEMG) activity of masticatory muscles in a young population with normal occlusion. MATERIAL AND METHODS: Twenty residents (5 men and 15 women, mean 24.27 ±2.59 years) of Dalian Stomatological Hospital were enrolled as the study participants. A trained operator collected the required information from the participants. The experiment was divided into 3 stages: pre-TENS acquisition, TENS application, and post-TENS acquisition. The pre-TENS stage was performed using surface electromyography (sEMG) (Myotronics Inc) to acquire the potential values of masticatory muscles in the following 3 states 5 times each: resting, intercuspal occlusion (ICO), and maximum voluntary clench (clenching). The potential values of the anterior of temporalis (TA), the masseter (MM), the sternocleidomastoid (SCM), and the anterior digastric (DA) muscles were collected in the resting state, and TA and MM were collected in the ICO and clenching states. During the TENS application phase, a TENS Unit device (J5 Myomonitor) (J5) was used on each participant for 45 minutes. The post-TENS acquisition phase involved the same procedure as the pre-TENS phase. The experimental data were recorded, and the normality of each group was analyzed using the Shapiro-Wilk test in a statistical software program (IBM SPSS Statistics, v26.0). The paired-sample t test was used to compare the differences in the mean values of sEMG and the asymmetry index (As); the independent-sample t test was used to compare the activity index (Ac) and torque index (To) (α=.05). RESULTS: Significant differences were observed in the mean potential values of TA, MM, LSCM, and RDA before and after TENS in the resting state and RTA, LMM, and RMM before and after TENS in the clenching state (P<.05). Moreover, although AsDA values showed a significant difference (P=.027) before and after TENS in the resting state, the differences in As values for the other muscles in the resting state were statistically similar. Furthermore, in each state, the mean values of Ac and To after TENS showed no significant differences before and after TENS (P>.05). CONCLUSIONS: The resting EMG values of the TA and MM differed significantly before and after TENS. After TENS, the resting EMG activity decreased, whereas the functional EMG activity tended to increase.
BACKGROUND: Periodontal disease may be an important modifiable risk factor for stroke. AIMS: To determine the contribution of markers of periodontal disease to stroke risk globally, within subpopulations, and by stroke subtypes. METHODS: INTERSTROKE is the largest international case-control study of risk factors for first acute stroke. All participants were asked a standardised set of questions about the presence or absence of painful teeth, painful gums or lost teeth, as markers of periodontal disease, within the previous year. The total number of reported variables was calculated per participant. Multivariable conditional logistic regression examined the association of these variables with acute stroke. RESULTS: In 26901 participants, across 32 countries, there was a significant multivariable association between lost teeth and stroke (OR 1.11, 95 % CI 1.01 - 1.22), but not painful teeth (OR 1.00, 95 % CI 0.91-1.10) or painful gums (OR 1.01, 95 % CI 0.89 - 1.14). When these symptoms were considered together there was a graded increased odds of stroke, with the largest magnitude of association seen if a patient reported all three of painful teeth, painful gums and lost teeth (OR 1.34, 95 % CI 1.00 - 1.79). CONCLUSIONS: Our findings suggest that features of severe periodontal disease are a risk factor for acute stroke. Periodontal disease should be considered as a potentially modifiable risk factor for stroke.
SCOPE: High-fat diet induced circadian rhythm disorders (CRD) are associated with metabolic diseases. As the main functional bioactive component in oat, ß-glucan (GLU) can improve metabolic disorders, however its regulatory effect on CRD remains unclear. In this research, the effects of GLU on high-fat diet induced insulin resistance and its mechanisms are investigated, especially focusing on circadian rhythm-related process. METHODS AND RESULTS: Male C57BL/6 mice are fed a low fat diet, a high-fat diet (HFD), and HFD supplemented 3% GLU for 13 weeks. The results show that GLU treatment alleviates HFD-induced insulin resistance and intestinal barrier dysfunction in obese mice. The rhythmic expressions of circadian clock genes (Bmal1, Clock, and Cry1) in the colon impaired by HFD diet are also restored by GLU. Further analysis shows that GLU treatment restores the oscillatory nature of gut microbiome, which can enhance glucagon-like peptide (GLP-1) secretion via short-chain fatty acids (SCFAs) mediated activation of G protein-coupled receptors (GPCRs). Meanwhile, GLU consumption significantly relieves colonic inflammation and insulin resistance through modulating HDAC3/NF-κB signaling pathway. CONCLUSION: GLU can ameliorate insulin resistance due to its regulation of colonic circadian clock and gut microbiome.
BACKGROUND: The study aimed to construct a standardized quality control management procedure (QCMP) and access its accuracy in the quality control of COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). METHODS: Considering the initial RT-PCR results without applying QCMP as the gold standard, a large-scale diagnostic accuracy study including 4,385,925 participants at three COVID-19 RT-PCR testing sites in China, Foshan (as a pilot test), Guangzhou and Shenyang (as validation sites), was conducted from May 21, 2021, to December 15, 2022. RESULTS: In the pilot test, the RT-PCR with QCMP had a high accuracy of 99.18% with 100% specificity, 100% positive predictive value (PPV), and 99.17% negative predictive value (NPV). The rate of retesting was reduced from 1.98% to 1.16%. Its accuracy was then consistently validated in Guangzhou and Shenyang. CONCLUSIONS: The RT-PCR with QCMP showed excellent accuracy in identifying true negative COVID-19 and relieved the labor and time spent on retesting.
COVID-19 , Quality Control , SARS-CoV-2 , Sensitivity and Specificity , Humans , China , COVID-19/diagnosis , COVID-19/prevention & control , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , COVID-19 Nucleic Acid Testing/methods , COVID-19 Nucleic Acid Testing/standards , Reverse Transcriptase Polymerase Chain Reaction/standards , Reverse Transcriptase Polymerase Chain Reaction/methods , Pilot Projects
Skeletal muscle is the largest metabolic organ of the human body. Maintaining the best quality control and functional integrity of mitochondria is essential for the health of skeletal muscle. However, mitochondrial dysfunction characterized by mitochondrial dynamic imbalance and mitophagy disruption can lead to varying degrees of muscle atrophy, but the underlying mechanism of action is still unclear. Although mitochondrial dynamics and mitophagy are two different mitochondrial quality control mechanisms, a large amount of evidence has indicated that they are interrelated and mutually regulated. The former maintains the balance of the mitochondrial network, eliminates damaged or aged mitochondria, and enables cells to survive normally. The latter degrades damaged or aged mitochondria through the lysosomal pathway, ensuring cellular functional health and metabolic homeostasis. Skeletal muscle atrophy is considered an urgent global health issue. Understanding and gaining knowledge about muscle atrophy caused by mitochondrial dysfunction, particularly focusing on mitochondrial dynamics and mitochondrial autophagy, can greatly contribute to the prevention and treatment of muscle atrophy. In this review, we critically summarize the recent research progress on mitochondrial dynamics and mitophagy in skeletal muscle atrophy, and expound on the intrinsic molecular mechanism of skeletal muscle atrophy caused by mitochondrial dynamics and mitophagy. Importantly, we emphasize the potential of targeting mitochondrial dynamics and mitophagy as therapeutic strategies for the prevention and treatment of muscle atrophy, including pharmacological treatment and exercise therapy, and summarize effective methods for the treatment of skeletal muscle atrophy.
Mitochondrial Dynamics , Mitophagy , Muscle, Skeletal , Muscular Atrophy , Humans , Muscular Atrophy/metabolism , Muscular Atrophy/pathology , Muscular Atrophy/therapy , Muscle, Skeletal/metabolism , Muscle, Skeletal/pathology , Animals , Mitochondria/metabolism , Mitochondria/pathology
Lithium metal, with ultrahigh theoretical specific capacity, is considered as an ideal anode material for the lithium-ion batteries. However, its practical application is severely plagued by the uncontrolled formation of dendritic Li. Here, a cation-loaded porous Mg2+-Zeolite layer is proposed to enable the dendrite-free deposition on the surface of Li metal anode. The skeleton channels of zeolite provide the low coordinated Li+-solvation groups, leading to the faster desolvation process at the interface. Meanwhile, anions-involved solvation sheath induces a stable, inorganic-rich SEI, contributing to the uniform Li+ flux through the interface. Furthermore, the co-deposition of sustained release Mg2+ realizes a new faster migration pathway, which proactively facilitates the uniform diffusion of Li on the lithium substrate. The synergistic modulation of these kinetic processes facilitates the homogeneous Li plating/stripping behavior. Based on this synergistic mechanism, the high-efficiency deposition with cyclic longevity exceeding 2100 h is observed in the symmetric Li/Li cell with Mg2+-Zeolite modified anode at 1 mA cm-2. The pouch cell matched with LiFePO4 cathode fulfills a capacity retention of 88.4% after 100 cycles at a severe current density of 1 C charge/discharge. This synergistic protective mechanism can give new guidance for realizing the safe and high-performance Li metal batteries.
