Therapeutic effect and mechanisms of traditional Chinese medicine compound (Qilong capsule) in the treatment of ischemic stroke.

Background Qilong capsule (QLC) is a well-known traditional Chinese medicine compound extensively used in clinical practice. It has been approved by the China's FDA for the treatment of ischemic stroke (IS). In our clinical trial involving QLC (ClinicalTrials.gov identifier: NCT03174535), we observed the potential of QLC to improve neurological function in IS patients at the 24th week, while ensuring their safety. However, the effectiveness of QLC beyond the initial 12-week period remains uncertain, and the precise mechanisms underlying its action in IS have not been fully elucidated. Purpose In order to further explore the clinical efficacy of QLC in treating IS beyond the initial 12-week period and systematically elucidate its underlying mechanisms. Study Design This study employed an interdisciplinary integration strategy that combines post hoc analysis of clinical trials, transcriptome sequencing, integrated bioinformatics analysis, and animal experiments. Methods In this study, we conducted a post-hoc analysis with 2302 participants to evaluate the effectiveness of QLC at the 12th week. The primary outcome was the proportion of patients achieving functional independence at the 12th week, defined as a score of 0-2 on the modified Rankin Scale (mRS), which ranges from 0 (no symptoms) to 6 (death). Subsequently, we employed RNA sequencing (RNA-Seq) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) techniques in the QLC trial to investigate the potential molecular mechanisms underlying the therapeutic effect of QLC in IS. Simultaneously, we utilized integrated bioinformatics analyses driven by external multi-source data and algorithms to further supplement the exploration and validation of QLC's therapeutic mechanism in treating IS. This encompassed network pharmacology analysis and analyses at the mRNA, cellular, and pathway levels focusing on core targets. Additionally, we developed a disease risk prediction model using machine learning. By identifying differentially expressed core genes (DECGs) between the normal and IS groups, we quantitatively predicted IS occurrence. Furthermore, to assess its protective effects and determine the key regulated pathway, we conducted experiments using a middle cerebral artery occlusion and reperfusion (MACO/R) rat model. Results Our findings demonstrated that the combination of QLC and conventional treatment (CT) significantly improved the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week compared to CT alone (n = 2,302, 88.65 % vs 87.33 %, p = 0.3337; n = 600, 91.33 % vs 84.67 %, p = 0.0165). Transcriptome data revealed that the potential underlying mechanism of QLC for IS is related to the regulation of the NF-κB inflammatory pathway. The RT-qPCR results demonstrated that the regulatory trends of key genes, such as MD-2, were consistent with those observed in the RNA-Seq analysis. Integrated bioinformatics analysis elucidated that QLC regulates the NF-κB signaling pathway by identifying core targets, and machine learning was utilized to forecast the risk of IS onset. The MACO/R rat model experiment confirmed that QLC exerts its anti-CIRI effects by inhibiting the MD-2/TLR-4/NF-κB signaling axis. Conclusion: Our interdisciplinary integration study has demonstrated that the combination of QLC with CT exhibits significant superiority over CT alone in improving functional independence in patients at the 12th week. The potential mechanism underlying QLC's therapeutic effect in IS involves the inhibition of the MD-2/TLR4/NF-κB inflammatory signaling pathway, thereby attenuating cerebral ischemia/reperfusion inflammatory injury and facilitating neurofunctional recovery. The novelty and innovative potential of this study primarily lie in the novel finding that QLC significantly enhances the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week. Furthermore, employing a "multilevel-multimethod" integrated research approach, we elucidated the potential mechanism underlying QLC's therapeutic effect in IS.

Global mapping and evolution of persistent fronts in Large Marine Ecosystems over the past 40 years.

Ocean fronts, characterized by narrow zones with sharp changes in water properties, are vital hotspots for ecosystem services and key regulators of regional and global climates. Global change is reshaping the distribution of material and energy in the ocean; however, it remains unclear how fronts have varied in the last few decades. Here, we present a global, fine-scale digital atlas of persistent fronts around Large Marine Ecosystems and demonstrate significant global increases in both their occurrence and intensity. In subtropical regions (around boundary currents and upwelling systems) and polar regions, persistent frontal occurrence and intensity are rapidly increasing, while in tropical regions, they remain stable or slightly decrease. These enhancements may be respectively related to changes in boundary currents, upwelling, and sea ice retreat. This spatially heterogeneous trend holds important implications for the redistribution of front-related ecosystem services and air-sea interactions but has not been captured by representative high-resolution climate projections models or observation-assimilated ocean models.

Regulation of transforming growth factor ß2-induced epithelial-mesenchymal transition and autophagy in lens epithelial cells by the miR-492/NPM1 axis.

A cataract is a clinically common blinding disease closely related to the ageing of the eye cells, which has become a major health killer in the elderly. Our research seeks to analyze the primary targets linked to the pathogenesis of cataracts during the ageing process. We performed bioinformatics analyses on the GSE101727 dataset to discover genes linked with ageing and cataracts. To explore the impacts of Nucleophosmin 1 (NPM1) on cell apoptosis, proliferation, as well as epithelial-mesenchymal transition (EMT) processes, in vitro tests such as western blotting, flow cytometry, and MTT were carried out. Additionally, the study incorporated transforming growth factor ß2 (TGF-ß2) to examine its function in cellular responses, chloroquine (CQ) to regulate autophagic flow, and H2O2 therapy to mimic oxidative stress. Our study discovered seven ageing-related genes, including NPM1, that had substantial relationships with cataracts. NPM1 overexpression was shown to boost cell proliferation and prevent apoptosis in SRA01/04 cells. Notably, NPM1 modulated the TGF-ß signalling pathway, influencing cell proliferation and EMT processes. miR-429 was shown to be adversely regulating NPM1 and autophagy-related proteins, as demonstrated by changes in their expression in response to TGF-ß2 treatment. Furthermore, NPM1 knockdown restored autophagy activity suppressed by miR-429 mimics, indicating a complex interaction of miR-429, NPM1, and TGF-ß2 pathways in regulating autophagy and EMT. Lens epithelial cell proliferation and apoptosis were largely regulated by NPM1, as well as autophagy and EMT, which were significantly mediated by TGF-ß2 and the miR-429/NPM1 axis. These results imply new possible targets for prognosis and therapy of cataracts.

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students.

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Association between dietary vitamin A intake from different sources and non-alcoholic fatty liver disease among adults.

Non-alcoholic fatty liver disease (NAFLD) has become an urgent public health issue with high global prevalence, but data on NAFLD are inconsistent. The association of total dietary vitamin A intake with the NAFLD risk was not well documented in previous studies. To explore the relationship between dietary vitamin A intake from different sources and NAFLD risk among American adults. Data were collected from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014. Logistic regression and restricted cubic spline models were used to estimate the relationship between total dietary vitamin A intake and NAFLD risk. 6,613 adult participants were included. After adjusting potential confounders, the odds ratios (ORs) with 95% confidence intervals (CIs) of NAFLD for the highest quartile intake of total vitamin A, preformed vitamin A, provitamin A carotenoids were respectively 0.86 (0.69-1.06), 0.97 (0.74-1.28), and 0.78 (0.61-0.99), compared to the lowest quartile. Stratifying gender and age, provitamin A carotenoids intake was inversely associated with NAFLD risk in females and participants aged < 45 years. Dose-response analysis indicated a linear negative relationship between provitamin A carotenoids intake and NAFLD risk. Provitamin A carotenoids intake was inversely associated with NAFLD, especially in women and those aged < 45 years among adult American.

Glutathione depletion and dihydroorotate dehydrogenase inhibition actuated ferroptosis-augment to surmount triple-negative breast cancer.

Ferroptosis is a promising therapeutic approach for combating malignant cancers, but its effectiveness is limited in clinical due to the adaptability and self-repair abilities of cancer cells. Mitochondria, as the pivotal player in ferroptosis, exhibit tremendous therapeutic potential by targeting the intramitochondrial anti-ferroptotic pathway mediated by dihydroorotate dehydrogenase (DHODH). In this study, an albumin-based nanomedicine was developed to induce augmented ferroptosis in triple-negative breast cancer (TNBC) by depleting glutathione (GSH) and inhibiting DHODH activity. The nanomedicine (ATO/SRF@BSA) was developed by loading sorafenib (SRF) and atovaquone (ATO) into bovine serum albumin (BSA). SRF is an FDA-approved ferroptosis inducer and ATO is the only drug used in clinical that targets mitochondria. By combining the effects of SRF and ATO, ATO/SRF@BSA promoted the accumulation of lipid peroxides within mitochondria by inhibiting the glutathione peroxidase 4 (GPX4)-GSH pathway and downregulating the DHODH-coenzyme Q (CoQH2) defense mechanism, triggers a burst of lipid peroxides. Simultaneously, ATO/SRF@BSA suppressed cancer cell self-repair and enhanced cell death by inhibiting the synthesis of adenosine triphosphate (ATP) and pyrimidine nucleotides. Furthermore, the anti-cancer results showed that ATO/SRF@BSA exhibited tumor-specific killing efficacy, significantly improved the tumor hypoxic microenvironment, and lessened the toxic side effects of SRF. This work presents an efficient and easily achievable strategy for TNBC treatment, which may hold promise for clinical applications.

The impact of two dietary patterns on hyperuricemia in adults: A meta analysis of observational studies.

BACKGROUND: Hyperuricemia (HUA) is a crucial factor contributing to some chronic diseases among adults. In past observational literatures, scholars have debated the effectiveness of dietary pattern on HUA and inconsistencies exist. Given this condition, the study aimed to provide a comparative assessment of the relation between dietary pattern and HUA risk and offer implications to policy makers. METHODS: A systematic research was undertaken in PubMed, Web of Science, Cochrane, Embase, Medicine, ScienceDirect and Medline to identify observational studies examining the effect of dietary pattern on HUA, and search period was from past to January 2022. Meta analysis was applied by using the Stata version 11.0 software. RESULTS: A total of 34,583 adults from 8 observational studies, 45,525 adults from 6 observational studies were included to examine the effectiveness of "healthy" and "meat/western" dietary patterns on HUA risk respectively. The findings suggested that "healthy" dietary pattern significantly decreased the HUA risk (OR = 0.73; 95% CI: 0.61-0.88) both in Eastern countries (OR = 0.79; 95% CI: 0.64-0.98) and Western countries (OR = 0.53; 95% CI: 0.30-0.92) while the "Meat/Western" pattern increased the HUA possibilities (OR = 1.26; 95% CI: 1.17-1.37). Stratified analysis exhibited that "healthy" pattern reduced HUA risk in adults was more effective in cohort study (OR = 0.79; 95% CI: 0.72-0.86). CONCLUSIONS: This study's findings highlighted the potential benefit of healthy dietary pattern in decreasing HUA risk. Accordingly, implementing policy makers of countries should enhance to appeal adults to keep a healthy diet, offer financial support to low-income staff, or provide guidelines for adult's dietary behavior changes. TRIAL REGISTRATION NUMBER: INPLASY: INPLASY202290034.

Decoding and synthesizing tonal language speech from brain activity.

Recent studies have shown that the feasibility of speech brain-computer interfaces (BCIs) as a clinically valid treatment in helping nontonal language patients with communication disorders restore their speech ability. However, tonal language speech BCI is challenging because additional precise control of laryngeal movements to produce lexical tones is required. Thus, the model should emphasize the features from the tonal-related cortex. Here, we designed a modularized multistream neural network that directly synthesizes tonal language speech from intracranial recordings. The network decoded lexical tones and base syllables independently via parallel streams of neural network modules inspired by neuroscience findings. The speech was synthesized by combining tonal syllable labels with nondiscriminant speech neural activity. Compared to commonly used baseline models, our proposed models achieved higher performance with modest training data and computational costs. These findings raise a potential strategy for approaching tonal language speech restoration.

Living in the era of codes: a reflection on China's health code system.

This article offers a critical analysis of China's health code system, a data-powered pandemic control and contact tracing system that supposedly subjects all individuals in the country to its panopticon control, a surveillance system that monitors and categorises the Chinese population into the healthy (green), the dubious (yellow), and the unhealthy (red). The article highlights the pretence of surveillance as care and the digital divide that normalises discrimination against the elderly and other digitally left-behind population. It also illustrates how, from policy making and technological design to user engagement, the health code system is implemented, optimised, and used in everyday life to meet the needs of the vulnerable population. The health code is better taken as a medium of adaptable and communicative process that can reset the relation between the system and the lifeworld. It is the process of interchange between the system and the lifeworld that deserves our critical attention.

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study.

Objective: Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson's disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML). Materials and methods: A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD. Results: At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson's Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan-Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing. Conclusion: In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.

Impacts of environmental regulation on innovation in the context of the Internet.

The relationship between environmental regulation and innovation has long been a key issue of scholarly interest. The Porter hypothesis posits that environmental regulations can effectively promote innovation. However, with the rapid development of the Internet, the relationship between environmental regulation and innovation has changed. Through a theoretical analysis, this paper finds that the development of the Internet can not only directly promote innovation but also inhibit innovation through environmental regulations. This paper uses Chinese city-level data from 2014 to 2016 to empirically test the relationship among the Internet, environmental regulation, and innovation. The study finds that (1) the increase in the Internet level and in the intensity of environmental regulations promotes innovation, and the results remain stable after the replacement of the Internet level metrics, (2) the increase in the Internet level inhibits the positive effect of environmental regulations on innovation, but its inhibitory effect is lower than the promotion effect of the Internet on innovation, and (3) the Internet and environmental regulations have a significant promotion effect on the application and acquisition of three types of patents, including invention patents, utility model patents and design patents, with the application and acquisition of utility model patents having the greatest promotion effect, and (4) the analysis of heterogeneity shows that environmental regulation has a greater effect on innovation in eastern and provincial capital cities, and the Internet has a greater effect on innovation in western and nonprovincial capital cities. Finally, this article puts forward policy recommendations based on three aspects: strengthening Internet construction, implementing environmental regulations and policies based on local conditions, and increasing support for R&D and innovation.

Clinical effectiveness of Qilong capsule in patients with ischemic stroke: A prospective, multicenter, non-randomized controlled trial.

BACKGROUND: The Qilong capsule (QLC) is a Chinese patented medicine characterized by an equal emphasis on replenishing Qi and activating blood circulation. In 2000, China's FDA approved the use of QLC for ischemic stroke (IS). However, there is not yet much high-quality evidence of the clinical effectiveness of QLC combined with conventional treatment (CT) for IS with Qi deficiency and blood stasis syndrome. PURPOSE: In this study, we conducted a prospective, multicenter, non-randomized controlled trial at 7 hospitals in China to investigate the clinical effectiveness of QLC combined with CT for IS with Qi deficiency and blood stasis syndrome. METHODS: Participants aged 35 to 80 years old diagnosed as IS with Qi deficiency and blood stasis syndrome in TCM were recruited. Participants were treated with QLC (intervention group) or non-QLC (control group). The intervention course of QLC was 12 weeks. All participants in two groups received standard treatment. All participants returned for in-person follow-up visits at the 12th week and 24th week. Primary outcome measures included a modified Rankin Scale (mRS), the National Institute of Health Stroke Scale (NIHSS), and the Barthel Index (BI). Secondary outcome measures included TCM syndromes (Qi deficiency syndrome score, blood stasis syndrome score), psychological index (self-rating depression scale, SDS; self-rating anxiety scale, SAS), blood lipid index, blood coagulation index, homocysteine, and favorable functional outcome (mRS 0 - 3). Multiple imputations were used for any missing data. Propensity score matching (PSM) was used to deal with any confounding factors (age, gender, scale score, etc.). Rank alignment transformation variance analysis (ART ANOVA) and generalized linear mixed model (GLMM) were introduced to improve the scientific and accuracy of repeated measurement data. All statistical calculations were carried out with R 3.6.1 statistical analysis software. RESULTS: A total of 2468 participants were screened from November 2016 to January 2019. Finally, 2302 eligible participants were included in the analysis. There were 1260 participants in the intervention group (QLC group) and 1042 participants in the control group (non-QLC group). After PSM matching, sub-samples of 300 participants in the QLC group and 300 participants in the non-QLC group were finally formed. The final results of clinical effectiveness are the same results shared by the total samples and sub-samples after PSM. In the 24th week after treatment, QLC combined with CT proved to be significantly better than CT alone in reducing the scores of mRS (p < 0.05), NIHSS (p < 0.001), Qi deficiency syndrome (p < 0.01), and blood stasis syndrome (p < 0.001), SAS (p < 0.05), as well as in improving BI score (p < 0.05). The favourable functional outcome (mRS score of 0 to 3 at week 12) was statistically different between QLC and non-QLC group in the sub-samples (p < 0.01, 97% vs 91.7%). The results of the ART ANOVA showed that the improvement of mRS (p < 0.01), BI (p < 0.05) and NIHSS (p < 0.001) in QLC group was better than non-QLC group when the interaction effect was considered. The results of GLMM showed that the reduction of mRS and NIHSS scores of patients in the QLC group were better than those of the non-QLC group (p < 0.001). The BI score of the QLC group in the sub-samples after PSM increased more than the non-QLC group (p < 0.001). There was no evidence showing that QLC can cause serious adverse reactions (ADRs) in treating patients with IS. CONCLUSION: QLC combined with CT was better than CT alone in reducing mRS score, NIHSS score, Qi deficiency syndrome score, blood stasis syndrome score, and SAS score, as well as improving BI score after treatment. Further high-quality RCTs are needed to confirm the positive results. The study protocol was embedded in a registry study that registered in the Clinical Trials USA Registry (registration No. NCT03174535).

Onset and Recurrence Characteristics of Chinese Patients with Noncardiogenic Ischemic Stroke in Chinese Medicine Hospital.

OBJECTIVE: To delineate the onset and recurrence characteristics of noncardiogenic ischemic stroke patients in China. METHODS: A prospective, multicenter and registry study was carried out in 2,558 patients at 7 representative clinical sub-centers during November 3, 2016 to February 17, 2019. A questionnaire was used to collect information of patients regarding CM syndromes and constitutions and associated risk factors. Additionally, stroke recurrence was defined as a primary outcome indicator. RESULTS: A total of 327 (12.78 %) patients endured recurrence events, 1,681 (65.72%) were men, and the average age was 63.33 ± 9.45 years. Totally 1,741 (68.06%) patients suffered first-ever ischemic stroke, 1,772 (69.27%) patients reported to have hypertension, and 1,640 (64.11%) of them reported dyslipidemia, 1,595 (62.35%) patients exhibited small-artery occlusion by The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification. Specifically, 1,271 (49.69%) patients were considered as qi-deficient constitution, and 1,227 (47.97%) patients were determined as stagnant blood constitution. There were 1,303 (50.94%) patients diagnosed as blood stasis syndrome, 1,280 (50.04%) patients exhibited phlegm and dampness syndrome and 1,012 (39.56%) patients demonstrated qi deficiency syndrome. And 1,033 (40.38%) patients declared intracranial artery stenosis, and 478 (18.69%) patients reported carotid artery stenosis. The plaque in 1,508 (41.36%) patients were of mixed. Particularly, 41.09% of them demonstrated abnormal levels of glycated hemoglobin levels. CONCLUSIONS: Recurrence in minor and small-artery stroke cannot be ignored. Hypertension, dyslipidemia, abnormal HbA1c, intracranial artery stenosis and carotid plaque were more common in stroke patients. Particularly, phlegm-dampness and blood stasis syndromes, as well as qi deficiency and blood stasis constitutions, were still the main manifestations of stroke. (Trial registration at ClinicalTrials.gov No. NCT03174535).

Solid fuels use for cooking and sleep health in adults aged 45 years and older in China.

Outdoor air pollution has been linked to poor sleep health, but limited studies have investigated the relationship between solid cooking fuels and sleep health in adults. Therefore, we analyzed data from the China Health and Retirement Survey (CHARLS), a national survey of about 17,000 residents aged over 45. Participants were restricted to those who participated in CHARLS 2011, 2013 and 2015 (n = 8,668). Sleep health was indicated by self-reported average sleep hours at night and the numbers of unrested days/week in CHARLS 2015. We analyzed cooking fuel types reported and assessed the duration of solid fuels usage as consistent (indicated use in all three surveys or 6 + years) or inconsistent use (indicated use in one or two surveys or 1-4 years). We found consistent use of solid fuels was associated with a shorter sleep duration (OR = 1.17 95% CI 1.01, 1.35 for ≤ 6 h vs. 7-9 h/day) and higher frequencies of feeling unrested (OR = 1.32 95% CI 1.12, 1.55 for ≥ 5 days/week vs. none) compared with cleaner fuels use. The associations for inconsistent solid fuels use and sleep health were in the similar direction but smaller in magnitude. Further research is needed to confirm our findings and evaluate the exposure impact of specific fuel types to inform intervention strategies.

Dynamic Brain Responses Modulated by Precise Timing Prediction in an Opposing Process.

The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However, much less is known about how the brain manages the prediction process over time using traditional neuropsychological paradigms. Here, we implemented an innovative paradigm for timing prediction to precisely study the temporal dynamics of neural oscillations. In the experiment recruiting 45 participants, expectation suppression was found for the overall electroencephalographic activity, consistent with previous hemodynamic studies. Notably, we found that N1 was positively associated with predictability while N2 showed a reversed relation to predictability. Furthermore, the matching prediction had a similar profile with no timing prediction, both showing an almost saturated N1 and an absence of N2. The results indicate that the N1 process showed a 'sharpening' effect for predictable inputs, while the N2 process showed a 'dampening' effect. Therefore, these two paradoxical neural effects of prediction, which have provoked wide confusion in accounting for expectation suppression, actually co-exist in the procedure of timing prediction but work in separate time windows. These findings strongly support a recently-proposed opposing process theory.

Magnetic bead-based electrochemical and colorimetric assays of circulating tumor cells with boronic acid derivatives as the recognition elements and signal probes.

Simple, sensitive and rapid detection of circulating tumor cells (CTCs) is of great importance for early diagnosis and therapy of cancers. Overexpression of sugar units on cell surface is related to the phenotypes of many cancers. Based on the boronate ester interaction, we reported the electrochemical and colorimetric detection of CTCs with high simplicity and sensitivity. Specifically, ferroceneboronic acid (FcBA) can be measured by differential pulse voltammetry and 4-mercaptophenylboronic acid (MPBA) can induce the aggregation and color change of gold nanoparticles (AuNPs). CTCs captured by the aptamer-modified magnetic beads (Apt-MBs) can sequestrate FcBA or MPBA molecules by the formation of boronate ester bonds, thus leading to the decrease in the electrochemical signal of FcBA or preventing the MPBA-triggered aggregation of AuNPs. Due to the overexpression of sugar groups on the surface of CTCs, the amplification-free methods exhibited high sensitivity and obviated the use of additional antibody or aptamer for the recognition of captured cells. With MCF-7 cancer cell as the model, 50 cells can be readily determined by the electrochemical and colorimetric methods. The proposed strategy is valuable for probing of cell glycosylation and designing of novel sensing devices for detection of sugar-containing biological macromolecules and cells.

Classification of auditory attention focuses during speech perception.

Passive brain-computer interfaces (BCIs) covertly decode the cognitive and emotional states of users by using neurophysiological signals. An important issue for passive BCIs is to monitor the attentional state of the brain. Previous studies mainly focus on the classification of attention levels, i.e. high vs. low levels, but few has investigated the classification of attention focuses during speech perception. In this paper, we tried to use electroencephalography (EEG) to recognize the subject's attention focuses on either call sign or number when listening to a short sentence. Fifteen subjects participated in this study, and they were required to focus on either call sign or number for each listening task. A new algorithm was proposed to classify the EEG patterns of different attention focuses, which combined common spatial pattern (CSP), short-time Fourier transformation (STFT) and discriminative canonical pattern matching (DCPM). As a result, the accuracy reached an average of 78.38% with a peak of 93.93% for single trial classification. The results of this study demonstrate the proposed algorithm is effective to classify the auditory attention focuses during speech perception.

Numerical study on the summertime patches of red tide in the adjacent sea of the Changjiang (Yangtze) River Estuary, China.

High incident rates of red tide have occurred off the coast of the Changjiang (Yangtze) River Estuary in summer, resulting from a magnified population growth discharging substantial nutrient loads into this vicinity. The presence of elevated Chlorophyll-a concentrations (≥36.3 µg/l), low suspended sediment concentrations in surface and mid-layers (<10 mg/l), a strong saline front and surface water temperature gradient, veering surface winds, and a bimodal shape to the Changjiang Diluted Water (CDW) revealed two red tide patches appearing between August 6-13, 2010. Two distinguishable hydrodynamic driving mechanisms, connected to these incidents, are diagnosed. Field observations and numerical simulations determined a red tide patch in the northeastern offshore area is caused by a northeast transport of the CDW truncated by a northwest intrusion of the Taiwan Warm Current (TWC) as winds deviated. Red tide occurrence in the southern nearshore area originated from a plume front where CDW interfaces with the TWC.

Implementing machine learning in bipolar diagnosis in China.

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.

Experimental sleep restriction effect on adult body weight: a meta-analysis.

BACKGROUND: Sleep is increasingly recognized as a potential risk for overweight and obesity. Observational studies have shown links between short sleep duration with weight gain. However, the findings from longitudinal studies in adults are conflicting. This review aimed to examine the effectiveness of experimental sleep restriction on adult body weight. METHOD: A systematic search was undertaken in MEDLINE, EMBASE, PsycINFO, and CENTRAL (Cochrane center register of controlled trials) to identify experimental studies examining the effectiveness of sleep restriction on body weight, and search period was from January 2005 to June 2018. Meta-analysis was applied by using the random model. RESULTS: A total of 275 adults from six experimental studies were included. The pooled standard mean difference in body weight and body fat was 0.44 (95% CI - 0.13 to 1.02) (Z = 1.51, p > 0.05) and 0.35 kg (95% CI - 0.19 to 0.88) (Z = 1.27, p > 0.05), respectively. The experimental sleep restriction did not result in significant differences in adult body weight or body fat. Subgroup analysis showed that there were differences in weight gain between genders and races. CONCLUSION: The finding from this review cannot support the hypothesis from observational studies that short sleep leads to weight gain.