Electrochemical detection of myoglobin using an ultrasensitive label-free sensor derived from cubic-ZIF67@Au-rGOF-NH2 composite of MOF and GOF.

Markers of myocardial injury, such as myoglobin (Mb), are substances swiftly released into the peripheral bloodstream upon myocardial cell injury or altered cardiac activity. During the onset of acute myocardial infarction, patients experience a significant surge in serum Mb levels. Given this, precise detection of Mb is essential, necessitating the development of innovative assays to optimize detection capabilities. This study introduces the synthesis of a three-dimensional hierarchical nanocomposite, Cubic-ZIF67@Au-rGOF-NH2, utilizing aminated reduced graphene oxide and zeolite imidazolium ester framework-67 (ZIF67) as foundational structures. Notably, this novel material, applied in a label-free electrochemical immunosensor, presents a groundbreaking approach for detecting myocardial injury markers. Experimental outcomes revealed ZIF67 and AuNPs exhibit enhanced affinity and growth on the 3D-rGOF-NH2 matrix, thus amplifying electrical conductivity while preserving the inherent electrochemical attributes of ZIF67. As a result, the Cubic-ZIF67@Au-rGOF-NH2 label-free electrochemical immunosensor exhibited a broad detection range and high sensitivity for Mb. The derived standard curve was ΔIp = 16.67552lgC+275.245 (R = 0.993) with a detection threshold of 3.47 fg/ml. Moreover, recoveries of standards spiked into samples ranged between 96.3% and 108.7%. Importantly, the devised immunosensor retained notable selectivity against non-target proteins, proving its potential clinical utility based on exemplary sample analysis performance.

Meta-analysis of pulsed-field ablation versus cryoablation for atrial fibrillation.

PURPOSE: The available data on the treatment strategy of pulsed field ablation (PFA) for patients with atrial fibrillation (AF) is limited. This study aims to provide a comparative analysis of the efficacy, safety, and procedural efficiency between PFA and cryoballoon ablation (CBA) for AF. METHODS: We conducted a comprehensive search of the EMBASE, PubMed, Cochrane Library, and ClinicalTrials.gov databases to identify trials comparing PFA with CBA for AF from their inception until December 2023. The odds ratio (OR) and mean difference (MD), along with a 95% confidence interval (CI), were utilized as measures of treatment effect. RESULTS: The analysis included 15 eligible trials with a total enrollment of 1880 patients. No significant differences were found in recurrent atrial arrhythmia (OR 0.83, 95% CI 0.64, 1.07) or periprocedural complications (OR 0.78, 95% CI 0.46, 1.30) between the two ablation techniques examined in this study. However, the PFA technique demonstrated a significantly shorter procedure time (MD -7.17, 95% CI -13.60, -0.73), but a longer fluoroscopy time (MD 2.53, 95% CI 0.87, 4.19). Similarly, PFA was found to be significantly associated with a decreased incidence of phrenic nerve palsy (OR 0.20, 95% CI 0.07, 0.59), but an increased incidence of cardiac tamponade (OR 4.07, 95% CI 1.15, 14.39). Moreover, there was a significantly higher release of troponin with PFA compared to CBA (MD 470.28, 95% CI 18.89, 921.67), while the increase in S100 protein and heart rate was significantly lower with PFA than with CBA (MD -64.41, 95% CI -105.46, -17.36), (MD -8.76, 95% CI -15.12, -2.40). CONCLUSION: The utilization of PFA provides a safer, time-saving, and tissue-specific procedure compared to CBA, while maintaining comparable success rates. This has the potential to enhance procedural efficiency and optimize resource utilization in clinical practice. These findings underscore the feasibility and promise of PFA as an alternative technique for PVI in patients with AF.

Role of sugars in the apical hook development of Arabidopsis etiolated seedlings.

KEY MESSAGE: The sugar supply in the medium affects the apical hook development of Arabidopsis etiolated seedlings. In addition, we provided the mechanism insights of this process. Dicotyledonous plants form an apical hook structure to shield their young cotyledons from mechanical damage as they emerge from the rough soil. Our findings indicate that sugar molecules, such as sucrose and glucose, are crucial for apical hook development. The presence of sucrose and glucose allows the apical hooks to be maintained for a longer period compared to those grown in sugar-free conditions, and this effect is dose-dependent. Key roles in apical hook development are played by several sugar metabolism pathways, including oxidative phosphorylation and glycolysis. RNA-seq data revealed an up-regulation of genes involved in starch and sucrose metabolism in plants grown in sugar-free conditions, while genes associated with phenylpropanoid metabolism were down-regulated. This study underscores the significant role of sugar metabolism in the apical hook development of etiolated Arabidopsis seedlings.

Identifying interactions among air pollutant emissions on diabetes prevalence in Northeast China using a complex network.

BACKGROUND: Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes. METHODS: After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China. RESULTS: Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO. CONCLUSIONS: Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions.

YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION: In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.

[Research on fault diagnosis of patient monitor based on text mining].

The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.

Exposure to ambient air pollution and metabolic kidney diseases: evidence from the Northeast China Biobank.

BACKGROUND: At present, there is no epidemiological evidence of the association between metabolic kidney diseases (MKD) and exposure to air pollution. METHODS: We investigated the association between exposure to long-term air pollution and the risk of developing MKD using samples from the Northeast China Biobank. RESULTS: Data from 29 191 participants were analyzed. MKD prevalence was 3.23%. Every standard deviation increment in PM2.5 increased the risk of MKD [odds ratio (OR) = 1.37, 95% confidence interval (CI) 1.19-1.58), diabetic kidney disease (DKD) (OR = 2.03, 95% CI 1.52-2.73), hypertensive kidney disease (BKD) (OR = 1.31, 95% CI 1.11-1.56), hyperlipidemic kidney disease (PKD) (OR = 1.39, 95% CI 1.19-1.63) and obese kidney disease (OKD) (OR = 1.34, 95% CI 1.00-1.81). PM10 increased the risk of MKD (OR = 1.42, 95% CI 1.20-1.67), DKD (OR = 1.38, 95% CI 1.03-1.85), BKD (OR = 1.30, 95% CI 1.07-1.58) and PKD (OR = 1.50, 95% CI 1.26-1.80). Sulfur dioxide increased the risk of MKD (OR = 1.57, 95% CI 1.34-1.85), DKD (OR = 1.81, 95% CI 1.36-2.40), BKD (OR = 1.44, 95% CI 1.19-1.74) and PKD (OR = 1.72, 95% CI 1.44-2.04). Ozone decreased the risk of PKD (OR = 0.83, 95% CI 0.70-0.99). Age, ethnicity and air pollution interacted to affect the risk of MKD, BKD and PKD. Associations between air pollution and CKD or metabolic disease were weaker than those with MKD. The association between air pollution and MKD became stronger when compared with participants with non-metabolic disease. CONCLUSIONS: Air pollution may cause MKD or facilitate the progression from metabolic disease to renal failure.

Associations between long term exposures to outdoor air pollution and indoor solid fuel use and depression in China.

Depression is one of the most common mental disorders. Effects of air pollution from outdoor and indoor on depression were inconsistent. We assessed 30,139 participants from Northeast China to explore the associations between long term exposures of outdoor and indoor solid fuel use and depressive symptoms. Multiple logistic regressions models as well as multiplicative interaction and additive interaction analysis were used. Outdoor exposures to air pollutants of particulate matter (with an aerodynamic diameter <2.5 µm, [PM2.5], odds ratio [OR] = 1.98 per standard deviation [SD], 95% confidence interval [CI]: 1.78, 2.19; with an aerodynamic diameter <10 µm, [PM10], OR = 1.83, 95% CI:1.68, 2.00), sulfur dioxide (SO2, OR = 1.42, 95% CI: 1.33, 1.52), and nitrogen dioxide (NO2, OR = 1.62, 95% CI: 1.49, 1.76) were significantly associated with higher occurrence of depressive symptoms. A significant linear trend for increased occurrence of depressive symptoms was observed in participants using both solid fuels for cooking and heating (P = 0.04). Indoor air pollution exposures from solid fuel use for heating (OR = 1.16, 95%CI: 1.00, 1.35) and high cooking frequency (OR = 1.17, 95%CI: 1.00, 1.37) were significantly associated with increased occurrence of depressive symptoms. We observed significant interactions of indoor solid fuel use and outdoor air pollution exposures on depressive symptoms (indoor fuel use for cooking and SO2, P value = 0.04; solid fuel use for heating and NO2, P value = 0.02). Solid fuel use for cooking weakened the associations between SO2(relative excess risk due to interaction [RERI] = -1.37, 95% CI: -1.88, -0.86) and depressive symptoms. Solid fuel use for heating weakened the associations between NO2 (RERI = -1.91, 95% CI: -2.55, -1.27) and depressive symptoms. Compared with individual associations, antagonistic interactions of outdoor air pollution and indoor solid fuel use on depressive symptoms might exist. Our findings contribute to better understandings for the associations between air pollution and depressive symptoms, which might be useful for developing effective strategies for depression prevention and air pollution control.

Household solid fuel use and peak expiratory flow in middle-aged and older adults in China: A large cohort study (2011-2015).

Indoor air pollution caused by solid fuel use in cooking and heating in China is common. The relationship between household solid fuel use and peak expiratory flow (PEF) in middle-aged and older adults in China has not been clarified. The aim of this study was to assess the relationship between long-term household solid fuel use (clean for both cooking and heating, solid for either cooking or heating, and solid for both cooking and heating) and PEF changes in middle-aged and older adults using a nationally representative prospective cohort. Covariance analysis was used to compare PEF changes in different indoor air pollution exposure groups. Separate analysis of cooking and heating as well as sub-group analyses by age, sex and smoking status were conducted, linear mixed growth model analysis was used to evaluate the association between cooking fuel type and PEF. A total of 6818 participants were enrolled in the cohort analysis. Results revealed that solid fuel use in cooking and heating separately or conjointly were associated with reduced PEF (solid fuel use in cooking: least square mean [LSM] = 19.9, 95% confidence interval [CI]: 11.5-28.2, P = 0.03; solid fuel use in heating: LSM = 19.4, 95% CI:11.2-27.5, P = 0.04; both solid fuel use: LSM = 17.6, 95% CI: 9.3-25.9, P for trend <0.0001), especially in participants aged >65 years (LSM = -9.22, 95% CI: 27.9-69.52, P for trend <0.0001), females (LSM = -6.41, 95% CI: 19.12-6.30, P for trend <0.0001) and current or former smokers (LSM = -21.55, 95% CI: 36.14 to -6.97, P < 0.02). Compared to that of participants using clean fuels for cooking, PEF of participants using solid fuels were decreased by 3.5 l/min per 2 years over a 4-year follow-up. This cohort study highlights the adverse effects of indoor air pollution on lung function in middle aged and older adults in China.

Effects of variations in meteorological factors on daily hospital visits for asthma: A time-series study.

Few studies have explored the relationships between meteorological factors and asthma. This study explored the associations between daily variations in meteorological factors and hospital visits for asthma among different ages, genders, and asthma subtypes in Shenyang, China. A distributed lag non-linear model with Poisson regression was used; confounding factors included day of the week, holidays, and seasonal patterns, and long-term trends and air pollutants were adjusted using natural cubic splines. The meteorological factor with the largest cumulative effect was barometric pressure, which increased the risk of hospital visits for adult asthma (relative risk [RR] = 2.414), followed by maximum wind speed, which increased hospital visits for asthma for children aged 0-5 years old (RR = 1.47), and pressure, which increased hospital visits for bronchial asthma (RR = 1.298). Meteorological factors showed stronger associations with asthma than air pollutants. Further research should focus more on the effects of meteorological factors on asthma.

Association between air pollutants and outpatient and emergency hospital visits for childhood asthma in Shenyang city of China.

Effects of air pollution on asthma vary in different study areas, and long-term time series research on the effects of air pollution on asthma outpatients and emergency hospital visits has not been conducted in Northeast China. We assessed the impact of air pollutants on the risk of asthma outpatients and emergency hospital visits in Shenyang, China. A distributed lag non-linear model with a Poisson regression was used to assess the short-term effects of air pollutants on asthma outpatient and emergency hospital visits between January 1, 2013 and December 31, 2017. Confounding factors were adjusted using natural cubic splines. Ozone (O3), carbon monoxide (CO), and suspended particulates < 10 µm in aerodynamic diameter (PM10) were positively associated with the number of asthma hospital visits. The largest cumulative effects of O3, CO, and PM10 on hospital visits were on lag day 2 (RR = 1.163, 95% CI 1.051-1.287) for 0-5-year-old childhood asthma, on lag day 3 (RR = 1.386, 95% CI 1.136-1.69) for asthma in winter, and on lag day 10 (RR = 1.148, 95% CI 0.942-1.399) for female asthma, respectively. The cumulative effect of air pollution represented by the air quality index (AQI) was largest on lag day 10 for 0-5-year-old childhood asthma with an increase of 28.6% (95% CI 6.5-55.4) hospital visits every IQR increment of the AQI. CO, O3, and PM10 were the main air pollutants in Shenyang city. Children with bronchitis asthma were more vulnerable to air pollution during the cold season.

Land use regression for spatial distribution of urban particulate matter (PM10) and sulfur dioxide (SO2) in a heavily polluted city in Northeast China.

Particulate material 10 µm (PM10) and sulfur dioxide (SO2) are representative air pollutants in Northeast China and may contribute more to the morbidity of respiratory and cardiovascular disease than may other pollutants. Up to now, there have been few studies on the relation between health effect and air pollution by PM10 and SO2 in Northeast China, which may be due to the lack of a model for determination of air pollution exposure. For the first time, we used daily concentration data and influencing factors (different type of land use, road length and population density, and weather conditions as well) to develop land use regression models for spatial distribution of PM10 and SO2 in a central city in Northeast China in both heating and non-heating months. The final models of SO2 and PM10 estimation showed good performance (heating months: R2 = 0.88 for SO2, R2 = 0.88 for PM10; non-heating months: R2 = 0.79 for SO2; R2 = 0.87 for PM10). Estimated concentrations of air pollutants were more affected by population density in heating seasons and land use area in non-heating seasons. We used the land use regression (LUR) models developed to predict pollutant levels in nine districts in Shenyang and conducted a correlation analysis between air pollutant levels and hospital admission rates for childhood asthma. There were high associations between asthma hospital admission rates and air pollution levels of SO2 and PM10, which indicated the usability of the LUR models and the need for more concern about the health effects of SO2 and PM10 in Northeast China. This study may contribute to epidemiological research on the relation between air pollutant exposure and typical chronic disease in Northeast China as well as providing the government with more scientific recommendations for air pollution prevention.

Picea wilsonii transcription factor NAC2 enhanced plant tolerance to abiotic stress and participated in RFCP1-regulated flowering time.

KEY MESSAGE: Picea wilsonii transcription factor PwNAC2 enhanced plant tolerance to salt and drought stress through multiple signaling pathway and interacted with PwRFCP1 to participate in flowering regulation. NAC is one of the largest transcription factor families in plants, however, its role is not yet fully understood. Here, we identified a transcription factor PwNAC2 in Picea wilsonii, which localized in nucleus with transcriptional activity in C-terminal region and can form homodimer by itself. Expression analysis by real-time PCR showed that PwNAC2 was induced by multiple abiotic stresses and phytohormones stimuli. PwRFCP1 (Resemble-FCA-contain-PAT1 domain), an interaction protein of PwNAC2 was screened via yeast two hybrid. Luciferase complementation assay confirmed the interaction in vivo and bimolecular fluorescence complementation assay showed the interaction in nucleus. PwNAC2 overexpression retarded Arabidopsis hypocotyls growth which is closely related to light, whereas promotion of hypocotyls growth by PwRFCP1 is independent on light. Under drought or salt treatment, overexpression of PwNAC2 in Arabidopsis showed more vigorous seed germination and significant tolerance for seedlings by ROS scavenging, reducing of membrane damage, slower water loss and increased stomatal closure. ABA or CBF-pathway marker genes were substantially higher in PwNAC2 transgenic Arabidopsis. Overexpression of PwRFCP1 promotes flowering in transgenic Arabidopsis, whereas PwNAC2 delayed flowering by altering the expression of FT, SOC1 and FLC. In addtioin, PwRFCP1 overexpression plants showed no higher tolerance to stress treatment than Col-0. Collectively, our results indicate that PwNAC2 enhanced plant tolerance to abiotic stress through multiple signaling pathways and participated in PwRFCP1-regulated flowering time.

Comparative transcriptome sequencing and de novo analysis of Vaccinium corymbosum during fruit and color development.

BACKGROUND: Blueberry is an economically important fruit crop in Ericaceae family. The substantial quantities of flavonoids in blueberry have been implicated in a broad range of health benefits. However, the information regarding fruit development and flavonoid metabolites based on the transcriptome level is still limited. In the present study, the transcriptome and gene expression profiling over berry development, especially during color development were initiated. RESULTS: A total of approximately 13.67 Gbp of data were obtained and assembled into 186,962 transcripts and 80,836 unigenes from three stages of blueberry fruit and color development. A large number of simple sequence repeats (SSRs) and candidate genes, which are potentially involved in plant development, metabolic and hormone pathways, were identified. A total of 6429 sequences containing 8796 SSRs were characterized from 15,457 unigenes and 1763 unigenes contained more than one SSR. The expression profiles of key genes involved in anthocyanin biosynthesis were also studied. In addition, a comparison between our dataset and other published results was carried out. CONCLUSIONS: Our high quality reads produced in this study are an important advancement and provide a new resource for the interpretation of high-throughput data for blueberry species whether regarding sequencing data depth or species extension. The use of this transcriptome data will serve as a valuable public information database for the studies of blueberry genome and would greatly boost the research of fruit and color development, flavonoid metabolisms and regulation and breeding of more healthful blueberries.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

[Research on Electrical Impedance Tomography Technology].

This article reviews the principle of electrical impedance tomography imaging and measurement system; focuses on electrical impedance tomography imaging detection system of incentive mode and several typical image reconstruction algorithm of electrical impedance imaging; and objectively compares and effectively evaluates several image reconstruction algorithm.

[The research and expectation on wearable health monitoring system].

Wearable health monitoring systems that use wearable biosensors capturing human motion and physiological parameters, to achieve the wearer's movement and health management needs. Wearable health monitoring system is a noninvasive continuous detection of human physiological information, data wireless transmission and real-time processing capabilities of integrated system, can satisfy physiological condition monitoring under the condition of low physiological and psychological load. This paper first describes the wearable health monitoring system structure and the relevant technology applied to wearable health monitoring system, and focuses on the current research work what we have done associated with wearable monitoring that wearable respiration and ECG acquisition and construction of electric multi-parameter body area network. Finally, the wearable monitoring system for the future development direction is put forward a simple expectation.

Life's Essential 8, genetic predisposition, and risk of incident adult-onset asthma: a prospective cohort study.

BACKGROUND: Adult-onset asthma (AOA) and cardiovascular diseases shared common risk factors and similar pathophysiologic resemblances. The American Heart Association (AHA) unveiled the life's essential 8 (LE8) to promote cardiovascular health (CVH). This study aimed to assess the overall impact of LE8 implementation on AOA prevention. METHODS: According to the guideline of AHA's Construct of CVH in 2022, LE8 score was calculated from 8 health status concerning diet, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. Cox proportional-hazards models were used to estimate effect sizes of associations between CVH, asthma genetic risk, and risk of incident AOA in participants selected from the UK Biobank study. RESULTS: A total of 6180 incident AOA cases occurred in 249,713 participants during an average of 11.60 y' follow-up. A higher LE8 score was associated with a lower risk of incident AOA with a significant linear trend (P < 0.0001). Every standard deviation increment of LE8 was associated with a 17% (HR: 0.83; 95% CI: 0.81, 0.85) lower risk of incident AOA. Compared with participants with low-CVH score, participants with moderate (HR: 0.72; 95% CI: 0.67, 0.78) and high CVH scores (HR: 0.52; 95% CI: 0.47, 0.58) were associated with a lower risk of incident AOA (P-trend < 0.0001). No significant multiplicative or additive interaction was found between LE8 score and genetic risks. Stratified analysis showed a consistent association between CVH and risk of incident AOA across different asthma polygenic risk score (PRS) levels. Compared with participants with high PRS and low CVH, participants with low PRS and high CVH experienced the lowest risk (HR: 0.28; 95% CI: 0.23, 0.34) of incident AOA. CONCLUSIONS: Our findings suggest that maintaining optimal CVH should be recommended as a preventive strategy for AOA, regardless of their asthma genetic risks.

Dietary carbohydrate types, genetic predisposition, and risk of adult-onset asthma: A longitudinal cohort study.

We aimed to investigate the longitudinal associations among carbohydrate intake types, genetic predisposition, and risk adult onset asthma (AOA). A dataset of 96,487 participants from UK Biobank was included with 1830 cases of incident AOA during an average follow-up of 9.68 years. Participants with the highest intake of total sugar, free sugar, and fiber intake, as compared to those with the lowest intake of total sugar, free sugar, and fiber intake, showed a 17 % and 22 % increased risk of incident AOA, and a 16 % decreased risk of AOA, respectively. Substitution of 5 % energy from free sugars with 5 % energy from non-free sugars was associated with a significantly lower risk of AOA (Hazard Ratio [HR] = 0.93, 95 % Confidence Interval [CI]: 0.88, 0.99). Participants with high genetic risk and the highest intake of free sugar showed a 112 % (HR = 2.12, 95%CI: 1.68, 2.68) increased risk of incident AOA. Participants with low genetic risk and highest intake of fiber showed a 50 % (HR = 0.50, 95%CI: 0.39, 0.64) reduced risk of AOA. This study highlights the critical role of carbohydrate types in AOA prevention, with an emphasis on reduced free sugar, moderate non-free sugar, and increased fiber intake.

Sandwich-type immunosensor based on aminated 3D-rGOF-NH2 and CMK-3-Fc-MgAl-LDH multilayer nanocomposites for detection of CA125.

Cancer antigen 125 (CA125)1 is the most important biological screening indicator used to monitor epithelial ovarian cancers, and it plays a vital role in distinguishing ovarian cancers from benign diseases. Biosensors show great potential in the analysis and detection of disease markers. In this study, we designed electrochemical sensors based on three-dimensional amino-functionalized reduced graphene oxide (3D-rGOF-NH2),2 MgAl layered double hydroxide nanocomposites containing ordered mesoporous carbon (CMK-3),3 and ferrocene carboxylic acids（Fc-COOH）4for the detection of CA125. 3D-rGOF-NH2 possesses good conductivity, a large surface area, and high porosity, enabling more immobilized nanoparticles to be deposited on its surface with excellent stability. CMK-3@Fc@MgAl-LDH nanocomposite was used as a carrier to enhance the immobilization of antibodies and the loading of Fc, conductors to enhance conductivity, and enhancers to gradually amplify the signal of Fc. The surface morphology, elemental composition, and surface groups of the materials were characterized using scanning electron microscopy (SEM),5 transmission electron microscopy (TEM),6 and X-ray diffraction (XRD)7 techniques. The response signal of the electrochemical sensor was measured by DPV. Under the optimal conditions, the electrochemical sensor obtained a linear detection range of 0.01 U/mL-100 U/mL with a detection limit of 0.00417 U/mL.