Search

Abstract Previous studies have indicated a potential association between the alanine aminotransferase to high-density lipoprotein cholesterol (ALT/HDL-C) ratio and the risk of Type 2 Diabetes Mellitus, but its relation with gestational diabetes mellitus (GDM) remains uncertain. This study aims to investigate the correlation between the ALT/HDL-C ratio in early pregnancy and the risk of GDM. This study is a secondary analysis based on an open-source cohort study. A total of 590 single pregnant women attending two hospitals in Korea up to 14 weeks gestation were included between November 2014 and July 2016. Logistic regression analysis, subgroup analysis, and smooth curve fitting were employed to explore the association between the ALT/HDL-C ratio and GDM risk. The predictive capability of the ALT/HDL-C ratio for GDM was assessed using ROC curve analysis. The average age of participants was 32.06 ± 3.80 years, with a GDM incidence rate of 6.27%. Multifactorial logistic regression analysis revealed that the serum ALT/HDL-C ratio is an independent influencing factor for GDM (OR = 1.08, 95% CI: 1.02–1.16). Furthermore, a non-linear relationship between the ALT/HDL-C ratio and GDM risk was observed, with a turning point at 5.51. The effect size (OR) on the left and right sides of the turning point were 0.75 (95% CI: 0.37–1.59) and 1.55 (95% CI: 1.18-2.00), respectively. Additionally, when combined with age, pre-pregnancy body mass index, parity, and insulin resistance index in a prediction model for GDM, the ALT/HDL-C ratio demonstrated improved sensitivity of prediction by reaching up to 67.6%, specificity of prediction by reaching up to 87.3%, and an area under curve value of 0.819 (95%CI: 0.743–0.894). In early pregnancy, the serum ALT/HDL-C ratio shows a positive correlation with maternal risk in a nonlinear manner. The combination of ALT/HDL-C ratio with maternal characteristics and metabolic indicators provides good predictive value for GDM. This study may facilitate optimization of GDM prevention in pregnant women and enable timely and effective intervention to enhance their prognosis.

Abstract Background Accumulating evidence suggests that exposure to air pollution acts as a potential trigger for neurological diseases (NDs), yet the current knowledge regarding the impact of ambient nitrogen dioxide (NO2) on the patients with NDs remains limited. In this study, we conducted a time-series study to evaluate the association between short-term exposure to NO2 and hospital visits for NDs in Xinxiang, China. Methods An over-dispersed Poisson generalized additive model was used to analyze the association between ambient NO2 concentrations and daily outpatient visits for NDs from January 1, 2015 to December 31, 2017. The model adjusted for meteorological factors, temporal trends, day of the week, and public holidays. The concentrations of air pollutants were collected from four air quality stations in Xinxiang. Results A total of 38, 865 outpatient visits for NDs were retrieved during the study period. 86.5% of the patients were below the age of 65 years. It was revealed that a 10 µg/m3 increase in NO2 at lag 0 was associated with a significant rise of 1.50% (95% CI: 0.45–2.56%) in outpatient visits for NDs, which was stronger during the cold season. However, the overall results from stratified analyses did not reach statistical significance. Conclusions Short-term exposure to NO2 is associated with increased outpatient visits for NDs. These findings underscore the need for implementing mitigating measures to reduce the neurological health effects of air pollutants.

Abstract Effects of short-term exposure to ambient air pollution on systemic immunological and inflammatory biomarkers in rural population have not been adequately characterized. From May to July 2021, 5816 participants in rural villages of northern Henan Province, China, participated in this cross-sectional study. Blood biomarkers of systemic inflammation were determined including peripheral white blood cells (WBC), eosinophils (EOS), basophils (BAS), monocytes (MON), lymphocytes (LYM), neutrophils (NEU), neutrophil-lymphocyte ratio (NLR), and serum high-sensitivity C-reactive protein (hs-CRP). The concentrations of ambient fine particulate matter (PM2.5), PM10, nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were assessed up to 7 days prior to the blood draw. A generalized linear model was used to analyze the associations between air pollution exposure and the above-mentioned blood biomarkers. Significantly positive associations were revealed between PM2.5, CO and WBC; CO, O3 and LYM; PM2.5, PM10, SO2, CO and NEU; PM2.5, PM10, SO2, CO and NLR; PM2.5, PM10, SO2, NO2, CO, O3 and hs-CRP. Meanwhile, negative associations were found between SO2 and WBC; PM2.5, PM10, NO2, CO, or O3 and EOS; PM2.5, SO2, or CO and BAS; SO2, NO2 or O3 and MON; PM2.5, PM10, SO2, or NO2 and LYM. Moreover, men, individuals with normal body mass index (BMI), current smokers, and those older than 60 years were found vulnerable to air pollution effects. Taken together, short-term exposure to air pollution was associated with systemic inflammatory responses, providing insight into the potential mechanisms for air pollution-induced detrimental systemic effects in rural residents.

With the establishment of smart grids, there is a growing demand for metering electric meters in urban areas. Given the diverse sizes and delicate nature of these meters, traditional scheduling solutions are unable to cater to the multifaceted requirements of urban environments, meters loading, and subsequent logistics scheduling. This study presents an intelligent scheduling model for electric meters in an urban context, taking into account various constraints such as urban traffic congestion, three-dimensional packing of metering devices, delivery time windows, and heterogeneous vehicles. To solve this, we design an improved whale optimization algorithm using a hybrid multi-phase heuristic approach (IWOA-HMOHA). Simulation results show that compared with the traditional meter logistics scheduling strategy, the IWOA-HMOHA algorithm reduces the objective function by 5.4%~ 26.1% compared with other similar algorithms. In addition, compared with the traditional first-in-last-out cargo packing method, the vehicle space utilization rate is improved by 12.62%. The proposed models and algorithms demonstrate excellent adaptability to a range of urban constraints, offering valuable insights and a robust framework essential for the development of logistics solutions in urban.

Exposure to ambient PM2.5 is associated with neurodegenerative disorders, in which microglia activation plays a critical role. Thus far, the underlying mechanisms for PM2.5-induced microglia activation have not been well elucidated. In this study, a human microglial cell line (HMC3) was used as the in vitro model to examine the inflammatory effect (hall marker of microglia activation) of PM2.5 and regulatory pathways. The expression of inflammatory mediators including interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2) as well as the brain derived neurotrophic factor (BDNF) were determined by ELISA and/or real-time PCR, respectively. Flow cytometry was used to measure the production of intracellular reactive oxygen species (ROS). Western blot was used to measure protein levels of Toll-like receptor 4 (TLR4), NF-κB inhibitor α (IκBα) and COX-2. It was shown that PM2.5 stimulation increased IL-6 and COX-2 expression but decreased BDNF expression in a dose-dependent manner. Further studies showed that PM2.5 triggered the formation of ROS and pre-treatment with the ROS scavenger acetylcysteine (NAC) significantly suppressed PM2.5-induced IL-6 and COX-2 expression. Moreover, the nuclear factor kappa B (NF-κB) inhibitor BAY11–7085 or the TLR4 neutralizing antibody markedly blocked PM2.5-induced IL-6 and COX-2 expression. However, NAC or BAY11–7085 exhibited minimal effect on PM2.5-induced BDNF down-regulation. In addition, pre-treatment with BAY11–7085 or TLR4 neutralizing antibody reduced ROS production induced by PM2.5, and NAC pre-treatment inhibited TLR4 expression and NF-κB activation induced by PM2.5. Collectively, PM2.5 treatment induced IL-6 and COX-2 but suppressed BDNF expression. PM2.5-induced IL-6 and COX-2 expression was mediated by interactive oxidative stress and TLR4/NF-κB pathway.

Abstract Fine particulate matter (PM2.5) pollution remains a major threat to public health. As the physical barrier against inhaled air pollutants, airway epithelium is a primary target for PM2.5 and influenza viruses, two major environmental insults. Recent studies have shown that PM2.5 and influenza viruses may interact to aggravate airway inflammation, an essential event in the pathogenesis of diverse pulmonary diseases. Airway epithelium plays a critical role in lung health and disorders. Thus far, the mechanisms for the interactive effect of PM2.5 and the influenza virus on gene transcription of airway epithelial cells have not been fully uncovered. In this present pilot study, the transcriptome sequencing approach was introduced to identify responsive genes following individual and co-exposure to PM2.5 and influenza A (H3N2) viruses in a human bronchial epithelial cell line (BEAS-2B). Enrichment analysis revealed the function of differentially expressed genes (DEGs). Specifically, the DEGs enriched in the xenobiotic metabolism by the cytochrome P450 pathway were linked to PM2.5 exposure. In contrast, the DEGs enriched in environmental information processing and human diseases, such as viral protein interaction with cytokines and cytokine receptors and epithelial cell signaling in bacterial infection, were significantly related to H3N2 exposure. Meanwhile, co-exposure to PM2.5 and H3N2 affected G protein-coupled receptors on the cell surface. Thus, the results from this study provides insights into PM2.5- and influenza virus-induced airway inflammation and potential mechanisms.

Elevated blood pressure is one of the major contributors to cardiovascular disease and premature death. The exposure to ambient fine particulate matter (PM2.5) is closely associated with changes in blood pressure, and even short-term exposure to PM2.5 can lead to an increase in blood pressure. PM2.5 is a complex mixture that exerts different toxicities and triggers increased blood pressure through various mechanisms. Therefore, in this article, we provided a comprehensive review of published studies on the effects of short-term exposure to PM2.5 and its components on blood pressure, and elaborated potential mechanisms from four aspects, including oxidative stress and inflammatory response, endothelial dysfunction, autonomic nervous system disorders and hypothalamus-pituitary-adrenal axis activation, and epigenome alteration. Given the limitations of existing research, future prospective studies can be conducted on diverse populations, using more precise exposure measurement methods and multi-omics approaches, to further elucidate the mechanisms underlying the effects of PM2.5 and its various components on blood pressure. The findings would provide a theoretical foundation for effective protection of public health, particularly vulnerable groups.

The detection of pain in persons with advanced dementia is challenging due to their inability to verbally articulate the pain they are experiencing. Pain Assessment in Advanced Dementia (PAINAD) is an observer-rated pain assessment tool developed based on non-verbal expressions of pain for persons with severe dementia. This study aimed to perform construct validation of PAINAD for pain assessment in persons with severe dementia in Malaysia. This was a prospective cross-sectional study conducted from 27 April 2022 to 28 October 2022 in eight public hospitals in Malaysia. The PAINAD scale was the index test, and the Discomfort Scale—Dementia of the Alzheimer Type (DS-DAT) and Nurse-Reported Pain Scale (NRPS) were the reference tests for construct and concurrent validity assessment. Pain assessment for the study subjects was performed by two raters concurrently at rest and during activity. The PAINAD score was determined by the first rater, whereas the DS-DAT and NRPS were assessed by the second rater, and they were blinded to each other’s findings to prevent bias. PAINAD showed good positive correlations ranging from 0.325 to 0.715 with DS-DAT and NRPS at rest and during activity, with a p-value of

Lithium-ion batteries are extensively utilised in various industries and everyday life. Typically, these batteries are considered retired when their state of health (SOH) drops below 80%. These retired batteries, known as secondary batteries, can be repurposed for applications that demand lower battery performance. Precise forecasting of the lifespan of secondary batteries is crucial for determining suitable operational management approaches. Initially, we use the CACLE dataset for thorough investigation. Therefore, to account for the unpredictable and random character of the application circumstances, we employ the U-chord long curvature feature extraction approach to minimise errors resulting from rotation and noise. Additionally, we utilise the discharged power as a feature. This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). This work comprises two experiments: Experiment 1 utilises the battery’s charging and discharging history data to train the model for estimating the SOH of the remaining cycles of the same battery. Experiment 2, on the other hand, employs the complete discharging data of the battery to train the model for predicting the SOH of the remaining cycles of other batteries. The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results indicate that the average MAE for SSA-LSSVM, LSTM, CNN, PSO-LSSVM, and GPR in Experiment 1 and Experiment 2 are 1.11%, 1.82%, 2.02%, 2.04%, and 12.18% respectively. The best prediction results are obtained by SSA-LSSVM.

Background Availability of long-term psychological interventions for personality disorders is limited because of their high intensity and cost. Research in evidence-based, low-intensity interventions is needed. Aims This study aimed to examine the feasibility, acceptability and potential impact of a low-intensity, digital guided self-help (GSH) intervention that is focused on emotion regulation, recovery-oriented and provides in-the-moment delivery for patients with personality disorders. Method We conducted a single-blind feasibility trial. A total of 43 patients with a personality disorder were recruited and randomly assigned to either a GSH arm (n = 22) or a treatment-as-usual arm (n = 21). The GSH intervention included a series of short videos offering psychoeducation and support, personalised feedback using text messages, and supportive telephone calls, for 4 weeks in addition to treatment as usual. Outcomes of emotional disturbance, emotion dysregulation, self-harm behaviours and decentring ability were measured at baseline, 4 weeks (end of intervention) and 8 weeks (follow-up). Results All patients who attended the first session continued until the last session. There was an interaction effect between time and group on anxiety (P = 0.027, Δη2 = 0.10), where the GSH group showed a significant reduction in anxiety at follow-up (P = 0.003, d = 0.25). The GSH group increased in decentring ability at the end of intervention (P = 0.007, d = −0.65), and the decrease in self-harm behaviours continued until follow-up (P = 0.02, d = 0.57). Conclusions The results suggest that a personalised digital GSH with a focus on recovery could reduce anxiety and self-harm behaviours at short-term follow-up.

Under specific circumstances, the liquefaction product of biomass polyol can be converted into hard polyurethane foam. In this study, the rigid foam material (RPUF) was created by reacting the liquefaction product of Salix psammophila with isocyanate. The mechanical properties, chemical structure, cell structure, and thermal stability of RPUF were characterized by mechanical properties tests, Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The impact strength, compressive strength, and flexural strength of RPUF after hygrothermal treatment showed a downward trend. FTIR showed that the change of temperature and humidity did not affect the chemical structure of RPUF. SEM showed that the destruction of cell structure of RPUF was increased with the extension of time. TGA showed that the initial decomposition temperature of RPUF was reduced by periodic hydrothermal treatment, and the residual carbon rate decreased with the extension of treatment time.

Abstract. Background:. The clinical importance of hypokalemia is likely underrecognized in Chinese dialysis patients, and whether its clinical effect was mediated by serum albumin is not fully elucidated. This study aimed to explore the association between serum potassium and mortality in dialysis patients of a Chinese nationwide multicenter cohort, taking albumin as a consideration. Methods:. This was a prospective nation-wide multicenter cohort study. Restricted cubic splines were used to test the linearity of serum potassium and relationships with all-cause (AC) and cardiovascular (CV) mortality and a subsequent two-line piecewise linear model was fitted to approach the nadir. A mediation analysis was performed to examine relations of albumin to potassium and mortalities. Results:. A total of 10,027 patients were included, of whom 6605 were peritoneal dialysis and 3422 were hemodialysis patients. In the overall population, the mean age was 51.7 ± 14.8 years, 55.3%(5546/10,027) were male, and the median dialysis vintage was 13.60 (4.70, 39.70) months. Baseline serum potassium was 4.30 ± 0.88 mmol/L. After a median follow-up period of 26.87 (14.77, 41.50) months, a U-shape was found between potassium and mortality, and a marked increase in risk at lower potassium but a moderate elevation in risk at higher potassium were observed. The nadir for AC mortality risk was estimated from piecewise linear models to be a potassium concentration of 4.0 mmol/L. Interestingly, the significance of the association between potassium and mortality was attenuated when albumin was introduced into the extended adjusted model. A subsequent significant mediation by albumin for potassium and AC and CV mortalities were found (P < 0.001 for both), indicating that hypokalemia led to higher mortality mediated by low serum albumin, which was a surrogate of poor nutritional status and inflammation. Conclusions:. Associations between potassium and mortalities were U-shaped in the overall population. The nadir for AC mortality risk was at a potassium of 4.0 mmol/L. Serum albumin mediated the association between potassium and AC and CV mortalities.

Abstract Epidemiological evidence has linked air pollution with adverse respiratory outcomes, but the mechanisms underlying susceptibility to air pollution remain unclear. This study aimed to investigate the role of glutathione S-transferase (GST) polymorphism in the association between air pollution and lung function levels. A total of 75 healthy young volunteers aged 18–20 years old were recruited for six follow-up visits and examinations. Spirometry was conducted to obtain lung function parameters such as forced vital capacity (FVC), and forced expiratory volume in 1 s (FEV1). Nasal fluid concentrations of interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and 8-epi-prostaglandin F2α (8-epi-PGF2a) were measured using ELISA kits. Linear mixed-effect models were used to evaluate the association of air pollutants with respiratory outcomes. Additionally, polymorphisms of glutathione S-transferase mu 1 (GSTM1) and glutathione S-transferase theta 1 (GSTT1) were estimated to explore its role in the association between air pollutants and lung function. We found that short-term exposure to atmospheric particulates such as PM2.5 and PM10 can cause an increase in nasal biomarkers of inflammation, oxidative stress, and lung function, while air gaseous pollutant exposure is linked with decreased lung function, except for CO. Stratification analyses showed that an increase in nasal inflammatory cytokines caused by exposure to atmospheric particulates is more obvious in subjects with GSTM1-sufficient (GSTM1 +) than GSTM1-null (GSTM1 − ), while elevated lung function levels due to air particles are more significant in subjects with the genotype of GSTM1 − when compared to GSTM1 +. As for air gaseous pollutants, decreased lung function levels caused by O3, SO2, and NO2 exposure is more manifest in subjects with the genotype of GSTM1 − compared to GSTM1 +. Taken together, short-term exposure to air pollutants is associated with alterations in nasal biomarkers and lung function levels in young healthy adults, and susceptible genotypes play an important mediation role in the association between exposure to air pollutants and inflammation, oxidative stress, and lung function levels.

Fine particulate matter (PM2.5) is the primary environmental stressor and a significant threat to public health. However, the effect of PM2.5 exposure on human nasal microbiota and its pathophysiological implication remain less understood. This study aimed to explore the associations of PM2.5 exposure with indices of nasal microbiota and biomarkers of nasal inflammation and oxidative stress. We conducted a panel study with 75 students in Xinxiang, Henan Province, China, from September to December 2017. Biomarkers of nasal inflammation and oxidative stress including interleukin-6 (IL-6), IL-8, tumor necrosis factor-α (TNF-α), 8-epi-prostaglandin F2 alpha (8-epi-PGF2α) and indices of nasal microbiota diversity and phenotypes were measured. Linear mixed-effect models and bioinformatic analyses were performed to assess the association of PM2.5 concentrations with the abovementioned biomarkers and indices. It was found that per 1 μg/m3 increase in PM2.5 was associated with increments of 13.15% (95 % CI: 5.53–20.76 %) and 78.98 % (95 % CI: 21.61–136.36 %) in TNF-α on lag2 and lag02. Indices of microbial diversity and phenotypes including equitability, Shannon index, biofilm forming, and oxidative stress tolerant decreased to different extent with the increment in PM2.5. Notably, thirteen differential microbes in Clostridia, Bacilli, and Gammaproteobacteria classes were recognized as keystone taxa and eight of them were associated with TNF-α, IL-6, or 8-epi-PGF2α. Moreover, environmental adaptation was the most critical functional pathway of nasal microbiota associated with PM2.5 exposure. In summary, short-term exposure to PM2.5 is associated with nasal inflammation, microbiota diversity reduction, and the microbiota phenotype alterations.

Exposure to particulate matter (PM) has been associated with increased hospital admissions for influenza. Airway epithelial cells are a primary target for inhaled environmental insults including fine PM (PM2.5) and influenza viruses. The potentiation of PM2.5 exposure on the effects of influenza virus on airway epithelial cells has not been adequately elucidated. In this study, the effects of PM2.5 exposure on influenza virus (H3N2) infection and downstream modulation of inflammation and antiviral immune response were investigated using a human bronchial epithelial cell line, BEAS-2B. The results showed that PM2.5 exposure alone increased the production of pro-inflammatory cytokines including interleukin-6 (IL-6) and IL-8 but decreased the production of the antiviral cytokine interferon-β (IFN-β) in BEAS-2B cells while H3N2 exposure alone increased the production of IL-6, IL-8, and IFN-β. Importantly, prior exposure to PM2.5 enhanced subsequent H3N2 infectivity, expression of viral hemagglutinin protein, as well as upregulation of IL-6 and IL-8, but reduced H3N2-induced IFN-β production. Pre-treatment with a pharmacological inhibitor of nuclear factor-κB (NF-κB) suppressed pro-inflammatory cytokine production induced by PM2.5, H3N2, as well as PM2.5-primed H3N2 infection. Moreover, antibody-mediated neutralization of Toll-like receptor 4 (TLR4) blocked cytokine production triggered by PM2.5 or PM2.5-primed H3N2 infection, but not H3N2 alone. Taken together, exposure to PM2.5 alters H3N2-induced cytokine production and markers of replication in BEAS-2B cells, which in turn are regulated by NF-κB and TLR4.

The widespread applications of silver nanoparticles (AgNPs) throughout our daily lives have raised concerns regarding their environmental health and safety (EHS). Despite an increasing number of studies focused on the EHS impacts of AgNPs, there remain significant knowledge gaps with respect to their potential health impacts on susceptible populations, such as lactating mothers and infants. Herein, we aimed to investigate the deleterious effects of AgNPs with different sizes (20 and 40 nm) and surface coatings (PVP and BPEI) on maternal mice and their offspring following lactation exposure at doses of 20, 100 and 400 μg/kg body weight. We discovered that AgNPs could accumulate in the maternal mammary glands and disrupt the epithelial barrier in a dose-dependent manner. Notably, BPEI-coated AgNPs caused more damage to the mammary glands than PVP-coated particles. Importantly, we observed that, while AgNPs were distributed throughout the blood and main tissues, they were particularly enriched in the brains of breastfed offspring after maternal exposure during lactation, exhibiting exposure dosage- and particle coating-dependent patterns. Compared to PVP-coated nanoparticles, BPEI-coated AgNPs were more readily transferred to the offspring, possibly due to their enhanced deposition in maternal mammary glands. Moreover, we observed reduced body weight, blood cell toxicity, and tissue injuries in breastfed offspring whose dams received AgNPs. As a whole, these results reveal that maternal exposure to AgNPs results in the translocation of AgNPs into offspring via breastfeeding, inducing developmental impairments in these breastfed offspring. This study provides important new insights into the EHS impacts of AgNP consumption during lactation.

Plain English summary Embodiment illusions involve the temporary experience of owning another person’s body or body parts. These illusions have been studied in people with eating disorders (EDs) as a means for (i) better understanding their self-perception issues (e.g., they tend to see their own appearance as being too big), and (ii) improving these self-perception issues (i.e., does experiencing embodiment reduce distortions involving body size?). ‘Classic’ embodiment illusions typically require participants to simultaneously experience visual, proprioceptive, and tactile sensory stimulation. However, COVID -19 pandemic restrictions have limited research in face-to-face settings which poses a challenge for the classic approach, mainly in relation to tactile input which would usually be provided by a researcher. A ‘tactile-reduced’ approach for eliciting embodiment illusions would, therefore, enable this field of research to continue, which in turn may benefit people with EDs. While several studies in non-clinical populations have demonstrated the effectiveness of a tactile-reduced approach, understanding surrounding this approach in ED populations is lacking. In this review, we offer a prospective set of suggestions regarding how tactile-reduced embodiment may be conducted with ED populations, both in terms of better understanding their (problematic) self-perception, and how such concerns may be improved.

BackgroundEmerging evidence has shown the damage of air pollution and the benefits of physical activity to human health, and the effects of air pollution and physical activity on the nervous system need more research.ObjectiveTo explore the effects of short-term air pollution exposure and physical activity on neural damage biomarkers in healthy elderly.MethodsUsing a design of panel study, physically and mentally healthy retired employees were recruited from Xinxiang Medical University, and were followed up five times regularly from December 2018 to April 2019. The demographic characteristics and physical activity information were obtained by questionnaire, and the weekly physical activity level was calculated according to intensity and duration of physical activity. Biomarkers of neural damage in serum were measured, including brain-derived neurotrophic factor (BDNF), neurofilament light chain (NF-L), neuron specific enolase (NSE), protein gene product 9.5 (PGP9.5), and S100 calcium-binding protein B (S100B). Air pollution data (including PM2.5, PM10, O3, SO2, CO, and NO2) of the follow-up period were collected. Generalized estimation equation was used to analyze the association of air pollution concentration and physical activity level with the concentration of neural damage biomarkers.ResultsA total of 29 volunteers were included in the study, with an average age of (63.5±5.9) years; there were 11 men accounting for 37.93%; more than half of them (62.07%) received above junior middle school education; the mean physical activity level was (80.23±54.51) MET-h·week−1. The daily average concentrations of PM2.5, PM10, O3, SO2, CO, and NO2 during the study period were (68.27±60.98) μg·m−3, (130.57±58.71) μg·m−3, (36.86±13.89) μg·m−3, (17.86±10.59) μg·m−3, (4.94±1.34) mg·m−3, and (50.83±8.03) μg·m−3, respectively. The average serum concentrations of BDNF, NF-L, NSE, PGP9.5, and S100B were (139.12±46.71) μg·L−1, (402.60±183.31) ng·L−1, (11.26±10.32) ng·L−1, (14.32±13.57) ng·L−1, and (127.57±41.74) ng·L−1, respectively. The results of generalized estimation equation showed that a higher concentration of PM2.5 or O3 was associated with increased serum NSE (OR=1.359, 95%CI: 1.224-1.509, P0.05).ConclusionAcute exposure to air pollution and high-level physical activity might affect the neural damage of elderly populations. Specifically, particulate matter (PM2.5) could increase NSE, while gaseous pollutants (O3, NO2, and SO2) could decrease NF-L and PGP9.5.

Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model.

Contact tracing is a way to track people who have been in contact with infected patients of COVID-19 and thereby effective control is achieved. Various countries have developed their own contact tracing applications which deploy the same or different protocol. It is of utmost importance to improve public awareness on the potential hidden risks of the respective protocols and applications and instill user confidence. The purpose of this research is to study the security and privacy of the existing contact tracing protocols to ensure that the security and privacy of users can be guaranteed. The protocols used by the applications include DP-3T protocol, TCN protocol, PEPP-PT protocol and BlueTrace protocol. The architecture of the protocols can be classified into centralised and decentralised architectures. In addition, the contact tracing applications in seven selected countries will be briefly analysed and compared.Some common user concerns are also outlined.

Ambient nitrogen dioxide (NO2)-induced adverse health effects have been studied, but documented evidence on neural systems is limited. This study aimed to determine the acute effect of NO2 exposure on nervous system damage biomarker levels in healthy older adults. Five rounds of follow-up among 34 healthy retired people were scheduled from December 2018 to April 2019 in Xinxiang, China. The real-time NO2 concentrations were measured using a fixed site monitor. Serum samples were acquired during each round to measure nervous system damage biomarker levels: brain-derived neurotrophic factor (BDNF), neurofilament light chain (NfL), neuron-specific enolase (NSE), protein gene product 9.5 (PGP9.5), and S100 calcium-binding protein B (S100B). A linear mixed-effect model was incorporated to analyze the association between short-term NO2 exposure and serum concentrations of the above-mentioned biomarkers. Stratification analysis based on sex, educational attainment, glutathione S-transferase theta 1 gene (GSTT1) polymorphism, and physical activity intensity was conducted to explore their potential modification effect. The NO2 concentration ranged from 34.7 to 59.0 µg/m3 during the study period. Acute exposure to ambient NO2 was significantly associated with elevated serum levels of NfL, PGP9.5, and BDNF. In response to a 10 µg/m3 increase in NO2 concentration, NfL and PGP9.5 levels increased by 76 % (95 % confidence interval [CI]: 12–140 %) and 54 % (95 % CI: 1–107 %) on the lag0 day, respectively, while BDNF levels increased by 49 % (95 % CI: 2–96 %) at lag4 day. The estimated effect of NO2 on NSE levels in GSTT1-sufficient participants was significantly higher than that in GSTT1-null participants. Intriguingly, the estimation of NO2 on PGP9.5 levels in females was significantly higher than that in males. Most two-pollutant models showed robust results, except for O3, which might have had confounding effects on NO2-induced BDNF stimulation. In summary, acute exposure to NO2 was associated with increased levels of serum nervous system damage biomarker levels including NFL, PGP9.5, and BDNF. The present study provided insights into NO2 exposure-induced adverse neural effects.