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Abstract Background Acute lung injury (ALI) is a severe condition with multifaceted causes, including inflammation and oxidative stress. This research investigates the influence of m6A (N6-methyladenosine) modification on GBP4, a protein pivotal for macrophage polarization, a critical immune response in ALI. Methods Utilizing a mouse model to induce ALI, the study analyzed GBP4 expression in alveolar macrophages. By overexpressing or knocking down GBP4, the study assessed its impact on M1 macrophage polarization. The role of YTHDF1 was also explored through knockdown experiments to determine its effect on GBP4 expression and macrophage polarization. Results Increased GBP4 expression was noted in ALI model mice, promoting M1 macrophage polarization. YTHDF1 was found to enhance GBP4 expression by recognizing m6A sites on its mRNA, which was linked to reduced inflammation in MLE-12 cells upon YTHDF1 knockdown. Conclusion The study emphasizes the crucial roles of GBP4 and YTHDF1 in ALI development and immune response regulation. It suggests m6A modification as a potential therapeutic target, contributing to the understanding of ALI’s molecular mechanisms and guiding future treatment strategies.

Abstract Molecular detection is important in biosensing, food safety, and environmental surveillance. The high biocompatibility, superior mechanical stability, and low cost make plasmon-free surface-enhanced Raman scattering (SERS) a promising sensing technique, the ultrahigh sensitivity of which is urgently pursued for realistic applications. As a proof of concept, we report a mechanochemical strategy, which combines the wrinkling and chemical functionalization, to fabricate a plasmon-free SERS platform based on 2D MnPS3 with a sub-attomolar detection limit. In detail, the formation of wrinkles in 2D MnPS3 enables a SERS substrate of the material to detect trace methylene blue molecules. The mechanism is experimentally revealed that the wrinkled structures contribute to the improvement of light-matter coupling. On this basis, decorating a wrinkled MnPS3 which has absorbed methylene blue with histamine dihydrochloride further lowers the detection limit to 10−19 M. Because the amino groups in histamine dihydrochloride molecules are crosslinkers that create more pathways to promote charge transfer between these substances. This work provides a guidance for the design of SERS sensors with single-molecule-level sensitivity.

Nitrate (NO3−) pollution in groundwater is a worldwide environmental issue, particularly in developed planting-breeding areas where there is a substantial presence of nitrogen-related sources. Here, we explored the key sources and potential health risks of NO3− in a typical planting-breeding area in the North China Plain based on dual stable isotopes and Monte Carlo simulations. The analysis results revealed that the NO3− concentration ranged from 0.02 to 44.6 mg/L, with a mean value of 7.54 mg/L, along with a significant spatial variability. Analysis by combining stable isotopes (δ15N-NO3− and δ18O-NO3−) with the Bayesian isotope mixing model (MixSIAR) revealed that soil N (60.3 %) and manure and sewage (35.9 %) contributed the most NO3− in groundwater, followed by chemical N fertilizer (2.9 %) and atmospheric N deposition (0.8 %). However, the contribution of N fertilizer may be underestimated because it has undergone a long-term applied history and have progressively accumulated in the soil, and then promoted the entry of groundwater under frequent rainfall and irrigation practices. From the probabilistic health risk assessment, a relatively low probability of exceeding the threshold (HI=1) was observed (0.2 % for adults and 2.59 % for children); nevertheless, children still face some nonnegligible risk, particularly for the oral ingestion of drinking water at high-pollution sites. Therefore, we highlight the importance of effective management of manure and sewage from breeding plants and reduction of chemical N fertilizer usage are suggested in developed agricultural areas.

Abstract Enabling pressure sensors with high resolution and a broad detection range is of paramount importance yet challenging due to the limitations of each known sensing method. Overlying different sensing mechanisms to achieve complementary functions is a promising approach, but it often leads to increased device thickness, crosstalk signals and complex signal channel management. Herein, we present a dual‐functional conformable pressure sensor that adopts a Janus thin film layout, enabling simultaneous piezoelectric and triboelectric signal detection capabilities between just one electrode pair, showing a most compact device configuration. Notably, despite its thin thickness (~80 μm for a packaged device), it exhibits a broad‐range detection capability with high signal resolution and fast response time, demonstrating a distinct signal‐relay characteristic corresponding to piezoelectricity and triboelectricity. Despite the slimness and simple structure, it shows an impressive signal resolution of 0.93 V·kPa−1 in the range of 0.1–140 kPa and 0.05 V·kPa−1 in the range of 140–380 kPa. Moreover, the device fabrication can be combined with the kirigami method to improve fitting to joint surfaces. This work introduces an innovative paradigm for designing advanced pressure sensing mechanisms, enabling a single device that can meet diverse application scenarios through its simplicity, slim layout, conformable, and self‐powered characteristics to adapt to multiple scenarios.

In low signal-to-noise ratio (SNR) conditions, to address the problem of poor speech enhancement effect of traditional neural networks, this paper combines Convolution Recurrent Neural Network (CRN) with Gated Linear Units (GLU) to extract speech characteristics. Meanwhile, considering the advantages of the Empirical Mode Decomposition (EMD) algorithm, this paper put forward a model for speech enhancement that is based on Adaptive Mean Median Empirical Modal Decomposition and Multilayer Gated Convolutional Recurrent Neural Networks (ME-MGCRN). The model first uses the improved EMD algorithm to split the original noisy speech into low-frequency and high-frequency components. After that, the noisy speech is denoised using noise correlation and completes the feature extraction process. Finally, the extracted feature information is fed into the MGCRN network to realize speech enhancement further. This paper analyzes the performance of the Adaptive Mean Median Empirical Modal Decomposition algorithm using the LibriSpeech ASR dataset. This paper compares and investigates the enhanced performance of the ME-MGCRN model with the baseline model and the traditional model in terms of evaluation metrics such as the perceptual evaluation of speech quality (PESQ) and short-time objective intelligence (STOI). The research indicates that the ME-MGCRN model proposed in this paper has improved in the evaluation indexes, such as PESQ and STOI, respectively, by 0.22% and 5.6% than the baseline model and the traditional model, and the speech enhancement effect is better when Huber is selected as the loss function.

Abstract Background Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images. Methods We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms. Results Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712–0.883). Significant differences were observed between the WSDL model and five ML models (P

Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification.

As the flight envelopes of turbine-based combined cycle (TBCC) engines expand, ensuring reliable ignition and flame stability under varying conditions becomes increasingly critical. Previous work has shown a significantly changed ignition performance and flow pattern in cavity-based combustors when strut structure parameters were altered, indicating a strong correlation between the ignition process, flame structure, and the strut configuration. This suggests that further investigation is required to determine the optimal strut design. Therefore, this study examines the impact of various strut configurations through numerical simulations, validated by high-speed imaging. Findings show that the tail-groove strut designs improve the flame propagation performance compared to the normal struts, with a critical depth beyond which further increases do not enhance performance. Changes in strut length have a lesser impact than depth. Flow analysis indicates that tail-groove struts create additional recirculation zones that enhance fuel atomization and flame stability. These results suggest that optimizing strut configurations is vital for achieving reliable ignition and flame stability, advancing the development of efficient engines across a wide range of operational conditions.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.

The interface of fresh-aged concrete represents a critical vulnerability within monolithic assembled monolithic concrete structures. In this paper, the shear performance of the interface between post-cast epoxy resin concrete and standard concrete is studied using experimental methods and finite element analysis. The objective is to furnish empirical data that support the broader adoption of epoxy resin concrete in assembled structures. A direct shear experiment of 19 Z-shaped samples and a computation of 20 finite element models were completed. The results from both experimental and computational analyses provided insights into several factors influencing the shear performance at the interface. These factors include the pre-cast part of concrete strength, the friction coefficient of the interface, the longitudinal reinforcement ratio at the interface, the compressive strength of concrete in the post-cast part, and confining stress. The findings indicate that utilizing epoxy resin concrete for post-cast material, roughing the interface, and setting keyways can enhance the shear performance of the interface so that it equals or even exceeds the cast-in situ sample. Optimal shear results are obtained when the compressive strength of the post-cast epoxy resin concrete closely matches that of the pre-cast conventional cement. Moreover, increasing the depth of the keyways rather than their width is more effective in improving the shear capacity of the sample. It is recommended that the depth of the keyway should be at least 30 mm, and its width should be no less than three times the depth. As the longitudinal reinforcement ratio at the interface increases, there is an enhancement in shear capacity coupled with a reduction in deformative performance. It is advisable to maintain this ratio below 1.0% to balance the strength and ductility effectively.

The horizontal joint is a critical component of the prefabricated shear wall structure, responsible for supporting both horizontal shear forces and vertical loads along with the wall, thereby influencing the overall structural performance. This study employs direct shear testing and finite element analysis to investigate the horizontal joint in walls with ring reinforcement. It examines the impact of various factors on joint shear performance, including the type of joint material, joint configuration, buckling length of ring reinforcement, strength of precast concrete, reinforcement ratio of ring reinforcement and dowel bars, and the effect of horizontal binding force. The findings indicate that the shear bearing capacity and stiffness of joints incorporating post-cast epoxy resin concrete and keyways are comparable or superior to those of integrally cast specimens. A larger buckling length in ring reinforcement may reduce shear strength, suggesting an optimal buckling length at approximately one-third of the joint width. As the strength of precast concrete increases, ductility decreases while bearing capacity increases, initially at an increasing rate that subsequently declines. Optimal results are achieved when the strength of precast concrete closely matches that of the post-cast epoxy concrete. Enhancing the reinforcement ratio of ring reinforcement improves shear capacity, but excessively high ratios significantly reduce ductility. It is recommended that the diameter of ring reinforcement be maintained between 10 mm and 12 mm, with a reinforcement ratio between 0.79% and 1.13%. Increasing horizontal restraint enhances stiffness and shear capacity but reduces ductility; thus, the axial compression ratio should not exceed 0.5.

Background: Sarcopenia, defined as the loss of muscle mass and strength, has been associated with increased hospitalization and mortality. Dietary pattern analysis is a whole diet approach which in this study was used to investigate the relationship between diet and sarcopenia. This study aims to estimate the prevalence of sarcopenia and explore possible factors associated with it among a large population in Beijing, China.Methods: A cross-sectional study with 1,059 participants aged more than 50 years was performed. Sarcopenia was defined based on the guidelines of the Asian Working Group for Sarcopenia. The total score of the MNA-SF questionnaire was used to analyse nutrition status. The baseline demographic information, diet structure and eating habits were collected by clinicians trained in questionnaire data collection and anthropometric and bioimpedance measurements.Results: The overall prevalence of sarcopenia was 8.8% and increased with age: 5%, 5.8%, 10.3% and 26.2% in the 50–59, 60–69, 70–79, and ≥80 years groups, respectively. Marital status (with or without a spouse) was not an independent factor associated with sarcopenia adjusted by age and sex. However, nutritional risk or malnutrition, vegetable diet, advanced age and spicy eating habits were risk factors for sarcopenia. Meanwhile, daily fruit, dairy and nut consumption were protective factors against sarcopenia adjusted by age, sex, income status and spouse status.Conclusion: Although further studies are required to explore the association between healthy dietary patterns and the risk of sarcopenia, the present study provides basic data for identifying correlates of sarcopenia in elderly Chinese individual.

Difficulties in separating tribo and piezoelectric hybrid signals can lead to an overestimated contribution of the latter. Here, authors propose a method to separate these hybrid signals in the time domain, precisely extracting piezoelectric charge transfer for performance evaluation.

IntroductionIn this paper, microbiota analysis was determined to analyze the structure and difference of intestinal microbiota between LBMJ (late-onset breast milk jaundice) infants and healthy individuals.MethodsWe collected fresh fecal samples from 13 infants with LBMJ and 13 healthy individuals, then determined the intestinal microbiota by 16 s rRNA sequencing. The differences of microbiota structure, diversity and functional characteristics between the two groups were analyzed, and the correlation between dominant genus and TcB (transcutaneous bilirubin) value was calculated.ResultsIn this study, there were no significant differences in maternal demographic characteristics, neonatal status and macronutrients in breast milk between the two groups (p > 0.05). There are differences in the structure of intestinal microbiota between LBMJ and the control group. At the genus level, the relative abundance of Klebsiella in the case group is high (p

IntroductionObesity has become a major health problem worldwide. Diet management is an important means of weight loss. The aim of this study was to explore the improvement effect of a calorie-restricted diet (CRD), 5 + 2 intermittent fasting (5 + 2 IF), and high protein diet (HPD) on weight composition and serum lipid level of overweight and obese people in a short period (3 months).MethodsFifty-three participants aged 18–60 years and with body mass index (BMI) ranging from 24 to 35 kg/m2 were screened for inclusion and exclusion criteria and were randomly divided into three groups, i.e., CRD, 5 + 2 IF, and HPD. Basic information, body composition, and venous blood samples were collected at baseline and after 3 months of intervention. Body composition was measured using a body composition analyzer (SK-V9), and serum lipids were assayed using the Roche Cobas e702 automatic biochemistry analysis system. The generalized estimating equation (GEE) was used to analyze and compare the repeated measurements of body composition and levels of serum lipids.ResultsThe results showed that total weight, BMI, body fat mass, muscle mass, visceral fat index, and waist and hip circumferences had significantly decreased in all three groups after 3 months of intervention, and the average weight loss was 3.3 ± 1.14, 4.12 ± 0.05, and 2.62 ± 0.13 kg in CRD, 5 + 2 IF, and HPD groups, respectively. The results of the GEE model indicated that compared with the CRD group, the 5 + 2 IF group showed a more significant decrease in weight (β = −0.272, P < 0.001), BMI (β = −0.091, P < 0.001), body fat mass (β = −0.172, P < 0.001), muscle (β = −0.043, P < 0.001), and visceral fat index (β = −0.019, P < 0.001), however, HPD has more advantages in visceral fat index loss (β = −0.011, P < 0.001) and lean body mass preserve (β = 0.229, P < 0.001).ConclusionOur findings showed that the 5 + 2 IF may be more effective in reducing total weight and body fat, and HPD may be more helpful in preventing lean body mass loss during a short-term weight loss intervention.

ObjectiveReports on negative results of metagenomic next-generation sequencing (mNGS) are scarce. We aimed to explore the diagnostic value of negative results in bronchoalveolar lavage fluid (BALF) mNGS and how to deal with the negative results in patients with severe respiratory disease.MethodsA retrospective analysis was performed on patients suspected severe community-acquired pneumonia who were admitted to the respiratory intensive care unit of the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2021. According to the final diagnosis as the reference standard, the negative results of mNGS were divided into a true negative group and a false negative group. For enrolled patients, we recorded their demographic data, imaging results, laboratory results, therapeutic processes, and prognoses.ResultsA total of 21 patients were enrolled in this study, including 16 true negative patients and 5 false negative patients. In the true negative group, interstitial lung diseases were the most and neoplastic diseases were following. In addition to mNGS, 9 patients underwent pathological examination, 7 patients were finally diagnosed by medical history, autoantibodies, and point-of-care (POC) ultrasound. 14 patients eventually discontinued antibiotics, 2 patients underwent antibiotic de-escalation, the average interval time of treatment adjustment was 3.56 ± 2.00 days. In the false negative group, the leading missed pathogen was fungi, followed by tuberculosis bacilli. In contrast to 2 patients underwent pathological examination, 3 patients were confirmed by routine microbiological tests.ConclusionsNegative results of BALF mNGS can help to rule out infection, but missed diagnoses may also exist. It should be re-evaluated with other clinical informations. Pathological examination or repeated mNGS may be viable options when the diagnosis cannot be confirmed.

Twisted phosphorene offers another attractive system to explore moiré physics. Here, the authors report emerging anisotropic moiré optical resonances in twisted monolayer/bilayer phosphorene, exhibiting strong twist dependence for angles as large as 19°.

The etiology of acute lung injury (ALI) is not clear, and the treatment of ALI presents a great challenge. This study aimed to investigate the pathogenesis and potential therapeutic targets of ALI and to define the target gene of Tanreqing (TRQ), which is a traditional Chinese medicine formula composed of five medicines, scutellaria baicalensis, bear bile powder, goat horn powder, honeysuckle and forsythia. Macrophage activation plays a critical role in many pathophysiological processes, such as inflammation. Although the regulation of macrophage activation has been extensively investigated, there is little knowledge of the role of long noncoding RNAs (lncRNAs) in this process. In this study, we found that lncRNA-SNHG1 expression is distinctly regulated in differently activated macrophages in that it is upregulated in LPS. LncRNA-SNHG1 knockdown attenuates LPS-induced M1 macrophage activation. The SNHG1 promoter was bound by NF-κB subunit p65, indicative of SNHG1 being a direct transcriptional target of LPS-induced NF-κB activation. SNHG1 acts as a proinflammatory driver that leads to the production of inflammatory cytokines and the activation of macrophages and cytokine storms by physically interacting with high-mobility group box 1 (HMGB1) in ALI. TRQ inhibited NF-κB signaling activation and binding of NF-κB to the SNHG1 promoter. In conclusion, this study defined TRQ target genes, which can be further elucidated as mechanism(s) of TRQ action, and provides insight into the molecular pathogenesis of ALI. The lncRNA-SNHG1/HMGB1 axis is an ideal therapeutic for ALI treatment.

A combined application of step and strut was put forward to achieve reliable ignition and flame stabilization. In this work, the ignition process and temperature distribution have been tested, and a new reduction approach applied to jet fuel oxidation mechanism was developed to present a flow map via tracking C and H reaction paths, then the minor and major reactions were verified according to relative occurrence probabilities. With the half decrease of mechanism size, bias occurred and was controlled within 1.8%. This reduction method had such characteristics as universality, intuition, and quantification, due to its inherent simplification theory. This simulation of ignition process was always consistent with experimental results, which depicted kernel generation, flamelet breakup and flame propagation. Also, the influence of inlet temperature on outlet temperature and component distribution was performed, the biases of experimental and numerical results were within 5%. Chemical characteristics of Kerosene/air premixed combustible had changed and side reactions occurred to jet fuel above 900 K, which led to a converse effect on flame spreading. The side reactions aggravated the increasing coproducts of CO and CH4, which caused the decrease of volumetric heat production.

Substitutional doping is a powerful strategy to modulate the properties and functionalities of two-dimensional (2D) materials while control of dopants during the process is still challenging. Recently, we invented a dissolution–precipitation (DP) method to grow 2D materials. Here, we further extend this method by developing a double-faced precursor supply DP growth strategy to substitutionally dope metal atoms into monolayer MoS2 lattices. In this double-faced precursor supply DP method, the Mo source and dopant source are supplied from the bottom and top surface of the glass substrate, respectively, to separate their diffusion paths. As a result, monolayer MoS2 incorporated with different concentrations of V atoms were grown by tuning the amount of V precursor, which exhibited different types of electrical transport properties. This new doping method is universal in growing several transition metal atom doped MoS2, including Re, Fe, and Cr, which will extend the applications of 2D materials.

BackgroundIn December 2019, a new disease named coronavirus disease 2019 (COVID-19) was occurred. Patients who are critically ill with COVID-19 are more likely to die, especially elderly patients. We aimed to describe the effect of age on the clinical and immune characteristics of critically ill patients with COVID-19.MethodsWe retrospectively included 32 patients with COVID-19 who were confirmed to have COVID-19 by the local health authority and who were admitted to the first affiliated hospital of Zhengzhou University in Zhengzhou, China between January 3 and March 20, 2020. Clinical information and experimental test data were retrospectively collected for the patients. The 32 patients in this study were all in a critical condition and were classified as severe, according to the guidelines of 2019-nCoV infection from the National Health Commission of the People's Republic of China. Data were compared between those ResultsOf 32 patients, 13 were under 60 years old, and 19 patients were ≥60 years old. The most common symptom among all patients upon admission was fever (93.8%, 30/32). Compared to younger patients, older patients exhibited increased comorbidities. Among patients who were 60 years and older, platelet count, direct bilirubin (DBIL), indirect bilirubin(IBIL), lactate dehydrogenase (LDH), B-type natriuretic peptide (BNP), C-reactive protein (CRP), procalcitonin (PCT), and interleukin-10 (IL-10) were significantly higher than in younger patients who were less than 60 years old. CD4+ T lymphocytes, CD8+ T lymphocytes, and NKT lymphocytes were decreased, CD4+/CD8+ T lymphocytes were significantly increased in all 32 patients, while there were no evident differences between younger and older patients. The CURB-65 (confusion, urea, respiratory, rate, blood pressure plus age ≥65 years), Acute Physiology and Chronic Health Evaluation (APACHE) II and pH value were significantly higher in older patients than in patients who were under 60 years old. However, the PaO2 and PaO2:FiO2 were lower in older patients than the younger. Compared to patients under 60 years old, patients who were 60 years and older tended to develop ARDS (15 [78.9%] vs 5 [38.5%]), septic shock (7 [36.8%] vs 0 [0.0%]) and were more likely to receive mechanical ventilation (13 [68.4%] vs 3[23.1%]). Dynamic trajectories of seven laboratory parameters were tracked on days 1, 3, 5 and 7, and significant differences in lymphocyte count (P = 0.026), D-dimer (P = 0.010), lactate dehydrogenase (P = 0.000) and C-reactive protein (P = 0.000) were observed between the two age groups.ConclusionsA high proportion of critically ill patients were 60 or older. Furthermore, rapid disease progression was noted in elderly patients. Therefore, close monitoring and timely treatment should be performed in elderly COVID-19 patients.

High-pressure hydrogen tanks which are composed of an aluminum alloy liner and a carbon fiber wound layer are currently the most popular means to store hydrogen on vehicles. Nevertheless, the aluminum alloy is easily affected by high-pressure hydrogen, which leads to the appearance of hydrogen embrittlement (HE). Serious HE of hydrogen tank represents a huge dangers to the safety of vehicles and passengers. It is critical and timely to outline the mainstream approach and point out potential avenues for further investigation of HE. An analysis, including the mechanism (including hydrogen-enhanced local plasticity model, hydrogen-enhanced decohesion mechanism and hydrogen pressure theory), the detection (including slow strain rate test, linearly increasing stress test and so on) and methods for the prevention of HE on aluminum alloys of hydrogen vehicles (such as coating) are systematically presented in this work. Moreover, the entire experimental detection procedures for HE are expounded. Ultimately, the prevention measures are discussed in detail. It is believed that further prevention measures will rely on the integration of multiple prevention methods. Successfully solving this problem is of great significance to reduce the risk of failure of hydrogen storage tanks and improve the reliability of aluminum alloys for engineering applications in various industries including automotive and aerospace.

Er3+/Yb3+ codoped tellurite-zinc-niobium (TZNb) glass was prepared by the melt-quenching method and used for the construction of a point all-fiber temperature sensor. The glass thermal stability and network structural properties were studied by differential thermal analysis and Raman spectrum, respectively. High glass transition temperature is beneficial to widen the working temperature range. The dependence of fluorescence intensity ratio (FIR) of green upconversion emissions on the surrounding temperature from 276 to 363 K was experimentally investigated and the maximum temperature sensitivity is 95 × 10−4 K−1 at 363 K. Strong green upconversion emission, broad temperature measurement range and high sensitivity indicate this point temperature sensor is a promising optical device for application on optical temperature sensing.