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Magnetic fields, as a form of physical energy, exert an influence on biological activities. However, our current understanding of the impact of magnetic fields on wheat yield remains limited. In this study, our objective was to investigate the effects of magnetic field treatment of wheat plants on their yield, root growth, absorption of nitrogen and phosphorus and soil bacterial diversity. The experiments were conducted at two agricultural research stations in China, Zhengzhou and Xuchang. Plants were treated with magnetic fields of 20, 40, 60, and 80 mT induced by permanent magnets for chronic exposure. Untreated plants were considered as controls. Our result showed that soil nutrients were found to have a substantial impact on wheat nitrogen and phosphorus absorption, and wheat nitrogen and phosphorus absorption significantly affected wheat yield. The change in soil nutrient content was caused by the change in soil bacterial community diversity and abundance, and increased soil nutrients increased wheat yield. The results suggest that magnetic field treatment stimulated wheat plant growth and yield, and changed soil nutrient content through improved soil bacterial community diversity and increased soil nitrogen and phosphorous absorption.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Abstract Background Lupus nephritis (LN) is the most common cause of kidney injury in systemic lupus erythematosus (SLE) patients and is associated with increased mortality. DNA methylation, one of the most important epigenetic modifications, has been reported as a key player in the pathogenesis of SLE. Hence, our article aimed to explore DNA methylation in CD4+ T cells from LNs to identify additional potential biomarkers and pathogenic genes involved in the progression of LN. Methods Our study enrolled 46 SLE patients with or without kidney injury and 23 healthy controls from 2019 to 2022. CD4+ T cells were sorted for DNA methylation genotyping and RNA-seq. Through bioinformatics analysis, we identified the significant differentially methylated CpG positions (DMPs) only in the LN group and validated them by Bisulfite PCR. Integration analysis was used to screen for differentially methylated and expressed genes that might be involved in the progression of LN, and the results were analyzed via cell experiments and flow cytometry. Results We identified 243 hypomethylated sites and 778 hypermethylated sites only in the LN cohort. Three of these DMPs, cg08332381, cg03297029, and cg16797344, were validated by Bisulfite PCR and could be potential biomarkers for LN. Integrated analysis revealed that the expression of BCL2L14 and IFI27 was regulated by DNA methylation, which was validated by azacytidine (5-aza) treatment. The overexpression of BCL2L14 in CD4+ T cells might induce renal fibrosis and inflammation by regulating the differentiation and function of Tfh cells. Conclusion Our study identified novel aberrant DMPs in CD4+ T cells only in LN patients and DNA methylation-regulated genes that could be potential LN biomarkers. BCL2L14 is likely involved in the progression of LN and might be a treatment target.

Abstract Background Community medical institutions play a vital role in China’s healthcare system. While the number of these institutions has increased in recent years, their construction contents remain insufficient. The potential of community medical institutions in preventing, screening, diagnosing, and treating non-communicable chronic diseases (NCDs) has not been fully utilized. This study aims to assess the status of construction contents in community medical institutions in Southwest China and examine how these contents influence the medical choices of NCD patients. Methods Descriptive statistics were used to evaluate the construction content of community medical institutions. Multiple-sets of multinomial logistic regression were employed to analyze the associations and marginal impacts between construction content and medical choices. Shapley value analysis was applied to determine the contribution and ranking of these impacts. Results Descriptive statistics revealed satisfactory construction contents in community medical institutions. Notably, factors such as service attitude, nursing services, expert consultations, charging standards, medical equipment, medical examinations, privacy protection, and referrals significantly influenced medical choices. Among these, service attitude, charging standards, and privacy protection had the most significant marginal improvement effects on NCD patients’ choices, with improvements of 12.7%, 10.2%, and 5.9%, respectively. The combined contribution of privacy protection, medical examinations, service attitude, charging standards, and nursing services to medical choices exceeded 80%. Conclusion Optimizing the service contents of community institutions can encourage NCD patients to seek medical care at grassroots hospitals. This study addresses crucial gaps in existing literature and offers practical insights for implementing new medical reform policies, particularly in underdeveloped regions of Southwest China focusing on hierarchical diagnosis and treatment.

Background: The real-world clinical impact of mNGS on BALF in the respiratory intensive care unit (RICU) is not yet fully understood. Methods: We investigated the clinical impact of mNGS on BALF samples obtained from 92 patients admitted to the RICU over a 2-year period. We utilized both mNGS and culture methods to evaluate the effectiveness of mNGS in diagnosing pulmonary infections. The clinical impact of mNGS were evaluated by the clinician committees. Results: Among the 92 diagnosed patients, 78 cases (84.7 %) were determined to have infectious diseases caused by pathogenic microorganisms, and the bacterial infections constituted the most prevalent diagnostic category. For mixed infection, the most common type was the Pneumocystis jironecii and cytomegalovirus co-infection. The mNGS results had a positive impact on the clinical management of 43 cases (46.7 %). Moreover, 19 cases (44.2 %) of positive clinical impacts were solely based on new diagnoses made possible by mNGS results. These new diagnoses were particularly helpful for identifying rare pathogens, which could not be detected by conventional diagnostic methods. Conclusions: The BALF mNGS has a positive real-world impact in RICU. Clinician committee play a critical role in ensuring the appropriate use of mNGS.

Objective: This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. Methods: Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. Results: With an advance warning set at three days, the SEAR model outperformed five primary models − logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model − in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. Conclusion: The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

Abstract Amphibians and reptiles, especially the critically endangered Chinese alligators, are vulnerable to climate change. Historically, the decline in suitable habitats and fragmentation has restricted the distribution of Chinese alligators to a small area in southeast Anhui Province in China. However, the effects of climate change on range‐restricted Chinese alligator habitats are largely unknown. We aimed to predict current and future (2050s and 2070s) Chinese alligator distribution and identify priority conservation areas under climate change. We employed species distribution models, barycenter migration analyses, and the Marxian model to assess current and future Chinese alligator distribution and identify priority conservation areas under climate change. The results showed that the lowest temperature and rainfall seasonality in the coldest month were the two most important factors affecting the distribution of Chinese alligators. Future predictions indicate a reduction (3.39%–98.41%) in suitable habitats and a westward shift in their distribution. Further, the study emphasizes that suitable habitats for Chinese alligators are threatened by climate change. Despite the impact of the Anhui Chinese Alligator National Nature Reserve, protection gaps persist, with 78.27% of the area lacking priority protected area. Our study provides crucial data for Chinese alligator adaptation to climate change and underscores the need for improved conservation strategies. Future research should refine conservation efforts, consider individual plasticity, and address identified limitations to enhance the resilience of Chinese alligator populations in the face of ongoing climate change.

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.

Abstract Background Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. Methods We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ( $${R}_{t}$$ R t ) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for $${R}_{t}$$ R t to quantify the contribution of O3 to variations in transmissibility. Results Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in $${R}_{t}$$ R t . O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in $${R}_{t}$$ R t . Conclusions O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Abstract Background Since the first report of carbapenem-resistant Klebsiella pneumoniae isolates in China in 2007, the prevalence of CRKP and CRE has increased significantly. However, the molecular characteristics of IMP-producing Klebsiella pneumoniae (IMPKp) are rarely reported. Methods A total of 29 IMPKp isolates were collected from a Chinese tertiary hospital from 2011 to 2017. Clinical IMPKp were identified by VITEK®MS, and further analyzed by whole-genome DNA sequencing with HiSeq and PacBio RSII sequencer. Sequencing data were analyzed using CSI Phylogeny 1.4, Resfinder, PlasmidFinder and the MLST tool provided by the Centre for Genomic Epidemiology. The analysis results were visualized using iTOL editor v1_1. The open reading frames and pseudogenes were predicted using RAST 2.0 combined with BLASTP/BLASTN searches against the RefSeq database. The databases CARD, ResFinder, ISfinder, and INTEGRALL were performed for annotation of the resistance genes, mobile elements, and other features. The types of bla IMP in clinical isolates were determined by BIGSdb-Pasteur. Integrons were drawn by Snapgene, and the gene organization diagrams were drawn by Inkscape 0.48.1. Results Four novel ST type, including ST5422, ST5423, ST5426 and ST5427 were identified. The IMP-4 and IMP-1 were the dominant IMP type. The majority of bla IMP-carrying plasmids belonged to IncN and IncHI5. Two novel bla IMP-carrying integrons (In2146 and In2147) were uncovered. A novel variant bla IMP-90 presented in novel integron In2147 has been identified. Conclusions IMPKp showed low prevalence in China. Novel molecular characteristics of IMPKp have been identified. Continuous monitoring of IMPKp shall also be carried out in the future.

Objective: Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs quickly and accurately holds great significance in preventing heart disease and reducing mortality. This research endeavors to outperform previous studies by developing a scientific neural network model capable of training and predicting ECG signals for 11 categories of arrhythmias, accounting for up to 5 co-existing labels. Methods: In this study, we initially address the issue of imbalanced datasets by employing Borderline SMOTE and Cluster Centroids techniques during preprocessing. Subsequently, we propose a novel SAR model that combines attention and resnet mechanisms. The dataset is subjected to a 10-fold validation process to train and evaluate the model. Finally, several metrics such as HammingLoss, RankingLoss, F1-score, AUC and Coverage are used to evaluate the model. Results: By evaluating the results of the tests, the average Hamming Loss is 1.12 %, the average Ranking Loss is 1.17 %, the average Micro F1-score is 98.46 %, the average Micro AUC is 98.76 %, and the average Coverage is 3.2762. The results show that the SAR model outperforms previous related studies on the task of classifying arrhythmia signals with multiple categories and labels. Conclusion: The SAR model demonstrated excellent performance in accurately classifying multi-category and multi-label arrhythmia signals, affirming its scientific validity. Compared with previous studies, the model achieves a certain improvement in performance, which can help cardiologists to achieve scientific and accurate diagnosis of arrhythmia diseases.

Abstract Background The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. Methods We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011–2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020–2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020–2021. Results The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011–2019, with an obvious winter-spring seasonality in Beijing. During the 2020–2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, “Cancelling public events” and “Restrictions on internal travel” measures played an important role in the reduction of ILI in Beijing, while the “restrictions on international travel” was statistically most associated with ILI reductions in the Hong Kong SAR. Conclusions Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future. Graphical Abstract

BackgroundInfluenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. ObjectiveThis study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. MethodsWe collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. ResultsThis study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. ConclusionsComplementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

A microfiber with large evanescent field encapsulated in PDMS is proposed and demonstrated for ultrasound sensing. The compact size and large evanescent field of microfiber provide an excellent platform for the interaction between optical signal and ultrasound wave, exhibiting a high sensitivity of 3.5 mV/kPa, which is approximately 10 times higher than the single-mode fiber sensor. Meanwhile, a phase feedback stabilization module is introduced into the coherent demodulation system for long-term stable measurement. In addition, a photoacoustic tomography experiment with the microfiber ultrasound sensor is implemented to verify the excellent performance on imaging, with the depth of 12 mm, the highest lateral resolution of 65 μm and axial resolution of 250 μm, respectively. The highly sensitive microfiber ultrasound sensor provides a competitive alternative for various applications, such as industrial non-destructive testing, biomedical ultrasound and photoacoustic imaging.

Abstract In seawater pipeline, the welding joint is a non-uniform structure composed of welding seam, base metal and heat affected zone. It has inhomogeneity in chemical composition, organizational structure, residual stress, etc. As local defects and high turbulence accelerate corrosion, the welding joint is often the weakest link in pipeline corrosion. Herein, the electrochemical corrosion behavior of B10 alloy welded joint in flowing seawater is studied from macroscopic and submicroscopic viewpoints using AC impedance, linear polarization, array electrode and morphological characterization. The results reveal that the corrosion rate of weld metal (WM), base metal (BM) and heat-affected zone (HAZ) decreased with the increase of time. Combined with SEM and EDS analysis, it can be seen that the increase in time led to the decomposition and accumulation of corrosion products, which gradually enhanced the corrosion resistance of welded joints. At the submicroscopic scale, WM acts as a cathode to mitigate corrosion during the later stages of high flow rate.

BackgroundIn megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. ObjectiveThe aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. MethodsWe collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. ResultsThe highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. ConclusionsThe newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

A miniaturized ultrasound sensor based on optical fiber is designed and realized for multichannel parallel ultrasound detection and photoacoustic imaging. The fiber optic sensor is composed of a polymer coating, a reflective mirror and a single-mode optical fiber, with only 125 µm in diameter. By integrating the coherent demodulation technology and multiplexing technology, which using a relatively cheap fixed wavelength laser, hundreds of sensors could work simultaneously. Meanwhile, highly sensitive ultrasound detection has been demonstrated with the noise equivalent pressure as low as 0.46 kPa and the sensor exhibits a nearly omnidirectional directivity. Furthermore, a photoacoustic imaging system based on three sensors working in parallel is demonstrated. High lateral resolutions of 165–217 µm and axial resolutions of 112–131 µm over a depth range of larger than 5 mm are obtained. A three-dimensional phantom imaging experiment is also demonstrated. Benefited from parallel detection, the imaging speed is three times faster than that of a single sensor. The miniaturized fiber optic ultrasound sensor probe provides a competitive alternative for mechanically scanning-free endoscopic imaging, which is beneficial from small size, omnidirectional directivity and parallel detection capability.

Crocodilians, which are a kind of animal secondary adaptation to an aquatic environment, their hindlimb can provide the power needed to engage in various life activities, even in low-oxygen water environments. The development of limbs is an important aspect of animal growth and development, as it is closely linked to body movement, support, heat production, and other critical functions. For the Chinese alligator, the hindlimb is one of the main sources of power, and its development and differentiation will directly influence the survival ability in the wild. Furthermore, a better understanding of the hindlimb developmental process will provide data support for the comparative evolutionary and functional genomics of crocodilians. In this study, the expression levels of genes related to hindlimb development in the Chinese alligator embryos during fetal development (on days 29, 35, 41, and 46) were investigated through transcriptome analysis. A total of 1675 differentially expressed genes (DEGs) at different stages were identified by using limma software. These DEGs were then analyzed using weighted correlation network analysis (WGCNA), and 4 gene expression modules and 20 hub genes were identified that were associated with the development of hindlimbs in the Chinese alligator at different periods. The results of GO enrichment and hub gene expression showed that the hindlimb development of the Chinese alligator embryos involves the development of the embryonic structure, nervous system, and hindlimb muscle in the early stage (H29) and the development of metabolic capacity occurs in the later stage (H46). Additionally, the enrichment results showed that the AMPK signaling pathway, calcium signaling pathway, HIF-1 signaling pathway, and neuroactive ligand–receptor interaction are involved in the development of the hindlimb of the Chinese alligator. Among these, the HIF-1 signaling pathway and neuroactive ligand–receptor interaction may be related to the adaptation of Chinese alligators to low-oxygen environments. Additionally, five DEGs (CAV1, IRS2, LDHA, LDB3, and MYL3) were randomly selected for qRT-PCR to verify the transcriptome results. It is expected that further research on these genes will help us to better understand the process of embryonic hindlimb development in the Chinese alligator.

Abstract Pseudomonas aeruginosa is a formidable pathogen that is responsible for a diverse spectrum of human infectious diseases, resulting in considerable annual mortality rates. Because of biofilm formation and its ability of rapidly acquires of resistance to many antibiotics, P. aeruginosa related infections are difficult to treat, and therefore, developing an effective vaccine is the most promising method for combating infection. In the present study, we designed a novel trivalent vaccine, PcrV28-294-OprI25-83-Hcp11-162 (POH), and evaluated its protective efficacy in murine pneumonia and burn models. POH existed as a dimer in solution, it induced better protection efficacy in P. aeruginosa lethal pneumonia and murine burn models than single components alone when formulated with Al(OH)3 adjuvant, and it showed broad immune protection against several clinical isolates of P. aeruginosa. Immunization with POH induced strong immune responses and resulted in reduced bacterial loads, decreased pathology, inflammatory cytokine expression and inflammatory cell infiltration. Furthermore, in vitro opsonophagocytic killing assay and passive immunization studies indicated that the protective efficacy mediated by POH vaccination was largely attributed to POH-specific antibodies. Taken together, these data provided evidence that POH is a potentially promising vaccine candidate for combating P. aeruginosa infection in pneumonia and burn infections.

The determination of hemoglobin (Hb) level is indispensable in the pathological study of many blood diseases. Graphene oxide (GO), with its excellent optical properties and great biocompatibility, has attracted significant attention and been widely utilized in biochemical detection. Here, we report an ultrasensitive Hb sensor based on a graphene oxide (GO)-coated microfiber. The GO was utilized as a linking layer deposited on the microfiber surface, which can provide an enhanced local evanescent light field and abundant bonding sites for Hb molecules. The optical microfiber with a compact structure and a strong evanescent light field served as the platform for biosensing. The surface morphology characterized by optical microscope, scanning electron microscope, and Raman spectroscopy offers detailed evidence for the success of GO deposition. The dynamic bonding between GO and target Hb molecules was monitored in real-time through an optical spectrum analyzer. An ultrahigh sensitivity of 6.02 nm/(mg/mL) with a detection limit of 0.17 μg/mL was achieved by tracking the resonant wavelength shift of spectra. It is important to highlight that the detection limit of GO-coated microfiber is 1–2 orders of magnitude lower than other reported fiber optic Hb sensors. Benefiting from high sensitivity, low cost, small size, and fast response, the proposed sensing microfiber coated with GO could be a competitive alternative in the diagnosis of blood diseases and a subject of further research in the medical field.