Posterior capsular radial sign: a novel method to confirm anterior vitreous cortex resection in phacovitrectomy.

BACKGROUND: The main purpose of this paper is to introduce a method that can accurately locate the posterior capsule of the lens to facilitate a relatively complete resection of the anterior vitreous body. METHODS: A total of 51 patients in the experimental group and control group were enrolled in this study. Phacoemulsification combined with vitrectomy was performed in all cases. After the cataract procedure was completed in the control group, the surgeon performed a conventional anterior vitrectomy with the operative eye. In the experimental group, anterior vitrectomy was performed according to the threadiness corrugation of the posterior capsule of the lens. During the operation, with the help of triamcinolone, two surgeons confirmed the resection of the anterior vitreous cortex; the best corrected visual acuity and intraocular pressure of all patients were recorded at 1 week, 1 month and 3 months after surgery. RESULTS: Fifty patients underwent phacoemulsification combined with vitrectomy, except one patient in the experimental group who was lost to follow-up. After surgery, no significant complications were observed in all patients except two patients in the control group with temporary increases in intraocular pressure. There was no significant difference in preoperative visual acuity between the two groups (t = 0.83, P = 0.25). Both groups had varying degrees of improvement in best corrected visual acuity at 1 week, 1 month and 3 months after surgery. Moreover, there was no significant difference in BCVA between the two groups at the three follow-up time points (t=-1.15, -1.65, -1.09, P = 0.53, 0.21, 0.23). After surgery, no significant complications were observed in all patients except two patients in the control group with temporary increases in intraocular pressure. Incomplete resection of the anterior vitreous cortex was observed in 2 patients in each group, but there was no significant difference (χ2 = 7.81, P > 0.05). CONCLUSION: In the process of cataract surgery combined with vitrectomy, thready corrugation appears in the posterior capsule of the lens and is an important sign of its localization. Anterior vitrectomy can be accomplished safely and effectively with the help of thread-like corrugation, and the surgical effect is almost the same as that of traditional surgery. Especially suitable for beginners in vitreous surgery.

Effect of Negative Online Reviews and Physician Responses on Health Consumers' Choice: Experimental Study.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of online medical services. Although some researchers have investigated how numerical ratings affect consumer choice, limited studies have focused on the effect of negative reviews that most concern physicians. OBJECTIVE: This study aimed to investigate how negative review features, including proportion (low/high), claim type (evaluative/factual), and physician response (absence/presence), influence consumers' physician evaluation process under conditions in which a physician's overall rating is high. METHODS: Using a 2×2×2 between-subject decision-controlled experiment, this study examined participants' judgment on physicians with different textual reviews. Collected data were analyzed using the t test and partial least squares-structural equation modeling. RESULTS: Negative reviews decreased consumers' physician selection intention. The negative review proportion (ß=-0.371, P<.001) and claim type (ß=-0.343, P<.001) had a greater effect on consumers' physician selection intention compared to the physician response (ß=0.194, P<.001). A high negative review proportion, factual negative reviews, and the absence of a physician response significantly reduced consumers' physician selection intention compared to their counterparts. Consumers' locus attributions on the negative reviews affected their evaluation process. Physician attribution mediated the effects of review proportion (ß=-0.150, P<.001), review claim type (ß=-0.068, P=.01), and physician response (ß=0.167, P<.001) on consumer choice. Reviewer attribution also mediated the effects of review proportion (ß=-0.071, P<.001), review claim type (ß=-0.025, P=.01), and physician response (ß=0.096, P<.001) on consumer choice. The moderating effects of the physician response on the relationship between review proportion and physician attribution (ß=-0.185, P<.001), review proportion and reviewer attribution (ß=-0.110, P<.001), claim type and physician attribution (ß=-0.123, P=.003), and claim type and reviewer attribution (ß=-0.074, P=.04) were all significant. CONCLUSIONS: Negative review features and the physician response significantly influence consumer choice through the causal attribution to physicians and reviewers. Physician attribution has a greater effect on consumers' physician selection intention than reviewer attribution does. The presence of a physician response decreases the influence of negative reviews through direct and moderating effects. We propose some practical implications for physicians, health care providers, and online medical service platforms.

Analysis and Compensation of Lorentz Force Magnetic Bearing Magnetic Flux Density Uniformity Error.

Aiming at the influence of the magnetic flux density uniformity error (MFDUE) of the Lorentz force magnetic bearing (LFMB) on the sensitivity accuracy of magnetically suspended control and sensing gyroscopes (MSCSGs) on the angular rate of a spacecraft, a high precision measurement method of the angular rate of a spacecraft based on the MFDUE compensation of LFMB is proposed. Firstly, the structure of MSCSG and the sensitivity principle of MSCSG to the spacecraft angular rate are introduced. The mechanism influencing the accuracy of MSCSG to spacecraft angular rate sensitivity is deduced based on the definition of magnetic flux density uniformity. Secondly, the 3D magnetic flux distribution of LFMB is analyzed using ANSYS. The relationship between the rotor tilt angle, tilt angular rate, and magnetic flux density is established. The induced current calculation model due to MFDUE is proposed, and the LFMB magnetic flux density error compensation is realized. Finally, the simulation results show that the estimation accuracy of the induced current by the proposed method can reach 96%, and the simulation and the experiment show that the error compensation method can improve the accuracy of MSCSG in measuring the spacecraft angular rate by 12.5%.

[Structural Design and Finite Element Analysis of New Type Posterior Integral Glaucoma Valve].

The treatment of refractory Glaucoma is a difficult problem in clinical ophthalmology. For refractory glaucoma patients with hyphema, shallow anterior chamber, anterior conglutination of peripheral chamber angle, corneal endothelium dystrophy or decompensated, at present, there is no effective treatment. In order to solve this problem, a new type posterior integral glaucoma valve with IOP control device was designed using medical titanium alloy, and the valve model was established by Abaqus software, and the stiffness and preload of the valve were analyzed by finite element method. The results showed that the opening and closing of the valve were controlled automatically by the pressure difference between the front and back of the valve, and the opening and flow rate of the valve increase dynamically with the increase of intraocular pressure, and finally reached the set ideal IOP value of steady state.

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images.

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models' classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models' overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

Developing an Automated Gas Sampling Chamber for Measuring Variations in CO2 Exchange in a Maize Ecosystem at Night.

The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were 0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: △CO2 = 0.079  ). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately.

Development of Response Surface Model of Endurance Time and Structural Parameter Optimization for a Tailsitter UAV.

This study designed a vertical take-off and landing tailsitter unmanned aerial vehicle (UAV) with a long endurance time. Nine parameters of the tailsitter UAV were investigated. Using a 2k full factorial test, 512 experiments on the nine parameters were conducted at their maximum and minimum values. The time coefficient and air resistance were calculated using the computational fluid dynamics (CFD) method under different parameter combinations. The analysis of variance determined that the specific factors influencing the time coefficient and air resistance were the root chord, wingtip chord, wingspan, and sweep angle. By carrying out a central composite design (CCD) test, 25 sample points of the four particular factors were constructed. The time coefficient and air resistance were simulated under different structural parameter combinations using the CFD method. CFD simulation was verified by carrying out a wind tunnel test, and the results revealed that the aerodynamic coefficient error was less than 5%, while the air resistance error was less than 6%. The response surface methodology (RSM) for the time coefficient and air resistance was established using a genetic aggregation method. A multi-objective genetic algorithm (MOGA) was used to optimize the parameters with regard to the maximum time coefficient and minimum air resistance. The optimal structural parameters were wing root chord length at 315 mm, wingtip chord length at 182 mm, wingspan length at 1198 mm, and sweep angle at 16°. Compared with the original layout and size, the time coefficient of the new design of the tailsitter UAV improved by 19.5%, while the air resistance reduced by 34.78%. The results obtained by this study are significant for the design of tailsitter UAVs.

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory.

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system's own original gas using its pump. CO2 and PM2.5/10 above the corn field are used as the test species to validate the accuracy of the CO2 gas and PM concentrations collected by UGSS. Deming regression analyses showed good agreement between the measurements from the UGSS and the ground sampling station (y = 1.027x - 11.239, Pearson's correlation coefficient of 0.98 for CO2; y = 0.992x + 0.704, Pearson's correlation coefficient of 0.99 for PM).The UGSS provides a measuring method that actively collects gases and PM for manual analyses in the laboratory.

Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale.

Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing.

Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments.

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

The potential agents from food for preventing leukopenia induced by benzene: garlic preparations.

Leukopenia is the early clinical manifestation of benzene poisoning. The aim of our research was to evaluate the preventive effects of three kinds of garlic preparations on benzene induced leukopenia. The mouse model of Leukopenia was established with benzene orally. At the same time, mice were administrated with garlic homogenate (GH), garlic oil (GO) or diallyl trisulfide (DATS) as preventional measures. The counts of white blood cells (WBC), the organ indexes, pathological examinations, blood biochemical parameters, weight gains, and food intakes were evaluated to observe the protective effect and potential adverse events. The results demonstrated that the counts of WBC increased by 144.04%, 140.07%, and 148.34%, respectively, after intervention by GH (400 mg/kg), GO (60 mg/kg) and DATS (30 mg/kg), compared with that in the model group. The spleen and thymus indexes in the benzene model group were 44.99% and 54.04% lower than those in the blank control group, the number of spleen nodules reduced and the thymus atrophy, which were restored by three garlic preparations at different degree. The results suggested that the three preparations all could prevent the leukopenia and protect the organ injuries induced by benzene. However, the spleen index and weight gains revealed that GH and GO brought more adverse events than DATS.

Oxidative Stress Mediated Hippocampal Neuron Apoptosis Participated in Carbon Disulfide-Induced Rats Cognitive Dysfunction.

Occupational exposure to carbon disulfide (CS2) exhibits central nervous systems toxicity. But the mechanism is unclear. The present study was designed to investigate the relationship between the CNS damage and cognitive dysfunction caused by CS2, and eventually reveal the possible oxidative-related mechanism of hippocampus pathological changes in CS2 exposed rats. Male Wistar rats were administrated with CS2 at dosage of 200, 400 and 600 mg/kg for consecutive 20 days, respectively. Cognitive performances were evaluated by Morris water maze tests. Thionin and immunohistochemical analysis were used to investigate the hippocampal neuron damage, and the expression of apoptosis related proteins (cleaved-caspase 3, Bax and Bcl-2) were detected to explore the possible mechanisms of neuronal loss. Oxidative stress parameters were checked by commercial assay kits. Rats exposed to CS2 displayed cognitive dysfunction manifested as decreased spatial learning ability and memory lesion. Pathological changes and significant neuron loss were observed in hippocampus, especially in CA1 and CA3 sub-regions. Mitochondria-dependent apoptosis pathway was implicated in the CS2-induced neuronal loss which was demonstrated by the up-regulation of cleaved-caspase 3 and Bax accompanied with down-regulation of Bcl-2. Furthermore, extensive oxidative stress induced by CS2 was also revealed by the measurement of ROS, RNS, MDA, GSH&GSSG and antioxidant enzymes (CAT, T-SOD, and GSH-Px). Our study suggested that oxidative stress mediated hippocampal neuron apoptosis might play an important role in CS2 induced CNS damage and cognitive dysfunction.

Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors.

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.

[Cross-sectional association between diabetes and obesity among the elderly of different genders in Yantai City].

OBJECTIVE: To explore the cross-sectional association between the incidence of diabetes and obesity among the elderly of different genders, which intends to provide the scientific basis for undertaking glycemia interventions in the early stage to be conducive to the old folks' health status in Yantai City. METHODS: A total of 986 old people (≥ 60 years old) were recruited from 4 districts in Laishan District Yantai City, Penglai City, Qixia City, Haiyang City by stratified cluster of random sampling and surveyed using questionnaires, while the physical examinations and blood glucose tests were conducted. The logistic regression model was used to analyze the cross-sectional association between the incidence of diabetes and obesity among the elderly of different genders in Yantai City. RESULTS: The rates of obesity and abdominal obesity were 10.04% and 60.85% among the old people in Yantai, respectively. The morbidity rate of diabetes was 10.85%. The influencing factors such as age, cultural standard, monthly income, past job category, smoking, drinking were adjusted, the fat old people had 3.121 times as much chance of suffering from obesity as the normal weight ones (OR = 3.121, 95% CI 1.978 - 5.119). And there was a gender difference between diabetes and obesity. The cross-sectional association between the incidence of diabetes and masculine obesity was of statistical significance alone (OR = 3.924, 95% CI 1.561 - 7.174). The elderly with the abdominal obesity 2.398 times as likely to suffer from diabetes as the elderly with the non-abdominal obesity (OR = 2.398, 95% CI 2.123 - 4.412). There was a gender difference between diabetes and abdominal obesity. The cross-sectional association between the incidence of diabetes and masculine abdominal obesity was of statistical significance alone (OR = 2.917, 95% CI 1.249 - 4.019). CONCLUSION: There are gender difference in the relationship between obesity, abdominal obesity and diabetes in the elderly in Yantai. BMI and waist circumference can be used as the predictive indexes of masculine diabetes.

Modulated dye retention for the signal-on fluorometric determination of acetylcholinesterase inhibitor.

A novel fluorometric assay method based on target-induced signal on was developed for acetylcholinesterase (AChE) inhibitor with obviously improved detection sensitivity. In this method, the AChE molecules catalyzed the hydrolysis of acetylthiocholine (ATCl) to form thiocholine, which in turn can specifically react with fluorescent squaraine derivative, a specific chemodosimeter for thiol-containing compounds, resulting in fluorescence quenching and offering a low fluorometric background for the further detection of AChE inhibitor. In the presence of AChE inhibitor, the catalytic hydrolysis of ATCl is blocked, and then the squaraine derivative remains intact and shows signal-on fluorescence. The amount of the remaining fluorescent squaraine derivative is positively correlated with that of the AChE inhibitor in solution. This new designed sensing system shows an obviously improved sensitivity toward target with a detection limit of 5 pg mL(-1) (0.018 nM) for the AChE inhibitor, comparing favorably with previously reported fluorometric methods. To our best knowledge, this new method is the first example of fluorometric enzymatic assay for AChE inhibitors based on such a signal-on principle and using a specific reaction, which has potential to offer an effective strategy for the detection of AChE inhibitors.

BMI and plasma lipid levels with risk of proliferative diabetic retinopathy: a univariable and multivariable Mendelian randomization study.

Background: The study aimed to determine whether a causal effect exists between body mass index (BMI) or plasma lipid levels and proliferative diabetic retinopathy (PDR) risk in humans. Methods: We utilized univariable (UVMR) and multivariable two-sample Mendelian randomization (MVMR) analyses to confirm the effects of BMI and plasma lipid levels on the risk of PDR. Genetic variants associated with BMI and three plasma lipids were obtained from GWAS summary datasets generated by many different consortia and were deposited in the MR-Base database. The GWAS summary data for PDR from the FinnGen biobank included 2,12,889 participants of European ancestry (8,681 cases and 2,04,208 controls). Inverse variance weighted (IVW) was applied as the main MR analysis. Sensitivity analysis was used to evaluate the robustness of our findings. Results: In the UVMR analysis, the causal associations of genetically predicted BMI with PDR presented a positive association (OR = 1.120, 95% CI = 1.076-1.167, P < 0.001), and the lower HDL-C level was associated with a higher risk of PDR (OR = 0.898, 95% CI = 0.811-0.995, P = 0.040). No evidence of an association between LDL-C or TG levels (P > 0.05) and PDR risk was found. In the MVMR analysis controlling for the HDL-C level, there was strong evidence for a direct causal effect of BMI on the risk of PDR (OR = 1.106, 95%CI = 1.049, 1.166, P < 0.001, IVW). After adjusting for BMI, there was no evidence for a direct causal effect of the HDL-C level on the risk of PDR (OR = 0.911, 95% CI = 0.823, 1.008, P = 0.072). Sensitivity analyses confirmed that the results were reliable and stable. Conclusion: Robust evidence was demonstrated for an independent, causal effect of BMI in increasing the risk of PDR. Further studies are required to understand the potential biological mechanisms underlying this causal relationship.

Acetylcholinesterase liquid crystal biosensor based on modulated growth of gold nanoparticles for amplified detection of acetylcholine and inhibitor.

A novel acetylcholinesterase (AChE) liquid crystal (LC) biosensor based on enzymatic growth of gold nanoparticles (Au NPs) has been developed for amplified detection of acetylcholine (ACh) and AChE inhibitor. In this method, AChE mediates the hydrolysis of acetylthiocholine (ATCl) to form thiocholine, and the latter further reduces AuCl(4)(-) to Au NPs without Au nanoseeds. This process, termed biometallization, leads to a great enhancement in the optical signal of the LC biosensor due to the large size of Au NPs, which can greatly disrupt the orientational arrangement of LCs. On the other hand, the hydrolysis of ATCl is inhibited in the presence of ACh or organophosphate pesticides (OPs, a AChE inhibitor), which will decrease the catalytic growth of Au NPs and, as a result, reduce the orientational response of LCs. On the basis of such an inhibition mechanism, the AChE LC biosensor can be used as an effective way to realize the detection of ACh and AChE inhibitors. The results showed that the AChE LC biosensor was highly sensitive to ACh with a detection limit of 15 µmol/L and OPs with a detection limit of 0.3 nmol/L. This study provides a simple and sensitive AChE LC biosensing approach and offers effective signal enhanced strategies for the development of enzyme LC biosensors.

The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis.

BACKGROUND: Medical informatics has attracted the attention of researchers worldwide. It is necessary to understand the development of its research hot spots as well as directions for future research. OBJECTIVE: The aim of this study is to explore the evolution of medical informatics research topics by analyzing research articles published between 1964 and 2020. METHODS: A total of 56,466 publications were collected from 27 representative medical informatics journals indexed by the Web of Science Core Collection. We identified the research stages based on the literature growth curve, extracted research topics using the latent Dirichlet allocation model, and analyzed topic evolution patterns by calculating the cosine similarity between topics from the adjacent stages. RESULTS: The following three research stages were identified: early birth, early development, and rapid development. Medical informatics has entered the fast development stage, with literature growing exponentially. Research topics in medical informatics can be classified into the following two categories: data-centered studies and people-centered studies. Medical data analysis has been a research hot spot across all 3 stages, and the integration of emerging technologies into data analysis might be a future hot spot. Researchers have focused more on user needs in the last 2 stages. Another potential hot spot might be how to meet user needs and improve the usability of health tools. CONCLUSIONS: Our study provides a comprehensive understanding of research hot spots in medical informatics, as well as evolution patterns among them, which was helpful for researchers to grasp research trends and design their studies.

ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation.

Recently, convolutional neural network(CNN) has achieved great success in medical image segmentation. However, due to the limitation of convolutional receptive field, the pure convolutional neural network is difficult to further improve its performance. Given the outstanding ability of transformers in extracting the long-range dependency, some works have successfully applied it to computer vision and achieved better results than CNN in some tasks. Based on transformers could remedy the shortage of CNN, in this paper, we propose ITUnet, a segmentation network using CNN and transformers as features extractor. The combination of CNN and transformers enables the network to learn both short- and long-range dependency of features, which is beneficial to segmentation tasks. We evaluate our method on a head-and-neck CT dataset which has 18 kinds of organs to be segmented. The experimental results demonstrate that our proposed method shows better accuracy and robustness, the proposed methods achieve the Dice score of 77.72 and the 95% Hausdorff Distance of 2.31, outperforming the existing methods.