Early detection of multiple endocrine neoplasia type 1: A case report

BACKGROUND Multiple endocrine neoplasias(MENs)are a group of hereditary diseases invol-ving multiple endocrine glands,and their prevalence is low.MEN type 1(MEN1)has diverse clinical manifestations,mainly involving the parathyroid glands,gastrointestinal tract,pancreas and pituitary gland,making it easy to miss the clinical diagnosis.CASE SUMMARY We present the case of a patient in whom MEN1 was detected early.A middle-aged male with recurrent abdominal pain and diarrhea was admitted to the hos-pital.Blood tests at admission revealed hypercalcemia and hypophosphatemia,and emission computed tomography of the parathyroid glands revealed a hy-perfunctioning parathyroid lesion.Gastroscopy findings suggested a duodenal bulge and ulceration.Ultrasound endoscopy revealed a hypoechoic lesion in the duodenal bulb.Further blood tests revealed elevated levels of serum gastrin.Surgery was performed,and pathological analysis of the surgical specimens revealed a parathyroid adenoma after parathyroidectomy and a neuroendocrine tumor after duodenal bulbectomy.The time from onset to the definitive diagnosis of MEN1 was only approximately 1 year.CONCLUSION For patients who present with gastrointestinal symptoms accompanied by hyper-calcemia and hypophosphatemia,clinicians need to be alert to the possibility of MEN1.

Establishment of a Multiplex Detection Method for Common Bacteria in Blood Based on Human Mannan-Binding Lectin Protein-Conjugated Magnetic Bead Enrichment Combined with Recombinase-Aided PCR Technology

Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannanbinding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP.Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays.Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05).Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.

Improving early diagnosis of multiple endocrine neoplasia type 1 by assessing the gastrointestinal symptoms,hypercalcemia,and elevated serum gastrin

Despite advancements in the field,early diagnosis of multiple endocrine neoplasia type 1(MEN1)remains unachievable.This letter to the editor highlighted the importance of carefully assessing gastrointestinal symptoms,hypercalcemia,and elevated serum gastrin levels,as suggested by Yuan et al in their paper.They focused on a patient with recurrent abdominal pain and diarrhea whose diagnostic path led to establishing a MEN1 diagnosis within a year.This emphasized the need for clinicians to consider MEN1 in patients with similar presentations,particularly when gastrointestinal symptoms persist or recur after discontinuation of proton pump inhibitors,especially knowing that early recognition and intervention are crucial for improving patient outcomes.

Iterative Signal Detection Based Soft Interference Cancellation for Satellite-Terrestrial Downlink NOMA Systems

To improve the bit error rate(BER)performance of multi-user signal detection in satelliteterrestrial downlink non-orthogonal multiple access(NOMA)systems,an iterative signal detection algorithm based on soft interference cancellation with optimal power allocation is proposed.Given that power allocation has a significant impact on BER performance,the optimal power allocation is obtained by minimizing the average BER of NOMA users.According to the allocated powers,successive interference cancellation(SIC)between NOMA users is performed in descending power order.For each user,an iterative soft interference cancellation is performed,and soft symbol probabilities are calculated for soft decision.To improve detection accuracy and without increasing the complexity,the aforementioned algorithm is optimized by adding minimum mean square error(MMSE)signal estimation before detection,and in each iteration soft symbol probabilities are utilized for soft-decision of the current user and also for the update of soft interference of the previous user.Simulation results illustrate that the optimized algorithm i.e.MMSE-IDBSIC significantly outperforms joint multi-user detection and SIC detection by 7.57dB and 8.03dB in terms of BER performance.

Survey and Detection of Disease and Pest Occurrence in Citrus Nurseries

[Objectives]The paper was to ascertain the prevalence of diseases and pests in a range of citrus nurseries situated in Guangdong Province and its neighboring provinces.[Methods]Citrus diseases and pests were systematically investigated,and citrus leaf samples were randomly collected from 15 citrus nurseries across 8 regions in Guangdong Province and its neighboring provinces.Quantitative polymerase chain reaction(qPCR)and reverse transcription polymerase chain reaction(RT-PCR)techniques were employed to detect diseases in the collected samples.Additionally,root and substrate samples were obtained,and root-knot nematodes were isolated using the Baermann funnel method.[Results]The positive detection rate of citrus huanglongbing(HLB)was recorded at 3%,indicating an increase in attention towards this disease compared to 2013.Additionally,the positive detection rate for citrus bacterial canker disease(CBCD)was found to be 16.5%.It was observed that the majority of nurseries with positive samples employed open field rearing practices without the use of mesh chambers,and the primary source of scions was self-propagation.The detection rate of citrus tristeza virus(CTV)was found to be the highest,with a positive detection rate of 63%,and the prevalence in disease-bearing nurseries reached as high as 90%.In comparison to 2013,there had been no improvement in the condition of seedlings affected by CTV.The positive detection rate of citrus yellow vein clearing virus(CYVCV)was found to be 38%,with 70%of the surveyed nurseries exhibiting the disease.The citrus varieties identified as carriers of the disease included‘Qicheng’,‘Shatangju’,‘Wogan’,and‘Gonggan’.Nematodes were isolated from the matrix and roots of seedlings grown in both container and open field environments.The susceptibility of container seedlings to nematodes was found to be 36.4%,while the susceptibility of open field seedlings was 38.6%.Statistical analysis indicated no significant difference in susceptibility between the two groups.[Conclusions]The disease detection rates associated with various seedling rearing methods and citrus varieties exhibited notable variability.Open field seedlings without the protection of mesh chambers demonstrated a higher susceptibility to disease.Additionally,the types of infectious diseases varied among the different citrus varieties.

MAPOD Analysis in Eddy Current Testing of Flaws Considering Multiple Response Signals and Multiple Flaw Parameters

The reliability of the eddy current testing (ECT) in flaw detection is quantitatively evaluated by theprobability of detection (POD). Precise and efficient modeling of POD gives direction for the implement of ECTon sites to avoid false or missing flaw detection. Traditional POD analysis focuses on single uncertain factor orsingle response signal with limited credibility in engineering. This paper considers multiple response signals andmultiple flaw parameters to perform POD. The flaw length, the flaw depth, the coil impedance, and the magneticflux density are comprehensively studied under various lift-off distances. A finite element model (FEM) of ECT isestablished and verified with experiments to obtain sufficient simulation data for discrete POD modeling. Thecontinuous POD function is then fitted based on the discrete values to show the superiority of integrating multiplefactors. A comparison with conventional POD analysis further demonstrates the higher reliability of ECT flawdetection considering multiple flaw parameters and multiple response signals, especially for small flaws.

Detection of artificial pornographic pictures based on multiple features and tree mode

It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this work, we studied how to detect artificial pornographic pictures, especially when they are on social networks. The whole detection process can be divided into two stages： feature selection and picture detection. In the feature selection stage, seven types of features that favour picture detection were selected. In the picture detection stage, three steps were included. 1） In order to alleviate the imbalance in the number of artificial pornographic pictures and normal ones, the training dataset of artificial pornographic pictures was expanded. Therefore, the features which were extracted from the training dataset can also be expanded too. 2） In order to reduce the time of feature extraction, a fast method which extracted features based on the proportionally scaled picture rather than the original one was proposed. 3） Three tree models were compared and a gradient boost decision tree （GBDT） was selected for the final picture detection. Three sets of experimental results show that the proposed method can achieve better recognition precision and drastically reduce the time cost of the method.

A New Method for the Detections of Multiple Faults Using Binary Decision Diagrams

With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram （BDD）. First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.

FAULT DETECTION FOR MULTIPLE-VALUED LOGIC CIRCUITS WITH FANOUT-FREE

The single fault and multiple fault detections for multiple-valued logic circuits are studied in this paper. Firstly, it is shown that the cardinality of optimal single fault test set for fanout-free m-valued circuits with n primary inputs is not more than n + 1, for linear tree circuits is two, and for multiplication modulo circuits is two if n is an odd number or if n is an even number and m ＞ 3, where the optimal test set of a circuit has minimal number of test vectors. Secondly,it is indicated that the cardinality of optimal multiple fault test set for linear tree circuits with n primary inputs is 1 + [n/(m - 1)], for multiplication modulo circuits is n+ 1, for fanout-free circuits that consist of 2-input linear tree circuits and 2-input multiplication modulo circuits is not greater than n+ 1, where [x] denotes the smallest integer greater than or equal to x. Finally,the single fault location approaches of linear tree circuits and multiplication modulo circuits are presented, and all faults in the two types of circuits can be located by using a test set with n + 1 vectors.

An Algebraic Detection Approach for Control Systems under Multiple Stochastic Cyber-attacks

In order to compromise a target control system successfully, hackers possibly attempt to launch multiple cyberattacks aiming at multiple communication channels of the control system. However, the problem of detecting multiple cyber-attacks has been hardly investigated so far. Therefore, this paper deals with the detection of multiple stochastic cyber-attacks aiming at multiple communication channels of a control system. Our goal is to design a detector for the control system under multiple cyberattacks. Based on frequency-domain transformation technique and auxiliary detection tools, an algebraic detection approach is proposed. By applying the presented approach, residual information caused by different attacks is obtained respectively and anomalies in the control system are detected. Sufficient and necessary conditions guaranteeing the detectability of the multiple stochastic cyber-attacks are obtained. The presented detection approach is simple and straightforward. Finally, two simulation examples are provided, and the simulation results show that the detection approach is effective and feasible. © 2014 Chinese Association of Automation.

Estimation of detection threshold in multiple ship target situations with HF ground wave radar

A credible method of calculating the detection threshold is presented for the multiple target situations, which appear frequently in the lower Doppler velocity region during the surveillance of sea with HF ground wave radar. This method defines a whole-peak-outlier elimination （WPOE） criterion, which is based on in-peak-samples correlation of each target echo spectra, to trim off the target signals and abnormal disturbances with great amplitude from the complex spectra. Therefore, cleaned background noise samples are obtained to improve the accuracy and reliability of noise level estimation. When the background noise is nonhomogeneous, the detection samples are limited and often occupied heavily with outliers. In this case, the problem that the detection threshold is overvalued can be solved. In applications on experimental data, it is verified that this method can reduce the miss alarm rate of signal detection effectively in multiple target situations as well as make the adaptability of the detector better.

Multiple PCR for Detection of Subclinical Mastitis in Dairy Cow

[ Objective] To develop a rapid, sensitive, specific and accurate diagnostic method for subclinical mastitis in dairy cow. [ Method] A total of 70 samples of suspected clinical mastitis cows were detected in gene level by multiple PCR. [Result] The 58 samples were positive with the developed multiple PCR method and the positive rate was 82.5% （58/70）. The 53 samples were positive with the traditional biochemical method and the positive rate was 75.5% （53/70）. As a result, the coincidence rate of both methods was 92%. [ Conclusion] The multiple PCR method is rapid and specific for the detection of pathogenic subclinical mastitis in dairy cow.

Application of modified multiple subarrays detection method to multibeam bathymetry system

Either amplitude based methods or phase based methods are usually used in multibeam bathymetry system and chosen in practical measurement by ad hoc criteria. The errors of the detection are often caused by choosing wrong detection method. In this paper a modified multiple subarrays amplitude-phase united detection method is proposed , which uses both the amplitude and phase of echo from bottom.This new method not only realizes the super-wide swath coverage, but also improves the detection performance by using the phase differences among subarrays in place of phases of subarrays to estimate the phase slope image. Experimental results are also analyzed and discussed to demonstrate the effectivity of the proposed approach.

Multiple reaction monitoring for the detection of disease-related synaptic proteins

Synaptic dysfunction occurs early in Alzheimer＇s disease （AD） and is acknowledged as a primary pathologic target for treatment. Synaptic degeneration is the pathological feature most strongly correlated with loss of cognitive function ante mortern （Terry et al., 1991）. Synapses are heavily damaged in hippocampal and neocortical regions of AD brain, whereas motor and occipital cortices are relatively spared （Honer et al., 1992）. Despite extensive work, the molecular mechanisms underlying synaptic degeneration are largely unknown.

Biological and molecular characterization of tomato brown rugose fruit virus and development of quadruplex RT-PCR detection

Tomato brown rugose fruit virus(ToBRFV) is a novel tobamovirus firstly reported in 2015 and poses a severe threat to the tomato industry. So far, it has spread to 10 countries in America, Asia, and Europe. In 2019, ToBRFV was identified in Shandong Province(ToBRFV-SD), China. In this study, it was shown that ToBRFV-SD induced mild to severe mosaic and blistering on leaves, necrosis on sepals and pedicles, and deformation, yellow spots, and brown rugose necrotic lesions on fruits. ToBRFV-SD induced distinct symptoms on plants of tomato, Capsicum annumm, and Nicotiana benthamiana, and caused latent infection on plants of Solanum tuberosum, Solanum melongena, and N. tabacum cv. Zhongyan 102. All the 50 tomato cultivars tested were highly sensitive to ToBRFV-SD. The complete genomic sequence of ToBRFV-SD shared the highest nucleotide and amino acid identities with isolate IL from Israel. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with tobacco mosaic virus(TMV). Furthermore, a quadruplex RT-PCR system was developed that could differentiate ToBRFV from other economically important viruses affecting tomatoes, such as TMV, tomato mosaic virus, and tomato spotted wilt virus. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV and provide an efficient and effective detection method for multiple infections, which is helpful in the management of ToBRFV.

Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an image.This technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same.When tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same object.Therefore,this paper attempts to determine the same object present in different images using color information among the unique information of the object.Thus,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television applications.The proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other images.To this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile range.The Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the object.Through the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different images.In the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 s.Through this,it is possible to detect and track the same object in real time when using the proposed method.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

A New Method for Image Tamper Detection Based on an Improved U-Net

With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.

Real-time RT-PCR Assay for the detection of Tahyna Virus

A real-time RT-PCR （RT-qPCR） assay for the detection of Tahyna virus was developed to monitor Tahyna virus infection in field-collected vector mosquito samples. The targets selected for the assay were S segment sequences encoding the nucleocapsid protein from the Tahyna virus. Primers and probes were selected in conserved regions by aligning genetic sequences from various Tahyna virus strains available from GenBank. The sensitivity of the RT-qPCR approach was compared to that of a standard plaque assay in BHK cells. RT-qPCR assay can detect 4.8 PFU of titrated Tahyna virus. Assay specificities were determined by testing a battery of arboviruses, including representative strains of Tahyna virus and other arthropod-borne viruses from China. Seven strains of Tahyna virus were confirmed as positive; the other seven species of arboviruses could not be detected by RT-qPCR. Additionally, the assay was used to detect Tahyna viral RNA in pooled mosquito samples. The RT-qPCR assay detected Tahyna virus in a sensitive, specific, and rapid manner; these findings support the use of the assay in viral surveillance.

Real-time RT-PCR Assay for the Detection of Culex flavivirus

Based on the Culex flavivirus (CxFV) E gene sequences in GenBank, CxFV-specific primers and probes were designed for real-time reverse transcription-polymerase chain reaction (RT-qPCR). The specificity test revealed that CxFV could be detected using RT-qPCR with the specific CxFV primers and probes; other species of arboviruses were not detected. The stability test demonstrated a coefficient of variation of <1.5%. A quantitative standard curve for CxFV RT-qPCR was established. Quantitative standard curve analysis revealed that the lower detection limit of the RT-qPCR system is 100 copies/mu L. Moreover, RT-qPCR was used to detect CxFV viral RNA in mosquito pool samples. In conclusion, we established a real-time RT-PCR assay for CxFV detection, and this assay is more sensitive and efficient than general RT-PCR. This technology may be used to monitor changes in the environmental virus levels.