Morphological features of allogenic nerve segment in rats after subcutaneous embedment

BACKGROUND: Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction. OBJECTIVE: To observe the gross shape, optical and electron microscope results of allogenic nerve segment in rats 2 weeks after subcutaneous embedment, and compare with subcutaneous emdedment of autologous nerve segment. DESIGN: A randomized and controlled experiment. SETTING: Department of Orthopaedics of Fifth People’s Hospital of Zhengzhou; Department of Orthopaedics, First Hospital Affiliated to Chongqing Medical University. MATERIALS: Totally 30 adult healthy Wistar male rats , with body mass of (200±20) g, were enrolled . Ten rats were chosen as the donors of allogenic nerve transplantation. The other 20 rats were randomly divided into 2 groups: allogenic nerve embedment group and autologous nerve embedment group, with 10 rats in each one. JEM-1220 transmission electron microscope (Japan) and Olympus BX50 optical microscope (Japan) were used. METHODS: This experiment was carried out at the laboratory of Orthopaedic Department, Chongqing Medical University from October 2000 to April 2002.① Sciatic nerve of donor rats for allogenic nerve transplantation was cut off at 5 mm distant from pelvic strait.15 mm sciatic nerve segment was chosen from lateral part as graft. allogenic nerve embedment group: 15 mm sciatic nerve form the donor rats was embedded in the posterior part of right legs. Autologous nerve embedment group: 15 mm sciatic nerve segment of autologous left side was embedded in the posterior side of right legs. ② Nerve segment embedded subcutaneously was taken out at postoperative 2 weeks and performed gross observation; then 5 samples chosen randomly respectively from 2 groups and given haematoxylin-eosin staining and observation under optical microscope (×400);The other 5 samples were made into ultrathin sections(0.5 μm)and observed under transmission electron microscope(×17 000). MAIN OUTCOME MEASURES: Gross shape, optical and electron microscope results of nerve segments of rats between two groups at 2 weeks after subcutaneous embedment. RESULTS: ① Results of gross observation: Appearance of nerve segment was similar between 2 groups. ②Results of optical observation: medullary sheath denaturation, axonotmesis, vascular engorgement, desmoplasia of adventitia and infiltration of inflammatory cells were all found in both 2 groups. Inflammatory reaction was a little more severe in the allogenic nerve embedment group than in the autologous nerve embedment groups. ③Results of electron microscope : Similar cataplasia and denaturation of medullary sheath and cataplasia of Schwann cell were all found in the 2 groups. CONCLUSION: Some inflammatory reaction occurs after allogenic nerve embedment, but the activity of Schwann cell is similar to that of peripheral nerve after autologous nerve embedment.

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Clinical Study and automatic diagnosis of electrocardiogram(ECG)data always remain a challenge in diagnosing cardiovascular activities.The analysis of ECG data relies on various factors like morphological features,classification techniques,methods or models used to diagnose and its performance improvement.Another crucial factor in themethodology is howto train the model for each patient.Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy.This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global trainingmethodology against an individual training methodology and identifying a gap between them.We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart RateVariability(HRV)thatmay aid real time application.This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.

Segmentation and measurement scheme for fish morphological features based on Mask R-CNN

The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture.Therefore,the accurate measurement of the morphological features is of great significance.However,the existing measurement methods mainly rely on manual measurement,which is operationally complex,low efficiency,and high subjectivity.To address these issues,this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN.Firstly,the fish body images are acquired by a home-made image acquisition device.Then,the fish images are preprocessed and labeled,and fed into the Mask R-CNN for training.Finally,the trained model is used to segment fish image,thus the morphological features indicators of the fish can be obtained.The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance.In pure background,the average relative errors(AREs)of all indicators measured all are less than 2.8%,and the AREs of body length and body width are less than 0.8%.In complex background,the AREs of all indicators are less than 3%,and the AREs of body length and body width is less than 1.8%.2020 China Agricultural University.Production and hosting by Elsevier B.V.on behalf of KeAi.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).1.Introduction With the advancing of its scientific and technological capabilities,China has made great achievements in the mariculture.The production accounts for more than 70%of the world’s overall mariculture output[1].The measurement of body length,body width and other morphological features of fish have wide application prospects in smart mariculture.Due to the difference in the quality and feeding ability of the Juvenile fish,the growth of the fish in the same pond is significantly different after a period of growth.Then,the fish needs to be classified.Grading can make fish grow better and improve feed utilization[2].The fish body length and body width are closely related to the weight of the fish.In the mariculture,the fishermen judge the growth of the fish by collecting the morphological feature of the fish,and use the information as an important reference for feeding,fishing and classification[3].At present,most of the measurement methods of fish body morphological features are manual,the operator usesmeasuring ruler to measure manually.It requires high technical level,and has high labor intensity and low efficiency.Furthermore,https://doi.

Advanced Insights into Goliathus orientalis (Coleoptera: Scarabaeidae) of Tanzania

Members of the Goliathus group in the order Coleoptera, family Scarabaeidae and the sub-family Cetoniidae is one among essential ecological insects worldwide with scanty documentation in Tanzania. The country was among the first point of collection of the specimen by Moser (1909) but subsequent attempts to locate and collect the specimen were unsuccessful possibly due to changed habitat and behaviour of Goliathus. Much of published work about the insect are based on the morphological characteristics of collected specimen which led to designation of six major species namely;Goliathus goliatus, G. cacicus, G. regius, G. albosignatus, G. orientalis and G. usambarensis, the last two being considered to exist in Tanzania. The current study reports on successful identification of habitats using biological cues and subsequently collected several Goliathus specimen that had been difficult to trace for more than a century. New insights into correct identity of the species by using a combination of morphological and molecular techniques have been set. The study has resolved that only one species, Goliathus orientalis with five sub-species exist in Tanzania.

Morphologic and texture features in classifying the malignant and benign breast nodules in ultrasonography

Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.

Area and volume ratios for prediction of visual outcome in idiopathic macular hole

AIM:To predict the visual outcome in patients undergoing macular hole surgery by two novel three-dimensional morphological parameters on optical coherence tomography(OCT): area ratio factor(ARF) and volume ratio factor(VRF).METHODS: A clinical case series was conducted,including 54 eyes of 54 patients with an idiopathic macular hole(IMH). Each patient had an OCT examination before and after surgery. Morphological parameters of the macular hole,such as minimum diameter,base diameter,and height were measured. Then,the macular hole index(MHI),tractional hole index(THI),and hole form factor(HFF) were calculated. Meanwhile,novel postoperative macular hole(MH) factors,ARF and VRF were calculated by three-dimensional morphology. Bivariate correlations were performed to acquire asymptotic significance values between the steady best corrected visual acuity(BCVA) after surgery and 2D/3D arguments of MH by the Pearson method with two-tailed test. All significant factors were analyzed by the receiver operating characteristic(ROC) curve analysis of SPSS software which were responsible for vision recovery. ROC curves analyses were performed to further discuss the different parameters on the prediction of visual outcome.RESULTS: The mean and standard deviation values of patients' age,symptoms duration,and follow-up time were 64.8±8.9y(range: 28-81),18.6±11.5d(range: 2-60),and 11.4±0.4mo(range: 6-24),respectively. Steady-postBCVA analyzed with bivariate correlations was found to be significantly correlated with base diameter(r=0.521,P<0.001),minimum diameter(r=0.514,P<0.001),MHI(r=-0.531,P<0.001),THI(r=-0.386,P=0.004),HFF(r=-0.508,P<0.001),and ARF(r=-0.532,P<0.001). Other characteristic parameters such as age,duration of surgery,height,diameter hole index,and VRF were not statistically significant with steady-post-BCVA. According to area under the curve(AUC) values,values of ARF,MHI,HFF,minimum diameter,THI,and base diameter are 0.806,0.772,0.750,0.705,0.690,and 0.686,respectively. However,Steady-post-BCVA analysis with bivariate correlations for VRF was no statistical significance. Results of ROC curve analysis indicated that the MHI value,HFF,and ARF was greater than 0.427,1.027 and 1.558 respectively which could correlate with better visual acuity.CONCLUSION: Compared with MHI and HFF,ARF could effectively express three-dimensional characteristics of macular hole and achieve better sensitivity and specificity. Thus,ARF could be the most effective parameter to predict the visual outcome in macular hole surgery.

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

Magnetic resonance imaging findings of carcinoma arising from anal fistula: A retrospective study in a single institution

BACKGROUND Magnetic resonance imaging(MRI)is currently the standard investigation for suspected perianal diseases.Carcinoma arising from anal fistula is very rare,and early diagnosis is often difficult.AIM To describe and summarize the MRI findings of carcinoma arising from anal fistula.METHODS In this retrospective study,records of ten patients diagnosed with carcinoma arising from anal fistula and confirmed by surgery(n=7)or biopsy(n=3)between June 2006 and August 2018 were analyzed.All patients underwent preoperative pelvic MRI.Morphologic features,signal characteristics,fistula between the mass and the anus,contrast enhancement of mass,signal and enhancement of peritumoral areas,and regional lymphadenopathy were assessed.RESULTS All ten tumors were solitary(8 mucinous adenocarcinomas and 2 adenocarcinomas).The maximum diameter of the tumors ranged from 3.4 cm to 12.4 cm(median:4.15 cm;mean:5.68 cm).Eight patients had a fistula between the mass and the anus.Contrast enhancement of the peritumoral areas was noted in three(3/5)patients.Perirectal or inguinal lymphadenopathy was noted in seven patients.Most lesions of mucinous adenocarcinoma were multiloculated and cauliflower-like,with a thin capsule and focally unclear boundary.They were markedly hyperintense on fat-suppressed T2WI,slightly hyperintense with focal hyperintensity on diffusion-weighted imaging(DWI),and hyperintense with focal hypointensity on apparent diffusion coefficient(ADC)map,with progressive mesh-like contrast enhancement.Adenocarcinomas had an infiltrative margin without a capsule and appeared heterogeneously hyperintense or slightly hyperintense on fat-suppressed T2WI,hyperintense on DWI,and hypointense on ADC map,with persistent heterogeneous enhancement.CONCLUSION Our study highlighted several characteristic and potentially helpful MRI findings to diagnose carcinomas arising from anal fistula.

Removal of baseline wander from ECG signal based on a statistical weighted moving average filter

Baseline wander is a common noise in electrocardiogram (ECG) results.To effectively correct the baseline and to preserve more underlying components of an ECG signal,we propose a simple and novel filtering method based on a statistical weighted moving average filter.Supposed a and b are theminimum and maximum of all sample values within a moving window,respectively.First,the whole region [a,b] is divided into M equal sub-regions without overlap.Second,three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region,denoted as [a 0,b 0 ] for simplicity.Third,for every sample point in the moving window,its weight is set to 1 if its value falls in [a 0,b 0 ];otherwise,its weight is 0.Last,all sample points with weight 1 are averaged to estimate the baseline.The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org.The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

Simulated impacts of 3D urban morphology on urban transportation in megacities: case study in Beijing

Urban morphology and morphology change and their impacts on urban transportation have been studied extensively in planar urban space.The essential feature of urban space,however,is its three-dimensionality(3D),and few studies have been conducted from a 3D perspective,overly limiting the accuracy of studies on the relationships between urban morphology and transportation.The aim of this paper is to simulate the impacts of 3D urban morphologies on urban transportation under the Digital Earth framework.On the basis of the principle that population distribution and movement are largely confined by 3D urban morphologies,which affect transportation,high spatial resolution remote sensing imagery and a thematic vector data-set were used to extract urban morphology and transportation-related variables.With a combination of three research methods-factor analysis,spatial regression analysis and Euclidean allocation-we provide an effective method to construct a simulation model.The paper indicates three general results.First,building capacity in the urban space has the most significant impact on traffic condition.Second,obvious urban space otherness,reflecting both use density characteristics and functional character-istics of urban space,mostly results in heavier traffic flow pressure.Third,no single morphology density indicator or single urban structure indicator can reflect its contribution to the pressure of traffic flow directly,but a combination of these different indicators has the ability to do so.