Multiple Capsule Research Articles

COVID19-ResCapsNet: A Novel Residual Capsule Network for COVID-19 Detection from Chest X-Ray Scans Images

In response to the urgent need for swift and accurate diagnosis of COVID-19 during the global pandemic, there has been a growing interest in leveraging advanced technology like deep learning for detection purposes. This project introduces a novel approach termed COVID19-ResCapsNet, which integrates deep neural networks with X-ray imaging data to predict the likelihood of COVID-19 onset in patients. The methodology comprises two key steps. Initially, features are extracted using an enhanced Residual feature extraction network, aimed at capturing crucial information from the X-ray images. Subsequently, multiple Capsule Networks are employed to discern between COVID-19 and non-COVID-19 cases. This hybrid model is assessed using two separate datasets of chest X-ray images: Dataset-1 containing 50 images and Dataset-2 containing 20 images. Remarkably, the proposed method achieves high classification accuracy rates of 0.9988 on Dataset-1 and 0.9933 on Dataset-2 for binary classification tasks. To validate its efficacy, the performance of the COVID19-ResCapsNet model is compared against various state-of-the-art pre-trained classification models. Additionally, the study investigates the impact of two crucial hyper parameters: optimizer selection and batch size. The findings underscore the effectiveness of the proposed COVID-19 detection model, suggesting its potential as a complementary tool in clinical settings for expedited and accurate diagnosis. Keywords: Deep learning, Capsule network, Residual network, COVID-19 detection.

Identification of predictors for short-term recurrence: comprehensive analysis of 296 retroperitoneal liposarcoma cases

BackgroundThe short-term (≤ 1 year) recurrence (STR) is the primary determinant impacting both the life quality and survival duration in patients who have undergone surgical resection for retroperitoneal liposarcoma (RPLS), a condition with intricate and ambiguous pathogenesis. The purpose of this study was to analyze the risk factors associated with STR in cases of RPLS and primary retroperitoneal liposarcoma (PRPLS).MethodsFor this retrospective observational study, a total of 296 RPLS cases were selected as research subjects, who experienced tumor recurrence during the follow-up period. The Local recurrence-free survival (LRFS) rates were estimated using the Kaplan–Meier method and subsequently compared between groups utilizing the log-rank test. The subsequent analyses involved univariate and multivariate logistic regression to identify predictors of STR in RPLS cases. Additionally, a logistic regression model was constructed for PRPLS.ResultsThe 1-, 3-, and 5-year LRFS rates of the 296 RPLS cases were 51.7%, 16.9%, and 7.1%, respectively. In the univariate analysis, several factors were found to be associated with STR, including preoperative neutrophil/lymphocyte ratio (NLR), smoking history, surgical frequency, combined organ excision, operative time, intraoperative bleeding, transfer to the intensive care unit (ICU), multiple primary tumors, tumor shape and capsule characteristics, histological subtype, and presence of tumor necrosis. The elevated preoperative NLR, surgical frequency of ≥ 3 times, transfer to the ICU, presence of multiple primary tumors, and tumor necrosis were identified as independent risk factors for STR in surgically resected RPLS. Conversely, diabetes, intact tumor capsule, and well-differentiated histological subtype appeared to be independent protective factors. Analysis for PRPLS revealed that tumor capsule and tumor necrosis were independent predictors of STR.ConclusionsThe elevated preoperative NLR, surgical frequency of ≥ 3 times, transfer to the ICU, presence of multiple primary tumors, tumor necrosis, and tumor capsule were expected to serve as predictive factors of STR for surgical resected RPLS and PRPLS.

Why should multiple dehiscences of the otic capsule be considered before surgically treating patients with superior semicircular canal dehiscence? A radiological monocentric review and a case series.

This review aims to draw attention to the multiple ipsilateral otic capsule dehiscences (OCDs), which may cause therapeutic failure in operated patients. A series of six severely disabled patients with symptoms and signs consistent with a superior semicircular canal dehiscence (SSCD) diagnosis, confirmed by a high-resolution CT scan, is presented here. Five of the patients underwent surgery, and in four of the cases, the postoperative results were poor and/or disappointing. The ethical principles underlying modern medicine encourage medical staff to learn from past experience even when the results are modest despite the accuracy of the treatment applied to a patient. Consequently, we reviewed the radiological records of symptomatic and asymptomatic patients diagnosed or referred to our center for confirmation over the past 5 years to determine the incidence of multiple OCD in this population. Multiple localizations of suspected OCD in the ipsilateral ear did not appear to be rare and were found in 29 of 157 patients (18.47%) in our retrospective review using high-resolution thin-sliced CT scans. The decision to perform surgery for a documented symptomatic superior SSCD should be made with caution only after ruling out concomitant lesser-known variants of OCD in the ipsilateral ear.

A method for real-time mechanical characterisation of microcapsules

Characterising the mechanical properties of flowing microcapsules is important from both fundamental and applied points of view. In the present study, we develop a novel multilayer perceptron (MLP)-based machine learning (ML) approach, for real-time simultaneous predictions of the membrane mechanical law type, shear and area-dilatation moduli of microcapsules, from their camera-recorded steady profiles in tube flow. By MLP, we mean a neural network where many perceptrons are organised into layers. A perceptron is a basic element that conducts input–output mapping operation. We test the performance of the present approach using both simulation and experimental data. We find that with a reasonably high prediction accuracy, our method can reach an unprecedented low prediction latency of less than 1 millisecond on a personal computer. That is the overall computational time, without using parallel computing, from a single experimental image to multiple capsule mechanical parameters. It is faster than a recently proposed convolutional neural network-based approach by two orders of magnitude, for it only deals with the one-dimensional capsule boundary instead of the entire two-dimensional capsule image. Our new approach may serve as the foundation of a promising tool for real-time mechanical characterisation and online active sorting of deformable microcapsules and biological cells in microfluidic devices.

MIRN: A multi-interest retrieval network with sequence-to-interest EM routing.

Vector-based retrieval have been widely adopted to process online users' diverse interests for recommendations. However, most of them utilize a single vector to represent user multiple interests (UMI), inevitably impairing the accuracy and diversity of item retrieval. In addition, existing work often does not take into account the scale and speed of the model, and high-dimensional user representation vectors need high computation cost, leading to inefficient item retrieval. In this paper, we propose a novel lightweight multi-interest retrieval network (MIRN) by incorporating sequence-to-interest Expectation Maximization (EM) routing to deal with users' multiple interests. By leveraging representation ability of the Capsule network, we design a multi-interest representation learning module that clusters multiple Capsule vectors from the user's behavior sequence to represent each of their interests respectively. In addition, we introduce a composite capsule clustering strategy for the Capsule network framework to reduce the scale of the network model. Furthermore, a Capsule-aware module incorporating an attention mechanism has been developed to guide model training by adaptively learning multiple Capsule vectors of user representations. The experimental results demonstrate MIRN outperforms the state-of-the-art approaches for item retrieval and gains significant improvements in terms of metric evaluations.

Virulence and antibiotic-resistance genes in Enterococcus faecalis associated with streptococcosis disease in fish

Enterococcus faecalis is associated with streptococcosis like infection in fish. A whole-genome sequence study was conducted to investigate the virulence factor and antibiotic-resistance genes in three fish pathogenic E. faecalis. Genomic DNA was extracted from three strains of E. faecalis isolated from streptococcosis infected Nile tilapia (strains BF1B1 and BFFF11) and Thai sarpunti (strain BFPS6). The whole genome sequences of these three strains were performed using a MiSeq sequencer (Illumina, Inc.). All three strains conserved 69 virulence factor such as genes associated with protection against oxidative stress, bacterial cell wall synthesis, gelatinase toxin, multiple biofilm-associated genes and capsule producing genes. Moreover, 39 antibiotic-resistance genes against sixteen major groups of antibiotics were identified in the genome sequences of all three strains. The most commonly used antibiotic Tetracycline resistance genes were found only in BFPS6 strain, whereas, Bacteriocin synthesis genes were identified in both BFFF11 and BFPS6 strain. Phylogenetic analysis revealed that strains BF1B1 and BFFF1 form a different cluster than BFPS6. This is one of the first whole-genome sequence study of fish pathogenic E. faecalis, unfold new information on the virulence factor and Antibiotic resistance genes linked to pathogenicity in fish.

Inertial migration of soft particles initially evenly spaced along the flow direction in a channel

The inertial migration of two-dimensional soft particles initially distributed uniformly in channel flow is numerically studied by employing an immersed-boundary lattice Boltzmann method coupled with a finite element method. To model a soft particle, a capsule with an elastic membrane separating the liquid inside from the outside is used. Validation tests show excellent agreement with previous numerical results of other researchers. Then, the effects of the number of capsules Np, the bending stiffness Eb, the initial position Y0′, and the Reynolds number Re on the dynamics of the capsules in channel flow are investigated in detail. Interestingly, we discover a new regime (labeled as regime E) in which a few capsules situated near the channel centerline travel much faster than the rest. Moreover, regime E is more prone to vanishing while the capsules are very soft. The normalized overall lateral position of the capsules σ increases almost linearly with Np when the capsules stabilize in a single-line particle train (designated as regime A). We also make an effort to explain the formation of the capsule deformation shape in regime A. The steady dynamics of a single capsule depend on whether the initial position is on the channel centerline. However, if the capsule is not released on the channel centerline, the steady capsule dynamics are independent of the initial position. But, it is not true for multiple capsule dynamics. These findings may help understand the inertial migration of capsules in channel flow.

E3GCAPS: Efficient EEG-based multi-capsule framework with dynamic attention for cross-subject cognitive state detection

Cognitive state detection using electroencephalogram (EEG) signals for various tasks has attracted significant research attention. However, it is difficult to further improve the performance of cross-subject cognitive state detection. Further, most of the existing deep learning models will degrade significantly when limited training samples are given, and the feature hierarchical relationships are ignored. To address the above challenges, we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection, termed as Efficient EEG-based Multi-Capsule Framework (E3GCAPS). Specifically, we use a self-expression module to capture the potential connections between samples, which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG. In addition, considering the strong correlation between cognitive states and brain function connection mode, the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data, in which multichannel 1D data greatly improving the training efficiency while preserving the model performance. The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset (FAAD) and the SJTU Emotion EEG Dataset (SEED). Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.

Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

Prediction of melting characteristics of encapsulated phase change material energy storage systems

This paper presents a numerical study of encapsulated phase change material (PCM) energy storage systems consisting of a single capsule or multiple capsules with different arrangements. A numerical model is developed for predicting the melting characteristics of encapsulated PCM with circular geometry, subjected to the flow of heat transfer fluid (HTF) over the capsules. The model also incorporates the effect of natural convection inside the PCM capsules. The single capsule system is analyzed to capture the effect of different parameters such as capsule size, fluid temperature, fluid velocity, and direction of flow. The model is applied to investigate multiple capsule systems to enhance the melting rate by the effective distribution of PCM capsules. Straight and alternate arrangements are compared for a varying number of capsules by maintaining the same latent energy storage capacity. Parametric studies are also performed for multiple capsule systems. Subsequently, a novel design of graded systems is proposed for improving the multi-capsule system by reducing the total duration of melting. The capsules are arranged with a percentage reduction in size along the flow direction, keeping the overall energy storage capacity constant. Simulation results show that there exists an optimum value of the reduction percentage for which the melting time is lowest.

Formulation and performance of slow-setting cement-based grouting paste (SCGP) for capsule grouting technology using orthogonal test

Capsule grouting technology (CGT) is a newly proposed technology to control the deformation of soil and underground structure. However, the conventional cement-based grouting slurry, with short setting time and poor fluidity, would not meet requirements for multiple capsule grouting. This study developed a slow-setting cement-based grouting paste (SCGP) based on ordinary Portland cement, bentonite, fly ash, and Etidronic acid as a retarder. The orthogonal test design and multiple linear regression analysis were used to optimize the material composition of SCGP, with the flowability, bleeding rate, initial setting time, and compressive strength as indexes. Moreover, the microstructure properties of the optimal SCGP were investigated. The results show that the influence of water-cement ratio and bentonite content on the fluidity and bleeding rate is more significant than that of other factors. The initial setting time of the paste would be greatly extended to nearly 60 h by adjusting the mixing ratio of retarder and raw materials. Although the retarder inhibits the early hydration reaction of the slurry, resulting in lower 7d strength, it does not affect the 28d compressive strength. Therefore, the optimal formulation of SCGP is recommended to be 1.1 for water-cement ratio, 30% for bentonite, 30% for fly ash, and 0.45% for retarder, which presents excellent performance for CGT.

With High-Risk Factors, Total Thyroidectomy is Preferred for Thyroid Cancer.

IntroductionBased on thyroid cancer data from patients treated in Liaocheng People’s Hospital in 2017, with Chinese national and regional characteristics, in this study we addressed the controversy of which initial thyroid surgical mode, lobectomy or total thyroidectomy, is most effective.Patients and MethodsClinical and pathological data from 552 patients with thyroid cancer, who were initially diagnosed and treated surgically, were collected from the Department of Thyroid Surgery. Among them, 40 patients underwent endoscopic surgery, with resection including lobectomy + central lymph node dissection of the affected lobe, while 512 cases underwent total thyroidectomy + central lymph node dissection.ResultsThe metastasis rate for all patients was 59.42%. Further, among patients with bilateral or multiple tumors or capsule invasion, the proportions who developed metastatic disease were 63.54% and 71.19% and 67.46%, respectively. Meanwhile, the incidence of contralateral accidental malignancy was 7.25% after postoperative paraffin pathology.DiscussionRates of neck lymph node metastasis are high, particularly among patients with risk factors for poor prognosis. It is recommended that, among patients with risk factors, initial treatment should comprise at least total thyroidectomy + central lymph node dissection in China, to avoid the risks associated with secondary surgery and effects on patient quality of life.

Chronic functional constipation is strongly linked to vitamin D deficiency.

BACKGROUNDFew studies have examined intestinal motility disorders, which are disabling conditions associated with chronic functional constipation, whose pathogenesis is actually not well-defined.AIMTo investigate the relationship between serum 25-hydroxyvitamin D levels and functional chronic constipation associated to intestinal motility disorders.METHODSWe performed a prospective case-control study, from May-June to November 2017. Glucose/lactulose breath tests, radiopaque markers (multiple capsule techniques) and wireless motility capsule analysis were used to assess colonic and oro-cecal transit time, after excluding small-intestinal bacterial overgrowth condition. Then, we measured 25-hydroxyvitamin D levels in patients with intestinal motility disorders and we further evaluated the influence of intestinal motility disorders on psychological symptoms/quality of life using validated questionnaires, the Irritable Bowel Syndrome Quality of life (IBS-QOL), the Short Form Health Survey 12, and the Hospital Anxiety and Depression Scale 14 (HADS-14 A and HADS-14 D).RESULTSWe enrolled 86 patients with chronic functional constipation associated to intestinal motility disorders and 86 matched healthy subjects. Patients with intestinal motility disorders had lower 25-hydroxyvitamin D levels (P < 0.001), and they showed a significant impairment of all health-related quality of life and psychological tests (IBS-QOL, Short Form Health Survey 12-Physical Component Summary, Short Form Health Survey 12-Mental Component Summary, HADS-14 A and HADS-14 D), as compared to the control group (P < 0.001), which significantly correlated with low vitamin D levels (r = - 0.57, P < 0.001; r = 0.21, P = 0.01; r = - 0.48, P < 0.001; r = - 0.57, P < 0.001; r = - 0.29, P < 0.001, respectively). At multivariate analysis vitamin D low levels remained a significant independent risk factor for the occurrence of intestinal motility disorder (odds ratio = 1.19; 95% confidence interval: 1.14-1.26, P < 0.001).CONCLUSIONVitamin D deficiency, anxiety and depression symptoms are commonly associated with chronic functional constipation induced by intestinal motility disorders. Vitamin D serum levels should be routinely measured in these patients.

Analysis of risk factors for central lymph node metastasis in papillary thyroid microcarcinoma

Objective: To analyze the risk factors of lymph node metastasis in central region of patients with papillary thyroid microcarcinoma (PTMC). To evaluate the reliability of different risk factors on the prognosis of cervical lymph node metastasis in PTMC patients, and to provide the clinical support for PTMC in the central area. Methods: The clinical data of 700 patients with PTMC treated with surgery from January 2015 to July 2017 were analyzed retrospectively. Risk factors for lymph node metastasis in central region were analyzed by single factor analysis, multivariate Logistic regression analysis and receiver operating characteristic curve (ROC) curve. Results: Central lymph node metastasis (CLNM) rate was 48.29% (338/700). Multifactor analysis indicated that age≤45 years old, male, multifocality, capsule invasion, the tumor calcification and tumor diameter ≥5 mm were independent risk factors for CLNM in patients with PTMC. In the risk prediction of CLNM, the optimal critical value of diameter prediction was 7 mm and the area under the curve (AUC) of ROC=0.647. The optimal threshold for age prediction was 41 years old and AUC=0.597. Single factor analysis for ROC curve showed that gender factor AUC=0.588, tumor number factor AUC=0.627, tumor location factor AUC=0.613. and calcification factor AUC=0.603. The ROC curve of multiple risk factors was analyzed according to age, gender, diameter, location, number of cancer foci and calcification, and AUC=0.768. Conclusions: Age less than 45 years old, male, multiple cancer foci, focal invasion and capsule invasion, calcification, and tumor diameter ≥5 mm are independent risk factors for CLNM of PTMC. With an accumulation of multiple risk factors, CLNM risk increases, and central lymph node dissection should be recommend.

Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification.

Tolerability and safety of real‑world use of pomalidomide in patients with relapsed/refractory multiple myeloma

Pomalidomide (POM) is a second-generation immunomodulatory agent with proven efficacy in patients with relapsed/refractory multiple myeloma (RRMM) proven to be refractory to previous treatment with lenalidomide (LEN) and bortezomib. We herein conducted a retrospective analysis of 14 RRMM patients receiving POM to determine its tolerability and safety in the clinical setting. The median age of the patients was 72 years (range, 58-84 years), and 85.7% of the patients were aged >70 years. The most frequent treatment dose was 3 mg/day. POM dose reductions were required in 54.5% (6/11) of the patients. The patient data were compared among three age groups (<70, 70-75 and >75 years) and there was only significant difference in daily POM treatment dose. The tolerability of POM must be confirmed, particularly in elderly patients. Dose reduction from 4 to 3 mg occurred during the second cycle in 83.3% (5/6) of the patients. It is important to determine the tolerability of POM in the early phases of treatment. The most frequently reported grade 3/4 hematological adverse events were neutropenia (64.3%), anemia (64.3%) and thrombocytopenia (57.1%). Although the median number of treatment cycles was 4 (range, 1-13), 21.4% (3/14) of the patients with a performance status (PS) of 3 were administered only 1 treatment cycle. The tolerability of POM was low among patients with poor PS and an aggressive treatment introduction should be avoided. However, 21.4% (3/14) of the patients were able to continue treatment for >1 year and some patients received long-term therapy. POM does not require dose modification for renal function, and multiple capsule doses are available, which is an advantage of POM compared with LEN. POM may be administered to late-stage RRMM patients in a real-world clinical setting, but elderly patients or those with poor PS must be treated with caution. In this manner, the treatment options for RRMM patients may be expanded by assessing the tolerability and safety of POM.

Diversity and distribution of Klebsiella capsules in Escherichia coli.

E. coli strains responsible for elevated counts (blooms) in freshwater reservoirs in Australia carry a capsule originating from Klebsiella. The occurrence of Klebsiella capsules in E. coli was about 7% overall and 23 different capsule types were detected. Capsules were observed in strains from phylogroups A, B1 and C, but were absent from phylogroup B2, D, E and F strains. In general, few A, B1 or C lineages were capsule-positive, but when a lineage was encapsulated multiple different capsule types were present. All Klebsiella capsule-positive strains were of serogroups O8, O9 and O89. Regardless of the phylogroup, O9 strains were more likely to be capsule-positive than O8 strains. Given the sequence similarity, it appears that both the capsule region and the O-antigen gene region are transferred to E. coli from Klebsiella as a single block via horizontal gene transfer events. Pan genome analysis indicated that there were only modest differences between encapsulated and non-encapsulated strains belonging to phylogroup A. The possession of a Klebsiella capsule, but not the type of capsule, is likely a key determinant of the bloom status of a strain.

Development of Multiple Capsule Robots in Pipe.

Swallowable capsule robots which travel in body cavities to implement drug delivery, minimally invasive surgery, and diagnosis have provided great potential for medical applications. However, the space constraints of the internal environment and the size limitations of the robots are great challenges to practical application. To address the fundamental challenges of narrow body cavities, a different-frequency driven approach for multiple capsule robots with screw structure manipulated by external electromagnetic field is proposed in this paper. The multiple capsule robots are composed of driven permanent magnets, joint permanent magnets, and a screw body. The screw body generates a propulsive force in a fluidic environment. Moreover, robots can form new constructions via mutual docking and release. To provide manipulation guidelines for active locomotion, a dynamic model of axial propulsion and circumferential torque is established. The multiple start and step-out frequencies for multiple robots are defined theoretically. Moreover, the different-frequency driven approach based on geometrical parameters of screw structure and the overlap angles of magnetic polarities is proposed to drive multiple robots in an identical electromagnetic field. Finally, two capsule robots were prototyped and experiments in a narrow pipe were conducted to verify the different motions such as docking, release, and cooperative locomotion. The experimental results demonstrated the validity of the driven approach for multiple capsule robots in narrow body cavities.

Spectrum and Frequency of Mutations Induced by Gamma Radiations in Three Varieties of Nigerian Sesame (Sesamum indicum L.)

Insufficient genetic variability is one of the major problems of plant breeding programmes, especially in sesame. Gamma radiation has been reported to be very effective in creating genetic variability in plants. Three varieties of Nigerian sesame were assessed for spectrum and frequency of mutation induced by Gamma radiations in M1 and M2 generations. The varieties (NCRIBEN-04E, NCRIBEN-01M and NCRIBEN-03L) were treated with four different doses of gamma rays (250, 350, 450 and 550 Gy). The treated and untreated seeds (control) were sown in planting bags (under field condition) to raise M1 plants. Four treatments: V1D5, V2D3, V3D2 and V3D4 (from M1 plants) were selected and bulked to obtain M2 populations. The results of M1 revealed four mutant fruit traits: multicarpellate capsule, multiple capsule per leaf axil, indehiscent capsule and terminal capsules. The highest frequencies of the traits in M1 generation were 2.50×10-2, 9.17×10-2, 1.67×10-2and3.33×10-2 respectively. The highest branching (7) was from NCRIBEN-01M, while the least (2) was from NCRIBEN-04E. The M2 plants were grouped into eight M2 lines. The dose range (250-550 Gy) was proved to be effective in inducing viable mutations in sesame.

Efficacy and Safety of the Newer Multiple Sclerosis Drugs Approved Since 2010.

Multiple sclerosis treatment faces tremendous changes as a result of the approval of new medications. The new medications have differing safety considerations and risks after long-term treatment, which are important for treating physicians to optimize and individualize multiple sclerosis care. Since the approval of the first multiple sclerosis capsule, fingolimod, the armamentarium of multiple sclerosis therapy has grown with the orally available medications dimethyl fumarate and teriflunomide. Fingolimod is mainly associated with cardiac side effects, dimethyl fumarate with bowel symptoms. Several reports about progressive multifocal leukoencephalopathy as a result of dimethyl fumarate or fingolimod therapy raised the awareness of fatal opportunistic infections. Alemtuzumab, a CD52-depleting antibody, is highly effective in reducing relapses but leads to secondary immunity with mainly thyroid disorders in about 30% of patients. Development of secondary B-cell-mediated disease might also be a risk of this antibody. The follow-up drug of the B-cell-depleting antibody rituximab, ocrelizumab, is mainly associated with infusion-related reactions; long-term data are scarce. The medication daclizumab high yield process, acting via the activation of CD56bright natural killer cells, can induce the elevation of liver function enzymes, but also fulminant liver failure has been reported. Therefore, daclizumab has been retracted from the market. Long-term data on the purine nucleoside cladribine in MS therapy, recently authorized in the European Union, have been acquired during the long-term follow-up of the cladribine studies. The small molecule laquinimod is currently under development. We review data of clinical trials and their extensions regarding long-term efficacy and side effects, which might be associated with long-term treatment.