Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer’s disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models’ outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer’s disease. Neurodegenerative diseases (NDs), known as tauopathies, constitute a group of more than 20 proteinopathies that represent a major global public health problem; among the most prevalent are Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). In AD, the Tau protein undergoes modifications that cause its aggregation and the formation of Neurofibrillary Tangles (NFTs), which together with amyloid beta positive plaques are the histopathological hallmark of this disease ,2,3,4; lThe epidemiological data from  highlighIn the case of tauopathies, pathological protein aggregation is considered a key event. Several research groups ,17 have Elucidation of the differences between pathological PTMs in the Tau protein that leads to its fibrillar polymeric form is fundamental to understanding the pathogenesis and differential diagnosis between the different tauopathies, representing a critical challenge for therapeutics .Machine and Deep Learning, specifically Convolutional Neural Networks (CNNs), have been used to address the problem of differentiating NDs in medical imaging, such as Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET), which are noninvasive means for detecting changes in brain function ,23,24. TThe ability of immunofluorescence to discriminate between cells, organelles or molecules within tissues and to analyze their interactions through the obtained images makes it an ideal data format for more advanced computational analysis . In partDeep Learning is a computational paradigm that has been exploited for medical image classification ,22,27,28Within the context of DL, Transfer Learning is a technique that has also been exploited for medical image classification . It consThe field of DL for classification of immunofluorescence microscopy imaging has been widely studied for HEp-2 cell classification. Rahman et al.  provide Neurons have been classified in immunofluorescence images of rat brains, where CNN showed better performance than Principal Component Analysis (PCA) with a Support Vector Machine. However, this research explains that their model may not be suitable for the hippocampus region given its dense neuronal population . ResNet-The only related work found on classification of immunofluorescence post-mortem brain imaging is presented by Alegro et al. , who proLin et al.  classifiGao et al.  provide As the conclusion of the literary review, IPMB images have not been used for studies of classification of NDs. While MRI and CT scan at a generalized level and are based on morphological data of brain tissue, IPMB images are based on brain tissue but at a molecular level, which is key to understanding the pathogenesis of NDs. Therefore, the design of a classification model among the different tauopathies, with a focus on aberrant PTMs suffered by the Tau polypeptide, constitutes the challenge of the present investigation.In this study, we modeled the different biomarkers concerning pathological PTMs in Tau polypeptide in the hippocampal and entorhinal cortex regions of the brain using a DL and Transfer Learning pipeline that classifies AD and PSP tauopathies on IPMB images, provided by the National Biobank of Dementias of the National Autonomous University of Mexico (UNAM). From a broad range of DL architectures, we developed the ResNet-IFT architecture, which is a ResNet-50-based architecture that proves to be efficient for obtaining models for classifying IPMB images. The models developed in this study test whether Transfer Learning or Transfer Learning and fine-tuning are helpful tools to develop the pipeline. This pipeline is followed by Guided Grad-CAM and Occlusion Analysis algorithms in order to obtain the actual differences in Tau polypeptide that lead to the classification of each disease. To our knowledge, the present work is the first one proposed to classify NDs from parameters computed by IPMB images.The following section presents the specifications of the IPMB images used for the project. We also introduce the datasets we constructed to carry out the experimentation. Next, we present four distinct architectures and a comprehensive comparison of their performances to obtain the best classifier. Within the section, we provide a brief explanation concerning the ResNet models and Transfer Learning for DL. Finally, we present the specifications and results from implementation of XAI algorithms, Guided Grad-CAM and Occlusion Analysis, to interpret the most significant regions of the IPMB images for an accurate classification of AD or PSP.The IPMB images used for the research were obtained in a collaborative project between the National Dementia Biobank of the UNAM and the Bioengineering Department of the Tecnologico de Monterrey. The images were obtained entirely from post-mortem tissues of patients with AD and PSP. All data were obtained following current laws, regulations and guidelines, such as sharing anonymized data that does not contain information that would establish the identity of individual deceased subjects.Four different brains were used.The brain areas used were Hippocampus CA1 and Entorhinal cortex.Two brains with diagnosed AD were used—one of a 90 year old female and another of an 81 year old male.Two male brains with diagnosed PSP were used—one of a 75 year old and another one of a 85 year old.The tissues of patients with AD used were of the Braak 5–6 stages.Delving deeper into the specifications of the brains used to obtain the IPMB images:Green channel: Inmunodetection with AT8 mouse IgG antibody  against Tau protein. AT8 antibody detects phosphorylations of Tau protein in amino acids: Serine 202 and Threonine 305. The presence of phosphates translates into chemical changes that give protein Tau aberrant behaviour.Red channel: Inmunodetection with Thiazine red, which is a molecule that binds to fibrillar insoluble structures of protein polymers. This molecule specifically binds to protein Tau in its polymer conformation.Blue channel: Inmunodetection with pS396 rabbit IgG antibody against Tau protein . The 396Merge channel: Visualization of the green, red and blue channel images together into one image.The IPMB images are a visual representation of the interaction of fluorochrome-coupled antibodies with their epitopes on the specific protein chemical structure. The images also represent molecules with chemical interactions of fibrillar forms such as NFT in the brain of patients. Experiments with three different biomarkers were used, resulting in four-channel imaging:Images were obtained using a 100× oil-immersion plan Apochromat objective (NA 1.4). Ten to fifteen consecutive single sections were sequentially scanned at 0.8–1.0 μm intervals for two or three channels throughout the z-axis of the sample.It is important to note that our images are captured with the same criteria and we block the nonspecific background signal when incubating the corresponding antibodies in the immunofluorescence. Moreover, it is important to highlight that the obtention of our dataset has been a work that has taken over ten years.For the development of the project, a pre-processing stage was not needed, the images were processed as they were delivered by the experts, already labeled.Three datasets were formed to evaluate the performance through experimentation, as shown in In order to explain our main contribution, i.e. the classifier and the model’s interpretability merit from XAI algorithms, we briefly address first the theory behind our choices of architectures to test.It is commonly understood that DL models work best when copious amounts of annotated data are available; however, for our research, even our largest dataset (D3) is relatively small (over 1300 images). Therefore, in order to achieve optimal performance, we considered a wide range of CNN architectures, starting with a multilayer perceptron and then increasing the model complexity, up to a pre-trained ResNet50 architecture using Transfer Learning and fine-tuning. In the related work, previously presented, artificial neural networks and Deep Learning models have been used for the classification of immunofluorescence images and the classification of neurodegenerative diseases. Therefore, we decided to test two artificial neural networks with different depths and two Deep Learning architectures with different complexity.Given the limitations of our datasets, we decided to use Transfer Learning, thus initializing the weights from an ImageNet pre-trained model, rather than randomly, since this would help extracting features from the IPMB images dataset. Moreover, Transfer Learning has been proven to save time and achieve a better performance than training an entire model from scratch . MoreoveSince the introduction of ResNet models by He et al. , ResNet-Homogeneous Transfer Learning: Source and target feature spaces are the same.Heterogeneous Transfer Learning: Source and target feature spaces are different.As we briefly explained in the Introduction, Transfer Learning uses a model trained on a source domain for a specified task and then re-uses that model for a different task . It is iTransfer Learning: Initializing the weights of the model using a pre-trained architecture on the ImageNet dataset. Then, we preload these weights to train the entire architecture with our developed dataset.Transfer Learning and fine-tuning: Initializing the weights of the model using a pre-trained architecture on ImageNet dataset, but training only the last convolution block and the final fully connected layer with our developed dataset, for some epochs. Afterwards, we unfreeze the entire architecture and continue to train the model for additional epochs.For the development of this project, we define two levels of Transfer Learning:From the explanations above, it should be noted that we follow a heterogeneous Transfer Learning approach, since we will use ImageNet pre-trained architectures for the classification of IPMB images. In addition, we tested both Transfer Learning and Transfer Learning plus fine-tuning.Sequential CNN: A Multi-Layer Perceptron with three fully connected linear layers with 18, 8 and 2 neurons. L2 regularization was used for the last linear layer. The training was performed for 30 epochs.Simple CNN: A CNN with 3 convolution layers, starting with 16 filters, then 32 and lastly 64 filters of 3 × 3 kernels. Each convolution was followed up by a max pooling operation with a 2 × 2 window. Then, we reduced the images to a one-dimensional vector and used two fully connected layers with 200 and 2 neurons, respectively. We applied a dropout layer with a rate of 0.5 between these two fully connected layers. Each convolution and linear operation was performed with L2 regularization. The model was trained for 30 epochs.ResNet-IFTF: The architecture ResNet-50, as provided in Keras, was used. This architecture is pre-trained on the ImageNet dataset. Transfer Learning and fine-tuning were applied for the development of this model. We froze the entire model up to the last activation layer encountered and added a convolution layer with 512 filters with kernels of size 1 × 1, a batch normalization layer, an activation layer, a global average pooling layer and an output layer with 2 neurons and L2 regularization. The model was trained for 15 epochs with the frozen part of the architecture and then for 10 additional epochs unfreezing the entire model.ResNet-IFT: The architecture ResNet-50 from Keras was used. This architecture was pre-trained on the ImageNet dataset. For this model, we used Transfer Learning using the entire pre-trained RESNET without any further fine-tuning. The entire architecture was trained for 15 epochs. We added a global average pooling layer and an output layer with L2 regularization. In comparison to the original ResNet-50 architecture, we eliminated the flattening layer between the global average pooling layer and the dense layer.To accomplish the goal of developing the most suitable model for IPMB images classification, we developed four different DL architectures:All neural network models were developed and trained in the open source package Keras of Tensorflow. The threshold used for classification in all neural networks was 0.5. In The XAI algorithms chosen to aid in the interpretation of our models are Guided Grad-CAM and Occlusion Analysis, which allow us to test both a back-propagation-based method and a perturbation-based method .In order to provide additional interpretations of the results, we look into the CNN’s internal logic using the Guided Grad-CAM algorithm . This viThis technique passes a fixed-size patch across the image, evaluating the class prediction for each patch location in the picture . Figure For the evaluation of the classification models, we used the metric accuracy  as our guide to define a successful model. This metric was chosen because our datasets are balanced. We used 10-fold cross validation to determine the standard deviation of the models developed per architecture. Each dataset was split into 80% for training and validation and 20% for evaluation or testing.The Guided Grad-CAM and the Occlusion experiments were evaluated by confirmation by human experts. For the XAI experiments, we were interested in obtaining an interpretation and insights on the importance of a certain prediction.The following section describes the accuracy metrics obtained for the models developed with the four CNN architectures previously described. Moreover, this section stresses the best classification model comparing the probability scores for a determined class. Finally, we present the findings for the interpretability study.As explained in the previous section, we developed four architectures and tested their accuracy to obtain the most suitable model for IPMB image classification. For each dataset , we tested each of the four architectures, thus developing a total of 12 models. In Since the accuracies obtained for the models using Sequential and Simple architectures are the lowest, but the accuracy of the models using ResNet-IFT and ResNet-IFTF are similar, we were able to visualize the effect of applying a level of Transfer Learning on a classification task for IPMB images. We decided to explore further the statistical meaning of the results of the models developed with the ResNet architectures. As we can see in As a final experiment to determine the best performing model between ResNet-IFT and ResNet-IFTF, we tested a random sample of images in order to obtain the prediction value per class, as shown in Furthermore, in The DL pipeline obtained from ResNet-IFT was saved and then loaded for the Guided Grad-CAM and Occlusion Analysis algorithms to obtain the final visualizations.Firstly, we obtained the visualizations of the activations of the entire models of ResNet-IFT trained on D1, D2 and D3. As we can see in We are able to see spots of non-immunoreactivity that are not colored in the original image thanks to the filters learned by the models. The convolution operation in CNN is an element-wise multiplication followed by a sum, between an input datum and a filter that gives us an output feature map. The convolutional layers execute the convolution operation with the filters learned by the models, in order to perform feature extraction. As the depth of the CNN increases, the complexity of the features learned by the CNN increases. Therefore, even though we are not able to appreciate completely some spots of non-immunoreactivity in the original image, these are reflected as a set of broad features that the models are able to abstract from the original image.From our experimentation, we could identify features of Tau protein that differentially associate between the hippocampal region and the entorhinal cortex of the brain. Although the results coincide with the studied areas of tangles by neurophysiologists, it is noteworthy that our CNN pipeline also located other discriminative criteria outside the zones of elongation of the polymeric filaments of the Tau protein or outside the body of the neurofibrillary tangle.In Regarding the blue channel, which correspond to pS396, it shows a relationship between the immunoreactive tangles in the periphery and the one located in the medial zone with round morphology. However, the criteria obtained with the model developed for D3 are not as enriching because they seem to point more to the immunoreactive structures in the periphery rather than to the central tangle. We find this result very interesting because of the implications of the phosphorylation of serine 396 of the Tau protein as an event considered closely associated in the final stages of NFT formation.From For PSP classification in the hippocampus, we can observe that there is a correlation in the relevant part of the image that does not fully agree with the immunoreactivity detected with the corresponding antibody d–f becauIn From our comprehensive experimentation, we can see that the Transfer Learning model displays the best prediction performance. Thus, this is the model we used to implement interpretability models to analyze and identify AD and PSP tauopathies from IPMB images. In addition, Guided Grad-CAM and Occlusion Analysis help us to obtain information about the molecular pathogenesis of the tauopathies that was not recognized in a conventional interpretation.From our initial experimentation, we can see that the Sequential and Simple CNN architectures do not generalize properly or abstract enough information to learn the features of the IPMB images dataset. We decided to start with a three-layer MLP since we were dealing with medical images from which we did not know the complexity for classification. As we can see, the spatial information is lost in this model and therefore it is not a good fit for the IPMB images. However, it is interesting that the Simple CNN-based models behaved slightly worse, as shown in our standard deviation analyses we carried out. However, in this case the Simple CNN-based models were affected by the random weight initialization instead of using pre-trained weights.In the case of the models developed using Transfer Learning and Transfer Learning plus fine-tuning, the Transfer Learning model using pre-trained weights of the ImageNet dataset achieved the best results. According to Guo et al. , it is nAs we can see in CNNs have been poorly considered in the histopathological study of neurodegenerative diseases and to a lesser extent focused on the training of algorithms at the level of fibrillar lesions such as NFTs. Our study points out that Transfer Learning demonstrates strong predictive performance. Therefore, the models developed with ResNet-IFT can implement a criterion of interpretability aided by Guided Grad-CAM and Occlusion Analysis to study and identify structural differences with IPMB images of AD and PSP.The use of Guided Grad-CAM and Occlusion Analysis showed that the presence of the main tangle in the images, except in the hippocampal region with AD immunoreactive to AT8 antibody, is relevant for classifying it as AD or PSP. The main difference for the classification of these tauopathies in the hippocampal region is that the most relevant structural features of PSP are located in the center of the quadrant despite the biomarker and for AD they are located in the center and periphery of the image.In the entorhinal cortex, the criteria focus mainly on the most prominent NFT for classification of AD or PSP despite its location in the image. These results represent a novel way to explore and understand the phenomenon of neurodegenerative diseases from immunostaining with specific biomarkers, since they show relevant information that is not salient in the original images, in contrast to the status quo in related research that focuses on the most complex NFT structures evidenced by immunoreactivity ,63,64.Hence, we provide additional criteria for the identification of AD that shows that there is relevant information on the periphery of the image. In addition, we note that whenever we have images of both the entorhinal cortex and the hippocampus for AD classification, peripheral structures in the image are relevant.Tang et al.  explain The heat zones assigned by Occlusion Analysis indicate a stronger association with both pS396 antibody immunoreactive areas and TR-positive staining than with AT8 antibody immunoreactivity a–c in thIt is likely that earlier events in pathological processing toward the amino-terminal end of the Tau polypeptide, such as phosphorylation at amino acid serine 202 and threonine 305, show less association than later events toward the carboxyl-terminal end . AccordiImportantly, we did not observe the same behavior in hippocampal NFTs in PSP patients d–f. For In summary, this evidence underlines the importance of the analysis by the occlusion algorithm for our further studies using early and late biomarkers in the pathological processing of Tau protein directed towards its amino-terminal and carboxyl-terminal end, respectively, which can be validated among different neurodegenerative diseases that have the common factor of pathological PTMs of the Tau polypeptide .Regarding the Occlusion Analysis in the NFTs of the entorhinal cortex of patients with AD and PSP , using tHowever, the results obtained with the algorithm trained with D2 are more associated with mature NFTs than those trained with D3, which could suggest that there are molecular differences between initiating and advanced events for these fibrillar structures. Another interesting result with Occlusion Analysis with D3 denotes areas outside the immunoreactive zones in AD a–c and nStill, the common areas found with Occlusion Analysis and Guided Grad-CAM are considered decisive for the classification of PSP and AD. This means that experts could focus their attention more specially on some areas of the image which could save research time. Importantly, clinical profile and histopathological analysis were key factors in selecting patients from our study; however, we have as a perspective to include an analysis of ML/DL algorithms trained with specific markers for PSP and EA, different from Tau biomarkers, in order to compare other variables in prediction methods.In this work, we obtained a CNN pipeline using ResNet-IFT architecture and XAI algorithms Guided Grad-CAM and Occlusion Analysis where the classification of AD and PSP in IPMB images achieved an accuracy of 98.41%, on average, using Transfer Learning. We conclude that in cases in which we want to use Transfer Learning and fine-tuning with a ResNet-50-based model, we may need to initialize weights from a similar domain. However, using Transfer Learning with a ResNet-50-based model pre-trained on the ImageNet dataset results in very effective models for classification of AD and PSP in IPMB images.Our study shows that there may be different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with PSP and AD, as identified with our models. Moreover, our work suggests that DL classification algorithms based on ResNet 50 can support the structural analysis of Tau polypeptide aggregation, which has been studied primarily with histopathological assays for decades. Based on antibody training that has been documented in the advanced stages of pathological Tau processing, the analysis of Guided Grad-CAM and Occlusion Analysis proposes immunoreactive areas of the neurofibrillary tangle and other areas of the quadrant that may be important for studying the aggregation behavior of this protein in AD and PSP. This methodology proposes that to study these diseases, criteria of shared spatial invariability can also be considered between the images that support the CNN models for the IPMB images. While structural patterns are identified in this study, further research is needed to identify the exact nature of this difference.This first study allows us to suggest that classifier models based on ResNet-50 architectures are valuable for the classification of AD and PSP IPMB images. Finally, these tools will help us to structure the following analyses using close-ups of images to classify AD and PSP using fluorescence images with antibodies against other PTMs that are associated with the formation of Tau filaments, such as conformational changes or endogenous proteolysis. Likewise, we propose to build a multiclass classifier where we carry out the study of structural characteristics comparing PTMs against non-fibrillar Tau controls. Moreover, we are considering whether, in our future work, to combine classification with object detection in order to classify neurodegenerative diseases and also to detect pre-defined features of interest.


The scale and impact of the COVID-19 pandemic are globally evident and present an unprecedented challenge to public health, causing devastating social and economic disruption as reported by the World Health Organization. There have been major differences in the responses and performance of national healthcare systems to the pandemic, and there is a need to thoroughly explore the roots of these variations.The financing model is a distinguishing feature of any healthcare system. Despite the strong criticism of the out-of-pocket payment model that is common in Low-to-Middle Income Countries , the peThe current pandemic represents a unique opportunity to explore whether the variation in the performance of healthcare systems to the pandemic is due to the healthcare financing model, or whether other factors have a greater influence on performance . Such anYang et al. use a methodological framework, applying the two-agent game analysis. Their findings show that regional cooperation in facing the epidemic maximizes the regional benefits brought by containing the epidemic and improving economic performance. Yet, despite the differences in economic scale and epidemic prevention and control preferences between countries, not all of them directly benefitted from cooperation. Accordingly, the cooperative incentive of each country has to be strengthened through the allocation of cooperative income.Zou et al.).The second contribution entitled “The DEMATEL method explores the interdependent relationship structure and weights for diagnosis-related groups system” examines the overall performance of medical services. It indicates that patients and managers should first focus on the capacity of medical service providers when making a choice or deciding how to use the results of the DRGs system  investment on enterprise financial risk under COVID-19. Their findings have revealed that the pandemic has increased the number of high-risk enterprises. R&D investment appears to be able to effectively reduce an enterprise's financial risk, and enterprises that attach importance to scientific research are relatively less affected by COVID-19. Compared with non-state-owned enterprises, R&D investment under state-owned enterprises can better help enterprises reduce financial risk. When the enterprise's financial risk is lower, the role of R&D investment in reducing financial risk is more significant.Antohi et al.).An intriguing study came discusses Romania in eastern Europe, presenting a post-Semashko health system model. These authors tested working hypotheses using an econometric linear regression model based on the financing budgetary function, which matches funding to the specific need for each expenditure heading. The authors concluded that under the impact of pandemic stress, measures to improve healthcare management, increase performance and streamline financial allocation are vulnerable and cannot counteract the effects that the pandemic has on the healthcare of the population as reflected by the morbidity and mortality indicators collected during the pandemic  countries, marked by long-term bold real GPD growth , are theThe goal of this Research Topic was to explore the impact of financing on the readiness of different healthcare systems to respond to emergencies . Health MJ prepared the manuscript draft. SM, SA, and DH revised the manuscript for important intellectual content. All authors fulfill ICMJE conditions for full authorship. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N = 25) and a control group (N = 28). Over a period of 3 months, the experimental group was instructed based on wiki-based writing instruction while the control group was taught traditionally. The data were collected by administering IELTS writing tasks and a writing self-efficacy scale employed for pre- and post-tests. The results of data analysis indicated that both classes substantially enhanced their writing skill and writing self-efficacy. Nevertheless, the experimental group performed better than the control group in terms of both dependent variables, leading the researchers to conclude that the wiki-based writing instruction was significantly effective in boosting writing skill and self-efficacy of Chinese EFL learners. These outcomes can offer some worthwhile implications for EFL instructors.As a popular Web 2.0 technology, wikis have gained momentum in educational contexts. To give further empirical support to the use of wikis in foreign language education, this research probed the usefulness of a wiki-based writing instruction on the writing skill and self-efficacy of Chinese English-as-a-foreign language (EFL) learners. For this purpose, 53 EFL students in a foreign language school in China were selected as the participants. The two classes were assigned to an experimental group ( Over the past few decades, there has been a widespread integration of internet and digital forms of communication in education and online application tools have become an integral part of teaching and learning . In the Consistent with these developments and changes, various researchers have highlighted the significance of blogs or wikis for writing classes and have suggested wikis as a significant tool for collaborative work in the realm of education in general  and EFL Due to the significance of dynamicity and context-specific nature of L2 learning, individual differences as well as the contextual factors have received more research attention by L2 scholars . As writDoes wiki-based writing instruction have any significant effect on the writing skills of Chinese EFL learners?Does wiki-based writing instruction have any significant effect on the writing self-efficacy of Chinese EFL learners?via wikis has recently become a focus of particular attention in various fields of study  and is now a significant part of many learner programs  and CMC have become critical tools by which technology can be implemented for the purpose of English writing . Among tprograms . As Wichprograms , Wiki plprograms . Regardiprograms . Wiki soprograms . AccordiThere exists a body of literature examining learners’ wiki writing process. For instance, in his study, A review of the existing literature indicates that although the wiki-based writing has attracted much attention in various contexts like EFL e.g., , empiricAs Since self-efficacy is context-dependent , online via linguistics skills, and Self-regulation pertaining to learners’ self-management and control of their own thoughts, feelings, and actions and also to learners’ perception about their cognitive and linguistic capacities while writing a text. EFL learners’ confidence in English writing can not only reflect their attitudes toward writing but it also can indicate learners’ likelihood to master or avoid the writing process  and a control group (N = 28). Participants’ age ranged from 18 to 23  and they reported that they had the experience of learning English from 7 to 15 years . This language school was preparing EFL students to take IELTS and whose purpose was to elevate the language competencies of the students in the four skills. In this course, the students had enrolled in the writing skill course with duration of 3 months, 2 h per week. The two groups were taught by a trained IELTS teacher who was also familiar with wikis.This was a quasi-experimental study in which two intact groups  were employed as the participants. In total, 53 Chinese EFL students took part in this study. These participants were students of two classes from a foreign language school in Zhejiang Province of China. Given the criteria of availability and willingness to participate, convenience sampling was used to select these EFL students who included male and female students. The two classes were assigned to an experimental group  was administered to two groups. This test is considered as an appropriate test of proficiency which can gage English abilities of various students with differing levels . To compEFL learners’ writing skill was assessed using sample IELTS tasks selected from ‘Collins writing for IELTS’ . To thisParticipants’ self-efficacy in doing writing tasks was measured using a 9-item self-report scale developed by via wiki collaborative discussions. They were also asked to do a written task  each session. There was a warm-up stage for each task prior to using wiki pages in order to activate the background knowledge of the participants. The instructor ensured that participants have sufficiently participated in group discussions and collaborative writing by monitoring wiki discussions and activities. In addition, tried to encourage the less active participants to become more actively involved in the wiki activities. The teacher also aided the students in providing more constructive and peer mediations on different aspects of writing.Wiki-based instruction was use for doing the EFL collaborative writing tasks in the experimental group. Initially, the instructor gave the learners a general discussion on how to employ wiki for doing writing tasks collaboratively. He explained the features of wiki including achieving, revising, and commenting in the forum. The participants of the experimental group were divided in groups of three or four individuals in order to construct their own wikis for doing writing tasks. These students were requested to do writing tasks together outside the class by giving feedback to each other on various aspects of writing in terms of language, content, and organization On the contrary, the participants of the control group were requested to do similar writing tasks collaboratively but without the use of wikis or any other technology devices. In fact, all the procedures, content, and tasks of the two groups were the same except for the fact that the collaborative writing tasks were not wiki-mediated. These students were also required to give peer-feedback to each other with regard to different aspects of writing organization, language use, and content. There was a schema activation phase like that of the experimental group in which the students were involved in brainstorming about the topic. Like the wiki group, the students of the control group were also divided in groups of three or four. The teacher also checked the group activities and peer mediations of the participants.In the first session of the course (week 1), the instructor provided the students with a general discussion of the purpose of the course. Then the pre-tests including the sample IELTS task 1 and writing self-efficacy scale was administered to the students of both groups. At the end of the treatment which lasted for about 13 weeks, the post-tests were given to the participants. More specifically, the last session (week 13) was devoted to students’ completing the writing self-efficacy scale and doing the sample IELTS task 2.The data analysis was carried out using IBM SPSS program (version 23). To address the research questions of this research, both descriptive and inferential statistics were utilized. With regard to the descriptive statistics, mean, standard deviation, skewness, kurtosis, and reliability coefficients were calculated. Concerning the inferential statistics, one-way analysis of covariance (ANCOVA) was used for each research question. In each ANCOVA analysis, the pre-test scores were a covariate, the post-test scores were considered as the dependent variables, and the instruction type with two levels  was the independent variable.Before calculating the descriptive statistics and performing ANCOVAs, some pre-requisite assumptions were checked. With regard to checking the normality of the data, skewedness and kurtosis values were taken into account. More specifically, the ratios of skewedness and kurtosis statistics to the standard errors were referred to.As seen in F  = 59.51,  = 23.11, . This result highlighted the fact that the use of wikis in the writing instruction significantly contributed to improving the writing self-efficacy of Chinese EFL students.Research question 2 was set to explore the effect of wiki-based instruction on the writing self-efficacy of Chinese EFL learners, As seen in The current research aimed to investigate the effect of wiki-based writing instruction on the writing skills and writing self-efficacy of Chinese EFL learners. Regarding the purposes of the current research and the formulated research questions, the results of the ANCOVAs by comparing the two dependent variables  between two study groups on the posttest scores after controlling for the pretest scores yielded two notable findings.First, the investigation of research question one revealed that the use of wiki-based writing improved writing performance of EFL learners. This result is in accordance with previous research on the usefulness of wikis in affecting learners’ writing skills e.g., . In addiSecond, the results of the data analysis for examining the second research question indicated that the use of wiki-based writing improved writing self-efficacy of Chinese EFL learners. The outcomes of this study are in line with the study by To lend further support to the importance of online application tools and Web 2.0 in EFL contexts, this study sought to investigate the effect of wiki-based instruction on the writing skills and writing self-efficacy of Chinese EFL learners. As demonstrated by the results, the current study indicated that wiki was an effective Web 2.0 tool for promoting and improving writing skills and writing self-efficacy of the EFL learners. The current research was the first attempt to probe the simultaneous relationships among the constructs of wiki-based instruction, writing skills, and writing self-efficacy of EFL students. Given the fact that wiki tools have a significant role in affecting writing self-efficacy and writing performance, EFL teachers and methodologists should consider wiki-based instruction intervention programs in order to facilitate EFL learners’ integration of wiki tools. The outcomes of the study can also offer some implications to policy makers and EFL teacher educators so that they can create more appropriate conditions for EFL instructors to design an effective online wiki context for EFL learners. Training teachers to use wikis more effectively will help their students to carry out writing collaboratively and successfully.In the same vein, further initiatives should be taken to enhance EFL learners’ writing self-efficacy and writing abilities by paying particular attention to utilizing effective strategies and approaches to promote their beliefs of competence in writing tasks and their abilities to practically write texts. Despite the fact that the results generated valuable insights into the benefits of implementing wiki tools in EFL learning context, the findings complete just a small piece of a complex puzzle and therefore further research is needed to contribute to a better understanding and some ambiguity about the nature, features, dimensionality, and consequences of integration of Web 2.0 tools, specifically wikis in the realm of EFL learning and teaching. In addition, it is apparent that self-efficacy plays an integral role in L2 learning , since iConcerning the limitations of this research, it is worth noting that we utilized just a quantitative research method. Therefore, next researchers are invited to triangulate their findings with adding qualitative or mixed-methods research designs to shed more light on the utility of wikis in EFL writing courses. Also, the external validity of the results will be enhanced if the future researchers replicate studies by recruiting larger samples of participants with various proficiency levels in EFL contexts other than China. Given the potential effectiveness of wikis, future researchers are encouraged to explore the effects of wikis on other EFL skills and components.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Wenzhou Medical University Academic Ethics Committee. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pes planus, one of the most common foot deformities, includes the loss of the medial arch, misalignment of the rearfoot, and abduction of the forefoot, which negatively affects posture and gait. Foot orthosis, which is effective in normalizing the arch and providing stability during walking, is prescribed for the purpose of treatment and correction. Currently, machine learning technology for classifying and diagnosing foot types is being developed, but it has not yet been applied to the prescription of foot orthosis for the treatment and management of pes planus. Thus, the aim of this study is to propose a model that can prescribe a customized foot orthosis to patients with pes planus by learning from and analyzing various clinical data based on a decision tree algorithm called classification and regressing tree (CART). A total of 8 parameters were selected based on the feature importance, and 15 rules for the prescription of foot orthosis were generated. The proposed model based on the CART algorithm achieved an accuracy of 80.16%. This result suggests that the CART model developed in this study can provide adequate help to clinicians in prescribing foot orthosis easily and accurately for patients with pes planus. In the future, we plan to acquire more clinical data and develop a model that can prescribe more accurate and stable foot orthosis using various machine learning technologies. With a complex and multifunctional structure, the foot plays an important role in supporting body weight and moving the body very efficiently while standing or walking . Pes plaRecently, with the rapid development of artificial intelligence (AI) technology, its application in the medical field is gradually expanding. Machine learning, a branch of AI technology, uses large amounts of data to learn patterns and predict outcomes. Supervised learning, in which the correct answers are included in the training data, is the most used machine learning algorithm in the medical field due to its high reliability and accuracy . SpecifiIt was suggested that the combination of two models for image and numerical data analysis in the classification of foot deformities can lead to more accurate diagnostic results than the case of using only a single model. Li et al.  presenteA customized foot orthosis is usually prescribed to correct a patient’s rearfoot alignment and movement based on various clinical data . Decision tree methods such as classification and regressing tree (CART), Chi-squared automatic interaction detector (CHAID), quick, unbiased, efficient statistical tree (QUEST), commercial version 4.5 (C4.5), and Interactive Dichotomizer version 3 (ID3) are the most effective and practical algorithms for classification and prediction . As a noUntil now, the decision tree-based CART algorithm has been widely used in the medical field. Tange et al.  proposedn input variables belong to different groups. The Gini index (GI) can be expressed asi, and c is the total class. The CART algorithm is a top-down decision tree, as shown in CART is a representative algorithm for generating decision trees. In practice, various tree-based machine learning methods  are implemented based on the CART algorithm . This alThis study used clinical data from a total of 1548 patients diagnosed with pes planus at the Department of Rehabilitation Medicine of Chungnam National University Hospital in Daejeon, Republic of Korea. Pes planus was diagnosed based on the results measured by a professional clinician with a goniometer  and an inclinometer ,29,30,31In this study, two types of foot orthosis (gait plate and arch support orthosis with heel cups) were used as a dependent variable in this study. A gait plate (GP) is a special type of orthosis that is prescribed to limit in-toeing gait due to increased internal hip rotation and femoral anteversion with pes planus . Arch suThe dataset was randomly split into two categories: the training dataset (70%) and the testing dataset (30%). A training dataset is the samples required to properly train a CART model. Once a CART classifier is developed, its performance is evaluated prior to application in the clinical field. The testing dataset is used to evaluate the predictive performance of the generated CART model on unobserved data. It is important to note that the model is trained through a 10-fold cross-validation procedure instead of using the validation dataset. T is defined asT, and the complexity parameter If the model is overtrained only on the training data, the error in the testing data increases, and its generality decreases. This phenomenon is called overfitting. Pruning is a technique to solve overfitting problems by controlling the complexity of the decision tree. Pruning can be divided into two categories: pre-pruning and post-pruning. Pre-pruning is the process of stopping the growth of a decision tree prematurely to prevent excessive node creation. The pre-pruning method is simple but not practical since it is difficult to accurately determine the end point of tree growth. In the post-pruning approach, a fully grown decision tree is truncated through evaluation criteria and replaced with leaf nodes. The cost complexity pruning (CCP) is the frequently used pruning method in CART. In the pruning process, the prediction accuracy of the decision tree on the training data is calculated, and finally, an optimal tree with a balance between complexity and error rate is obtained. The cost complexity (CC) of a tree The confusion matrix is used to calculate evaluation metrics, including accuracy, sensitivity, precision, and f1 score, and the related equations are :(3)AccurAll the experiments were carried out based on Python 3.10.2 on a 64-bit computer with an Intel i5-7500 (3.4 GHz) CPU and 16 GB RAM. A flow chart of the overall study procedure is shown in The 15 rules for prescribing the two types of foot orthosis (GP or ASOHC) are shown in In this study, we confirmed the classification accuracy of the CART model for foot orthosis prescription using evaluation metrics. As a result, it was found that the classification accuracy was relatively low compared to the previous study. Previous studies have mainly proposed a method for learning and classifying foot types using sensors or image data. Although these data can provide accurate information for determining whether a patient has pes planus, there are limitations in understanding the physical characteristics or functional movements of a patient with pes planus . In thisWe developed the CART model that prescribes two types of foot orthoses in consideration of the various biomechanical characteristics of patients with pes planus. Based on the evaluation results on feature importance, it was confirmed that RCSPA, age, EASTJ, HIR, TMA, FFRF, and IASTJ were important parameters in prescribing foot orthosis. The relatively high importance of the RCSP angle may be due to the prescription of the footrest to increase the arch height and adjust the alignment of the rearfoot ,37. Age The limitations of this study are as follows. First, there were insufficient quality training data for patients with pes planus. Second, the types of pes planus , the time of onset, and other diseases related to pes planus were not considered. Finally, there are no indicators to evaluate the clinical effectiveness of a prescribed foot orthosis. Therefore, in future research, based on high-quality training data, we will develop a machine learning algorithm that can compare and analyze the difference in foot shape and functional movement of patients with pes planus before and after the prescription of a foot orthosis.As interest in healthcare has recently increased, the need for technology that can provide disease prediction, preventive medicine, and patient-specific treatment based on learning and the analysis of vast amounts of data is being emphasized. CART is one of the most-used algorithms in the medical field because of its excellent performance in analyzing training data and predicting and classifying new data with unique patterns in the data. In this study, we present a CART algorithm-based method that can prescribe a customized foot orthosis to flat-footed patients with relatively high prevalence. As a result, 15 rules were generated based on the importance of the 9 variables related to foot orthosis prescription identified. The main advantage of the CART algorithm-based foot orthosis prescription strategy proposed in this study is that the visualized results can be helpful in the decision-making process of experts. The results of this study support that the CART model, which can be easily and quickly understood, can be effectively utilized when a clinician prescribes a foot orthosis. It is expected that more accurate and stable foot orthosis prescriptions will be possible if more clinical data are secured, and a new model incorporating various machine learning technologies is presented in the future.


Cardiac diseases and failure make up one of largest contributions to global mortality and significantly detriment the quality of life for millions of others. Disorders in the valves of the left ventricle are a prominent example of heart disease, with prolapse, regurgitation, and stenoses—the three main valve disorders. It is widely known that mitral valve prolapse increases the susceptibility to cardiac arrhythmia. Here, we investigate stenoses and regurgitation of the mitral and aortic valves in the left ventricle using a synergistic low-order numerical model. The model synergy derives from the incorporation of the mechanical, chemical, and electrical elements. As an alternative framework to the time-varying elastance (TVE) method, it allows feedback mechanisms at work in the heart to be considered. The TVE model imposes the ventricular pressure–volume relationship using a periodic function rather than calculating it consistently. Using our synergistic approach, the effects of valve disorders on the mechano-electric-feedback (MEF) are investigated. The MEF is the influence of cellular mechanics on the electrical activity, and significantly contributes to the generation of arrhythmia. We further investigate stenoses and regurgitation of the mitral and aortic valves and their relationship with the MEF and generation of arrhythmia. Mitral valve stenosis is found to increase the sensitivity to arrhythmia-stimulating systolic stretch, and reduces the sensitivity to diastolic stretch. Aortic valve stenosis does not change the sensitivity to arrhythmia-stimulating stretch, and regurgitation reduces it. A key result is found when valve regurgitation is accompanied by diastolic stretch. In the presence of MEF disorder, ectopic beats become far more frequent when accompanied by valve regurgitation. Therefore, arrhythmia resulting from a disorder in the MEF will be more severe when valve regurgitation is present. Out of the many cardiovascular diseases plaguing millions of people throughout the world, disorders in the heart’s valves make up a significant proportion. These disorders frequently lead to death or morbidity, particularly in the ageing population . DisordeDespite the heart’s apparent complexity, it remains a well-regulated organ and this is aided by many feedback mechanisms at work over multiple scales and domains; from the micro cellular scale of the myocytes to the macro organ level scale. The MEF is one of the main feedback mechanisms. Along with the electro-excitation coupling (ECC), these two mechanisms help maintain cardiac stability and synchronicity. As their names suggest, the MEF is the feedback of local mechanical cell stretch on the electrical dynamics, while the ECC operates in the opposite direction. For comprehensive reviews of the MEF see ,9. WhilsComputer and mathematical modelling is a powerful way to investigate complex systems as it allows for system visualisation, hypotheses and predictions to be examined at relatively low cost compared to experimental methods . ComputeIn this study a 0D mathematical model of the left ventricle with valve stenoses and regurgitation is developed, by modifying the synergistic cardiovascular model by Kim and Capoccia . The modThe cardiovascular system model is made of three parts presented in c’. Equation   χτc in EEquation  below. bel belPv acp and q representing slow and fast electrical activity. Equations p and q rquations  describeThe constant parameters Capoccia  describerefer to ,32,33. TEquation , simulatIn the circulation equations above, two constant parameters The equivalent electrical diagram for the complete model just described is shown in We first examine the effects of mitral valve pathologies without a dysfunction of the MEF. x axis of the figures give the severity of regurgition, where 1 denotes normal or control, 2 denotes mild, 3 denotes moderate, and 4 denotes severe. The model parameter values used to model each severity are provided in m reduces for the same reason that the systemic pressure falls too.Aortic valve pathologies make up the majority of valve disorders leading to mortalities. The effect of aortic valve stenoses corresponding to Before exploring the impact of valve disorders on the MEF, a brief examination of valve motion is made. To model valve motion, Likewise for the aortic valve, used by  who alsop to ventricular stress p increases faster to greater values, but falls more gradually which prolongs the repolarisation process causing a longer action potential (AP duration or APD); an effect also seen by Kim and Capoccia [The response of MEF dysfunction to valve stenoses and regurgitation are now explored using the current model  by increasing the MEF parameters Capoccia  conduct Capoccia  and noteCapoccia ,45.q) to a rise in ventricular volume V, thereby mimicking the effect of diastolic mechanical stretch. A rise in q shown in the last figure column can be seen to increase, whilst that of p reduces. This suggests that the AP depolarisation is stronger, and the repolarisation process weaker, hence the APD reduces. Unlike The MEF parameter traction ,34. HencThe parameter It can be seen that the sensitivity to systolic stretch is increased slightly with mitral valve stenosis, as the value of r volume . With a With a severe mitral valve regurgitation, the first appearance of bifurcation occurs when Equation . That isWith the parameter Our aim was to develop a new model by extending a synergistic reduced-order mathematical model of the cardiovascular system  to incluMitral valve stenosis without a dysfunction of the MEF is found to cause a reduction in ventricular and systemic pressures and reduction in the cardiac output and stroke volume. The atrial pressure increases slightly. The EDV and ESV both reduce, shifting the P-V loop to the right. The extent of this reduction and shift is of similar order to  for a coDysfunction of the MEF is modelled by increasing the coupling between the mechanical part of the model and the electrical. Two parameters are used for this coupling; ted. See ,33 for tValve disorders do however have quantitative effects. Specifically, they change the sensitivity of the heart to arrhythmia-stimulating stretch. Sensitivity to systolic stretch is compared between cases by looking at the value of Sensitivity to diastolic stretch is compared between cases by finding the approximate value of rhythmia ,48,49.Limitations of the model: The model inherits the same limitations as the model by Kim and Capoccia , namely This study describes the successful modification and use of the cardiovascular mathematical model developed by Kim and Capoccia ,32 to stFinally, we demonstrated how to incorporate valve opening and closing times within our model, indicating some potentially interesting results. It remains a future work to perform detailed analysis on valve motion. It would be also be interesting to extend our study to a continuous model such that the effects of wave dynamics can be studied, particularly in the context of MEF. Extending the electrical model to include a more detailed description of the cellular ion channels is also of interest as the particular channels involved in generating a significant MEF effect can be better studied.


For treatment of carpometacarpal thumb joint osteoarthritis, a trapeziectomy with an alternative suspension technique can be performed as the primary surgery or as the secondary after a failed primary surgery. This study evaluates the midterm follow-up  for this technique using patient-reported outcome measures.After trapeziectomy, an alternative suspension technique is performed with a flexor carpi radialis tendon strip. Leaving the insertion intact, the strip is tunneled through a drill hole in the base of the first metacarpal and then through a drill hole in the second metacarpal neck and then sutured back onto itself. This suspends the first metacarpal to the shaft of the second metacarpal, creating a strong, V-shaped suspension. As the technique is performed in both the primary and secondary surgery, we analyzed both groups separately. As the primary outcome, we evaluated pain and function with the Patient-Rated Wrist and Hand Evaluation. Further, we evaluated the Disabilities of the Arm, Shoulder and Hand and Short Form 12 questionnaire scores from eligible patients. Finally, we correlated pain and function to quality of life.The median Patient-Rated Wrist and Hand Evaluation score was 16.0  after the primary surgery and 46  after the secondary surgery. Patients after the primary surgery also scored better on the Disabilities of the Arm, Shoulder, and Hand questionnaire compared to patients after the secondary surgery. The Short Form 12 questionnaire physical scores were negatively correlated with the Disabilities of the Arm, Shoulder, and Hand questionnaire scores for the primary group  and negatively correlated with the Patient-Rated Wrist and Hand Evaluation pain scores for the secondary group .Trapeziectomy with this alternative suspension technique for treatment of carpometacarpal thumb joint osteoarthritis shows good patient-reported outcome measures for primary surgery and poor patient-reported outcome measures after the secondary surgery.Therapeutic IV. For advanced stages of osteoarthritis in CMC-1, many surgeons use trapeziectomy with ligament reconstruction and thumb suspension as the treatment of choice.,,Between 2006 and 2015, 53 thumbs in 47 patients were treated with a trapeziectomy and AST for CMC-1 osteoarthritis. We used this cohort for a cross-sectional study design, because limited preoperative measurements were available. As the primary outcomes, we used the Dutch language version (DLV) of the Patient-Rated Wrist and Hand Evaluation (PRWHE) to assess pain and function.After getting approval from the institutional review board , we invited patients to participate in the study by mail or email. After obtaining written informed consent, we sent the patients 4 PROM questionnaires between December 2016 and March 2017. We reviewed all medical charts retrospectively for demographics, perioperative details, and complications.Trapeziectomy with AST was performed in the primary setting and after failed previous treatment for CMC-1 osteoarthritis with another surgical technique. Indications for surgery were painful CMC-1 osteoarthritis that did not respond to nonsurgical treatment (such as hand therapy and orthosis fabrication) and a radiographic stage 2–4 on the Eaton and Glickel radiographic classification scale for patients with no history of surgical treatment for CMC-1 osteoarthritis.This alternative procedure was specifically suitable for patients with evident subluxation or a tendency to subside. In the primary setting, the choice for this technique was made after shared decision making and after discussing all possible techniques. In case of a failed previous surgery, AST was the preferred treatment of choice.This choice was based on subsidence after former surgery, with abutment of the first metacarpal on the distal scaphoid. A single surgeon performed all operations (A.H.S). We analyzed patients after the primary and secondary surgery separately. The patients with bilateral complaints had 2 operations, 1 for each hand. For the analysis, the questionnaires were filled twice, separately for the left and the right hand.Spaans et alOn the volar side of the wrist, a 10–15-cm strip of one-third of the FCR tendon is harvested through 3 separate incisions, leaving the insertion intact at the volar base of the second metacarpal. In case of revision surgery, and if the FCR tendon strip was previously used in the primary surgery, a toe extensor is harvested and used for the suspension. The toe extensor is then secured on the insertion of the FCR tendon on the base of the second metacarpal to provide the same vector for the suspension as with the use of a FCR tendon strip. The tendon strip is passed through the tunnel in the first metacarpal , then over the adductor muscle and under the extensor pollicis longus (EPL) tendon, and subsequently through the distally placed tunnel in the second metacarpal , after which the tendon is sutured back upon itself on the first metacarpal base. This construction lead to a strong, V-shaped suspension of the first metacarpal to the second metacarpal, and proximal migration should be prevented in case of an axial load on the first metacarpal. The thumb should be positioned in maximal radial and palmar abduction and an adducted position should be prevented. To prevent impingement of the first and second metacarpals, the remaining part of the FCR can be used as an interposition between the 2 metacarpal bases. The remaining end of the FCR tendon strip was placed in the space left by excising the trapezium. The capsule and skin were closed and a thumb spica cast was applied for 4 weeks. After 4 weeks, unloaded range-of-motion exercises were started, progressing to loaded range-of-motion exercises after 12 weeks.We assessed pain and function, measured with PRWHE-DLV scores, as the primary outcomes.P value less than .05, they were considered to be statistically significant.We reported descriptive statistics as medians and interquartile ranges (IQRs) because of their nonnormal distribution, or as absolute values. The nonparametric Spearman’s rank correlation test was used to correlate the validated questionnaires with quality of life. When variables had a Of the 205 patients treated for CMC-1 osteoarthritis between 2006 and 2015, a total of 53 thumbs in 47 patients were treated with trapeziectomy and AST in our center . Other tThis resulted in a total of 29 patients  for analysis , and we There was a significant, negative correlation for the SF-12 physical score and the DASH questionnaire for the primary group . For theTwo patients experienced postoperative complications. The first patient developed persistent pain after surgery and underwent a resection of the distal pole of the scaphoid after trapeziectomy and AST. A second patient developed a complex regional pain syndrome.During the operation for the AST, another intervention was performed in 1 patient. This patient underwent an arthrodesis of the first metacarpophalangeal joint in the same operation.At the midterm follow-up, our study shows low pain scores and good function, as measured with the PRWHE, together with excellent satisfaction after trapeziectomy with AST for CMC-1 osteoarthritis in the primary surgery. After the secondary or salvage surgery, the PRWHE and DASH outcomes were poor.The aim of this study was to subjectively assess the results of a trapeziectomy with an AST. This specific technique was designed to improve thumb height with a suspension that could reduce the chance to subsidence, with the intention of alleviating pain, restoring joint stability, and increasing function of the thumb. We measured function and pain with the validated PRWHE and DASH questionnaires.,,Other studies, which reported on PROMs after different techniques of trapeziectomy followed by a ligament reconstruction or suspension, reported higher scores of the DASH and PRWHE than those reported for the primary group after AST, suggesting better function and less pain after AST in this study at the midterm follow-up.A randomized controlled trial by Marks et alWhen comparing the outcomes of the primary and secondary surgery groups, the primary group scored much better on the PROMs. This trend of mediocre-to-poor outcomes on PROMs after the secondary CMC-1 surgery for osteoarthritis has been described before.Interestingly, for the primary group, pain was the main reason for operation, whereas for the secondary group, pain and function were the main reasons for operation. Despite function and pain being scored as equally important to those undergoing the secondary operation, the results on function and pain, as measured by the PRWHE, were poor after the secondary surgery, which suggests neither better function nor less pain after the surgery. However, without preoperative scores, it is difficult to know how much these patients improved.The DASH scores and quality of life, as measured with the SF-12 physical score, showed a negative correlation: that is, a lower DASH score (better function) correlated with a higher SF-12 physical score . The secondary group showed a negative correlation between the PRWHE pain score and the SF-12 physical score: that is, better physical quality of life when less pain was noted. This suggests that the physical quality of life is more correlated to pain for patients after the secondary surgery than for patients after the primary surgery. This correlates with the outcome that a higher percentage of people in the secondary surgery group choose to have a secondary surgery because of pain .The study has several limitations. Unfortunately, in our study the preoperative subjective measures were insufficient and therefore could not be included. Hence, we could not determine how much the patients improved from their preoperative status. Furthermore, we did not perform measurements of thumb subsidence; hence, we could not correlate the PROMs to migration of the thumb, range of motion, or strength. Another limitation was that no postoperative radiographs were obtained, and a radiographic analysis of the suspension could not be performed.In conclusion, this study shows similar results when compared with other studies. Future studies should focus on radiology in combination with clinical outcomes to demonstrate the benefits of this technique.Good outcomes were found after the primary AST and poor outcomes were found for the secondary AST on midterm PROMs with the PRWHE, DASH, and SF-12. This suggests that AST is suitable for the primary surgery and less suitable for the secondary surgery. Patient expectations should be adjusted accordingly.


Real-time smart applications are now possible because to developments in communication and sensor technology. Wireless sensor networks (WSNs) are used to collect data from specific disaster sites, such as fire events, gas leaks, land mines, earthquake, landslides, etc., where it is necessary to know the exact location of the detected information to safely rescue the people. For instance, the detection and disposal of explosive materials is a difficult task because land mines consistently threaten human life. Here, the T-based Routing Topology (TRT) is suggested to gather data from sensors , Infra-Red sensors, etc.), Global Positioning System (GPS), and cameras in land mine-affected areas. Buried explosive materials can be found and located with high accuracy. Additionally, it will be simpler to eliminate bombs and reduce threats to humans. The efficiency of the suggested data collection method is evaluated using Network Simulator-2 (NS-2). Also, the proposed T-based routing topology requires a minimal number of nodes to cover the entire searching area and establish effective communication. In contrast, the number of nodes participating in the sensing area grows, as the depth of the tree increases in the existing tree topology-based data gathering. And for cluster topology, the number of nodes deployment depends on the transmission range of the sensor nodes. Recent developments in sensor and communication technologies are playing a vital role in many smart applications due to their adaptability, low cost, quick expansion, and availability . WirelesOne of the dangers to human life around the world is the use of land mine bombs. A land mine bomb is an explosive device used in battle to destroy enemy targets and vehicles that is buried in or on the ground . The RoyNumerous mine awareness initiatives were established to inform the public and promote safe conduct among residing communities. According to the United Nations (UN), it would take approximately 1100 years to entirely remove all hidden land mines from the earth. According to the land mine monitor database, more than 12,000 casualties from anti-personnel and anti-vehicle land mines were reported between 1999 and 2017. The present routing algorithms are closely related to static WSNs which deploy numerous sensor nodes to expand coverage and improve data accuracy. When using the present random node deployment and routing technique, it is very difficult to pinpoint the exact position of events in the hazardous region, such as the detection of land mine bombs, gas leaks, and radiation emitting sources. Furthermore, the mobile WSN’s random node placement is not sufficient to provide full coverage for the land mine search. Furthermore, it does not guarantee that the location being tracked will be secure or qualified as a safe zone. According to the UN, finding the exact location of the buried land mine is still required in order to dispose of the explosive safely. The land mine is typically located using a metal detector, a dog, a rat, and a person. However, if the land mine is not located precisely, it can cause severe damage, death and financial loss.In order to address these problems, it is planned to employ a completely automated technology to locate and destroy landmines without endangering people. An energy-efficient T-based Routing Topology (TRT) is proposed, in which the robotic vehicle incorporates the sensor nodes and Global Positioning System (GPS) to find and lock the precise location of land mines.Dividing the network area into strips to minimize the number of deployed sensor nodes and achieve effective tracking of the objects.Measuring the moving speed of sensor node based on the sensing range and length of the detector’s stick.Introducing T-based Routing Topology (TRT) to ensure the congestion free data collection.Analyzing the performance of the proposed data routing protocol with respect to the Packet Delivery Ratio (PDR), Average End-to-End Delay (AEED), Average Energy Consumption (AEC) and control overhead.The major goal of this paper is to create networks in the targeted area and to gather information about the buried device, including its precise location and picture. This technique use a metal detector to locate the landmine, and a GPS map is utilized to pinpoint exactly where it is buried. The operator can safely detonate the land mine by using the camera to record the location of locked land mines. The following are the main contributions of the suggested methods:The remainder of the paper is organized as follows: There have been many strategies used recently to locate the buried land mine. The dog’s primary training involves finding the RDX-exploded substance in the target area. it has a 60-centimeter detecting range for objects buried in the ground. The rats were taught to scratch a hidden area on their foot to indicate the presence of land mines by rewarding them with food every time they did so. A catastrophic loss could occur if buried weapons are not precisely located due to poorly trained dogs and rats. Additionally, various types of explosive material in the affected area were found using bees, bacteria, and plants ,10,11.Radio waves were used by the ground penetrating radar to examine the characteristics of a return signal from the explosive components. Infrared detection picks up the electromagnetic variation in the buried explosive surface due to the signal’s reflection and emission. The temperature variation close to the explosive’s buried surface was recorded by the thermal-based detector. Due to the fact that different materials exhibit different types of signal reflection, the ultrasound-based detection generates a variety of acoustic properties depending on the materials ,10. One The Internet of Things (IoT) has been designed with the standard Transmission Control Protocol (TCP) to offer secure data connections without traffic congestion. However, it could not be used directly to WSNs due to the restrictions imposed by sensor nodes. For the analysis of mobile ad-hoc networks, a probabilistic, energy-aware broadcast calculus is proposed in ,20 to enIn traditional WSNs, the sensor nodes were manually or randomly placed in remote areas to sense the necessary events and send the data directly to the BS. The main disadvantages of direct communication are high power consumption and short network lifetime . To overOne of the most popular routing mechanisms utilized in WSNs was Low energy Adaptive Clustering Hierarchy (LEACH) , in whicThe clustering algorithm and Travelling Salesman Problem (TSP) based routing was presented in  to estabNumerous routing protocols have been evolved in the past years to collect the data from the targeted area. However, the existing protocols are not suitable for all the applications in real time. For example, in tree topology based data collection, the number of nodes involved in each level is The majority of research on WSNs focuses on periodic data collection, in which all sensor node nodes send data to the base station at regular intervals.Most of the protocols utilise a large number of sensor nodes to collect data or detect an event in a specific area.The majority of protocols are designed for a standard application and are intended to collect data from the sensing area and transmit it to the base station in a multihop fashion.From the literature survey, it can be summarized as follows:In this paper, the T-based routing algorithm is specifically designed for collecting the data in landmine detection which utilizes a minimal number of nodes to cover large area and establish an effective communication using strip based network. It also extends the communication between the sensor node and BS by deploying the router nodes.Traditional methods use a metal detector, dog, rat, and human to locate the land mine, but failing to pinpoint its exact location result in catastrophic harm, including damage, death, and financial loss. To solve these issues, it is planned to deploy a fully automated system for collecting the data efficiently without affecting the human lives. The unmanned surface vehicle contains the sensor nodes to locate and lock the exact position of the land mines using the Global Positioning System (GPS). The camera, GPR, electro-magnetic induction sensor and GPS are used to capture the location of locked land mines that enables the operator to safely detonate the land mine. Still what is missing..?Poor data communication between the sensing node and base station is being caused by a lack of network coverage.The data routing and location identification of landmine in real time is limited.Still, it has wrong location marking, low operating speed and effectiveness.This work introduces an energy-efficient T-based routing topology that searches and locates land mines precisely utilizing camera-based WSNs and GPS.Additionally, the strip-based network partitioning is implemented to facilitate the detection of land mines and to improve network coverage with a minimal deployment of nodes. The user can also locate a precise location and minimize the hazards to human life by using the camera image. The general diagram of proposed model is shown in The major assumptions of the networks are (i) All sensor nodes are aware of its exact location in the targeted sensing region. (ii) All sensor nodes are identical in terms of mobility, memory, bandwidth, battery life, and compression technique. (iii) The transmission range of the sensor nodes can be adjusted depending on the distance between them (iv) The base station is located at the sensing zone border and the entire sensor node can interact directly with BS.G and the vertices. Additionally, the node’s speed, direction of motion, transmission range, and location are taken into account as representations of the sensor nodes in the sensing region using Cartesian coordinates. The robotic vehicle with sensor nodes is shown in The base station is positioned at one end of the network boundary. All sensor node moves in the same direction at a constant speed. The links between the nodes in the network model are represented by an undirected connectivity graph The targeted sensing area is divided into the strip based on the length of sensor/detector holding pole Equation .(1)dc= required to cover the searching area is calculated asThe network region is splitting into the strips and each strip is covered by a single Mobile Node (MN). From the deployment, each node knows its direct hop neighbors. The mobile node in the central strip is called as Coordinator Node (CN) that performs two operations: (i) coordinating the operation of nodes (ii) deploying the router node. The main role of coordinator is to deploy the router nodes based on the transmission range k is the length of packets. The energy consumption for transmitting the packets d, as given in Equation (The communication energy consumption model utilized in the proposed model is similar to that of . The eneEquation .(5)er=kEquation .(6)et=kIn the proposed method, the metal detector is attached to the pole with GPS, and camera is mounted on the vehicle head. The metal detector pole is rotated from This article mainly focuses to cover the entire searching area with minimal numbers of sensor deployed USVs. For this, the sensing area is divided into strips based on the sensing range of metal detector and pole length. The number of strips in the sensing area is derived based on the Equation  and eachAlgorithm 1: PRM-ACK based route formation.The proposed mechanism makes use of event-based data collection mode by transmitting the Path Reservation Message (PRM), in which the source node reserves the routing to the CN when it detects the events. Only the direct neighbours of each node are aware of information like neighbours ID and distance. Initially, the source node transmits PRM using its default transmission range, which includes the source node ID and hop count. The nodes located in the source node transmission range open the PRM message and determine whether it is from its immediate neighbour or not. The source node identifies that the next hop node has been successfully reserved when it receives the acknowledgment (ACK) from its direct-hop neighbour before the timeout. If the source node does not receive the acknowledgment from its immediate neighbour, then it retransmits the PRM with incremented hop count value (HC = 2). It means that the source node receives the ACK from its second-hop neighbours and other nodes in the B is currently sending its data to the CN, at the same time node C wants to send its data and node C sends PRM to node B. But, node B sends back the wait message instead of ACK which means that node B either currently sends its own data or processes the PRM message of other node. It therefore avoids the collision and interference. Another case, node A detects the event during the process of node A knows itself to wait for completing the node As shown in B sends PRM to reserve the path when it wants to send the data. And node A doesn’t send ACK or wait message before its scheduled time bound. The source node increases the transmission range and transmits the PRM again to reserve the routing path towards the CN. By this way, it reserves the path to CN, otherwise the source node directly sends data to the BS.It is noted that from The deployed sensor-based detector gathers data on the sensed data, the position of the explosive material, and an exact location picture. Sensor nodes compress the sensed data to effectively handle the sensor node resource and prolong the network lifetime. The Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) is used in the image compression techniques to improve the image quality. However, both transformations reveal an increased complexity that requires more time, memory, and processing power. As a result, it has a constraint to apply in WSN since it consumes more energy.The image compression method based on LBT and Golomb + Multiple Quantization (MQ) coders described in  has a loMinimum number of sensor nodes is used to cover a large network region that decreases the complexity and cost of installation.It offers an effective technique for implementing a fully automated land mine detection system that reduces human participation and prevents human life risk.Horizontal search efficiently monitors the affected region of the land mine allowing easy identification of the secure region, while random node deployment does not ensure the entire sensing area.The suggested PRM-ACK based routing avoids collision and congestion. So, it is highly appropriate for the collection of event-driven data.The suggested data routing technique offers a precise location of the buried place of explosive material enhancing the safety of land mine disposal.Features of the proposed node deployment and data collection in land mine detection are given as follows:Energy consumed by each sensor node depends on the sensing of event, image compression, GPS, exchange of control packets for path reservation, and number of packets processed by each node. Total energy consumed by the sensor node for data transmission Equation .(7)etd=Equation .(8)etc=ts (ecb)  is givenNoting r as the compression ratio, it’s better for camera sensor to compress the raw data before sending than to directly send the raw data, when Total energy consumed by the total image size (Equation (10)etipEquation (11)etsnThe end-to-end delay for data transmission depends on the number of hops, transmission delay k denotes the length of the data packets, b is the transmission bit rate, Equation (12)ld=qv is the propagation speed.The propagation and transmission delay is determined using Equations (14) and (15)End-to-end delay The time complexity for the proposed model is MN reserves the path by exchanging PRM and ACK when it detects the target. The maximum path length between the CN and MN  and the performance of the proposed protocols is evaluated with respect to average energy consumption, average end-to-end delay, packet delivery ratio and control overhead. The simulation setup is given in The total targeted sensing area for this experimental setup is 100 × 100 mgiven in  which waThe performance of the proposed protocol is evaluated from It is observed from the As shown in From The energy consumption of the proposed protocol is evaluated with respect to the range of transmission as shown in It is seen from In A minimal number of sensor nodes are used by the proposed data routing system to cover a large area, which decreases the cost of node placement and makes it easy to track where land mines have been detonated. It is sensible and safe to monitor and dispose of the explosive material thanks to the GPS and camera-based data gathering, which provides the precise position of the sensed event. The complexity of processing and memory is decreased by using the LBT and Golomb + Multiple Quantization coders based image compression approach. Additionally, it can set up a T-based routing topology between the source and BS to allow event-driven based data gathering. And, it ensures a congestion-free data transmission by reserving the path based on the PRM-ACK mechanism. As a result, the suggested TRT mechanism effectively uses battery power and extends the network lifetime. It delivers better results in terms of average end-to-end delay, average energy consumption, packet delivery ratio and control overhead. Also, it is ideally suited for land mine detection and other applications of a similar nature. In the future, a test bed will be developed to evaluate the performance of proposed protocol for the real time applications.


Gram-negative bacteria are intrinsically resistant to many commercialized antibiotics. The outer membrane (OM) of Gram-negative bacteria prevents the entry of such antibiotics. Outer membrane vesicles (OMV) are naturally released from the OM of Gram-negative bacteria for a range of purposes, including competition with other bacteria. OMV may carry, as part of the membrane or lumen, molecules with antibacterial activity. Such OMV can be exposed to and can fuse with the cell surface of different bacterial species. In this review we consider how OMV can be used as tools to deliver antimicrobial agents. This includes the characteristics of OMV production and how this process can be used to create the desired antibacterial activity of OMV. Membrane vesicles (MV) are formed and released by a broad range of cells, from bacteria to human cells, with different nomenclature attributed according to the budding cell ,2,3. TheDepending on their constitution and biogenesis, MV secreted by Gram-negative bacteria can be classified as outer membrane vesicles (OMV), outer-inner membrane vesicles, explosive outer membrane vesicles or tube-shaped membranous structures . The mosAntibiotic resistance is due to the bacteria’s ability of adaptation to the action of antimicrobial molecules as well as to prevent them reaching the target site ,16. OMV Although different types of MV are secreted by bacteria, here we focus on OMV, the archetypal vesicles of Gram-negative bacteria. We discuss key aspects of bacterial OMV and their potential role as a delivery tool of molecules with antibacterial activity. Emphasis will be given to the biogenesis and the antimicrobial activity of OMV. Different studies have shown OMV activity against different bacterial species due to the carriage of antibiotic molecules, or due to some molecules that are naturally present in OMV such as lysins, which behave as an antibacterial molecule when exposed to recipient bacteria .Gram-negative bacteria have an envelope comprising an OM and an inner membrane (IM) with a periplasmic space in between, which contains a layer of peptidoglycan (PG). In the OM there are lipopolysaccharides (LPS) linked covalently by the lipidic moiety and proteins bound as β-barrels, while in the IM the proteins are bound as α-helical .The destabilization of ligations in a bacterium’s cell wall can lead to the detachment of the OM from the cell membrane. Consequently, the natural stabilization of the molecular charges allows the formation of the OMV ,19. OMV The first step to the formation of OMV is the disruption of the connection OM-PG-IM without damage or loss of membrane integrity . To explSalmonella spp. ..2+ and CP. aeruginosa vesicles do not merge with the cell wall of S. aureus, but rather tend to attach and release their content, such as gentamicin and autolysins, into the extracellular matrix, allowing enzyme activity and therefore antibacterial activity [Aneurinibacillus thermoaerophilus and Bacillus spp., prevent the passage of vesicles through the cell wall but not the attachment of the vesicles from P. aeruginosa [Vesicles from several Gram-negative bacterial species can promote the lysis of other bacteria, despite the specific interactions related with different PG chemotypes mentioned above . For insactivity . Althougruginosa .In all these cases, the equilibrium of the membrane charges seems to be mandatory. It is necessary that new studies determine if there is a difference between the merging of the different vesicles with other species of bacteria and what cell signaling is associated with the merging after the attachment to the cell wall ,99,100.E. coli and S. aureus, respectively, with an antibiotic coat have in vitro and in vivo antibacterial efficacy against S. aureus. However, this study only considered the entrance of the vesicles into the infected macrophages and not into the bacteria. Nonetheless, it was proven that it is possible to use the OMV membrane as a coat for antibiotics such as vancomycin and rifampicin with a diameter of approximately 90 nm [E. coli. Unlike the previous study, it was proven the entrance of these vesicles into E. coli bacteria but not into S. aureus [Nowadays, new techniques are developed as a different approach to antibiotic delivery, specifically the coating of nanoparticles with OMV membrane and nanotransformation of antibiotics ,102. Thely 90 nm . A recen. aureus .E. coli and P. aeruginosa bacteria [A novel mechanism of repurposing antibiotics is the sonochemistry that is capable of nanotransforming vancomycin, an antibiotic used against Gram-positive bacteria, to pass through and disrupt the OMs of Gram-negative bacteria. This technique has proven to be effective against bacteria .Due to the similarity to the OM composition, natural OMV represent an advantage relative to synthetic nanoparticles, which lack the intercellular interaction’s essential ability to promote trafficking and delivery of antibiotics into bacterial cells . HoweverOMV from Gram-negative bacteria have innate antibacterial activity due to the incorporation of several enzymes such as lysins. The incorporation of non-natural molecules into OMV with additional antibacterial effect has been shown. This suggests that a delivery system could be developed to overcome the OM barrier of the Gram-negative bacteria and that bacterial OMV can be used to deliver antibiotics to targeted populations of Gram-negative and -positive pathogenic bacteria. The OMV antimicrobial effect will depend on their cargo and the bacterial species targeted, as well as the resistance mechanism present in the target bacterial cells. The repurposing of antibiotics that are ineffective due to the permeability barrier of the cell wall of bacteria may also be possible with the use of OMV.The industrial production of OMV can be enhanced by changing the growth conditions or through different techniques that force cell damage; an alternative to OMV production with active lumen content is coating of the OMV with nanoparticles. However, several technical challenges that hamper the use of OMV remain, such as the types of MV isolated, the purification yield, and the optimal technique to produce vesicles with desired lumen content, especially hydrophilic molecules which cannot enter by passive loading. The reduction of the LPS toxicity is another point that needs to be optimized in order to reduce the immunogenic potential of OMV; for instance, the engineering of strains with altered LPS  might beOverall, despite the challenges that must still be overcome, OMV represent a cost-effective and safe drug delivery tool, representing a promising alternative for the treatment of bacterial infections caused by antibiotic-resistant bacteria and for the repurposing of antibiotics that are not usually effective against Gram-negative bacteria.


We propose a terahertz frequency multiplier based on high order harmonic generation in a GaAs-based miniband superlattice driven by an electric field. The performance of the frequency multiplier is analyzed using the balance equation approach, which incorporates momentum and energy relaxation processes at different lattice temperatures. It is found that the generated high-order harmonic power is sensitive to temperature changes. The peak power appears around resonance between driving terahertz frequency and intrinsic Bloch frequency. In the presence of the magnetic field, the peak power shifts towards a stronger static electric field region. The simulated results about the dependence of the second and third harmonic powers on a DC electric field are in qualitative consistence with the experiments. The proposed terahertz frequency multiplier based on semiconductor superlattice, being compact and efficient, is provided as a good candidate for terahertz wave generation. Semiconductor superlattice  is a peric field ,6, etc. Over the past twenty years, terahertz technology has attracted lots of interest due to its wide and important applications, such as nondestructive testing , imagingcillator  can radicillator . The radcillator .Recently, the superlattice harmonic multiplier has been proposed to generate millimeter and terahertz waves. Many theoretical ,24 and eDespite the rapid progress in fundamental sciences of superlattices and its application in terahertz sources, some key issues regarding the nature and performance of frequency multiplier based on miniband superlattice driven by electric and magnetic fields remain less understood. In this paper, we theoretically study the performance of the superlattice frequency multiplier by applying the balance equation approach. It is found that increasing lattice temperature can suppress the output power. The distribution of output power peak for frequency doubling and tripling is contributed from resonance between AC frequency and Bloch frequency. The paper is organized as follows: in z-axis) in the miniband of a GaAs-based superlattice. The superlattice is driven by an external terahertz radiation and a magnetic field B. The motion of electrons is confined in the lowest miniband and the inter-miniband tunneling effect is ignored. Within the tight-binding approximation, the electron energy dispersion of semiconductor superlattice can be written in the formd is the period of superlattice, ℏ is the Plank constant.In this section, we consider the motion of electrons along the growth direction (z axis) and a magnetic field perpendicular to it, i.e., along the x-axis. The electrons confined in the miniband are accelerated by the applied fields and scattered by disorders and phonons, leading to an overall drift motion and heating of the electron system. The macroscopic average states of the superlattice can be described by the momentum and energy balance equations, in which the average drift velocity and average electron energy are both functions of the external fields. The balance equations for electron motions in the miniband are in the form [y- and z-axis, respectively. z-axis. e is the electron charge, Now, we apply an external electric field parallel to the growth direction of superlattice PnNow, we consider the emitted power of the second and third harmonics at different lattice temperatures Since working at room temperature is an important feature of terahertz devices, we now focus on the harmonic generation of semiconductor superlattice at To focus on the characteristic of  in Ref. . The superiments . It is nFinally, we demonstrate that the high order harmonic output power can be tuned by introducing a magnetic field perpendicular to the growth axis. In summary, using the balance equation approach with relaxation time depending on lattice temperature and miniband width, we theoretically analyze harmonic coefficients of drift velocity and emitted harmonic power of a semiconductor superlattice frequency multiplier in terahertz frequency range. It is found that high lattice temperature can suppress the peak of