Hybrid treatment of varied orthodontic appliances for a patient with skeletal class II and temporomandibular joint disorders:A case report and review of literature

BACKGROUND The relation between orthodontic treatment and temporomandibular disorders(TMDs)is under debate;the management of TMD during orthodontic treatment has always been a challenge.If TMD symptoms occur during orthodontic treatment,an immediate pause of orthodontic adjustments is recommended;the treatment can resume when the symptoms are managed and stabilized.CASE SUMMARY This case report presents a patient(26-year-old,female)with angle class I,skeletal class II and TMDs.The treatment was a hybrid of clear aligners,fixed appliances and temporary anchorage devices(TADs).After 3 mo resting and treatment on her TMD,the patient’s TMD symptom alleviated,but her anterior occlusion displayed deep overbite.Therefore,the fixed appliances with TAD were used to correct the anterior deep-bite and level maxillary and mandibular deep curves.After the levelling,the patient showed dual bite with centric relation and maximum intercuspation discrepancy on her occlusion.After careful examination of temporomandibular joints(TMJ)position,the stable bite splint and Invisible Mandibular Advancement appliance were used to reconstruct her occlusion.Eventually,the improved facial appearance and relatively stable occlusion were achieved.The 1-year follow-up records showed there was no obvious change in TMJ morphology,and her occlusion was stable.CONCLUSION TMD screening and monitoring is of great clinical importance in the TMD susceptible patients.Hybrid treatment with clear aligners and fixed appliances and TADs is an effective treatment modality for the complex cases.

Class Imbalanced Problem:Taxonomy,Open Challenges,Applications and State-of-the-Art Solutions

The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the power of data-driven machine learning(ML)and deep learning(DL)techniques.The data imbalance presents major hurdles for classification and prediction problems in machine learning,restricting data analytics and acquiring relevant insights in practically all real-world research domains.In visual learning,network information security,failure prediction,digital marketing,healthcare,and a variety of other domains,raw data suffers from a biased data distribution of one class over the other.This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas.We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations.This article has also identified open challenges for further research in the direction of class data imbalance.

Simple and effective method for treating severe adult skeletal class II malocclusion: A case report

BACKGROUND Severe skeletal class II malocclusion is the indication for combined orthodontic and orthognathic treatment.CASE SUMMARY A woman with a chief complaint of a protruding chin and an inability to close her lips requested orthodontic camouflage.The treatment plan consisted of extracting the right upper third molar,right lower third molar,left lower second molar,and left upper third molar and moving the maxillary dentition distally using a convenient method involving microimplant nail anchors,push springs,long arm traction hooks,and elastic traction chains.After 52 months of treatment,her overbite and overjet were normal,and her facial profile was favorable.CONCLUSION This method can be used for distal movement of the maxillary dentition and to correct severe skeletal class II malocclusion in adults.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

Improved super-elastic Ti-Ni alloy wire for treating adult skeletal class III with facial asymmetry:A case report

BACKGROUND Correcting severe skeletal class III malocclusion with facial asymmetry in adults through orthodontic treatment alone is difficult.CASE SUMMARY In this case report,we describe orthodontic treatment and lower incisor extraction without orthognathic surgery for a 27-year-old man with a transverse discrepancy.The extraction sites were closed using an elastic chain.The use of intermaxillary elastics,improved super-elastic Ti-Ni alloy wire,and unilateral multibend edgewise arch wire was crucial for correcting facial asymmetry and the midline deviation.CONCLUSION After treatment,the patient had a more symmetrical facial appearance,acceptable overjet and overbite,and midline coincidence.The treatment results remained stable 3 years after treatment.This case report demonstrates that a minimally invasive treatment can successfully correct severe skeletal class III malocclusion with facial asymmetry.

ASARA,a prediction model based on Child-Pugh class in hepatocellular carcinoma patients undergoing transarterial chemoembolization

Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.

Correlation between the Six-Minute Walk Test and Subjective Functional Class in Patients with Heart Failure

Introduction: The Six-Minute Walk Test (6MWT) is an inexpensive method to objectively evaluate physical capacity or limitation and stratify prognosis in patients with Heart Failure (HF). Since the clinical perception of symptoms may be adapted or compromised, regular evaluation from medical interviews often fails to determine functional classification. This study aimed to assess the correlation between New York Heart Association Functional Class (NYHA-FC) and the distance walked in the 6MWT. Methods: We conducted a cross-sectional observational study that included patients with HF with reduced ejection fraction followed up at an outpatient service of a teaching hospital, from August 2018 to April 2019. Patients in NYHA-FC I, II, or III were included. We compared NYHA-FC subjectively obtained during the consultation with the 6MWT performed after medical consultation, and the correlation between these two parameters was assessed. Results: The study included 70 patients with HF, 41 (58.6%) of whom were female. The mean age was 61.2 ± 12.7 years. The most prevalent etiologies were dilated idiopathic cardiomyopathy (35.7%) followed by ischemic cardiomyopathy (25.7%). The mean ejection fraction was 34.1% ± 9.8%. The average distance walked in the 6MWT by NYHA-FC I patients was 437.8 ± 95.8 meters, NYHA-FC II 360.1 ± 96.4, and NYHA-FC III 248.4 ± 98.3. Functional class measured by the 6MWT was different than that estimated by NYHA-FC in 34 patients (48.6%), 23 (32.9%) for a higher functional class and 11 (15.7%) for a lower one (p = 0.07). Pearson’s correlation coefficient between NYHA-FC and the 6MWT was -0.55. Conclusion: There was a moderate correlation between the subjective NYHA-FC and the 6MWT. The 6MWT revealed a different classification from NYHA-FC in almost half of the patients. Among those who presented discrepancies between methods, 6MWT reclassification towards a higher functional class was more common.

Survey of Multidrug Resistance and Class I Integron Prevalence in Chicken-derived Salmonella

[Objective] This study aimed to investigate the multidrug resistance and prevalence of class I integrons in Salmonel a. [Method] Salmonel a strains were isolated from chicken farms in Shandong Province. Kauffmann-White classification method was employed to analyze the serotypes of Salmonel a strains. Minimum in-hibition concentration （MIC） of Salmonel a strains against 19 common antimicrobial drugs was analyzed determined with microdilution method. The class I integrons and carried drug resistance gene cassettes were detected by PCR. [Result] A total of 311 Salmonel a strains were isolated and classified into two serotypes, including 133 Salmonel a Indiana strains and 178 Salmonel a Enteritidis strains. Drug sensitivity test showed that the isolated Salmonel a strains were general y resistant to sulfadiazine, sulfamethoxazole, nalidixic acid, ampicil in, tetracycline, doxycycline and trimethoprim, with a multidrug resistance rate of 91.0% （283/311）; 99% strains were sensitive to amikacin and colistin. PCR assay indicated that the detection rate of class I integrons was 65.0% （202/311）; the positive rate of class I integrons in Salmonel a strains with multidrug resistance was 92.6%; among 202 positive strains, six strains carried gene cassette dfr17-aadA5. [Conclusion] According to the above results, class I integrons exist general y in Salmonel a and are closely associated with the multidrug resistance of Salmonel a strains.

The Driving Strategy of Online Ideological and Political Class in Universities Based on SWOT Analysis

The epidemic provided practical opportunities for online classes and also tested its drawbacks. To analyze the internal strengths (S) and weaknesses (W) as well as the external opportunities (O) and threats (T) of the online ideological and political class with SWOT theory, based on which to explore targeted solutions, is a “symptomatic” approach to improving the quality of the online ideological and political class. The analysis results are as follows: S includes “not limited by time and space”, “comprehensive teaching monitoring data”, “diverse and innovative interactive forms”, and “abundant high-quality teaching resources”. W refers to “increased difficulty in classroom control”, “teachers’ psychological pressure caused by online teaching”, and “limited by hardware conditions”. O means the opportunity provided by the modernization of ideological and political education and epidemics. T indicates external threats, such as limitations from the network and the severe impact of wrong values on the internet. The driving strategy for above case could be “upgrading of online teaching platforms”, “building a teaching team skilled in information technology teaching”, “improving hardware facilities” and “joining forces to confront value crisis”.

Effect of Herpes Simplex Virus-2 Infection in Vitro on the Expression of HLA Class II Antigen of Monocytes

Objectives: In order to investigate the role of mono-cytes and human leukocyte antigen (HLA) class II an-tigen in herpes simplex virus-2 (HSV-2) infection, westudied the effect of HSV-2 infection in vitro on theexpression of HLA class II antigen on monocytes.Methods: Monocytes were infected with HSV-2(Strain 333). Culture cells were collected 1, 3, 5 and 7days after infection. The levels of expression of HLAclass II antigen were measured by using alkaline phos-phatase antialkaline phosphatase method (APAAP).Results: The levels of the expression of HLA class IIantigen on monocytes significantly decreased on thefirst day of post-infection, and then gradually returnedto levels found in the controls by the 7th day post-infection.Conclusion: HLA class II antigen expression onmonocytes was inhibited with HSV-2 infection, whichmight be one means of virus escape at an early phase.The expression of HLA class II antigen may play animportant role in herpes simplex viurs-2 pathogenic-ity and immunity.

一株Class I新城疫病毒中国分离株分子特性的研究

对从健康家鸭中分离到的一株Class I新城疫病毒分离株Duck/China/08-004/2008进行了遗传进化特性研究。利用RT-PCR扩增了该分离株F基因的主要功能区片段,并进行了克隆与序列分析。序列测定结果已经登录到GenBank,登录号为EU589149。该分离株F蛋白裂解位点的组成为112E-Q-Q-E-R-L117,具有典型新城疫弱毒株的分子特征。同源性分析表明其与国内普遍使用的弱毒疫苗株LaSota和V4核苷酸的同源性较低(分别为55.4%和57.7%)。通过构建F基因的遗传进化树,结果表明该分离株在分类地位上属于ClassI,与我国目前普遍使用的弱毒疫苗株LaSota(Class II中的基因II型)和V4(Class II中的基因I型)处在不同的进化分支。通过构建57株ClassI新城疫病毒的遗传进化树,表明本分离株与近年来香港活禽市场分离株较为类似,同属于基因3型。

Class I patatin基因5′侧翼区驱动的GUS基因在马铃薯块茎中专一性表达

将1.4kb Class I patatin基因的5′侧翼区与GUS基因融合,构建了双元表达载体pPATIs(含patatin部分信号顺序)和pPATI(不含patatin部分信号顺序)。pPATI通过基因枪介导在块茎切片中获得了瞬间表达。以上建构物通过农杆菌介导转入了马铃薯品种Desiree。X-Gluc染色(PATIs不能染色)及PCR结果证实已获得转基因植株。利用离体块茎诱导系统,GUS的表达进一步用荧光进行定量检测,结果显示,PATI-GUS的转基因植株中GUS比活性均以块茎明显高于茎段,达10-20倍。蔗糖浓度的升高,PATI-GUS植株中的GUS比活性无明显变化,与前人报道有不同。此外,光照促进PATI-GUS的表达。

基于NP基因的新城疫病毒Class I实时荧光定量RT-PCR方法的建立及验证

新城疫病毒是影响养禽业健康发展的主要病原之一。新城疫病毒只有1个血清型,但根据亲源关系可以划分为Class I和ClassⅡ两大类。为快速定量检测Class I新城疫病毒,根据其NP基因的保守序列,设计合成1对引物和Taq Man探针,以Class I新城疫病毒JS-18-05株NP基因阳性重组质粒为标准品模板,建立荧光定量PCR标准曲线,首次建立了一种敏感、特异、重复性好的快速检测新城疫病毒Class I核酸载量的Taq Man荧光定量RT-PCR方法。该方法在102~108拷贝范围内具有良好的线性关系,可检测到初始模板中1μl 10拷贝的病毒核酸,与传统的病毒分离方法具有相近的敏感性,且这两种方法对33株Class I、ClassⅡ新城疫病毒分离株尿囊液样品检测结果符合率为100%。

HLA Class I Expressions on Peripheral Blood Mononuclear Cells in Colorectal Cancer Patients

Objective： To investigate the expression change of human leukocyte antigen （HLA） class I on human peripheral blood mononuclear ceils （PBMCs） at both mRNA and protein levels, and to evaluate its roles in the development of colorectal cancer （CRC）. Methods： In the present study, 50 patients with CRC, 35 patients with benign colorectal lesion and 42 healthy volunteers were enrolled. Expression levels of HLA class I mRNA and protein were determined using real-time quantitative reverse transcription PCR （RT-PCR） and flow cytometry analysis, respectively. Results： The expression levels of HLA class I mRNA and proteins were not influenced by age and gender. The relative ratios of HLA class I mRNA were 0.99±0.27 in healthy controls, 0.76±0.19 in benign patients, and 0.48±0.21 in CRC patients. Mean fluorescence intensities of HLA class I were 145.58±38.14 in healthy controls, 102.05±35.98 in benign patients and 87.44±34.01 in CRC patients. HLA class I on PBMCs was significantly down-regulated at both mRNA and protein levels in patients with stage III and IV CRC. CRC patients with lymph node metastasis also showed a decreased HLA class I expression at protein level. Conclusion： HLA class I expressions on PBMCs are associated with staging of CRC and lymph node metastasis. Monitoring the expression of HLA class I on PBMCs may provide useful information for diagnosis and metastasis judgement of CRC.

MHC Class I Exon 4 in the Multiocellated Racerunners(Eremias multiocellata): Polymorphism, Duplication and Selection

The major histocompatibility complex （MHC） is a dynamic genetic region with an essential role in the adaptive immunity of jawed vertebrates. The MHC polymorphism is affected by many processes such as birth-and- death evolution, gene conversion, and concerted evolution. Studies investigating the evolution of MHC class I genes have been biased toward a few particular taxa and model species. However, the investigation of this region in nonavian reptiles is still in its infancy. We present the first characterization of MHC class I genes in a species from the family Lacertidae. We assessed genetic diversity and a role of selection in shaping the diversity of MHC class I exon 4 among 37 individuals of Eremias multiocellata from a population in Lanzhou, China. We generated 67 distinct DNA sequences using cloning and sequencing methods, and identified 36 putative functional variants as well as two putative pseudogene-variants. We found the number of variants within an individual varying between two and seven, indicating that there are at least four MHC class I loci in this species. Gene duplication plays a role in increasing copy numbers of MHC genes and allelic diversity in this species. The class I exon 4 sequences are characteristic of low nucleotide diversity. No signal of recombination is detected, but purifying selection is detected in β2-microglobulin interaction sites and some other silent sites outside of the function-constraint regions. Certain identical alleles are shared by Eremias multiocellata and E. przewalskii and E. brenchleyi, suggesting trans-species polymorphism. The data are compatible with a birth-and-death model of evolution.

Clinical review considerations of class I PI3K inhibitors in hematolymphatic malignancies by Center for Drug Evaluation

Several phosphoinositide 3-kinase(PI3 K) inhibitors are currently approved to treat hematolymphatic malignant diseases worldwide, and many drugs that have the same target are in the clinical research stage. In March 2022,duvelisib became the first PI3 K inhibitor approved in China indicated for the treatment of hematolymphatic malignant diseases. Meanwhile, linperlisib and copanlisib have almost completed the technical review of the clinical specialty. The Center for Drug Evaluation(CDE) of the China National Medical Products Administration(NMPA) found that class I PI3 K inhibitors can cause various degrees of immune-related adverse events, which are associated with action mechanisms, affecting the benefit-risk assessment of the drugs. On April 21, 2021, the United States Food and Drug Administration(FDA) convened the Oncologic Drugs Advisory Committee(ODAC)meeting to discuss the safety of PI3 K inhibitors indicated for hematolymphatic malignancies and their related risk of death. The hematological tumor group of CDE of the China NMPA summarized and combined the data on PI3 K inhibitors listed or under technical review for marketing authorization applications and found that such products may have unique efficacy and safety characteristics in Chinese patients with malignant lymphoma.

Handling Class Imbalance in Online Transaction Fraud Detection

With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction.However,the fraud cases have also increased causing the loss of money to the consumers.Hence,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time.Generally,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem.In this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets.Also,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.

Bioinformatics Analysis of Class I Chitinase in Tobacco

This study almed to analyze the characteristics of Chi I （cIass I chitinase） in tobacco by using bioinformatics methods and tooIs so as to Iay a foundation for further study on disease resistance of Chi I in tobacco. The amino acid sequence characteristics and phyIogenetic reIationships of Chi Is of Nicotiana tabacum, Ara-bidopsis thaliana, Oryza sativa and Phaseolus vulgaris from NCBI were predicted and analyzed by using bioinformatics methods. The resuIts showed that the moIec-uIar weights of Chi Is with signal peptides ranged from 28 to 37 kDa. The se-quences of Chi Is, which al contalned both N-terminal chitin binding domaln （Chit BD1）andcatalyticdomaln（CIyco hydro-19）,werehighIyconserved.Thesequences of Nt-ChiA, Nt-ChiB and Nt-ChiC, three Chi Is from Nicotiana tabacum had high simiIarity, so their phyIogenetic reIationships were also cIose. The reIationships be-tween Chi Is from Nicotiana tabacum and one of the Chi Is from Arabidopsis thaliana and Phaseolus vulgaris were cIoser than those between Chi Is from Nico-tiana tabacum and Oryza sativa. The resuIts indicated that the properties of Chi I in tobacco were almost the same as those in other pIants. This study wiI provide a basis for further researches on disease resistance proteins in tobacco.

Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions

In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.