Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

Predictors of Abnormal Vaginal Discharge among Women of Reproductive Age in Southeast Nigeria

Background: An abnormal vaginal discharge is a common complaint among women of reproductive age, and it can indicate serious conditions like pelvic inflammatory disease and cervical cancer. This study aimed to assess the predictors of abnormal vaginal discharge in women of reproductive age group in Imo State, Southeast Nigeria. Methods: A cross-sectional study was conducted among 368 women of reproductive age group attending the clinic at Federal University Teaching Hospital Owerri, in Imo State, Nigeria. Respondents were recruited using a systematic sampling technique. Data were collected using a pre-tested interviewer-administered questionnaire. Multivariable analysis was performed to determine predictors of abnormal vaginal discharge. Statistical significance was set at p Results: The mean age of the respondents was 30 ± 4.5 years. Predictors of abnormal vaginal discharge were: age 36 - 45 years (OR: 4.5;95% C.I: 1.023 - 8.967, p = 0.041), being a student (OR: 2.4: 95% C.I: 1.496 - 7.336, p = 0.003), use of oral contraceptives (OR: 3.4;95% C.I: 1.068 - 6.932, p = 0.010), use of water cistern (OR: 4.7;C.I: 1.654 - 5.210, p = 0.028) anal hygiene practices (OR: 2.7;95% C.I: 1.142 - 4.809, p Conclusion: These findings suggest that targeted sexual and reproductive health interventions should be provided to reduce the risk of abnormal vaginal discharge in women of reproductive age group.

Effect of low-speed waterjet pressure on the rock-breaking performance of unsubmerged cavitating abrasive waterjet

Unsubmerged cavitating abrasive waterjet(UCAWJ)has been shown to artificially create a submerged environment that produces shear cavitation,which effectively enhances rock-breaking performance.The shear cavitation generation and collapse intensity depend on the pressure difference between the intermediate high-speed abrasive waterjet and the coaxial low-speed waterjet.However,the effect of the pressure of the coaxial low-speed waterjet is pending.For this purpose,the effect of low-speed waterjet pressure on rock-breaking performance at different standoff distances was experimentally investigated,and the effects of erosion time and ruby nozzle diameter on erosion performance were discussed.Finally,the micromorphology of the sandstone was observed at different locations.The results show that increased erosion time and ruby nozzle diameter can significantly improve the rock-breaking performance.At different standoff distances,the mass loss increases first and then decreases with the increase of low-speed waterjet pressure,the maximum mass loss is 10.4 g at a low-speed waterjet pressure of0.09 MPa.The surface morphology of cavitation erosion was measured using a 3D profiler,the increase in both erosion depth and surface roughness indicated a significant increase in the intensity of the shear cavitation collapse.At a low-speed waterjet pressure of 0.18 MPa,the cavitation erosion surface depth can reach 600μm with a roughness of 127μm.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.

Is fluoroscopy-free single-use flexible ureteroscopy a feasible treatment for kidney stones with abnormal renal anatomy?

ObjectiveThis study aimed to evaluate the feasibility of the fluoroscopy-free single-use flexible ureteroscopy procedure in the treatment of kidney stones with abnormal renal anatomy compared to normal renal anatomy.MethodsForty patients with abnormal (Group A) and 80 patients with normal (Group B) renal anatomy who had 10–20 mm renal stones were included. They were treated with LithoVue single-use flexible ureteroscopy (Boston Scientific, Marlborough, MA, USA) after ureteric dilatation by two different size semi-rigid ureteroscopes. This technique was chosen as the aim was to exclude any ureteric pathology (e.g., stone or stricture), confirm the placement of a safe guidewire, avoid balloon dilatation of the ureter, and achieve safe insertion of a 12 Fr, 35/45 cm ureteric access sheath with optical and tactile sign and without fluoroscopy image for guidance.ResultsThe mean ages were 43 years and 45 years in Group A and Group B, respectively. The mean stone burden was 14.62 (standard deviation: 5.35) mm^(3) and 14.79 (standard deviation: 4.58) mm^(3) in Group A and Group B, respectively. There is no significant difference between both groups according to the mean operative time, hospital stay, or stone-free rate. The stone-free rate was about 93% in both groups when the stone size was between 10 mm and 15 mm, and less than 54% when the stone size was more than 15 mm to 20 mm. In the majority of cases (80.0% in Group A and 92.5% in Group B), we completed the procedure without fluoroscopy. The perioperative complication rates were comparable in the two groups.ConclusionFluoroscopy-free single-use flexible ureteroscopy, when performed by expert urologists, is a feasible treatment for pre-stented patients with kidney calculi of ≤15 mm with abnormal renal anatomy.

Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

A Clinical Study to Assess the Effectiveness of Oral Combination Kit Therapy in Syndromic Management of Abnormal Vaginal Discharge (FEMINE Study) in Kinshasa, Democratic Republic of Congo

Background: Vaginal discharge is one of most common and nagging problems that women face. About 20% - 25% of women who visit gynecology department complain of vaginal discharge and leucorrhoea. An orally administered combination kit, containing 2 g secnidazole, 1 g azithromycin and 150 mg fluconazole (Azimyn FS Kit), has been successfully evaluated in clinical trials and used in several countries for management syndromic vaginal discharge due to infections. Methods: This is a longitudinal study which aimed to verify the clinical efficacy of the combined oral kit containing secnidazole, azithromycin and fluconazole (Azimyn FS Kit®) in the syndromic treatment of abnormal vaginal discharge in patients received in outpatient consultations in Kinshasa/DR Congo from March to September 2023. Results: Majority of patients had whitish vaginal discharge (51.6%) of average abundance (56.2%), accompanied by pruritus in 72.1% of cases, and dyspareunia in 23.5% of cases and hypogastralgia in 40.2% of cases. One week after treatment with the Azimyn FS® combined kit, at the greatest majority of patients (97.3%), abnormal vaginal discharge had decreased by more than 50% (84.1%). Two weeks after treatment with the Azimyn FS® combined kit, almost all patients (97.3%) no longer had abnormal vaginal discharge which had completely disappeared. Conclusion: A single dose of secnidazole, azithromycin and fluconazole in the form of an oral combi-kit (Azimyn FS Kit) has shown excellent therapeutic effectiveness in the syndromic treatment of abnormal vaginal discharge wherein patients were treated without diagnostic confirmation.

First record of abnormal body coloration in a rockfish Sebastes koreanus(Scorpaenoidei:Sebastidae)from coastal water of China based on morphological characteristics and DNA barcoding

The first record of abnormal body coloration in Sebastes koreanus Kim and Lee,1994,from the Yellow Sea of China,was documented based on morphological characteristics and DNA barcoding.The two rockfish specimens were collected from the coastal waters of Qingdao,China,and the whole body and all fins of them were red.Of the two red-colored rockfish,there were tiny deep red spots on each fin,2 red radial stripes behind and below the eyes and 1 large deep red blotch on the opercula,while the similar stripe and spot patterns are also present in the S.koreanus specimens with normal body coloration.The countable characteristics of the two specimens are in the range of the morphometry of S.koreanus.To further clarify the species identity and taxonomic status of the two specimens,DNA barcode analysis was carried out.The genetic distance between the red-colored rockfish and S.koreanus was 0,and the minimum net genetic distances between the red-colored rockfish and other Sebastes species except for S.koreanus were 3.0%,which exceeds the threshold of species delimitation.The phylogenetic analysis showed that the DNA barcoding sequences of the two red-colored rockfish clustered with the S.koreanus sequences.The above results of DNA barcode analysis also support that the two red-colored rockfish could be identified as the species of S.koreanus.The mechanism of color variation in S.koreanus is desirable for further research and the species could be an ideal model to study the color-driven speciation of the rockfishes.

Heart abnormality associates with a wide spectrum of psychiatric disorders:Evidence from Mendelian randomization analyses

Psychiatric disorders and heart abnormality are closely interconnected.Previous knowledge has been well-established that psychiatric disorders can lead to increased cardiovascular morbidity and even sudden cardiac death.Conversely,whether heart abnormality contributes to psychiatric disorders remains rarely studied.The work by Zhang et al pointed out that chronic heart failure had effects on the anxiety and depression(AD)severity,and indices including left ventricular ejection fraction,N-terminal pro-brain natriuretic peptide and interleukin-6 were independent risk factors for AD severity.In addition to the aforementioned AD,we herein find that heart failure might additionally impact the development of autism spectrum disorder and post-traumatic stress disorder(albeit P>0.05),and significantly protects against the presence of attention deficit hyperactivity disorder(ADHD),[odds ratio(OR)=0.61,P=0.0071]by using a Mendelian randomization analysis.Bradycardia is also a protective factor for ADHD(OR=0.61,P=0.0095),whereas hypertrophic cardiomyopathy is a mild risk factor for schizophrenia(OR=1.02,P=0.032).These data suggest a wide spectrum of psychiatric disorders secondary to heart abnormality,and we highlight more psychiatric care that should be paid to patients with heart abnormality.

High-grade serous carcinoma of the fallopian tube in a young woman with chromosomal 4q abnormality:A case report

BACKGROUND Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome.This study presents a rare association between chromosome 4q abnormalities and fallopian tube highgrade serous carcinoma(HGSC)in a young woman.CASE SUMMARY A 35-year-old woman presented with acute dull abdominal pain and a known chromosomal abnormality involving 4q13.3 duplication and 4q23q24 deletion.Upon arrival at the emergency room,her abdomen appeared ovoid and distended with palpable shifting dullness.Ascites were identified through abdominal ultrasound,and computed tomography revealed an omentum cake and an enlarged bilateral adnexa.Blood tests showed elevated CA-125 levels.Paracentesis was conducted,and immunohistochemistry indicated that the cancer cells favored an ovarian origin,making us suspect ovarian cancer.The patient underwent debulking surgery,which led to a diagnosis of stage IIIC HGSC of the fallopian tube.Subsequently,the patient received adjuvant chemotherapy with carboplatin and paclitaxel,resulting in stable current condition.CONCLUSION This study demonstrates a rare correlation between a chromosome 4q abnormality and HGSC.UBE2D3 may affect crucial cancer-related pathways,including P53,BRCA,cyclin D,and tyrosine kinase receptors,thereby possibly contributing to cancer development.In addition,ADH1 and DDIT4 may be potential influencers of both carcinogenic and therapeutic responses.

Lightweight and polarized self-attention mechanism for abnormal morphology classification algorithm during traditional Chinese medicine inspection

Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphol-ogy in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.Methods First,this paper establishes a dataset of abnormal morphology for Chinese medi-cine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting.Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10.Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.

Analysis of the Efficacy of Prenatal B-Ultrasound in Diagnosing Fetal Abnormalities

Objective:To explore the positive significance of using prenatal B-ultrasound in diagnosing fetal abnormalities.Methods:A total of 200 pregnant women who visited Shaanxi Provincial People’s Hospital between January 2023 and January 2024 were recruited as the research subjects.All pregnant women received prenatal examinations.A retrospective analysis was carried out to analyze the positive significance of prenatal B-ultrasound examination in the diagnosis of fetal abnormalities.Results:Prenatal B-ultrasound examination detected 10 cases of fetal abnormalities,with a detection rate of 5.00%.When compared with the postnatal examination results of 5.50%,the difference was insignificant(P>0.05).Moreover,comparing the fetal limb abnormalities and cardiovascular abnormalities in prenatal B-ultrasound examination and postnatal examination,one case of congenital heart disease was missed in the prenatal B-ultrasound examination,and the others were consistent with the postnatal examination results,with a coincidence rate of 90.91%,indicating a high compliance rate.Conclusion:Fetal abnormalities have a great impact on mothers,babies,and families,and it is particularly important to strengthen diagnosis during this process.Prenatal B-ultrasound examination can improve the accuracy of diagnosis of fetal abnormalities and can be promoted in clinical practice as a basis for screening fetal abnormalities.

Optimizing the Isolation Forest Algorithm for Identifying Abnormal Behaviors of Students in Education Management Big Data

With the changes in educational models,applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education.In response to the increasing amount of student data in universities,this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management.Firstly,it uses a logistic regression algorithm to update the calculation method of isolated forest weights and then uses residual statistics to eliminate redundant forests.Finally,it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm.On this basis,improvements have also been made to the traditional gated loop unit network.It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data.The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1s.The recognition accuracy of the improved gated loop unit network is 0.965,and the training time is 0.16s.In summary,the constructed model can effectively identify abnormal data of college students,thereby helping educators to detect students’problems in time and helping students to improve their learning status.

Application of Abnormal Touchdown PCR with High Degeneracy Primer in Amplification of Large-Family Genes

[Objective] The aim was to explore the special methods for amplification of large-family genes by using primers with high degeneracy.[Method] By using the primers with high degeneracy,conventional PCR,conventional touchdown PCR and the optimized abnormal touchdown PCR were respectively carried out to amplify the genomic DNA of Cyprinus carpio.[Result] Only one evident electrophoretic band and a few Sox genes were obtained by using normal PCR;no obvious electrophoretic band but dispersive product was obtained by normal touchdown PCR;ideal result was obtained by the abnormal touchdown PCR that three evident electrophoretic bands and much more Sox genes were amplified.[Conclusion] The research provided theoretical basis for the optimization and selection of PCR amplification conditions of the large-family genes.

CATGUT EMBEDDING FOR TREATMENT OF SIMPLE OBESITY COMPLICATED WITH ABNORMAL BLOOD FAT

Objective To compare the therapeutic effect of catgut embedding combined with diet plus exercise with that of diet plus exercise in treating simple obesity complicated with abnormal blood fat. Methods A prescription of diet plus exercise was given to the patients in a control group, and catgut embedding was added to the prescription in a treatment group. It was given once every 15 days for the 1st three treatments, and once every month for the 2^nd three treatments. Six treatments constituted a therapeutic course. Body weight and blood fat were measured before and half a year since treatment. Results After treatment, BMI, TO, TG and LDL were very significantly improved in the treatment group （P〈0.01）, and BMI and TG were very significantly （P〈0.01） and TO and LDL were significantly （P〈0.05） improved in the control group. HDL was not obviously improved in both groups （P〉0.05）. The improvement of TO, TG and LDL in the treatment group was significantly more than that in the control group （P〈0.01）, but no difference in improvement of BMI and HDL was found between the two groups （P〉0.05）. Conclusion The therapy of diet plus exercise was effective in body weight reduction and lowering blood fat in patients with simple obesity complicated with abnormal blood fat, and catgut embedding used in combination was even better in lowering blood fat.