Study of the Effect of Two New Types of IUDs,TCu380A and GyneFix on the Positive Rate of Chlamydia Trahmatis

To analyze the effect of two types of IUDs, TCu380A and GyneFix on the positive rate of Chlamydia Trahmatis (CT). Methods The TCu380A and GyneFix IUDs were compared in a randomized was for the one year and two year positive rate of Chlamydia Trahmatis (CT) and with the control respectively. Results The one year positive rate of CT antigens was 5.63% of TCu380A and two year was 4.92%; the one year positive rate of CT antigens was 4.62% and two year was 5.08% of GyneFix. There was no significant difference in the positive rate of CT antigen between the TCu380A IUD, and GyneFix IUDs groups, while there were significant differences between the TCu380A IUD, GyneFix IUD and the controls (15.18%) respectively. Conclusion Both IUDs provide highly effective protection against CT infection.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

SARS-CoV-2 infection rates after different vaccination schemes:An online survey in Turkey

Objective:To identify effects of various nationwide vaccination protocols on the evolution of new SARS-CoV-2 infections among adult population and to evaluate the safety of mRNA(BioNTech/Pfizer)vaccine.Methods:Totally 10735 adult volunteers that received at least one dose of BioNTech/Pfizer or triple doses of CoronaVac participated in this cross-sectional-online survey between 1 and 10 September 2021.The information was collected covering a 5-month period from April 2021 to September 2021.Information about people who were vaccinated with only single and double dose CoronaVac were not included in this study.Results:At least one side effect after single and double dose of BioNTech/Pfizer and triple doses of CoronaVac were observed in 42.1%,42.5%and 10.9%,respectively.The most common side effects were shoulder/arm pain,weakness/fatigue,muscle/joint pain and headache.The side effects were the most frequent in single BioNTech/Pfizer,while it was the least in triple CoronaVac.The rate of positive PCR tests before vaccination was 17.6%,and decreased to 3.0%after vaccination.The rates of positive SARS CoV-2-PCR were 18.8%,3.5%,3.1%,0.5%and 4.6%in single BioNTech/Pfizer,double BioNTech/Pfizer,double CoronaVac+single BioNTech/Pfizer,double CoronaVac+double BioNTech/Pfizer and triple CoronaVac,respectively.While 1.8%of PCR positive COVID-19 cases needed intensive unit care in the pre-vaccination period,intensive care unit was required in 0%,1.5%,2.4%,0%and 4.2%after single BioNTech/Pfizer,double BioNTech/Pfizer,double CoronaVac+single BioNTech/Pfizer,double CoronaVac+double BioNTech/Pfizer and triple CoronaVac,respectively.Reinfection rate after vaccination was 0.4%.Conclusions:The rarity of COVID-19 infection after vaccination suggests that efficacy of vaccines is maintained.On the other hand,the data underscore the critical importance of continued public health mitigation.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma

BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.METHODS A total of 2773 eligible patients were divided into the training cohort(70%)and the validation cohort(30%).Optimal cutoff values were calculated using the X-tile software for continuous variables.Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival(OS)and cancer-specific survival(CSS)-related prognostic factors.Two nomograms were successfully constructed.The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis.RESULTS The 95%CI in the training group was 0.719(0.690-0.749)and 0.733(0.702-0.74),while that in the validation group was 0.739(0.696-0.782)and 0.750(0.701-0.800)for the OS and CSS nomogram prediction models,respectively.In the validation group,the AUC of the three-year survival rate was 0.762 and 0.770,while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms,respectively.The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades.The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system.CONCLUSION The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.

Investigation on HTLV Infection among Voluntary Blood Donors in Wuzhou City

Objective: To understand the infection of HTLV among voluntary blood donors in Wuzhou City, and to provide reference for the national health administrative department to formulate blood safety screening strategies. Methods: The HTLV double-antigen sandwich ELISA reagent was used to screen the blood samples of unpaid blood donors, and the reactive samples in the initial screening were subjected to a double-well retest;Specimens that were still reactive in the retest were further confirmed by viral nucleic acid amplification test (PCR) and western blotting (WB). Results: A total of 9 of 20,222 unpaid blood donation samples were screened to be reactive, and the screening response rate was 0.04%;Two samples of HTLV-1 nucleic acid and western blotting (WB) were confirmed to be positive, and the other seven samples were negative;The confirmed positive rate was 0.01%. Conclusion: There was a certain positive rate of HTLV-1 serological screening among the non remunerated blood donors in Wuzhou City, and the confirmation test confirmed that there was a certain risk of HTLV infection;In order to further understand the HTLV infection of blood donors in this city, we should further increase the number of screening samples, so as to obtain more reliable and accurate data in this region, and provide data and reference for the health administration department to formulate HTLV screening strategies for blood donors.

Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.

Study on the Diagnostic Value of Combined Detection of Sperm Quality, Sex Hormone and Ovulation in Infertility

Objective: To analyze the application value of combined detection of sperm quality, sex hormone and ovulation in the diagnosis of infertility. Methods: The study was conducted from June 2021 to June 2022. Sixty infertile couples who received IVF cycle treatment in our hospital during this period were selected as the observation group, and 60 couples with good sperm quality and follicle number ≥ 5 who conceived naturally after gynecological disease treatment were selected as the control group during the same period. The sperm quality, sex hormones and ovulation of the two groups were observed, and the change of positive rate was detected by the combined detection method. Results: Compared with the control group, the observation group had less semen (2.82 ± 0.12) ml, lower concentration (69.17 ± 1.28) × 106/ml, normal sperm morphology rate (2.92% ± 0.11%), lower survival rate (70.25% ± 1.16%), higher deformed sperm index (1.39 ± 0.11), and significant differences between groups (P < 0.05);The levels of FSH (7.15 ± 1.33) U/L, LH (5.13 ± 0.53) mU/ml, E2 (72.34 ± 5.11) ng/L, AMH (3.87 ± 0.67) ng/ml and AFC (7.15 ± 0.76) in the control group were significantly better than those in the observation group (P < 0.05). Compared with the single detection method of the three groups, the positive detection rate of the combined diagnosis method was higher, and the difference between the groups was significant (P < 0.05). Conclusion: For the diagnosis of infertility, the combined detection method of sperm quality, sex hormone and ovulation can effectively clarify the problems existing in the couple, significantly improve the detection rate of the cause of the patient, and is more conducive to guiding clinical symptomatic treatment, which is worthy of promotion and reference.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.