M184V is a single-base mutation in the YMDD domain of reverse transcriptase (RT). The M184V resistance-associated mutation is related to virological unresponsiveness to lamivudine (3TC) and emtricitabine (FTC) and induces high-level resistance to these two antiretroviral agents. M184V is rapidly selected in the setting of non-suppressive antiretroviral therapy (ART) and accumulates in the HIV reservoir. There were continuous efforts to evaluate the impact of M184V mutation on the treatment outcomes in people with HIV (PWH). Since lamivudine remains an extensively used part of recommended antiretroviral combinations, M184V is commonly detected in patients with virological failure. ART guidelines do not recommend the use of drugs impacted by resistance-associated mutations as they have been confirmed to comprise a risk factor for virological failure. However, previous authors suggest that 3TC/FTC can remain active even in the presence of M184V. Obtaining the viral resistance profile for each individual both treated or ART-naïve is a key challenge for lifelong treatment management.Given the potential benefits of lamivudine in the ART combinations, the investigation of M184V remains of high interest for clinicians and researchers. This is a review of literature for the challenges in treating both naïve and experienced individuals carrying the M184V resistance mutation, including virological failure, virological suppression, and resistance to ART.

Water pollution is a common problem for dams situated within an urban or agricultural catchment. This can negatively affect the hydro ecosystem, drinking, recreational and other uses of water. In this study, the drinking water quality class of the Roodeplaat Dam, South Africa which faces pollution problems was modeled using machine learning algorisms in Python Jupyter Notebook 6.0.0. Eleven monthly water quality parameters recorded at five sampling stations from January 1981 to September 2017 were used for training and testing the model. Five machine learning classifiers: Gaussian Naïve Bayes (GNB), K-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machines (SVM), and Linear Regression (LR) at a test size of 20%, 25%, 30%, and 40% were used to classify water into five classes (Excellent to Very bad). It was investigated that the dam water has only three classes good, medium, and bad. The prediction accuracies of machine learning algorithms from the highest to the lowest were 96.39%, 96.17%, 92.25%, 90.20, and 54.19% for KNN, DT, SVM, GNB, and LR, respectively. Therefore, KNN at a test size of 30% was recommended to classify the water quality of Roodeplat Dam accurately. Hence, machine learning algorithms can be used to identify the class of water quality before the water is treated and distributed for drinking use.

In this preprint, we introduce a dataset containing students enrolment applications combined with the related result of their filing procedure. The dataset contains 73 variable. Student candidates, at the time of applying for study, fill a web form for filing the procedure. A committee at Tilburg University review each single application and decide if the student is admissible or not. This dataset is suitable for algorithmic studies and has been used in a comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms. They have been used for predicting the decision of the committee in admitting candidates at various bachelor programs. Our analysis shows that, in this particular case, a combination of the approaches outperform a both of them in term of precision and recall.

Bacillus Calmette-Guérin (BCG) therapy for patients with non-muscle invasive bladder cancer (NMIBC) faces limitations in efficacy and significant side effects, aggravated by a recent global shortage. In this prospective clinical study, we report outcomes of sequential intravesical adminis-tration of gemcitabine and docetaxel (Gem/Doce) as the first-line treatment in BCG-naïve patients with high-risk NMIBC (HR NMIBC). From October 2019 until April 2022, we enrolled 52 patients and followed treatment protocol set by the University of Iowa. Follow-up assessments were con-ducted every 3 months. In this cohort, 25 (48.1%) patients were diagnosed with T1HG bladder cancer, 10 (19.2%) patients had carcinoma in situ (CIS), and 17 (32.7%) patients had a combination of CIS+T1HG. Median time to first recurrence in T1HG, CIS and T1HG+CIS groups was 11, 10.5 and 8.8 months, respectively. The recurrence-free survival was 98.1%, 94.2% and 80.8% at 6, 9 and 12 months, respectively. The rate of progression-free survival was 100%, 98.1% and 92.3% at 6, 9 and 12 months, respectively. We demonstrated the safety and efficacy of Gem/Doce therapy in BCG-naïve patients with HR NMIBC during a one-year follow-up. Further research with extended follow-up, as well as direct comparison of Gem/Doce and other anticancer agents, are essential.

Keywords: pluripotency; naïve; stem; imprinting; primordial germ cells; trophoblast; CDK8; Mediator; enhancers.

SQL injection attacks are one of the most common types of attacks on web applications. These attacks exploit vulnerabilities in the application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network (RNN) autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then compared with several other machine learning models, including ANN, CNN, Decision Tree, Naïve Bayes, SVM, Random Forest, and Logistic Regression. The experimental result showed that the proposed approach achieved an accuracy of 94% and an F1 score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison with other models covered in the study.

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. Cricket, the mainstream and widely played sport across India which has the most noteworthy fan base. Indian Premier League follows 20-20 format which is very unpredictable. IPL match predictor is a ML based prediction approach where the data sets and previous stats are trained in all dimensions covering all important factors such as: Toss, Home Ground, Captains, Favorite Players, Opposition Battle, Previous Stats etc, with each factor having different strength with the help of KNIME Tool and with the added intelligence of Naive Bayes network and Eulers strength calculation formula.