Detection of BCR-ABL kinase domain mutations in CD34+ cells from newly diagnosed chronic phase CML patients and their association with imatinib resistance

BCR-ABL kinase domain (KD) mutations, the most common cause of imatinib resistance, are infrequently detected in newly diagnosed chronic-phase chronic myeloid leukemia (CP-CML) patients. Recent studies indicate pre-existing mutations (PEMs) can be detected in a higher percentage of CML patients using CD34+ stem/progenitor cells, and these mutations may correlate with imatinib resistance. We investigated KD mutations in CD34+ stem cells from 100 CP-CML patients by multiplex ASO-PCR and sequencing ASO-PCR products at the time of diagnosis. PEMs were detected in 32/100 patients and included F311L, M351T, and T315I. After a median follow-up of 30 months (range 8-48), all patients with PEMs exhibited imatinib resistance. Of 68 patients without PEMs, 24 developed imatinib resistance. Mutations were detected in 21 of these patients by ASO-PCR and KD sequencing. All 32 patients with PEMs had the same mutations. In imatinib-resistant patients without PEMs, we detected F311L, M351T, Y253F, and T315I mutations. All imatinib-resistant patients without T315I and Y253F mutations responded to imatinib dose escalation. In conclusion, BCR-ABL PEMs can be detected in a substantial number of CP-CML patients when investigated using CD34+ stem/progenitor cells. These mutations are associated with imatinib resistance, and mutation testing using CD34+ cells may facilitate improved, patient-tailored treatment

An Automatic Mass Screening System for Cervical Cancer Detection Based on Convolutional Neural Network

Cervical cancer is the fourth most common type of cancer and is also a leading cause of mortality among women across the world. Various types of screening tests are used for its diagnosis, but the most popular one is the Papanicolaou smear test, in which cell cytology is carried out. It is a reliable tool for early identification of cervical cancer, but there is always a chance of misdiagnosis because of possible errors in human observations. In this paper, an auto-assisted cervical cancer screening system is proposed that uses a convolutional neural network trained on Cervical Cells database. The training of the network is accomplished through transfer learning, whereby initializing weights are obtained from the training on ImageNet dataset. After fine-tuning the network on the Cervical Cells database, the feature vector is extracted from the last fully connected layer of convolutional neural network. For final classification/screening of the cell samples, three different classifiers are proposed including Softmax regression (SR), Support vector machine (SVM), and GentleBoost ensemble of decision trees (GEDT). The performance of the proposed screening system is evaluated for two different testing protocols, namely, 2-class problem and 7-class problem, on the Herlev database. Classification accuracies of SR, SVM, and GEDT for the 2-class problem are found to be 98.8%, 99.5%, and 99.6%, respectively, while for the 7-class problem, they are 97.21%, 98.12%, and 98.85%, respectively. These results show that the proposed system provides better performance than its previous counterparts under various testing conditions

User-Centric Context-Aware Location-Based Service for ATM’s Users

The article discusses a context-aware system designed to help Automated Teller Machine (ATM) users quickly locate a working ATM with cash. Many people rely on ATMs for quick cash withdrawals, but often waste time searching for a working machine. The proposed system takes into account the user’s environmental context, such as their activity, the availability of cash in the ATM, the on/off status of the machine, and the presence of a line or crowd at the ATM booth. The objective of the system is to enhance the ATM locator according to the user’s specific needs, utilizing advanced features to recommend the best option for ATM customers based on their current situation. This user-centric approach aims to provide a more efficient and effective system for ATM users

Feline Lower Urinary Tract Disease – Report of Four Cases

This report describes the lower urinary tract disease (LUTD) in four male cats with two different etiologies. All animals were under three years of age and on commercial dry diet. Treatment guidelines prescribed for obstructive and non-obstructive cases were followed. This appears to be the first clinical report on feline LUTD in Pakistan

Molecular docking of Diospyrin as a LOX inhibitory compound

Diospyros lotus is traditionally used in various diseases including inflammation. In the current study an effort has been made to identify a bioactive constituent from D. lotus in order to scientifically validate its use in inflammatory disorders. Diospyrin was isolated from D. lotus and exhibited significant lipoxygenase (LOX) inhibitory activity (IC50 value: 31.89 ± 0.14 μmol). Molecular docking revealed significant molecular interactions between Diospyrin and LOX showing promising potential for further optimization as a potential anti-inflammatory lead compound

Blockchain-Enabled healthcare system for detection of diabetes

Blockchain has penetrated numerous domains such as, industries, government agencies, online voting, and healthcare, etc. Among these domains, healthcare is one of the trending and most important one, which consists of a control system and an Electronic Health Records (EHRs). Diabetes is one of the most rapidly growing chronic diseases that increases the death ratio across the globe. This paper presents a Blockchain-enabled diabetes disease detection framework that provides an earlier detection of this disease by using various machine learning classification algorithms and maintains the EHRs of the patients in a secure manner. Our EHRs sharing framework combines symptom-based disease prediction, Blockchain, and interplanetary file system (IPFS) in which the patient's health information are collected via wearable sensor devices. This information is then sent to EHRs manager, where an ML model is executed for further processing to collect the desired results. The results along with the physiological parameters are then stored in the Blockchain with the approval of concerned patient and his/her practitioner. It is anticipated that our proposed system will help the healthcare society in order to store, process, and share the patient health information in a secure manner

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%

Comparative analysis of analytical and numerical approximations for the flow and heat transfer in mixed convection stagnation point flow of Casson fluid

A mathematical description of non-Newtonian mixed convective Casson fluid stagnation point flow and transfer of heat exists in terms of partial differential equations. We considered the same to study it further under the effects of unsteadiness and varied film thickness parameters. For inclusion of these parameters in the flow model study we modified the available similarity transformations. The governing equations with three independent variables are converted into ordinary differential equations by employing the modified invertible transformations. For the mass and heat transfer in the mixed convection stagnation point unsteady flow of Casson fluid over a stretching sheet, a detailed comparative analysis is carried out in this paper, of the analytical and numerical approximation techniques. The Homotopy Analysis Method is applied for the analytical solutions while the Runge-Kutta with a Shooting Method (RKF45) and Finite Difference Method are used for obtaining the numerical solutions. With these solution schemes we present an analysis of velocity and temperature profiles under the effects of embedded parameters such as the Casson fluid parameter, unsteadiness parameter, mixed convection parameter, Prandtl number, Eckert number, and stretching ratio. The results are presented in both graphical and tabulated forms and they illustrate the dependence of mass and heat transfer characteristics of Casson fluid upon the embedded parameters. Further, we have shown an agreement between the analytical and the approximate solutions for the considered flow and heat transfer which reflects a validation of the results presented here

Jeelo Dobara (Live Life Again): a cross-sectional survey to understand the use of social media and community experience and perceptions around COVID-19 vaccine uptake in three low vaccine uptake districts in Karachi, Pakistan

Objective To gather preliminary insights through formative research on social media usage, and experiences, attitudes and perceptions around COVID-19 and COVID-19 vaccination in three high-risk, underserved districts in Karachi, Pakistan.Design Cross-sectional mixed-method design.Participants 392 adults (361 surveys and 30 in-depth interviews (IDI)) from districts South, East and Korangi in Karachi, Pakistan.Main outcome measures Social media usage and knowledge, perception and behaviour towards COVID-19 infection and vaccination.Results Using social media was associated with an increased probability of getting vaccinated by 1.61 units. Most of the respondents (65%) reported using social media, mainly to watch videos and/or keep in touch with family/friends. 84.76% knew of COVID-19 while 88.37% knew about the COVID-19 vaccination, with 71.19% reported vaccine receipt; reasons to vaccinate included belief that vaccines protect from the virus, and vaccination being mandatory for work. However, only 56.7% of respondents believed they were at risk of disease. Of the 54 unvaccinated individuals, 27.78% did not vaccinate as they did not believe in COVID-19. Despite this, 78.38% of respondents scored high on vaccine confidence. In IDIs, most respondents knew about COVID-19 vaccines: ‘This vaccine will create immunity in your body. Therefore, I think we should get vaccinated’, and over half knew how COVID-19 spreads. Most considered COVID-19 a serious public health problem and thought it important that people get vaccinated. However, there was a low-risk perception of self as only a little over half felt that they were at risk of contracting COVID-19.Conclusion With our conflicting results regarding COVID-19 vaccine confidence, that is, high vaccine coverage but low perception of risk to self, it is likely that vaccine coverage is more a result of mandates and coercion than true vaccine confidence. Our findings imply that interactive social media could be valuable in fostering provaccine sentiment