High-Throughput Screening Assay In 384-Well Format For The Identification Of Anti-Trypanosomal Agents Against Trypanosoma Brucei Rhodesiense And Mode Of Cell Death Study

In order to accelerate the discovery of novel leads for the treatment of Human African Trypanosomiasis (HAT), it is necessary to have a simple and sensitive assay to identify positive hits by whole cell viability based high-throughput screening (HTS). In this study, the HTS assay was developed in 384-well format using clear plate and black plate, for comparison. Assay robustness and reproducibility were determined under the optimized conditions in 384-well plate was well tolerated in the HTS assay, including percentage of coefficient of variation of 4.68% and 4.74%, signal-to-background ratio of 12.75 and 12.07, and Z’ factor of 0.79 and 0.82 in clear- and black-384-well plate, respectively

Dependency of NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Cancer cells undergo transcriptional reprogramming to drive tumor progression and metastasis. Using cancer cell lines and patient-derived tumor organoids, we demonstrate that loss of the negative elongation factor (NELF) complex inhibits breast cancer development through downregulating epithelial-mesenchymal transition (EMT) and stemness-associated genes. Quantitative multiplexed Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins (qPLEX-RIME) further reveals a significant rewiring of NELF-E-associated chromatin partners as a function of EMT and a co-option of NELF-E with the key EMT transcription factor SLUG. Accordingly, loss of NELF-E leads to impaired SLUG binding on chromatin. Through integrative transcriptomic and genomic analyses, we identify the histone acetyltransferase, KAT2B, as a key functional target of NELF-E-SLUG. Genetic and pharmacological inactivation of KAT2B ameliorate the expression of EMT markers, phenocopying NELF ablation. Elevated expression of NELF-E and KAT2B is associated with poorer prognosis in breast cancer patients, highlighting the clinical relevance of our findings. Taken together, we uncover a crucial role of the NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis

Activities of Daily Living Recognition Using Deep Learning Approaches

Alzheimer’s disease has become a prevalent disease faced by the elderly in Malaysia. Studies believe that early symptoms of the disease can be detected via activities of daily living. Activities of daily living is a term that collectively refer to the basic or fundamental activities performed independently to care for oneself. In this paper, a deep learning approach is presented for activities of daily living recognition to classify daily life activities such as drinking from the cup, eating at a table, reading the book, using the telephone, and walking. A number of long short-term memory (LSTM) variants have been tested in this study. Experiments results demonstrate a promising accuracy of 94% can be achieved using the public Toyota Smarthome dataset

(+)-Spectaline and iso-6-spectaline induce a possible cross-talk between autophagy and apoptosis in Trypanosoma brucei rhodesiense

In our previous study, two known piperidine alkaloids (+)-spectaline (1) and iso-6-spectaline (2) were isolated from the leaves of Senna spectabilis and showed no toxic effect on L6 cells. In view of the potential use of piperidine alkaloids in S. spectabilis for the treatment of sleeping sickness, further investigation on the cell death actions of the parasite after treatment with compound 1 and 2 suggested that the treated parasites died by a process of autophagy based on the characteristic morphological alterations observed in intracellular T. b. rhodesiense. In search for apoptosis, interestingly, trypanosomes treated with high concentration of compound 1 and 2 after 72 h significantly induced an early apoptosis-like programmed cell death (PCD) such as phosphatidylserine (PS) exposure, loss of mitochondrial membrane potential and caspases activation. No DNA laddering discriminated late apoptosis event. Taken together, these findings demonstrated the potential of compound 1 and 2 as a natural chemotherapeutic capable of inducing a possible cross-talk between autophagy and apoptosis in T. b. rhodesiense

Visual-based vehicle detection with adaptive oversampling

In this study, the class imbalance issue in vehicle detection was addressed. Specifically, certain classes such as Tow Truck were found to have significantly fewer samples compared to others such as normal trucks. This imbalance could be adversely impacted algorithm performance, favouring abundant classes over underrepresented ones. After thorough analysis, an adaptive dataset augmentation approach was proposed for the underrepresented classes. Evaluation was first performed on classic and state-of-the-art object detection methods. All experiments were undertaken on a tiny dataset called Multimedia University Diversity Dataset (MMUVD). The fastest training process and the highest mean average precision (mAP), which stood at 0.686 for mAP50 and 0.439 for mAP50-95, were demonstrated by You Only Look Once version 8 nano (YOLOv8n). By applying adaptive oversampling to the dataset and retesting it again on YOLOv8n, mAP50 was improved to 0.950 and mAP50-95 to 0.717, respectively. Notably, the contribution lay in identifying the optimal detection algorithm for vehicle detection, and the proposed adaptive oversampling method ensured consistent performance across all classes, enhancing the overall accuracy and reliability of the system

Fall risk prediction using temporal gait features and machine learning approaches

Introduction: Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible. Methods: This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers. Results: Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model’s ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task. Discussion: The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model’s generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals

Team 7: Topological Analysis of Infrastructure Network

from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 6 Workshop 18Critical Infrastructures work together to produce goods and services. For example, the power station generates electricity and the water purification station uses the electricity to produce drinking water. Disruption of Civil Infrastructures will affect our national security, economic well being and way of life. This provides a primary motivation to model and understand the interaction between infrastructures. Based on our works in military modeling and simulation (M&S), we have extended these M&S methodologies to the area of Critical Infrastructure Protection. However, we observed that it take a reasonable amount of modeling effort to model a large network of infrastructures

Improved bioavailability of levodopa using floatable spray-coated microcapsules for the management of Parkinson’s disease

Oral administration of levodopa (LD) is the gold standard in managing Parkinson’s disease (PD). Although LD is the most effective drug in treating PD, chronic administration of LD induces levodopa-induced dyskinesia. A continuous and sustained provision of LD to the brain could, therefore, reduce peak-dose dyskinesia. In commercial oral formulations, LD is co-administrated with an AADC inhibitor (carbidopa) and a COMT inhibitor (entacapone) to enhance its bioavailability. Nevertheless, patients are known to take up to five tablets a day because of poor sustained-releasing capabilities that lead to fluctuations in plasma concentrations. To achieve a prolonged release of LD with the aim of improving its bioavailability, floatable spray-coated microcapsules containing all three PD drugs were developed. This gastro-retentive delivery system showed sustained release of all PD drugs, at similar release kinetics. Pharmacokinetics study was conducted and this newly developed formulation showed a more plateaued delivery of LD that is void of the plasma concentration fluctuations observed for the control (commercial formulation). At the same time, measurements of LD and dopamine of mice administered with this formulation showed enhanced bioavailability of LD. This study highlights a floatable, sustained-releasing delivery system in achieving improved pharmacokinetics data compared to a commercial formulation.MOE (Min. of Education, S’pore)Accepted versio

Role transition: A descriptive exploratory study of assistant nurse clinicians in Singapore

Aim: To explore the role‐transition experiences of assistant nurse clinicians after their first year of appointment. Background: The National Nursing Taskforce was set up in Singapore to examine the professional development and recognition of nurses. It created the assistant nurse clinician role as an avenue for the nurses’ career development. The role was intended to assist nurse managers to guide the nursing team in the assessment, planning, and delivery of patient care. Methods: A qualitative descriptive study design was adopted. A purposive sample of 22 registered nurses from six acute care institutions and two polyclinics in Singapore participated in the face‐to‐face interviews. An inductive content analysis approach was used to analyse the data. Results: Four themes emerged: (a) promotion to assistant nurse clinician is a form of recognition and vindication; (b) there was uncertainty about the expected role of the assistant nurse clinician; (c) experience eases transition; and (d) there was a need for peer support, mentorship, and training. Conclusions: The job description of the assistant nurse clinician needs to be better defined to provide greater clarity about their clinical and administrative duties and what is expected of their performance. Implications for Nursing Management: It is essential for nurse managers to provide successful role transition strategies to help the newly appointed assistant nurse clinicians to become efficient and effective leaders