Machine Learning Techniques for Antimicrobial Resistance Prediction of from Whole Genome Sequence Data.

Aim: Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas .

Method: A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.

Exploring antibiotic resistance genes, mobile gene elements, and virulence gene factors in an urban freshwater samples using metagenomic analysis.

Antibiotic resistance genes (ARGs) and antimicrobial resistance elements (AMR) are novel environmental contaminants that pose a significant risk to human health globally. Freshwater contains a variety of microorganisms that might affect human health; its quality must be assessed before use. However, the dynamics of mobile genetic elements (MGEs) and ARG propagation in freshwater have rarely been studied in Singapore.

Incidence and molecular characterization of ESBL-producing and colistin-resistant Escherichia coli isolates recovered from healthy food-producing animals in Pakistan.

Objectives: To investigate the occurrence and molecular features of ESBL-producing and colistin-resistant Escherichia coli isolates recovered from healthy food-producing animals in Pakistan.

Methods: A total of 153 E. coli isolates were recovered from 250 faecal samples collected from livestock and poultry.

Microbiomes and Resistomes in Biopsy Tissue and Intestinal Lavage Fluid of Colorectal Cancer.

The gut microbiome plays a crucial role in colorectal cancer (CRC) tumorigenesis, but compositions of microorganisms have been inconsistent in previous studies due to the different types of specimens. We investigated the microbiomes and resistomes of CRC patients with colonic biopsy tissue and intestinal lavage fluid (IVF). Paired samples (biopsy tissue and IVF) were collected from 20 patients with CRC, and their gut microbiomes and resistomes were measured by shotgun metagenomics.

Li-Pos: A Light Positioning Framework Leveraging OFDM for Visible Light Communication.

The design of solid-state lighting is vital, as numerous metrics are involved in their exact positioning, and as it is utilized in various processes, ranging from intelligent buildings to the internet of things (IoT). This work aims to determine the power and delay spread from the light source to the receiver plane. The positions of the light source and receiver were used for power estimation.

Physical Inactivity, Non-Communicable Diseases and National Fitness Plan of China for Physical Activity.

Background: China has the world's largest population, going under health transition due to industrialization, urbanization, and a sedentary lifestyle. About 82% of China's disease burden is due to the prevalence of non-communicable diseases (NCDs). Physical activity (active travel) is the best preventive measure against NCDs.

Nexus between green logistic operations and triple bottom line: evidence from infrastructure-led Chinese outward foreign direct investment in Belt and Road host countries.

The Belt and Road initiative (BRI) mainly relies on the traditional and underdeveloped logistical trade routes including the terrestrial and oceanic Silk Road. The poor logistic infrastructure across the BRI region not only restricts trade potential and economic progress but also creates several social and environmental challenges. Therefore, this study investigates the relationship between green logistic operations, economic, environmental, and social indicators of countries along with the BRI.

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method.

Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care.

Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria.

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work.

A hybrid Forecast Cost Benefit Classification of diabetes mellitus prevalence based on epidemiological study on Real-life patient's data.

The increasing ratio of diabetes is found risky across the planet. Therefore, the diagnosis is important in population with extreme risk of diabetes. In this study, a decision-making classifier (J48) is applied over a data-mining platform (Weka) to measure accuracy and linear regression on classification results to forecast cost/benefit ratio in diabetes mellitus patients along with prevalence.

A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data.

The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.