Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

In this paper, we introduce machine learning approaches that are used to prioritize outpatients (OP) according to their current health state, resulting in self-optimizing heterogeneous networks (HetNet) that intelligently adapt according to users' needs. We use a naïve Bayesian classifier to analyze data acquired from OPs' medical records, alongside data from medical Internet of Things (IoT) sensors that provide the current state of the OP. We use this machine learning algorithm to calculate the likelihood of a life-threatening medical condition, in this case an imminent stroke. An OP is assigned high-powered resource blocks (RBs) according to the seriousness of their current health state, enabling them to remain connected and send their critical data to the designated medical facility with minimal delay. Using a mixed integer linear programming formulation (MILP), we present two approaches to optimizing the uplink side of a HetNet in terms of user-RB assignment: a Weighted Sum Rate Maximization (WSRMax) approach and a Proportional Fairness (PF) approach. Using these approaches, we illustrate the utility of the proposed system in terms of providing reliable connectivity to medical IoT sensors, enabling the OPs to maintain the quality and speed of their connection. Moreover, we demonstrate how system response can change according to alterations in the OPs' medical conditions

Quantitative ultrasound imaging of therapy response in bladder cancer in vivo.

Background and aimsQuantitative ultrasound (QUS) was investigated to monitor bladder cancer treatment response in vivo and to evaluate tumor cell death from combined treatments using ultrasound-stimulated microbubbles and radiation therapy.MethodsTumor-bearing mice (n=45), with bladder cancer xenografts (HT- 1376) were exposed to 9 treatment conditions consisting of variable concentrations of ultrasound-stimulated Definity microbubbles [nil, low (1%), high (3%)], combined with single fractionated doses of radiation (0 Gy, 2 Gy, 8 Gy). High frequency (25 MHz) ultrasound was used to collect the raw radiofrequency (RF) data of the backscatter signal from tumors prior to, and 24 hours after treatment in order to obtain QUS parameters. The calculated QUS spectral parameters included the mid-band fit (MBF), and 0-MHz intercept (SI) using a linear regression analysis of the normalized power spectrum.Results and conclusionsThere were maximal increases in QUS parameters following treatments with high concentration microbubbles combined with 8 Gy radiation: (ΔMBF = +6.41 ± 1.40 (±SD) dBr and SI= + 7.01 ± 1.20 (±SD) dBr. Histological data revealed increased cell death, and a reduction in nuclear size with treatments, which was mirrored by changes in quantitative ultrasound parameters. QUS demonstrated markers to detect treatment effects in bladder tumors in vivo

Minipool Caprylic Acid Fractionation of Plasma Using Disposable Equipment: A Practical Method to Enhance Immunoglobulin Supply in Developing Countries

Plasma-derived immunoglobulin G (IgG) is on WHO’s Essential Medicines List, yet developing countries face severe shortages of this critical treatment. Infusion of IgG prepared from locally-collected plasma provides an advantageous mix of antibodies to viral and bacterial pathogens found in the living environment, and this can reduce recurrent infections in immune-deficient patients. We developed a simple manufacturing process using disposable equipment (blood bags, hemodialyzer, and filters) to isolate immunoglobulins from minipools of 20 plasma donations. This process yields a ca. 90% pure virally-inactivated immunoglobulin fraction at 50–60% recovery. Anti-hepatitis B and anti-rubella immunoglobulins were enriched fourfold to sixfold. The product was free of in-vitro thrombogenic and proteolytic activity, confirming its expected clinical safety profile. Virus validations showed caprylic acid treatment robustly inactivated or removed infectivity of lipid-enveloped viruses, including human immunodeficiency virus (HIV) and hepatitis C virus model. This simple and cost-effective process is implemented in Egypt to prepare experimental batches for clinical evaluation. It can enhance immunoglobulin supplies to treat immunodeficient patients through passive transmission of antibodies directed against local pathogens. The method requires minimal training and reasonable infrastructure, and is a practical means to prepare convalescent hyperimmune IgG during infectious outbreaks such as the current Ebola episode.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto Clodomiro Picado (ICP)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí

Comparative analysis of within-host diversity among vaccinated COVID-19 patients infected with different SARS-CoV-2 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly evolving RNA virus that mutates within hosts and exists as viral quasispecies. Here, we evaluated the within-host diversity among vaccinated and unvaccinated individuals (n = 379) infected with different SARS-CoV-2 Variants of Concern. The majority of samples harbored less than 14 intra-host single-nucleotide variants (iSNVs). A deep analysis revealed a significantly higher intra-host diversity in Omicron samples than in other variants (p value < 0.05). Vaccination status and type had a limited impact on intra-host diversity except for Beta-B.1.315 and Delta-B.1.617.2 vaccinees, who exhibited higher diversity than unvaccinated individuals (p values: <0.0001 and <0.0021, respectively). Three immune-escape mutations were identified: S255F in Delta and R346K and T376A in Omicron-B.1.1.529. The latter 2 mutations were fixed in BA.1 and BA.2 genomes, respectively. Overall, the relatively higher intra-host diversity among vaccinated individuals and the detection of immune-escape mutations, despite being rare, suggest a potential vaccine-induced immune pressure in vaccinated individuals.The authors are grateful for the leadership and assistance provided by the Ministry of Public Health in Qatar, the virology laboratory staff at Hamad Medical Corporation, and Qatar Biobank (QBB) team. This project was funded by Qatar National Research Fund (QNRF; Project number UREP28-164-3-048) and Qatar University (Project number QUCG-BRC-22/23-547). The article processing charges were paid from grant no. QUCG-BRC-2022/23-578