The fast pace of today's world has presented several challenges in the area of healthcare. Depression, hypertension, diabetes, cancers and several infectious diseases are just some of the common outcomes associated with the high speed stress-filled lifestyle. Early diagnosis has been the goal for prompt arrest and management of these health conditions. This has been a challenge in recent times. However, great scientific advancement with improved potential in medical diagnosis has equally been a giant stride in times like these. Early disease detection even before symptoms' presentation, improved imaging of internal body structure, as well as ease of diagnostic procedures, have been developed with the help of a new branch of laboratory medicine termed nanodiagnostics. Use of microchips, biosensors, nanorobots, nano identification of single celled structures, and microelectromechanical systems are current techniques being developed for use in nanodiagnostics. This piece of write up takes a panoramic view of available nanotechnological advances in current use for medical diagnosis and projecting into future possibilities and potentials for an improved health care delivery.

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.

Nanotoxicology, a branch of bionanoscience focuses on the study of the hazardous interactions between nanomaterials and the ecosystem and ascertaining its consequent implications. Nanomaterial-cell interactions are dependent on numerous factors such as size, shape, type and surface coatings/charge of nanomaterials. These factors in association with cell membrane factors such as charge and formation of the protein corona influence the uptake and internalization of these particles leading to their potential toxicity. Understanding the different routes of exposure, their transport, behaviour and eventual fate is also of importance. Toxicities that occur to the living systems are consequences of various causes/dysfunctions such as ROS production, loss of membrane integrity, releases of toxic metal ions that bind with specific cell receptors and undergo certain conformations that inhibit normal cell function resulting in cytotoxicity, genotoxicity and possible cell necrosis. This paper attempts to review the available research pertaining to nanomaterial-cell interactions and their potential toxicity.

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Silver nanoparticles were synthesized using eco-friendly method with the extract of Carica papaya as a reducing and stabilizing agent. Metronidazole 200 mg was loaded as a model drug to the silver nanoparticles. The percentage yield of the metronidazole nanoparticle was high (96.00%). The entrapment efficiency 85.60% while the loading capacity was 8.90%. Differential scanning calorimetry showed there was no interaction between the reducing agent and model drug. Characterization of the metronidazole malpractices using UV-vis spectroscopy, zeta sizer, scanning electron microscopy (SEM) was performed. The UV-Vis spectroscopy showed surface plasmon resonance of 435nm for the silver nanoparticle. The mean particle size was 250 nm while the polydispersity index was 0.22. The metronidazole nanoparticle showed an extended and controlled release profile. The kinetics of release was zero-order (R 2 = 0.9931) for the metronidazole nanoparticle while the metronidazole normal release tablet followed Higuchi kinetics (R 2 = 0.9745).