The resemblance of lipid membrane models to physiological membranes determines how well molecular dynamics (MD) simulations imitate the dynamic behavior of cell membranes and membrane proteins. Physiological lipid membranes are composed of multiple types of phospholipids, and the leaflet compositions are generally asymmetric. Here we describe an approach for self-assembly of a Coarse-Grained (CG) membrane model with physiological composition and leaflet asymmetry using the MARTINI force field. An initial set-up of two boxes with different types of lipids according to the leaflet asymmetry of mammalian cell membranes stacked with 0.5 nm overlap, reliably resulted in the self-assembly of bilayer membranes with leaflet asymmetry resembling that of physiological mammalian cell membranes. Self-assembly in the presence of a fragment of the plasma membrane protein syntaxin 1A led to spontaneous specific positioning of phosphatidylionositol(4,5)bisphosphate at a positively charged stretch of syntaxin consistent with experimental data. An analogous approach choosing an initial set-up with two concentric shells filled with different lipid types results in successful assembly of a spherical vesicle with asymmetric leaflet composition. Self-assembly of the vesicle in the presence of the synaptic vesicle protein synaptobrevin 2 revealed the correct position of the synaptobrevin transmembrane domain. This is the first CG MD method to form a membrane with physiological lipid composition as well as leaflet asymmetry by self-assembly and will enable unbiased studies of the incorporation and dynamics of membrane proteins in more realistic CG membrane models.

Diagnosing infectious diseases using quantitative polymerase chain reaction (qPCR) offers a conclusive result in determining the infection, the strain or type of pathogen, and the level of infection. However, due to the high-cost instrumentation involved and the complexity in maintenance, it is rarely used in the field to make a quick turnaround diagnosis. In order to provide a higher level of accessibility than current qPCR devices, a set of 3D manufacturing methods is explored as a possible option to fabricate a low-cost and portable qPCR device. The key advantage of this approach is the ability to upload the digital format of the design files on the internet for wide distribution so that people at any location can simply download and feed into their 3D printers for quick manufacturing. The material and design are carefully selected to minimize the number of custom parts that depend on advanced manufacturing processes which lower accessibility. The presented 3D manufactured qPCR device is tested with 20-μL samples that contain various concentrations of lentivirus, the same type as HIV. A reverse-transcription step is a part of the device’s operation, which takes place prior to the qPCR step to reverse transcribe the target RNA from the lentivirus into complementary DNA (cDNA). This is immediately followed by qPCR which quantifies the target sequence molecules in the sample during the PCR amplification process. The entire process of thermal control and time-coordinated fluorescence reading is automated by closed-loop feedback and a microcontroller. The resulting device is portable and battery-operated, with a size of 12 × 7 × 6 cm³ and mass of only 214 g. By uploading and sharing the design files online, the presented low-cost qPCR device may provide easier access to a robust diagnosis protocol for various infectious diseases, such as HIV and malaria.

Neuronal exocytosis facilitates the propagation of information through the nervous system pertaining to bodily function, memory, and emotions. Using amperometry, the sub-millisecond dynamics of exocytosis can be monitored and the modulation of exocytosis due to drug treatment or neurodegenerative diseases can be studied. Traditional single-cell amperometry is a powerful technique for studying the molecular mechanisms of exocytosis, but it is both costly and labor-intensive to accumulate statistically significant data. To surmount these limitations, we have developed a silicon-based electrode array with 1024 on-chip electrodes that measures oxidative signal in 0.1 millisecond intervals. Using the developed device, we are able to capture the modulation of exocytosis due to Parkinson’s disease treatment (L-Dopa), with statistical significance, within 30 total minutes of recording. The validation study proves our device’s capability to accelerate the study of many pharmaceutical treatments for various neurodegenerative disorders that affect neurotransmitter secretion to a matter of minutes.

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted.