The multilayered myelin sheath is a critical component of both central and peripheral nervous systems, forming a protective barrier against axonal damage and facilitating the movement of nervous impulses. It is primarily composed of cholesterol (CHL1), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), sphingomyelin (SM), and galactosylceramide (GalCer) lipids. For rat sciatic nerve myelin (part of the peripheral nervous system, PNS), it has been found that cholesterol and unsaturated fatty acid contents are significantly lower in diabetic than in non-diabetic conditions. In this study, lipid compositions from experimental data are used to create four model rat sciatic nerve myelin lipid bilayers: PI-containing (nondiabetic and diabetic) and PS-containing (non-diabetic and diabetic), which were then simulated using the all-atom CHARMM36 force field. Simulation results of diabetic membranes indicate less rigid, more laterally expansive, and thinner bilayers as well as potentially reduced interactions between GalCer on opposing myelin leaflets, supporting a direct role of the cholesterol content decrease in instigating myelin deterioration and diabetic peripheral neuropathy. Compared to PI-lipids, PS-lipids were found to cause higher inter-lipid spacing and decreased order within membranes as a result of their smaller headgroup size and higher interlipid hydrogen bonding potential, which allow them to more frequently reside deeper in the membrane plane and produce pushing effects on other lipids. GalCer deuterium order parameters and non-diabetic headgroup-to-headgroup bilayer thicknesses were compared to experimental data, exhibiting close alignment, supporting the future usage of these models to study the PNS myelin sheath.

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Within the biopharmaceutical sector, there exists the need for a contactless multiplex sensor, which can accurately detect metabolite levels in real time for precise feedback control of a bioreactor environment. Reported spectral sensors in the literature only work when fully submerged in the bioreactor and are subject to probe fouling due to a cell debris buildup. The use of a short-wave infrared (SWIR) hyperspectral (HS) cam era allows for efficient, fully contactless collection of large spectral datasets for metabolite quantification. Here, we report the development of an interpretable deep learning system, a convolution metabolite regression (CMR) approach that detects glucose and lactate concentrations using label-free contactless HS images of cell-free spent media samples from Chinese hamster ovary (CHO) cell growth flasks. Using a dataset of <500 HS images, these CMR algorithms achieved a competitive test root-mean-square error (RMSE) performance of glucose quantification within 27 mg/dL and lactate quantification within 20 mg/dL. Conventional Raman spectroscopy probes report a validation performance of 26 and 18 mg/dL for glucose and lactate, respectively. The CMR system trains within 10 epochs and uses a convolution encoder with a sparse bottleneck regression layer to pick the best-performing filters learned by CMR. Each of these filters is combined with existing interpretable models to produce a metabolite sensing system that automatically removes spurious predictions. Collectively, this work will advance the safe and efficient adoption of contactless deep learning sensing systems for fine control of a variety of bioreactor environments.

The degradation effect of mold on the coating in a hot and humid environment is one of the important factors that cause layer failure. Combined with the wire beam electrode (WBE) and the traditional surface analysis technique, the local biodegradation of the coatings and the corrosion behaviors of metal substrates can be characterized accurately by a WBE. Herein, a WBE was used to study the degradation impact of Talaromyces funiculosus (T. funiculosus) isolated from a tropical rainforest environment on the corrosion of polyurethane (PU) coating. After immersion for 14 days, the local current density distribution of the WBE surface can reach ~10−3 A/cm2 in the fungal liquid mediums but maintains ~10−7 A/cm2 in sterile liquid mediums. The |Z|0.01Hz value of the high current densities area (#85 electrode) was 1.06 × 109 Ω cm2 in a fungal liquid medium after 14 days of immersion. After being attacked by T. funiculosus, the degradation of the PU was more severe, and there were wrinkles, cracks, blisters, and even micro-holes distributed randomly on the surface of electrodes. This resulted from the self-corrosion caused by the T. funiculosus degradation of the coating; the corrosion caused by the electric coupling effect of the coating was introduced. Energy dispersive spectroscopy (EDS) and Raman spectra results showed that the corrosion products were flakey and globular, which consisted of γ-FeOOH, γ-Fe2O3, and α-FeOOH.

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