Environmental stiffness restores mechanical homeostasis in vimentin-depleted cells.

Recent experimental evidence indicates a role for the intermediate filament vimentin in regulating cellular mechanical homeostasis, but its precise contribution remains to be discovered. Mechanical homeostasis requires a balanced bi-directional interplay between the cell's microenvironment and the cellular morphological and mechanical state-this balance being regulated via processes of mechanotransduction and mechanoresponse, commonly referred to as mechanoreciprocity. Here, we systematically analyze vimentin-expressing and vimentin-depleted cells in a swatch of in vitro cellular microenvironments varying in stiffness and/or ECM density. We find that vimentin-expressing cells maintain mechanical homeostasis by adapting cellular morphology and mechanics to micromechanical changes in the microenvironment. However, vimentin-depleted cells lose this mechanoresponse ability on short timescales, only to reacquire it on longer time scales. Indeed, we find that the morphology and mechanics of vimentin-depleted cell in stiffened microenvironmental conditions can get restored to the homeostatic levels of vimentin-expressing cells. Additionally, we observed vimentin-depleted cells increasing collagen matrix synthesis and its crosslinking, a phenomenon which is known to increase matrix stiffness, and which we now hypothesize to be a cellular compensation mechanism for the loss of vimentin. Taken together, our findings provide further insight in the regulating role of intermediate filament vimentin in mediating mechanoreciprocity and mechanical homeostasis.

Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution.

Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis cannot provide direct insights without the complete simulation. In this study, we present an ML-based approach for predicting the solvent accessible surface area (SASA) of a nanoparticle (NP), denoting its efficacy, from a fraction of the MD simulations data. The proposed framework uses a time series model for simulating the MD, resulting in an intermediate state, and a second model to calculate the SASA in that state. Empirically, the solution can predict the SASA value 260 timesteps ahead 7.5 times faster with a very low average error of 1956.93. We also introduce the use of an explainability technique to validate the predictions. This work can reduce the computational expense of both processing and data size greatly while providing reliable solutions for the nanomedicine design process.

Neurally adjusted ventilatory assist in ventilated very preterm infants: A crossover study.

OBJECTIVE: To assess the effects of neurally adjusted ventilatory assist (NAVA) ventilation on oxygenation and respiratory parameters in preterm infants. STUDY DESIGN: An observational crossover study with a convenience sample of 19 infants born before 30 gestational weeks. Study parameters were recorded during 3-h periods of both NAVA and conventional ventilation. The proportion of time peripheral oxygen saturation (SpO2 ) and cerebral regional oxygen saturation (cRSO2 ) were within their target ranges, plus the number and severity of desaturation episodes were analyzed. In addition, electrical activity of the diaphragm (Edi), neural respiratory rates, and peak inspiratory pressures (PIPs) were recorded. RESULTS: Infants were born at a median age of 264/7 gestational weeks (range: 230/7 -293/7 ); the study was performed at a median age of 20 days (range: 1-82). The proportion of time SpO2 was within the target range, the number of peripheral desaturations or cRSO2 did not differ between the modes. However, the desaturation severity index was lower (131 vs. 152; p = .03) and fewer manual supplemental oxygen adjustments (1.3 vs. 2.2/h; p = .006) were needed during the period of NAVA ventilation following conventional ventilation. The mean Edi (8.1 vs. 11.4 µV; p < .006) and PIP values (14.9 vs. 19.1; p < .001) were lower during the NAVA mode. CONCLUSIONS: Although NAVA ventilation did not increase the proportion of time with optimal saturation, it was associated with decreased diaphragmatic activity, lower PIPs, less severe hypoxemic events, and fewer manual oxygen adjustments in very preterm infants.