Mechanistic Study of Membrane Disruption by Antimicrobial Methacrylate Random Copolymers by the Single Giant Vesicle Method.

Cationic amphiphilic polymers have been a platform to create new antimicrobial materials that act by disrupting bacterial cell membranes. While activity characterization and chemical optimization have been done in numerous studies, there remains a gap in our knowledge on the antimicrobial mechanisms of the polymers, which is needed to connect their chemical structures and biological activities. To that end, we used a single giant unilamellar vesicle (GUV) method to identify the membrane-disrupting mechanism of methacrylate random copolymers. The copolymers consist of random sequences of aminoethyl methacrylate and methyl (MMA) or butyl (BMA) methacrylate, with low molecular weights of 1600-2100 g·mol-1. GUVs consisting of an 8:2 mixture of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-(1'-rac-glycerol), sodium salt (POPG) and those with only 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) were prepared to mimic the bacterial (Escherichia coli) or mammalian membranes, respectively. The disruption of bacteria and mammalian cell membrane-mimetic lipid bilayers in GUVs reflected the antimicrobial and hemolytic activities of the copolymers, suggesting that the copolymers act by disrupting cell membranes. The copolymer with BMA formed pores in the lipid bilayer, while that with MMA caused GUVs to burst. Therefore, we propose that the mechanism is inherent to the chemical identity or properties of hydrophobic groups. The copolymer with MMA showed characteristic sigmoid curves of the time course of GUV burst. We propose a new kinetic model with a positive feedback loop in the insertion of the polymer chains in the lipid bilayer. The novel finding of alkyl-dependent membrane-disrupting mechanisms will provide a new insight into the role of hydrophobic groups in the optimization strategy for antimicrobial activity and selectivity.

Quantum Neural Networks and Topological Quantum Field Theories.

Our work intends to show that: (1) Quantum Neural Networks (QNNs) can be mapped onto spin-networks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theory (TQFT); (2) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNNs, relating it to the topological features of the graph structures involved.

Length of Hospital Stay after Total Knee Arthroplasty: A Correlation Study on 1200 Patients.

In countries with a high average population age, total knee arthroplasty is still carried out in an inpatient setting. The roadmap to performing major surgery on an outpatient basis passes through the understanding of those perioperative features that correlate with higher lengths of hospital stay (LOS). A retrospective database of 1200 patients was reviewed for retrieving preoperative and perioperative factors including anthropometric and demographic data, comorbidities, preoperative laboratory assessment, and surgical time. Considering the LOS as a discrete series, data were analyzed by means of logistic regression with multiple univariate and multivariate models. The results showed a median length of hospital stay of 3 (IQR 3, 4) days. According to multiple univariate analysis, arterial hypertension (p = 0.008), diabetes mellitus (p = 0.028), CCI score (p < 0.001), ASA score (p = 0.006), surgical time (p < 0.001) and intraoperative blood loss (p < 0.001) were significantly associated with the duration of hospital stay in days. Moreover, preoperative hemoglobin value was inversely correlated to the LOS (p = 0.008). Multivariate analysis showed a significant correlation between LOS and surgical time and intraoperative blood loss. Many factors influence the permanence of the inpatient and acting on those variables, by stabilizing comorbidities and optimizing laboratory values, may reduce the overall healthcare burden.