Rationally Tailored Mesoporous Hosts for Optimal Protein Encapsulation.

Proteins play important roles in the therapeutic, medical diagnostic, and chemical catalysis industries. However, their potential is often limited by their fragile and dynamic nature outside cellular environments. The encapsulation of proteins in solid materials has been widely pursued as a route to enhance their stability and ease of handling. Nevertheless, the experimental investigation of protein interactions with rationally designed synthetic hosts still represents an area in need of improvement. In this work, we leveraged the tunability and crystallinity of metal-organic frameworks (MOFs) and developed a series of crystallographically defined protein hosts with varying chemical properties. Through systematic studies, we identified the dominating mechanisms for protein encapsulation and developed a host material with well-tailored properties to effectively encapsulate the protein ubiquitin. Specifically, in our mesoporous hosts, we found that ubiquitin encapsulation is thermodynamically favored. A more hydrophilic encapsulation environment with favorable electrostatic interactions induces enthalpically favored ubiquitin-MOF interactions, and a higher pH condition reduces the intraparticle diffusion barrier, both leading to a higher protein loading. Our findings provide a fundamental understanding of host-guest interactions between proteins and solid matrices and offer new insights to guide the design of future protein host materials to achieve optimal protein loading. The MOF modification technique used in this work also demonstrates a facile method to develop materials easily customizable for encapsulating proteins with different surface properties.

BacterAI maps microbial metabolism without prior knowledge.

Training artificial intelligence (AI) systems to perform autonomous experiments would vastly increase the throughput of microbiology; however, few microbes have large enough datasets for training such a system. In the present study, we introduce BacterAI, an automated science platform that maps microbial metabolism but requires no prior knowledge. BacterAI learns by converting scientific questions into simple games that it plays with laboratory robots. The agent then distils its findings into logical rules that can be interpreted by human scientists. We use BacterAI to learn the amino acid requirements for two oral streptococci: Streptococcus gordonii and Streptococcus sanguinis. We then show how transfer learning can accelerate BacterAI when investigating new environments or larger media with up to 39 ingredients. Scientific gameplay and BacterAI enable the unbiased, autonomous study of organisms for which no training data exist.

Mechanical versus bioprosthetic valves in chronic dialysis: a systematic review and meta-analysis.

BACKGROUND: Many patients with end-stage kidney disease (ESKD) have valvular heart disease requiring surgery. The optimal prosthetic valve is not established in this population. We performed a systematic review and meta-analysis to assess outcomes of patients with dialysis-dependent ESKD who received mechanical or bioprosthetic valves. METHODS: We searched Cochrane Central, Medline and Embase from inception to January 2020. We performed screening, full-text assessment, risk of bias and data collection, independently and in duplicate. Data were pooled using a random-effects model. RESULTS: We identified 28 observational studies (n = 9857 patients, including 6680 with mechanical valves and 3717 with bioprosthetic valves) with a median follow-up of 3.45 years. Twenty-two studies were at high risk of bias and 1 was at critical risk of bias from confounding. Certainty in evidence was very low for all outcomes except bleeding. Mechanical valves were associated with reduced mortality at 30 days (relative risk [RR] 0.79, 95% confidence interval [CI] 0.65-0.97, I 2 = 0, absolute effect 27 fewer deaths per 1000) and at 6 or more years (mean 9.7 yr, RR 0.83, 95% CI 0.72-0.96, I 2 = 79%, absolute effect 145 fewer deaths per 1000), but increased bleeding (incidence rate ratio [IRR] 2.46, 95% CI 1.41-4.27, I 2 = 59%, absolute effect 91 more events per 1000) and stroke (IRR 1.63, 95% CI 1.21-2.20, I 2 = 0%, absolute effect 25 more events per 1000). CONCLUSION: Mechanical valves were associated with reduced mortality, but increased rate of bleeding and stroke. Given very low certainty for evidence of mortality and stroke outcomes, patients and clinicians may choose prosthetic valves based on factors such as bleeding risk and valve longevity. STUDY REGISTRATION: PROSPERO no. CRD42017081863.

Noninferiority Randomized Controlled Trials.

From 2005 to 2015, the publication of noninferiority trials increased by six-fold. Noninferiority trials assess whether a new treatment's efficacy is comparable with that of the standard of care and have several appeals. Noninferiority trials can evaluate for both noninferiority and superiority of a new treatment. In addition, multiple treatment modalities exist, and new treatments may be advantageous for reasons beyond efficacy. Common elements of trial design such as the research question, outcomes, statistical analysis, and interpretation of results differ meaningfully between noninferiority trials and superiority trials. The noninferiority margin, constancy assumption, and assay sensitivity are unique aspects of noninferiority trials. As with all randomized controlled trials, patient engagement in and reporting of noninferiority trials are also important. In this review, we discuss the methodological considerations and limitations of noninferiority trials.

Outcomes of sutureless aortic valve replacement versus conventional aortic valve replacement and transcatheter aortic valve replacement, updated systematic review, and meta-analysis.

BACKGROUND: Sutureless aortic valve replacement (SuAVR) is an alternative to surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). This study compares the effectiveness of SuAVR to SAVR and TAVR. METHODS: We searched MEDLINE and EMBASE from inception to July 2021 for studies evaluating SuAVR, SAVR, and TAVR in adults with aortic stenosis. We performed screening, full-text assessment, data collection, and risk of bias evaluation independently and in duplicate. We evaluated risk of bias using by Cochrane and CLARITY's tools, and certainty in evidence using the GRADE framework. Data were pooled using a random-effects model. RESULTS: We identified one randomized and 78 observational studies (n = 60,689; SuAVR vs. SAVR = 39,171, vs. TAVR = 21,518). All studies were at high or unclear risk of bias, with very-low certainty in effect estimates. Compared to TAVR, SuAVR demonstrates no significant difference in mortality at 30-days (odds ratio [OR]: 0.52, 95% confidence interval [CI: 0.85, 1.16], I2 = 0%), but decreased odds at 2-years (OR: 0.39, 95% CI [0.17, 0.88], I2 = 0%). SuAVR also reduced odds of mild paravalvular regurgitation (OR: 0.11, 95% CI [0.06, 0.21], I2 = 50%). Compared to SAVR, SuAVR was associated with a similar mortality at 30-days (OR: 0.99, 95% CI [0.85, 1.16], I2 = 0%) and 2-years (OR: 0.99, 95% CI [0.43-2.30], I2 = 7%). SuAVR significantly increased odds of permanent pacemaker implantation (OR: 2.5, 95% CI [2.25, 2.77], I2 = 0%). Pooled effect estimates were consistent with results from the randomized trial comparing SuAVR and SAVR. CONCLUSION: Based on very-low quality evidence, SuAVR is associated with similar short- and midterm outcomes compared to TAVR and SAVR. Comparative randomized data with long-term follow-up are required to clarify the role of SuAVR.

A Randomized Comparison of 2 Robotic Virtual Reality Simulators and Evaluation of Trainees' Skills Transfer to a Simulated Robotic Urethrovesical Anastomosis Task.

OBJECTIVE: To determine, via a randomized comparison study, whether robotic simulator-acquired skills transfer to performance of a urethrovesical anastomosis (UVA) on a 3-dimensional-printed bladder model using the da Vinci Robot. MATERIALS AND METHODS: Medical students, surgical residents, and fellows were recruited and divided into 2 groups: Group 1 (G1) (junior trainees) and Group 2 (G2) (senior trainees). Participants were randomized to identical simulator training curricula on the dV-Trainer (dV-T) or da Vinci Surgical Skills Simulator (dVSSS). Participants then completed a UVA task on a 3-dimensional-printed bladder model using the da Vinci robot. Three blinded expert robotic surgeons rated videotaped performances of the UVA task using validated assessment tools, namely, the Global Evaluative Assessment of Robotic Skills (GEARS; overall procedure) and Robotic Anastomosis Competence Evaluation (RACE; specific to UVA). RESULTS: Thirty-nine participants (G1 = 23 and G2 = 16) completed the study. Participants in G2 had significantly more simulation and surgical experience compared with G1 (P <.05). UVA scores of the dVSSS group were higher compared with dV-T (GEARS: P = .09; RACE: P = .01). In the G1 cohort, dVSSS scores were significantly higher than dV-T (GEARS: P = .01; RACE: P <.01). In the G2 cohort, scores were statistically similar (GEARS: P = .32; RACE: P = .91). CONCLUSION: Compared with the dV-T, the dVSSS training led to superior GEARS and RACE scores for performance of the UVA task in junior trainees, but not in senior trainees. The dVSSS can be used to improve surgical skills acquisition in less experienced trainees in a safe and effective manner.