DataSheet1_Frequency dependent growth of bacteria in living materials.pdf

The fusion of living bacteria and man-made materials represents a new frontier in medical and biosynthetic technology. However, the principles of bacterial signal processing inside synthetic materials with three-dimensional and fluctuating environments remain elusive. Here, we study bacterial growth in a three-dimensional hydrogel. We find that bacteria expressing an antibiotic resistance module can take advantage of ambient kinetic disturbances to improve growth while encapsulated. We show that these changes in bacterial growth are specific to disturbance frequency and hydrogel density. This remarkable specificity demonstrates that periodic disturbance frequency is a new input that engineers may leverage to control bacterial growth in synthetic materials. This research provides a systematic framework for understanding and controlling bacterial information processing in three-dimensional living materials.</p

Additional file 1 of Multiparametric mapping by cardiovascular magnetic resonance imaging in cardiac tumors

Additional file 1. Supplementary Methods and Materials. Table S2. Supplementary Table 2

Additional file 1 of Prognostic value of right atrial strain derived from cardiovascular magnetic resonance in non-ischemic dilated cardiomyopathy

Additional file 1: Figure S1. Patient inclusion flowchart. CMR, cardiovascular magnetic resonance. Table S1. Univariable Cox analysis for all-cause mortality and composite heart failure endpoint. Table S2. Multivariable analysis of right atrial strain among patients with LVEF < 35%

Additional file 1 of Optimized data-independent acquisition approach for proteomic analysis at single-cell level

Additional file 1. Table S1. Large amount peptide amount and peptide amount per cell. Table S2. Detail information of 30 MS runs. Table S3. Information of single-cell level co-searching libraries. Table S4. Information of ten-cell level co-searching libraries. Table S5. Identified protein groups towards library size

Regional amyloid distribution and impact on mortality in light-chain amyloidosis: a T1 mapping cardiac magnetic resonance study

Background: T1 mapping allows quantitative assessment of “diffuse” deposition of amyloid protein in the myocardium. Early detection of cardiac involvement and potential prognostic improvement could benefit patients with AL amyloidosis. Objectives: This study aims to evaluate the regional variation of amyloid infiltration in the left ventricle and the prognostic value of T1 mapping in patients with AL amyloidosis. Methods: We prospectively enrolled 77 patients with AL amyloidosis who underwent cardiac magnetic resonance on a 3.0-T scanner. Native T1 and extracellular volume (ECV) were quantitated on the basal, mid, and apical levels of the left ventricle. Late gadolinium enhancement (LGE) pattern (no or non-specific LGE, sub-endocardial LGE, and transmural LGE) was also assessed. Forty healthy subjects served as controls. The primary end point was all-cause mortality. Results: Basal ECV (26.9 ± 2.8% versus 31.1 ± 4.9%, p p = .003) were significantly higher than apical ECV in patients with transmural LGE. During the follow-up period (median duration, 28 months; 25th–75th percentile, 13.5–38.0 months), 46 patients died. Basal ECV has the largest area under the curve of 0.845 (95% CI, 0.747–0.917) to predict all-cause mortality. Multivariable Cox analysis indicated that basal ECV was an independent prognostic factor and showed incremental prognostic value beyond NYHA class, Mayo stage, and LGE pattern. Conclusion: We demonstrated that T1 mapping may have the potential to detect a characteristic amyloid deposition with a decreasing gradient from base to apex. Furthermore, myocardial ECV indicated that basal amyloid infiltration provided robust and incremental prognostic value in patients with AL amyloidosis.</p

Additional file 1 of Cardiovascular magnetic resonance characterization of rheumatic mitral stenosis: findings from three worldwide endemic zones

Additional file 1. Additional tables

Epidemiological characteristics of the cases used to identify transmission risk factors.

Note that the data do not include all the study samples: for 5 clusters we were not able to identify a likely transmission event, and these clusters were excluded from this analysis. Transmitters are defined as those individuals estimated to be likely transmitters and/or likely index cases detected by TransPhylo. The figure shows estimated odd ratios for each risk factor tested. *Fisher’s exact test. Comparisons were made between transmitter cases and the rest of the clustered samples.</p

Weighted mean number of unsampled tuberculosis cases.

For each posterior transmission tree, we associate a weighting factor tk, where k is the number of sampled cases for which transmission happened after diagnosis, and t = 0.1. This accounts for the fact that individuals are treated once diagnosed, and so are less likely to transmit. This figure shows the mean number of unsampled cases for one of the simulated clock rates (0.363). The results for all clock rates appear in S5 Fig.</p

The posterior probability that each individual is the index case for a cluster versus the time of diagnosis of the individual.

The individual with highest posterior probability to be the index case is shown in red for each cluster. In some clusters, the first diagnosed case was the estimated index case, in that it had the highest probability of being the index case (e.g., CL002). In contrast, in the majority of clusters the most likely index case was not the first diagnosed individual (e.g., CL010 and CL023) or was not sampled (e.g., CL016 and CL003). The Psamp values are the posterior probability that the index case was any of the sampled individuals—in some clusters (e.g., CL003) the index case was likely to have been an unsampled individual.</p

Main characteristics of the study population.

Main characteristics of the study population.</p