Abstract With their capability to undergo unlimited self-renewal and to differentiate into all cell types in the body, induced pluripotent stem cells (iPSCs), reprogrammed from somatic cells of individual patients with defined factors, have unlimited potential in cell therapy and in modeling complex human diseases. Significant progress has been achieved to improve the safety of iPSCs and the reprogramming efficiency. To avoid the cancer risk and spontaneous reactivation of the reprogramming factors associated with the random integration of viral vectors into the genome, several approaches have been established to deliver the reprogramming factors into the somatic cells without inducing genetic modification. In addition, a panel of small molecule compounds, many of which targeting the epigenetic machinery, have been identified to increase the reprogramming efficiency. Despite these progresses, recent studies have identified genetic and epigenetic abnormalities of iPSCs as well as the immunogenicity of some cells derived from iPSCs. In addition, due to the oncogenic potential of the reprogramming factors and the reprogramming-induced DNA damage, the critical tumor suppressor pathways such as p53 and ARF are activated to act as the checkpoints that suppress induced pluripotency. The inactivation of these tumor suppression pathways even transiently during reprogramming processes could have significant adverse impact on the genome integrity. These safety concerns must be resolved to improve the feasibility of the clinic development of iPSCs into human cell therapy.

This paper considers the problem of testing the equality of two unspecified distributions. The classical omnibus tests such as the Kolmogorov-Smirnov and Cramér-von Mises are known to suffer from low power against essentially all but location-scale alternatives. We propose a new two-sample test that modifies the Neyman's smooth test and extend it to the multivariate case based on the idea of projection pursue. The asymptotic null property of the test and its power against local alternatives are studied. The multiplier bootstrap method is employed to compute the critical value of the multivariate test. We establish validity of the bootstrap approximation in the case where the dimension is allowed to grow with the sample size. Numerical studies show that the new testing procedures perform well even for small sample sizes and are powerful in detecting local features or high-frequency components.

Reaction-equilibrium constants determine the metabolite concentrations necessary to drive flux through metabolic pathways. Group-contribution methods offer a way to estimate reaction-equilibrium constants at wide coverage across the metabolic network. Here, we present an updated group-contribution method with 1) additional curated thermodynamic data used in fitting and 2) capabilities to calculate equilibrium constants as a function of temperature. We first collected and curated aqueous thermodynamic data, including reaction-equilibrium constants, enthalpies of reaction, Gibbs free energies of formation, enthalpies of formation, entropy changes of formation of compounds, and proton- and metal-ion-binding constants. Next, we formulated the calculation of equilibrium constants as a function of temperature and calculated the standard entropy change of formation (Δ_fS^∘) using a model based on molecular properties. The median absolute error in estimating Δ_fS^∘ was 0.013 kJ/K/mol. We also estimated magnesium binding constants for 618 compounds using a linear regression model validated against measured data. We demonstrate the improved performance of the current method (8.17 kJ/mol in median absolute residual) over the current state-of-the-art method (11.47 kJ/mol) in estimating the 185 new reactions added in this work. The efforts here fill in gaps for thermodynamic calculations under various conditions, specifically different temperatures and metal-ion concentrations. These, to our knowledge, new capabilities empower the study of thermodynamic driving forces underlying the metabolic function of organisms living under diverse conditions.

Nanoparticle surfactants (NPSs), formed by using dynamic interactions between nanoparticles and complementary ligands at the liquid-liquid interface, have emerged as "smart emulsifiers" with attributes of high emulsification efficiency, long-term stability, and on-demand emulsification/demulsification capabilities. However, only pH-responsiveness can be adopted for the assembly of reported NPSs formed by electrostatic interactions. Here, we propose an alternative design strategy, by taking advantage of the ferrocenium (Fc⁺ ) sulfate ion pair, to develop a new type of cellulose nanocrystal (CNC) surfactant. The Fc⁺ groups are sensitive to pH, redox reagents and voltage, imparting the CNC surfactants and derived Pickering emulsions with multi-stimuli-responsiveness, and showing promising applications in controllable delivery, release, and biphasic biocatalysis.

As a major metabolite of pyrethroid pesticides, 3-phenoxybenzoic acid (3-PBA) can be an indicator of health risk and human exposure assessment. Based on nanobodies (Nbs), we have developed a rapid flow-through dot enzyme linked immunosorbent assay (dot ELISA) and gold nanoparticle (GNP) lateral-flow immunoassay for detecting 3-PBA. The limit of detection (LOD) values for detecting 3-PBA by flow-through dot ELISA and GNP lateral-flow immunoassay were 0.01 ng mL^-1 and 0.1 ng mL^-1, respectively. The samples (urine and lake water) with and without 3-PBA were detected by both nanobody-based flow-through dot ELISA and GNP lateral-flow immunoassay, as well as liquid chromatography-mass spectrometry (LC-MS) for validation. There was good consistency between the results of the immunoassays. This demonstrated that the two developed nanobody-based immunoassays are suitable for rapid detection of 3-PBA.

Interstitial atoms usually diffuse much faster than vacancies, which is often the root cause for the ineffective recombination of point defects in metals under irradiation. Here, via ab initio modeling of single-defect diffusion behavior in the equiatomic NiCoCrFe(Pd) alloy, we demonstrate an alloy design strategy that can reduce the diffusivity difference between the two types of point defects. The two diffusivities become almost equal after substituting the NiCoCrFe base alloy with Pd. The underlying mechanism is that Pd, with a much larger atomic size (hence larger compressibility) than the rest of the constituents, not only heightens the activation energy barrier (E_a) for interstitial motion by narrowing the diffusion channels but simultaneously also reduces E_a for vacancies due to less energy penalty required for bond length change between the initial and the saddle states. Our findings have a broad implication that the dynamics of point defects can be manipulated by taking advantage of the atomic size disparity, to facilitate point-defect annihilation that suppresses void formation and swelling, thereby improving radiation tolerance.

High- (and medium-) entropy alloys have emerged as potentially suitable structural materials for nuclear applications, particularly as they appear to show promising irradiation resistance. Recent studies have provided evidence of the presence of local chemical order (LCO) as a salient feature of these complex concentrated solid-solution alloys. However, the influence of such LCO on their irradiation response has remained uncertain thus far. In this work, we combine ion irradiation experiments with large-scale atomistic simulations to reveal that the presence of chemical short-range order, developed as an early stage of LCO, slows down the formation and evolution of point defects in the equiatomic medium-entropy alloy CrCoNi during irradiation. In particular, the irradiation-induced vacancies and interstitials exhibit a smaller difference in their mobility, arising from a stronger effect of LCO in localizing interstitial diffusion. This effect promotes their recombination as the LCO serves to tune the migration energy barriers of these point defects, thereby delaying the initiation of damage. These findings imply that local chemical ordering may provide a variable in the design space to enhance the resistance of multi-principal element alloys to irradiation damage.

Objective

To determine the impact on referral patterns of using a Multivariate Index Assay, CA125, modified-American College of Obstetricians and Gynecologists referral guidelines, and clinical assessment among patients undergoing surgery for an adnexal mass after initial evaluation by nongynecologic oncologists.

Study design

Overall, 770 patients were enrolled by nongynecologic oncologists from 2 related, multiinstitutional, prospective trials and analyzed retrospectively. All patients had preoperative imaging and biomarker analysis. The subset of patients enrolled by nongynecologic oncologists was analyzed to determine the projected referral patterns and sensitivity for malignancy based on multivariate index assay (MIA), CA125, modified-American College of Obstetricians and Gynecologists (ACOG) guidelines, and clinical assessment compared with actual practice.

Results

The prevalence of malignancy was 21.3% (n = 164). In clinical practice, 462/770 patients (60.0%) were referred to a gynecologic oncologist for surgery. Triage based on CA125 predicted referral of 157/770 patients (20.4%) with sensitivity of 68.3% (95% confidence interval [CI], 60.8-74.9). Triage based on modified-ACOG guidelines would have resulted in referral of 256/770 patients (33.2%) with a sensitivity of 79.3% (95% CI, 72.4-84.8). Clinical assessment predicted referral of 184/763 patients (24.1%) with a sensitivity of 73.2% (95% CI, 65.9-79.4). Risk stratification using multivariate index assay would have resulted in referral of 429/770 (55.7%) patients, with sensitivity of 90.2% (95% CI, 84.7-93.9). MIA demonstrated statistically significant higher sensitivity (P < .0001) and lower specificity (P < .0001) for detecting malignancy compared with clinical assessment, CA125, and modified-ACOG guidelines.

Conclusion

In this study population, use of MIA as a risk stratification test was associated with referral patterns by nongynecologic oncologists comparable to actual clinical practice and higher sensitivity for malignancy than other adnexal mass triage algorithms.

Background

The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.

Results

In this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.

Conclusions

Overall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches.