Unmasking Bacterial Identities: Exploiting Silver Nanoparticle 'Masks' for Enhanced Raman Scattering in the Rapid Discrimination of Diverse Bacterial Species and Antibiotic-Resistant Strains.

Unraveling bacterial identity through Raman scattering techniques has been persistently challenging due to homogeneously amplified Raman signals across a wide variety of bacterial molecules, predominantly protein- or nucleic acid-mediated. In this study, we present an approach involving the use of silver nanoparticles to completely and uniformly "mask" adsorption on the surface of bacterial molecules through sodium borohydride and sodium chloride. This approach enables the acquisition of enhanced surface-enhanced Raman scattering (SERS) signals from all components on the bacterial surface, facilitating rapid, specific, and label-free bacterial identification. For the first time, we have characterized the identity of a bacterium, including its DNA, metabolites, and cell walls, enabling the accurate differentiation of various bacterial strains, even within the same species. In addition, we embarked on an exploration of the origin and variability patterns of the main characteristic peaks of Gram-positive and Gram-negative bacteria. Significantly, the SERS peak ratio was found to determine the inflection point of accelerated bacterial death upon treatment with antimicrobials. We further applied this platform to identify 15 unique clinical antibiotic-resistant bacterial strains, including five Escherichia coli strains in human urine, a first for Raman technology. This work has profound implications for prompt and accurate identification of bacteria, particularly antibiotic-resistant strains, thereby significantly enhancing clinical diagnostics and antimicrobial treatment strategies.

Designing cancer screening trials for reduction in late-stage cancer incidence.

Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Plasmonic Bowl-Shaped Nanopore for Raman Detection of Single DNA Molecules in Flow-Through.

Plasmonic nanopores combined with Raman spectroscopy are emerging as platforms for single-molecule detection and sequencing in label-free mode. Recently, the ability of identifying single DNA bases or amino acids has been demonstrated for molecules adsorbed on plasmonic particles and then delivered into the plasmonic pores. Here, we report on bowl-shaped plasmonic gold nanopores capable of direct Raman detection of single λ-DNA molecules in a flow-through scheme. The bowl shape enables the incident laser to be focused into the nanopore to generate a single intense hot spot with no cut off in pore size. Therefore, we achieved ultrasmall focusing of NIR light in a spot of 3 nm. This enabled us to detect 7 consecutive bases along the DNA chain in flow-through conditions. Furthermore, we found a novel electrofluidic mechanism to manipulate the molecular trajectory within the pore volume so that the molecule is pushed toward the hot spot, thus improving the detection efficiency.

Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules.

Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points. We propose a new criterion for DSRs that accounts for benefit-cost tradeoff during the course of disease surveillance. We develop two methods to estimate the time-invariant DSRs optimizing the proposed criterion, and establish asymptotic properties for the estimated parameters of biomarkers indexing the DSRs. The first approach estimates the optimal decision rules for each individual at every stage via regression modeling, and then estimates the time-invariant DSRs via a classification procedure with the estimated time-varying decision rules as the response. The second approach proceeds by optimizing a relaxation of the empirical objective, where a surrogate function is utilized to facilitate computation. Extensive simulation studies are conducted to demonstrate the superior performances of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study (PASS).

Autoxidation Mechanism and Kinetics of Methacrolein in the Atmosphere.

Elucidating the autoxidation of volatile organic compounds (VOCs) is crucial to understanding the formation mechanism of secondary organic aerosols, but it has been proven to be challenging due to the complexity of reactions under atmospheric conditions. Here, we report a comprehensive theoretical study of atmospheric autoxidation in VOCs exemplified by the atmospherically important methacrolein (MACR), a major oxidation product of isoprene. The results indicate that the Cl-adducts and H-abstraction products of MACR readily react with O2 and undergo subsequent isomerizations via H-shift and cyclization, forming a large variety of lowly and highly oxygenated organic molecules. In particular, the first- and third-generation oxidation products derived from the Cl-adducts and the methyl-H-abstraction complexes are dominated in the atmospheric autoxidation, for which the fractional yields are remarkably affected by the NO concentration. The present findings have important implications for a systematical understanding of the oxidation processes of isoprene-derived compounds in the atmospheric environments.

Development of an Endonuclease V-Assisted Analytical Method for Sequencing Analysis of Deoxyinosine in DNA.

Deoxyinosine (dI) is a highly mutagenic lesion that preferentially pairs with deoxycytidine during replication, which may induce A to G transition and ultimately contribute to carcinogenesis. Therefore, finding the site of dI modification in DNA is of great value for both basic research and clinical applications. Herein, we developed a novel method to sequence the dI modification site in DNA, which utilizes endonuclease V (EndoV)-dependent deamination repair to specifically label the modification site with biotin-14-dATP that allows the affinity enrichment of dI-bearing DNA for sequencing. We have achieved efficient determination of the location of the modified nucleotide in dI-bearing plasmid DNA with the assistance of EndoV-dependent deamination repair. We have also successfully applied this approach to locate the dI modification sites in the mitochondrial DNA of human cells. Our method should be generally applicable for genome-wide sequencing analysis of dI modifications in living organisms.

Causal relationship between type 1 diabetes and hypothyroidism: A Mendelian randomization study.

OBJECTIVES: Although an association between type 1 diabetes (T1D) and hypothyroidism has been found in multiple observational studies, whether T1D plays a causal role in the development of hypothyroidism remains uncertain. Therefore, this Mendelian randomization (MR) study aimed to investigate the causal association between T1D and hypothyroidism. METHODS: Independent single-nucleotide polymorphisms associated with T1D with genome-wide significance were selected as instrumental variables from a large genome-wide association study (GWAS) of T1D. Hypothyroidism GWAS summary statistics were obtained from the Thyroidomics Consortium. The inverse-variance weighted (IVW) method was used as the primary analysis for estimating the effect of the exposure on the outcome. We also used MR-Egger, the weighted median method, MR-Robust, and other methods to confirm the results. RESULTS: T1D had a positive causal association with hypothyroidism [IVW, odds ratio (OR) = 1.083, 95% confidence interval (CI), 1.046-1.122; p < .001]. MR-Egger regression indicated that directional pleiotropy did not bias the result (intercept = 0.006; p = .295). The causal association was verified in an independent validation set (IVW, OR = 1.099, 95% CI, 1.018-1.186; p = .017). The results were robust according to various MR methods, and the results of the reverse MR analysis did not support reverse causation (p > .05). CONCLUSIONS: The MR analysis results indicated a causal association between T1D and hypothyroidism. Therefore, it is recommended that patients with T1D undergo thyroid function tests regularly to minimize the risk of undiagnosed hypothyroidism among young patients with T1D.

Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records.

Electronic health records (EHRs) have become a platform for data-driven granular-level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely, we consider a penalized multilevel generalized linear model. We decompose regional contribution into smooth and sparse signals, which are automatically identified by a combination of fusion and sparse penalties imposed on the likelihood function. In addition, we weigh the proposed likelihood to account for the missingness and potential nonrepresentativeness arising from the EHR data. We develop a novel alternating minimization algorithm, which is computationally efficient, easy to implement, and guarantees convergence. Simulation studies demonstrate superior performance of the proposed method. Finally, we apply our method to the University of Wisconsin Population Health Information Exchange database.

Aerosol mass spectrometry of neutral species based on a tunable vacuum ultraviolet free electron laser.

A vacuum ultraviolet free electron laser (VUV-FEL) photoionization aerosol mass spectrometer (AMS) has been developed for online measurement of neutral compounds in laboratory environments. The aerosol apparatus is mainly composed of a smog chamber and a reflectron time-of-flight mass spectrometer (TOF-MS). The indoor smog chamber had a 2 m3 fluorinated ethylene propylene film reactor placed in a temperature- and humidity-controlled room, which was used to generate the aerosols. The aerosols were sampled via an inlet system consisting of a 100 µm orifice nozzle and aerodynamic lenses. The application of this VUV-FEL AMS to the α-pinene ozonolysis under different concentrations reveals two new compounds, for which the formation mechanisms are proposed. The present findings contribute to the mechanistic understanding of the α-pinene ozonolysis in the neighborhood of emission origins of α-pinene. The VUV-FEL AMS method has the potential for chemical analysis of neutral aerosol species during the new particle formation processes.

Restricted survival benefit with right-censored data.

The hazard ratio is widely used to quantify treatment effects. However, it may be difficult to interpret for patients and practitioners, especially when the hazard ratio is not constant over time. Alternative measures of the treatment effects have been proposed such as the difference of the restricted mean survival times, the difference in survival proportions at some fixed follow-up time, or the net chance of a longer survival. In this paper, we propose the restricted survival benefit (RSB), a quantity that can incorporate multiple useful measurements of treatment effects. Hence, it provides a framework for a comprehensive assessment of the treatment effects. We provide estimation and inference procedures for the RSB that accommodate censored survival outcomes, using methods of the inverse-probability-censoring-weighted U$U$ -statistic and the jackknife empirical likelihood. We conduct extensive simulation studies to examine the numerical performance of the proposed method, and we analyze data from a randomized Phase III clinical trial (SWOG S0777) using the proposed method.

Selective Ligand Sensitization of Lanthanide Nanoparticles for Multilevel Information Encryption with Excellent Durability.

Durable and multilevel information encryption technology has been of great importance in recent decades. Here, an inkjet printer-adaptable invisible ink was prepared with lanthanide nanoparticles, and optical decoding of information could only be achieved when specific ligand dipicolinic acid was utilized in the presence of UV illumination. In addition, the proposed protocols displayed long shelf life (>one year) and excellent durability even at harsh conditions such as in the presence of strong acids (1 M HCl) and alkalis (1 M NaOH). Meanwhile, such invisible inks could be further employed on a soft matrix via screen-printing, holding great potential for practical applications.

Magneto-Optical Activity in Nonmagnetic Hyperbolic Nanoparticles.

Active nanophotonics can be realized by controlling the optical properties of materials with external magnetic fields. Here, we explore the influence of optical anisotropy on the magneto-optical activity in nonmagnetic hyperbolic nanoparticles. We demonstrate that the magneto-optical response is driven by the hyperbolic dispersion via the coupling of metallic-induced electric and dielectric-induced magnetic dipolar optical modes with static magnetic fields. Magnetic circular dichroism experiments confirm the theoretical predictions and reveal tunable magneto-optical activity across the visible and near infrared spectral range.

Corticosterone induced apoptosis of mouse oviduct epithelial cells independent of the TNF-α system.

It has been reported in recent studies that restraint stress on pregnant mice during the preimplantation stage elevated corticotrophin-releasing hormone (CRH) and glucocorticoid levels in the serum and oviducts; furthermore, CRH and corticosterone (CORT) impacted preimplantation embryos indirectly by triggering the apoptosis of oviductal epithelial cells (OECs) through activation of the Fas system. However, it remains unclear whether TNF-α signaling is involved in CRH- and/or glucocorticoid-induced apoptosis of OECs. In the present study, it was shown that culture with either CRH or CORT induced significant apoptosis of OECs. The culture of OECs with CRH augmented both FasL expression and TNF-α expression. However, culture with CORT increased FasL, but decreased TNF-α, expression significantly. Although knocking down/knocking out FasL expression in OECs significantly ameliorated the proapoptotic effects of both CRH and CORT, knocking down/knocking out TNF-α expression relieved only the proapoptotic effect of CRH but not that of CORT. Taken together, our results demonstrated that CRH-induced OEC apoptosis involved both Fas signaling and TNF-α signaling. Conversely, CORT-induced OEC apoptosis involved only the Fas, but not the TNF-α, signaling pathway. The data obtained are crucial for our understanding of the mechanisms by which various categories of stress imposed on pregnant females impair embryo development, as well as for the development of measures to protect the embryo from the adverse effects of stress.

Glucocorticoids impair oocyte competence and trigger apoptosis of ovarian cells via activating the TNF-α system.

Mechanisms by which female stress and particularly glucocorticoids impair oocyte competence are largely unclear. Although one study demonstrated that glucocorticoids triggered apoptosis in ovarian cells and oocytes by activating the FasL/Fas system, other studies suggested that they might induce apoptosis through activating other signaling pathways as well. In this study, both in vivo and in vitro experiments were conducted to test the hypothesis that glucocorticoids might trigger apoptosis in oocytes and ovarian cells through activating the TNF-α system. The results showed that cortisol injection of female mice (1.) impaired oocyte developmental potential and mitochondrial membrane potential with increased oxidative stress; (2.) induced apoptosis in mural granulosa cells (MGCs) with increased oxidative stress in the ovary; and (3.) activated the TNF-α system in both ovaries and oocytes. Culture with corticosterone induced apoptosis and activated the TNF-α system in MGCs. Knockdown or knockout of TNF-α significantly ameliorated the pro-apoptotic effects of glucocorticoids on oocytes and MGCs. However, culture with corticosterone downregulated TNF-α expression significantly in oviductal epithelial cells. Together, the results demonstrated that glucocorticoids impaired oocyte competence and triggered apoptosis in ovarian cells through activating the TNF-α system and that the effect of glucocorticoids on TNF-α expression might vary between cell types.

Designing precision medicine trials to yield a greater population impact.

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.

Learning-based biomarker-assisted rules for optimized clinical benefit under a risk constraint.

Novel biomarkers, in combination with currently available clinical information, have been sought to improve clinical decision making in many branches of medicine, including screening, surveillance, and prognosis. Statistical methods are needed to integrate such diverse information to develop targeted interventions that balance benefit and harm. In the specific setting of disease detection, we propose novel approaches to construct a multiple-marker-based decision rule by directly optimizing a benefit function, while controlling harm at a maximally tolerable level. These new approaches include plug-in and direct-optimization-based algorithms, and they allow for the construction of both nonparametric and parametric rules. A study of asymptotic properties of the proposed estimators is provided. Simulation results demonstrate good clinical utilities for the resulting decision rules under various scenarios. The methods are applied to a biomarker study in prostate cancer surveillance.

Constructing dynamic treatment regimes with shared parameters for censored data.

Dynamic treatment regimes are sequential decision rules that adapt throughout disease progression according to a patient's evolving characteristics. In many clinical applications, it is desirable that the format of the decision rules remains consistent over time. Unlike the estimation of dynamic treatment regimes in regular settings, where decision rules are formed without shared parameters, the derivation of the shared decision rules requires estimating shared parameters indexing the decision rules across different decision points. Estimation of such rules becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address these challenges, we propose two novel methods: censored shared-Q-learning and censored shared-O-learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different decision points and estimated simultaneously. We use simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Heart Study to derive treatment rules for cardiovascular disease.

Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules.

Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule (ITR), by which suitable decisions on treatment assignments can be made to optimize patients' clinical outcome. For binary treatment settings, outcome weighted learning (OWL) and several of its variations have been proposed recently to estimate the ITR by optimizing the conditional expected outcome given patients' information. However, for multiple treatment scenarios, it remains unclear how to use OWL effectively. It can be shown that some direct extensions of OWL for multiple treatments, such as one-versus-one and one-versus-rest methods, can yield suboptimal performance. In this paper, we propose a new learning method, named Multicategory Outcome weighted Margin-based Learning (MOML), for estimating ITR with multiple treatments. Our proposed method is very general and covers OWL as a special case. We show Fisher consistency for the estimated ITR, and establish convergence rate properties. Variable selection using the sparse l 1 penalty is also considered. Analysis of simulated examples and a type 2 diabetes mellitus observational study are used to demonstrate competitive performance of the proposed method.

Hyperbolic Meta-Antennas Enable Full Control of Scattering and Absorption of Light.

We introduce a novel concept of hybrid metal-dielectric meta-antenna supporting type II hyperbolic dispersion, which enables full control of absorption and scattering of light in the visible/near-infrared spectral range. This ability lies in the different nature of the localized hyperbolic Bloch-like modes excited within the meta-antenna. The experimental evidence is corroborated by a comprehensive theoretical study. In particular, we demonstrate that two main modes, one radiative and one non-radiative, can be excited by direct coupling with the free-space radiation. We show that the scattering is the dominating electromagnetic decay channel, when an electric dipolar mode is induced in the system, whereas a strong absorption process occurs when a magnetic dipole is excited. Also, by varying the geometry of the system, the relative ratio of scattering and absorption, as well as their relative enhancement and/or quenching, can be tuned at will over a broad spectral range, thus enabling full control of the two channels. Importantly, both radiative and nonradiative modes supported by our architecture can be excited directly with far-field radiation. This is observed to occur even when the radiative channels (scattering) are almost totally suppressed, thereby making the proposed architecture suitable for practical applications. Finally, the hyperbolic meta-antennas possess both angular and polarization independent structural integrity, unlocking promising applications as hybrid meta-surfaces or as solvable nanostructures.

On-Demand Intracellular Delivery of Single Particles in Single Cells by 3D Hollow Nanoelectrodes.

Delivery of molecules into intracellular compartments is one of the fundamental requirements in molecular biology. However, the possibility of delivering a precise number of nano-objects with single-particle resolution is still an open challenge. Here we present an electrophoretic platform based on 3D hollow nanoelectrodes to enable delivery of single nanoparticles into single selected cells and monitoring of the single-particle delivery by surface-enhanced Raman scattering (SERS). The gold-coated hollow nanoelectrode capable of confinement and enhancement of electromagnetic fields upon laser illumination can distinguish the SERS signals of a single nanoparticle flowing through the nanoelectrode. Tight wrapping of cell membranes around the nanoelectrodes allows effective membrane electroporation such that single gold nanorods are delivered on demand into a living cell by electrophoresis. The capability of the 3D hollow nanoelectrodes to porate cells and reveal single emitters from the background in continuous flow is promising for the analysis of both intracellular delivery and sampling.