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.

Monolithic 3D nanoelectrospray emitters based on a continuous fluid-assisted etching strategy for glass droplet microfluidic chip-mass spectrometry.

Glass microfluidic chips are suitable for coupling with mass spectrometry (MS) due to their flexible design, optical transparency and resistance to organic reagents. However, due to the high hardness and brittleness of glass, there is a lack of simple and feasible technology to manufacture a monolithic nanospray ionization (nESI) emitter on a glass microchip, which hinders its coupling with mass spectrometry. Here, a continuous fluid-assisted etching strategy is proposed to fabricate monolithic three-dimensional (3D) nESI emitters integrated into glass microchips. A continuous fluid of methanol is adopted to protect the inner wall of the channels and the bonding interface of the glass microfluidic chip from being wet-etched, forming sharp 3D nESI emitters. The fabricated 3D nESI emitter can form a stable electrospray plume, resulting in consistent nESI detection of acetylcholine with an RSD of 4.5% within 10 min. The fabricated 3D emitter is integrated on a glass microfluidic chip designed with a T-junction droplet generator, which can realize efficient analysis of acetylcholine in picoliter-volume droplets by nESI-MS. Stability testing of over 20 000 droplets detected by the established system resulted in an RSD of 9.1% over approximately 180 min. The detection of ten neurochemicals in rat cerebrospinal fluid droplets is achieved. The established glass droplet microfluidic chip-MS system exhibits potential for broad applications such as in vivo neurochemical monitoring and single-cell analysis in the future.

Association between type 1 diabetes and systemic lupus erythematosus: a Mendelian randomization study.

OBJECTIVES: Observational studies have shown that there is a bidirectional relationship between type 1 diabetes (T1D) and systemic lupus erythematosus (SLE); the causality of this association remains elusive and may be affected by confusion and reverse causality. There is also a lack of large-scale randomized controlled trials to verify. Therefore, this Mendelian randomization (MR) study aimed to investigate the causal association between T1D and SLE. METHODS: We aggregated data using publicly available genome-wide association studies (GWAS), all from European populations. Select independent (R2 < 0.001) and closely related to exposure (P < 5 × 10-8) as instrumental variables (IVs). The inverse-variance weighted (IVW) method was used as the primary method. We also used MR-Egger, the weighted median method, MR-Robust, MR-Lasso, and other methods leveraged as supplements. RESULTS: T1D had a positive causal association with SLE (IVW, odds ratio [OR] = 1.358, 95% confidence interval [CI], 1.205 - 1.530; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.137, 95% CI, 1.033 - 1.251; P = 0.001). SLE had a positive causal association with T1D (IVW, OR = 1.108, 95% CI, 1.074 - 1.144; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.085, 95% CI, 1.046 - 1.127; P < 0.001). These results have also been verified by sensitivity analysis. CONCLUSION: The MR analysis results indicated a causal association between T1D and SLE. Therefore, further research is needed to clarify the potential biological mechanism between T1D and SLE. Key Points • Observational studies have shown that there is a bidirectional relationship between T1D and SLE. • We evaluated causal effects between T1D and SLE by Mendelian randomization analyses. • The MR analysis results indicated a causal association between T1D and SLE.

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).

Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient Electronic Health Records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes.

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.

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.

Path Planning Algorithm for Unmanned Surface Vessel Based on Multiobjective Reinforcement Learning.

It is challenging to perform path planning tasks in complex marine environments as the unmanned surface vessel approaches the goal while avoiding obstacles. However, the conflict between the two subtarget tasks of obstacle avoidance and goal approaching makes the path planning difficult. Thus, a path planning method for unmanned surface vessel based on multiobjective reinforcement learning is proposed under the complex environment with high randomness and multiple dynamic obstacles. Firstly, the path planning scene is set as the main scene, and the two subtarget scenes including obstacle avoidance and goal approaching are divided from it. The action selection strategy in each subtarget scene is trained through the double deep Q-network with prioritized experience replay. A multiobjective reinforcement learning framework based on ensemble learning is further designed for policy integration in the main scene. Finally, by selecting the strategy from subtarget scenes in the designed framework, an optimized action selection strategy is trained and used for the action decision of the agent in the main scene. Compared with traditional value-based reinforcement learning methods, the proposed method achieves a 93% success rate in path planning in simulation scenes. Furthermore, the average length of the paths planned by the proposed method is 3.28% and 1.97% shorter than that of PER-DDQN and dueling DQN, respectively.

Hypothalamic-Pituitary-Adrenal Hormones Impair Pig Fertilization and Preimplantation Embryo Development via Inducing Oviductal Epithelial Apoptosis: An In Vitro Study.

Previous studies show that stressful events after ovulation in sows significantly impaired the embryo cleavage with a significant elevation of blood cortisol. However, the effects of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH) and cortisol on fertilization and embryo development remain to be specified, and whether they damage pig embryos directly or indirectly is unclear. This study demonstrated that embryo development was unaffected when pig parthenotes were cultured with different concentrations of CRH/ACTH/cortisol. However, embryo development was significantly impaired when the embryos were cocultured with pig oviductal epithelial cells (OECs) in the presence of CRH/cortisol or cultured in medium that was conditioned with CRH/cortisol-pretreated OECs (CRH/cortisol-CM). Fertilization in CRH/cortisol-CM significantly increased the rates of polyspermy. CRH and cortisol induced apoptosis of OECs through FAS and TNFα signaling. The apoptotic OECs produced less growth factors but more FASL and TNFα, which induced apoptosis in embryos. Pig embryos were not sensitive to CRH because they expressed no CRH receptor but the CRH-binding protein, and they were tolerant to cortisol because they expressed more 11-beta hydroxysteroid dehydrogenase 2 (HSD11B2) than HSD11B1. When used at a stress-induced physiological concentration, while culture with either CRH or cortisol alone showed no effect, culture with both significantly increased apoptosis in OECs. In conclusion, CRH and cortisol impair pig fertilization and preimplantation embryo development indirectly by inducing OEC apoptosis via the activation of the FAS and TNFα systems. ACTH did not show any detrimental effect on pig embryos, nor OECs.

Microcontact printing of gold nanoparticle at three-phase interface as flexible substrate for SERS detection of MicroRNA.

The rigidity of traditional solid-state surface-enhanced Raman spectroscopy (SERS) substrate hampers their application in the curved structure for nonplanar surface test and in-situ detection. Traditionally, the flexible Raman substrates are often prepared by transferring printing of patterned nanoparticles on the flexible materials such as polymer, paper, etc. However，the replicate patterns are often produced by high-cost instruments. In this study, a low-cost and flexible SERS substrate is prepared by using a microcontact printing technology to transfer three-phase-assembled nanoparticles on a polydimethylsiloxane film, which can stabilize the assembled nanoparticles. Combining with the endonuclease Nt.BbvCI assisted amplification method, a SERS biosensor is constructed for microRNA 21 (miRNA 21) assay. This platform presents a wide dynamic range (100 fM ∼1 nM), achieving a fabulous sensitivity with limit of detection of 11.96 fM for miRNA 21. Furthermore, after being bent 90° for 50 times, the Raman intensity of the flexible substrate shows a negligible change. This versatile flexible substrate exhibits considerable potential for SERS analysis, which also opens a new avenue for preparing flexible devices.

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.

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.

Comparison of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy: A retrospective cohort study.

PURPOSE: To compare the clinical efficacy and safety of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy (PCNL). MATERIALS AND METHODS: We retrospectively analyzed the data of 1,258 patients who underwent PCNL for kidney stones from August 2017 to July 2020 in our hospital. Among them, 682 patients had double-J stents inserted (DJ group) and 576 patients had ureteral catheters (UC group). We analyzed baseline patient characteristics, perioperative outcomes, and complications in both groups. RESULTS: The puncture success rate was 97.9% and 97.4% in the DJ and UC groups, respectively (p>0.05). The operation time was 74.5±37.8 minutes in the DJ group compared with 80.8±38.5 minutes in the UC group (p=0.004). The total stone-free rate in the DJ and UC groups was 80.5% and 78.7%, respectively (p>0.05). The incidence of perioperative complications was relatively low in both groups and showed no obvious differences. In the subgroup analysis, the operation time for patients with no obvious or mild hydronephrosis preoperatively was significantly shorter in the DJ group than in the UC group (p<0.05). However, there were no significant differences among patients who had moderate or severe hydronephrosis preoperatively. CONCLUSIONS: It is feasible, safe, and effective to create artificial hydronephrosis by insertion of pre-indwelling double-J stents in PCNL surgery. Furthermore, the operation time was significantly shorter in the DJ group than in the group with pre-indwelling ureteral catheters, especially in patients who had no obvious or mild hydronephrosis preoperatively.

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.

Label-Free Optical Analysis of Biomolecules in Solid-State Nanopores: Toward Single-Molecule Protein Sequencing.

Sequence identification of peptides and proteins is central to proteomics. Protein sequencing is mainly conducted by insensitive mass spectroscopy because proteins cannot be amplified, which hampers applications such as single-cell proteomics and precision medicine. The commercial success of portable nanopore sequencers for single DNA molecules has inspired extensive research and development of single-molecule techniques for protein sequencing. Among them, three challenges remain: (1) discrimination of the 20 amino acids as building blocks of proteins; (2) unfolding proteins; and (3) controlling the motion of proteins with nonuniformly charged sequences. In this context, the emergence of label-free optical analysis techniques for single amino acids and peptides by solid-state nanopores shows promise for addressing the first challenge. In this Perspective, we first discuss the current challenges of single-molecule fluorescence detection and nanopore resistive pulse sensing in a protein sequencing. Then, label-free optical methods are described to show how they address the single-amino-acid identification within single peptides. They include localized surface plasmon resonance detection and surface-enhanced Raman spectroscopy on plasmonic nanopores. Notably, we report new data to show the ability of plasmon-enhanced Raman scattering to record and discriminate the 20 amino acids at a single-molecule level. In addition, we discuss briefly the manipulation of molecule translocation and liquid flow in plasmonic nanopores for controlling molecule movement to allow high-resolution reading of protein sequences. We envision that a combination of Raman spectroscopy with plasmonic nanopores can succeed in single-molecule protein sequencing in a label-free way.

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.

Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.

Novel biomarkers, in combination with currently available clinical information, have been sought to enhance clinical decision making in many branches of medicine, including screening, surveillance and prognosis. An individualized clinical decision rule (ICDR) is a decision rule that matches subgroups of patients with tailored medical regimen based on patient characteristics. We proposed new approaches to identify ICDRs by directly optimizing a risk-adjusted clinical benefit function that acknowledges the tradeoff between detecting disease and over-treating patients with benign conditions. In particular, we developed a novel plug-in algorithm to optimize the risk-adjusted clinical benefit function, which leads to the construction of both nonparametric and linear parametric ICDRs. In addition, we proposed a novel approach based on the direct optimization of a smoothed ramp loss function to further enhance the robustness of a linear ICDR. We studied the asymptotic theories of the proposed estimators. Simulation results demonstrated good finite sample performance for the proposed estimators and improved clinical utilities when compared to standard approaches. The methods were applied to a prostate cancer biomarker study.

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.

Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.

With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.