Electrochemical Knocking-Down of Zn Metal Clusters into Single Atoms.

Single Atoms Catalysts (SACs) have emerged as a class of highly promising heterogeneous catalysts, where the traditional bottom-up synthesis approaches often encounter considerable challenges in relation to aggregation issues and poor stability. Consequently, achieving densely dispersed atomic species in a reliable and efficient manner remains a key focus in the field. Herein, we report a new facile electrochemical knock-down strategy for the formation of SACs, whereby the metal Zn clusters are transformed into single atoms. While a defect-rich substrate plays a pivotal role in capturing and stabilizing isolated Zn atoms, the feasibility of this novel strategy is demonstrated through a comprehensive investigation, combining experimental and theoretical studies. Furthermore, when studied in exploring for potential applications, the material prepared shows a remarkable improvement of 58.21% for the Li+ storage and delivers a capacity over 300 Wh kg-1 after 500 cycles upon the transformation of Zn clusters into single atoms.

Click Chemistry in Lithium-Metal Batteries.

Lithium-metal batteries (LMBs) are considered the "holy grail" of the next-generation energy storage systems, and solid-state electrolytes (SSEs) are a kind of critical component assembled in LMBs. However, as one of the most important branches of SSEs, polymer-based electrolytes (PEs) possess several native drawbacks including insufficient ionic conductivity and so on. Click chemistry is a simple, efficient, regioselective, and stereoselective synthesis method, which can be used not only for preparing PEs with outstanding physical and chemical performances, but also for optimizing the stability of solid electrolyte interphase (SEI) layer and elevate the cycling properties of LMBs effectively. Here it is primarily focused on evaluating the merits of click chemistry, summarizing its existing challenges and outlining its increasing role for the designing and fabrication of advanced PEs. The fundamental requirements for reconstructing artificial SEI layer through click chemistry are also summarized, with the aim to offer a thorough comprehension and provide a strategic guidance for exploring the potentials of click chemistry in the field of LMBs.

Switch Volmer-Heyrovsky to Volmer-Tafel Pathway for Efficient Acidic Electrocatalytic Hydrogen Evolution by Correlating Pt Single Atoms with Clusters.

As cost-effective catalysts, platinum (Pt) single-atom catalysts (SACs) have attracted substantial attention. However, most studies indicate that Pt SACs in acidic hydrogen evolution reaction (HER) follow the slow Volmer-Heyrovsky (VH) mechanism instead of the fast kinetic Volmer-Tafel (VT) pathway. Here, this work propose that the VH mechanism in Pt SACs can be switched to the faster VT pathway for efficient HER by correlating Pt single atoms (SAs) with Pt clusters (Cs). Our calculations reveal that the correlation between Pt SAs and Cs significantly impacts the electronic structure of exposed Pt atoms, lowering the adsorption barrier for atomic hydrogen and enabling a faster VT mechanism. To validate these findings, this work purposely synthesize three catalysts: l-Pt@MoS2, m-Pt@MoS2 and h-Pt@MoS2 with low, moderate, and high Pt-loading, having different distributions of Pt SAs and Cs. The m-Pt@MoS2 catalyst with properly correlating Pt SAs and Cs exhibits outstanding performance with an overpotential of 47 mV and Tafel slope of 32 mV dec-1. Further analysis of the Tafel values confirms that the m-Pt@MoS2 sample indeed follows the VT reaction mechanism, aligning with the theoretical findings. This study offers a deep understanding of the synergistic mechanism, paving a way for designing novel-advanced catalysts.

Frameworked Electrolytes: A Pathway Towards Solid Future of Batteries.

All-solid-state batteries (ASSBs) represent a highly promising next-generation energy storage technology owing to their inherently high safety, device reliability, and potential for achieving high energy density in the post-ara of lithium-ion batteries, and therefore extensive searches are ongoing for ideal solid-state electrolytes (SSEs). Though promising, there is still a huge barrier that limits the large-scale applications of ASSBs, where there are a couple of bottleneck technical issues. In this perspective, a novel category of electrolytes known as frameworked electrolytes (FEs) are examined, where the solid frameworks are intentionally designed to contain 3D ionic channels at sub-nano scales, rendering them macroscopically solid. The distinctive structural design of FEs gives rise to not only high ionic conductivity but also desirable interfaces with electrode solids. This is achieved through the presence of sub-nano channels within the framework, which exhibit significantly different ion diffusion behavior due to the confinement effect. This perspective offers a compelling insight into the potential of FEs in the pursuit of ASSBs, where FEs offer an exciting opportunity to overcome the limitations of traditional solid-state electrolytes and propel the development of ASSBs as the holy grail of energy storage technology.

Lightweight, Strong, and Transparent Wood Films Produced by Capillary Driven Self-Densification.

Wood delignification and densification enable the production of high strength and/or transparent wood materials with exceptional properties. However, processing needs to be more sustainable and besides the chemical delignification treatments, energy intense hot-pressing calls for alternative approaches. Here, this study shows that additional softening of delignified wood via a mild swelling process using an ionic liquid-water mixture enables the densification of tube-line wood cells into layer-by-layer sheet structures without hot-pressing. The natural capillary force induces self-densification in a simple drying process resulting in a transparent wood film. The as-prepared films with ≈150 µm thickness possess an optical transmittance ≈70%, while maintaining optical haze >95%. Due to the densely packed sheet structure with a large interfacial area, the reassembled wood film is fivefold stronger and stiffer than the delignified wood in fiber direction. Owing to a low density, the specific tensile strength and elastic modulus are as high as 282 MPa cm3 g-1 and 31 GPa cm3 g-1. A facile and highly energy efficient wood nanotechnology approach are demonstrated toward more sustainable materials and processes by directly converting delignified wood into transparent wood omitting polymeric matrix infiltration or mechanical pressing.

Interfacial Synergy in Mo2C/MoC Heterostructure Promoting Sequential Polysulfide Conversion in High-Performance Lithium-Sulfur Battery.

A rational design of sulfur host is the key to conquering the"polysulfide shuttle effects" by accelerating the polysulfide conversion. Since the process involves solid-liquid-solid multistep phase transitions, purposely-engineered heterostructure catalysts with various active regions for catalyzing conversion steps correspondingly are beneficial to promote the overall conversion process. However, the functionalities of the materials surface and interface in heterostructure catalysts remain unclear. In this work, an Mo2C/MoC catalyst with abundant Mo2C surface-interface-MoC surface tri-active-region is developed by in situ converting the MoZn-metal organic framework. The experimental and simulation studies demonstrate the interface can catch long-chain polysulfides and promote their conversion. Instead, the Mo2C and MoC tend to accommodate the short-chain polysulfide and accelerate their conversion and the Li2S dissociation. Benefitting from the high catalytic ability, the Li-S battery assembled with the Mo2C/MoC-S cathode shows more discrete redox reactions and delivers a high initial capacity of 1603.6 mAh g-1 at 1 C charging-discharging rate, which is over twofolds of the one assembled using individual hosts, and 80.4% capacity can be maintained after 1000 cycles at 3 C rate. This work has demonstrated a novel synergy between the interface and material surface, which will help the future design of high-performance Li-S batteries.

Efficient estimation of Cox model with random change point.

In clinical studies, the risk of a disease may dramatically change when some biological indexes of the human body exceed some thresholds. Furthermore, the differences in individual characteristics of patients such as physical and psychological experience may lead to subject-specific thresholds or change points. Although a large literature has been established for regression analysis of failure time data with change points, most of the existing methods assume the same, fixed change point for all study subjects. In this paper, we consider the situation where there exists a subject-specific change point and two Cox type models are presented. The proposed models also offer a framework for subgroup analysis. For inference, a sieve maximum likelihood estimation procedure is proposed and the asymptotic properties of the resulting estimators are established. An extensive simulation study is conducted to assess the empirical performance of the proposed method and indicates that it works well in practical situations. Finally the proposed approach is applied to a set of breast cancer data.

A flexible time-varying coefficient rate model for panel count data.

Panel count regression is often required in recurrent event studies, where the interest is to model the event rate. Existing rate models are unable to handle time-varying covariate effects due to theoretical and computational difficulties. Mean models provide a viable alternative but are subject to the constraints of the monotonicity assumption, which tends to be violated when covariates fluctuate over time. In this paper, we present a new semiparametric rate model for panel count data along with related theoretical results. For model fitting, we present an efficient EM algorithm with three different methods for variance estimation. The algorithm allows us to sidestep the challenges of numerical integration and difficulties with the iterative convex minorant algorithm. We showed that the estimators are consistent and asymptotically normally distributed. Simulation studies confirmed an excellent finite sample performance. To illustrate, we analyzed data from a real clinical study of behavioral risk factors for sexually transmitted infections.

New Set of Isobaric Labeling Reagents for Quantitative 16Plex Proteomics.

Peptide labeling by isobaric tags is a powerful approach for the relative quantitative analysis of proteomes in multiple groups. There has been a revolution in the innovation of new isobaric reagents; however, great effort is being made to expand simultaneous labeling groups to identify more labeled peptides and reduce reporter ion signal suppression. We redesigned the original chemical structure of the deuterium isobaric amine-reactive tag developed in our laboratory. We optimized the synthetic pathway to create a new set of 16-plex isobaric tags (IBT-16plex). The novel reagent enabled almost complete labeling of peptides within 90 min, with all labeling reporter ions exhibiting comparable MS/MS signals. Compared to a typical 16plex reagent, TMTpro-16plex, the peptides and proteins identified by IBT-16plex in trypsinized HeLa cells were significantly increased by 14.8 and 8.6%, respectively. Moreover, differences in peptide abundance within 10-fold among multiple groups were barely suppressed in IBT-16plex, whereas the dynamic range in TMTpro-16plex-labeled groups was smaller. After quantitative examination of MCF7 cell proteins, IBT-16plex was confirmed as feasible and useful for evaluating protein responses of glucose-starved MCF7 cells to a glucose-rich medium.

Baseline C-reactive protein predicts efficacy of the first-line immune checkpoint inhibitors plus chemotherapy in advanced lung squamous cell carcinoma: a retrospective, multicenter study.

AIMS: To investigate the predictive value of baseline C-reactive protein (CRP) levels on the efficacy of chemotherapy plus immune checkpoint inhibitors (ICI) in patients with advanced lung squamous cell carcinoma (LSCC). MATERIALS AND METHODS: In this retrospective multicenter study spanning from January 2016 to December 2020, advanced LSCC patients initially treated with chemotherapy or a combination of chemotherapy and ICI were categorized into normal and elevated CRP subgroups. The relationship between CRP levels and treatment outcomes was analyzed using multivariate Cox proportional hazards models and multivariate logistic regression, focusing primarily on the progression-free survival (PFS) endpoint, and secondarily on overall survival (OS) and objective response rate (ORR) endpoints. Survival curves were generated using the Kaplan-Meier method, with the log-rank test used for comparison between groups. RESULTS: Of the 245 patients evaluated, the 105 who received a combination of chemotherapy and ICI with elevated baseline CRP levels exhibited a significant reduction in PFS (median 6.5 months vs. 11.8 months, HR, 1.78; 95% CI: 1.12-2.81; p = 0.013) compared to those with normal CRP levels. Elevated CRP was identified as an independent risk factor for poor PFS through multivariate-adjusted analysis. However, among the 140 patients receiving chemotherapy alone, baseline CRP levels did not significantly influence PFS. Furthermore, within the combination therapy group, there was a notable decrease in the ORR (51% vs. 71%, p = 0.035), coupled with a significantly shorter OS (median 20.9 months vs. 31.5 months, HR, 2.24; 95% CI: 1.13-4.44; p = 0.033). CONCLUSION: In patients with advanced LSCC, elevated baseline CRP levels were identified as an independent predictive factor for the efficacy of combination therapy with chemotherapy and ICI, but not in chemotherapy alone. This suggests that CRP may be a valuable biomarker for guiding treatment strategies.

Group variable selection for the Cox model with interval-censored failure time data.

Group variable selection is often required in many areas, and for this many methods have been developed under various situations. Unlike the individual variable selection, the group variable selection can select the variables in groups, and it is more efficient to identify both important and unimportant variables or factors by taking into account the existing group structure. In this paper, we consider the situation where one only observes interval-censored failure time data arising from the Cox model, for which there does not seem to exist an established method. More specifically, a penalized sieve maximum likelihood variable selection and estimation procedure is proposed and the oracle property of the proposed method is established. Also, an extensive simulation study is performed and suggests that the proposed approach works well in practical situations. An application of the method to a set of real data is provided.

Semiparametric regression analysis of length-biased and partly interval-censored data with application to an AIDS cohort study.

Length-biased data occur often in many scientific fields, including clinical trials, epidemiology surveys and genome-wide association studies, and many methods have been proposed for their analysis under various situations. In this article, we consider the situation where one faces length-biased and partly interval-censored failure time data under the proportional hazards model, for which it does not seem to exist an established method. For the estimation, we propose an efficient nonparametric maximum likelihood method by incorporating the distribution information of the observed truncation times. For the implementation of the method, a flexible and stable EM algorithm via two-stage data augmentation is developed. By employing the empirical process theory, we establish the asymptotic properties of the resulting estimators. A simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well and is more efficient than the conditional likelihood approach. An application to an AIDS cohort study is also provided.

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring.

BACKGROUND: Failure time data frequently occur in many medical studies and often accompany with various types of censoring. In some applications, left truncation may occur and can induce biased sampling, which makes the practical data analysis become more complicated. The existing analysis methods for left-truncated data have some limitations in that they either focus only on a special type of censored data or fail to flexibly utilize the distribution information of the truncation times for inference. Therefore, it is essential to develop a reliable and efficient method for the analysis of left-truncated failure time data with various types of censoring. METHOD: This paper concerns regression analysis of left-truncated failure time data with the proportional hazards model under various types of censoring mechanisms, including right censoring, interval censoring and a mixture of them. The proposed pairwise pseudo-likelihood estimation method is essentially built on a combination of the conditional likelihood and the pairwise likelihood that eliminates the nuisance truncation distribution function or avoids its estimation. To implement the presented method, a flexible EM algorithm is developed by utilizing the idea of self-consistent estimating equation. A main feature of the algorithm is that it involves closed-form estimators of the large-dimensional nuisance parameters and is thus computationally stable and reliable. In addition, an R package LTsurv is developed. RESULTS: The numerical results obtained from extensive simulation studies suggest that the proposed pairwise pseudo-likelihood method performs reasonably well in practical situations and is obviously more efficient than the conditional likelihood approach as expected. The analysis results of the MHCPS data with the proposed pairwise pseudo-likelihood method indicate that males have significantly higher risk of losing active life than females. In contrast, the conditional likelihood method recognizes this effect as non-significant, which is because the conditional likelihood method often loses some estimation efficiency compared with the proposed method. CONCLUSIONS: The proposed method provides a general and helpful tool to conduct the Cox's regression analysis of left-truncated failure time data under various types of censoring.

Intraocular nano-microscale drug delivery systems for glaucoma treatment: design strategies and recent progress.

Glaucoma is a leading cause of irreversible visual impairment and blindness, affecting over 76.0 million people worldwide in 2020, with a predicted increase to 111.8 million by 2040. Hypotensive eye drops remain the gold standard for glaucoma treatment, while inadequate patient adherence to medication regimens and poor bioavailability of drugs to target tissues are major obstacles to effective treatment outcomes. Nano/micro-pharmaceuticals, with diverse spectra and abilities, may represent a hope of removing these obstacles. This review describes a set of intraocular nano/micro drug delivery systems involved in glaucoma treatment. Particularly, it investigates the structures, properties, and preclinical evidence supporting the use of these systems in glaucoma, followed by discussing the route of administration, the design of systems, and factors affecting in vivo performance. Finally, it concludes by highlighting the emerging notion as an attractive approach to address the unmet needs for managing glaucoma.

Dose-dependent pharmacokinetics of midazolam in rats: influence of hepatic first-pass metabolism.

To characterise the dose-dependent pharmacokinetics of midazolam and evaluate the intestinal and hepatic first-pass effects on midazolam in Sprague-Dawley rats, the concentrations and area under the concentration-time curve (AUC) of midazolam in the portal and systemic plasma were simultaneously determined with a double cannulation method.It was found that about 75% of the dose was left in the portal blood with different oral administration doses, while the bioavailability in the liver was 37.86% at 20 mg/kg, significantly higher than 9.16% at 2 mg/kg.The disproportional increase in AUC of midazolam and significant decrease in exposure of metabolites were observed in systemic plasma after hepatic portal vein administration. And in the in vitro study, the formation rate of the metabolites of midazolam significantly decreased when midazolam was at 300 µM compared with 100 µM.These results indicated that not only the saturation of first-pass metabolism but also the inhibition of hepatic metabolism is responsible for the nonlinear PK of midazolam. Thus, a rational dose should be chosen when midazolam is used as a probe in the drug-drug interaction study, particularly for orally administered drugs that undergo hepatic first-pass metabolism.

Variable selection for nonparametric additive Cox model with interval-censored data.

The standard Cox model is perhaps the most commonly used model for regression analysis of failure time data but it has some limitations such as the assumption on linear covariate effects. To relax this, the nonparametric additive Cox model, which allows for nonlinear covariate effects, is often employed, and this paper will discuss variable selection and structure estimation for this general model. For the problem, we propose a penalized sieve maximum likelihood approach with the use of Bernstein polynomials approximation and group penalization. To implement the proposed method, an efficient group coordinate descent algorithm is developed and can be easily carried out for both low- and high-dimensional scenarios. Furthermore, a simulation study is performed to assess the performance of the presented approach and suggests that it works well in practice. The proposed method is applied to an Alzheimer's disease study for identifying important and relevant genetic factors.

A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies.

The case-cohort design was developed to reduce costs when disease incidence is low and covariates are difficult to obtain. However, most of the existing methods are for right-censored data and there exists only limited research on interval-censored data, especially on regression analysis of bivariate interval-censored data. Interval-censored failure time data frequently occur in many areas and a large literature on their analyses has been established. In this paper, we discuss the situation of bivariate interval-censored data arising from case-cohort studies. For the problem, a class of semiparametric transformation frailty models is presented and for inference, a sieve weighted likelihood approach is developed. The large sample properties, including the consistency of the proposed estimators and the asymptotic normality of the regression parameter estimators, are established. Moreover, a simulation is conducted to assess the finite sample performance of the proposed method and suggests that it performs well in practice.

Regression analysis of general mixed recurrent event data.

In modern biomedical datasets, it is common for recurrent outcomes data to be collected in an incomplete manner. More specifically, information on recurrent events is routinely recorded as a mixture of recurrent event data, panel count data, and panel binary data; we refer to this structure as general mixed recurrent event data. Although the aforementioned data types are individually well-studied, there does not appear to exist an established approach for regression analysis of the three component combination. Often, ad-hoc measures such as imputation or discarding of data are used to homogenize records prior to the analysis, but such measures lead to obvious concerns regarding robustness, loss of efficiency, and other issues. This work proposes a maximum likelihood regression estimation procedure for the combination of general mixed recurrent event data and establishes the asymptotic properties of the proposed estimators. In addition, we generalize the approach to allow for the existence of terminal events, a common complicating feature in recurrent event analysis. Numerical studies and application to the Childhood Cancer Survivor Study suggest that the proposed procedures work well in practical situations.

The histone deacetylase SIRT6 promotes glycolysis through the HIF-1α/HK2 signaling axis and induces erlotinib resistance in non-small cell lung cancer.

Erlotinib is a first-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). Overcoming erlotinib resistance is crucial to improve the survival of advanced non-small cell lung cancer (NSCLC) patients with sensitive EGFR mutations. It is also an important clinical problem that urgently needs a solution. In this study, we explored strategies to overcome erlotinib resistance from the perspective of energy metabolism. SIRT6 is a histone deacetylase. Here, we found that high expression of SIRT6 is associated with poor prognosis of lung adenocarcinoma, especially in EGFR-mutated NSCLC patients. The next cell experiment found that SIRT6 expression increased in erlotinib-resistant cells, and SIRT6 expression was negatively correlated with the sensitivity of NSCLC to erlotinib. Inhibition of SIRT6 promoted erlotinib-induced apoptosis in erlotinib-resistant cells, and glycolysis in drug-resistant cells was also inhibited. Functional studies have shown that SIRT6 increases glycolysis through the HIF-1α/HK2 signaling axis in drug-resistant cells and inhibits the sensitivity of NSCLC cells to erlotinib. In addition, the HIF-1α blocker PX478-2HCL attenuated the glycolysis and erlotinib resistance induced by SIRT6. More importantly, we confirmed the antitumor effect of SIRT6 inhibition combined with erlotinib in NSCLC-bearing mice. Our findings indicate that the cancer metabolic pathway regulated by SIRT6 may be a new target for attenuating NSCLC erlotinib resistance and has potential as a biomarker or therapeutic target to improve outcomes in NSCLC patients.

Rapid Progress in Intelligent Radiotherapy and Future Implementation.

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.