Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale.

When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected in a systematic review do not report the continuous measures on the same scale. The standardization process transfers the MDs to be unit-free measures that can be synthesized across studies. As such, some evidence synthesis researchers tend to prefer the SMD over the MD. However, other researchers have concerns about the interpretability of the SMD. The standardization process could also yield additional heterogeneity between studies. In this paper, we use simulation studies to illustrate that, in a scenario where the continuous measures are on the same scale, the SMD could have considerably poorer performance compared with the MD in some cases. The simulations compare the MD and SMD in various settings, including cases where the normality assumption of continuous measures does not hold. We conclude that although the SMD remains useful for evidence synthesis of continuous measures on different scales, the SMD could have substantially greater biases, greater mean squared errors, and lower coverage probabilities of CIs than the MD. The MD is generally more robust to the violation of the normality assumption for continuous measures. In scenarios where continuous measures are inherently comparable or can be transformed to a common scale, the MD is the preferred choice for an effect measure.

A Bayesian model for combining standardized mean differences and odds ratios in the same meta-analysis.

In meta-analysis practice, researchers frequently face studies that report the same outcome differently, such as a continuous variable (e.g., scores for rating depression) or a binary variable (e.g., counts of patients with depression dichotomized by certain latent and unreported depression scores). For combining these two types of studies in the same analysis, a simple conversion method has been widely used to handle standardized mean differences (SMDs) and odds ratios (ORs). This conventional method uses a linear function connecting the SMD and log OR; it assumes logistic distributions for (latent) continuous measures. However, the normality assumption is more commonly used for continuous measures, and the conventional method may be inaccurate when effect sizes are large or cutoff values for dichotomizing binary events are extreme (leading to rare events). This article proposes a Bayesian hierarchical model to synthesize SMDs and ORs without using the conventional conversion method. This model assumes exact likelihoods for continuous and binary outcome measures, which account for full uncertainties in the synthesized results. We performed simulation studies to compare the performance of the conventional and Bayesian methods in various settings. The Bayesian method generally produced less biased results with smaller mean squared errors and higher coverage probabilities than the conventional method in most cases. Nevertheless, this superior performance depended on the normality assumption for continuous measures; the Bayesian method could lead to nonignorable biases for non-normal data. In addition, we used two case studies to illustrate the proposed Bayesian method in real-world settings.

Redox Trimetallic Nanozyme with Neutral Environment Preference for Brain Injury.

Metal nanozyme has attracted wide interest for biomedicine, and a highly catalytic material in the physiological environment is highly desired. However, catalytic selectivity of nanozyme is still highly challenging, limiting its wide application. Here, we show a trimetallic (triM) nanozyme with highly catalytic activity and environmental selectivity. Enzyme-mimicked investigations find that the triM system possesses multi-enzyme-mimetic activity for removing reactive oxygen species (ROS) and reactive nitrogen species (RNS), such as 1O2, H2O2, •OH, and •NO. Importantly, triM nanozyme exhibits the significant neutral environment preference for removing the •OH, 1O2, and •NO free radical, indicating its highly catalytic selectivity. The density functional theory (DFT) calculations reveal that triM nanozyme can capture electrons very easily and provides more attraction to reactive oxygen and nitrogen species (RONS) radicals in the neutral environment. In vitro experiments show that triM nanozyme can improve the viability of injured neural cell. In the LPS-induced brain injury model, the superoxide dismutase (SOD) activity and lipid peroxidation can be greatly recovered after triM nanozyme treatment. Moreover, the triM nanozyme treatment can significantly improve the survival rate, neuroinflammation, and reference memory of injured mice. Present work provides a feasible route for improving selectivity of nanozyme in the physiological environment as well as exploring potential applications in brain science.

Hollow PtPdRh Nanocubes with Enhanced Catalytic Activities for In Vivo Clearance of Radiation-Induced ROS via Surface-Mediated Bond Breaking.

Catalytic nanomaterials can be used extrinsically to combat diseases associated with a surplus of reactive oxygen species (ROS). Rational design of surface morphologies and appropriate doping can substantially improve the catalytic performances. In this work, a class of hollow polyvinyl pyrrolidone-protected PtPdRh nanocubes with enhanced catalytic activities for in vivo free radical scavenging is proposed. Compared with Pt and PtPd counterparts, ternary PtPdRh nanocubes show remarkable catalytic properties of decomposing H2 O2 via enhanced oxygen reduction reactions. Density functional theory calculation indicates that the bond of superoxide anions breaks for the energetically favorable status of oxygen atoms on the surface of PtPdRh. Viability of cells and survival rate of animal models under exposure of high-energy γ radiation are considerably enhanced by 94% and 50% respectively after treatment of PtPdRh nanocubes. The mechanistic investigations on superoxide dismutase (SOD) activity, malondialdehyde amount, and DNA damage repair demonstrate that hollow PtPdRh nanocubes act as catalase, peroxidase, and SOD analogs to efficiently scavenge ROS.

Black Phosphorus Quantum Dot Induced Oxidative Stress and Toxicity in Living Cells and Mice.

Black phosphorus (BP), as an emerging successor to layered two-dimensional materials, has attracted extensive interest in cancer therapy. Toxicological studies on BP are of great importance for potential biomedical applications, yet not systemically explored. Herein, toxicity and oxidative stress of BP quantum dots (BPQDs) at cellular, tissue, and whole-body levels are evaluated by performing the systemic in vivo and in vitro experiments. In vitro investigations show that BPQDs at high concentration (200 µg/mL) exhibit significant apoptotic effects on HeLa cells. In vivo investigations indicate that oxidative stress, including lipid peroxidation, reduction of catalase activity, DNA breaks, and bone marrow nucleated cells (BMNC) damage, can be induced by BPQDs transiently but recovered gradually to healthy levels. No apparent pathological damages are observed in all organs, especially in the spleen and kidneys, during the 30-day period. This work clearly shows that BPQDs can cause acute toxicities by oxidative stress responses, but the inflammatory reactions can be recovered gradually with time for up to 30 days. Thus, BPQDs do not give rise to long-term appreciable toxicological responses.

Catalytic topological insulator Bi2Se3 nanoparticles for invivo protection against ionizing radiation.

Bi2Se3 nanoparticles (NPs) have attracted wide interests in biological and medical applications. Layer-like Bi2Se3 with high active surface area is promising for free radical scavenging. Here, we extended the medical applications of Bi2Se3 NPs further to in vivo protection against ionizing radiation based on their superior antioxidant activities and electrocatalytic properties. It was found that Bi2Se3 NPs can significantly increase the surviving fraction of mice after exposure of high-energy radiation of gamma ray. Additionally, the Bi2Se3 NPs can help to recover radiation-lowered red blood cell counts, white blood cell counts and platelet levels. Further investigations revealed that Bi2Se3 NPs behaved as functional free radical scavengers and significantly decreased the level of methylenedioxyamphetamine. In vivo toxicity studies showed that Bi2Se3 NPs did not cause significant side effects in panels of blood chemistry, clinical biochemistry and pathology.

Ultrasmall Pt Clusters Reducing Radiation-Induced Injuries via Scavenging Free Radicals.

High energy ionizing radiation was widely used in medical diagnosis and cancer radiation therapy. The high dose of X ray or γ ray can cause the damage of cancerous tissue as well as healthy tissue during therapy. Therefore, it is urgent to develop chemical agents to protect the healthy tissue from high energy ray invasion. Here, the ultrasmall Pt clusters were employed as the anti-radiation agents for protecting healthy cells and improving survival rate of irradiated mice. It was found that Pt clusters can reduce the DNA damages in irradiated cells. In vivo experiments show that the Pt clusters treatment can improve the survival rate of irradiated mice up to 30%. As a contrast, only-irradiated mice without Pt clusters treatment completely died after 15 days. The detailed biological experiments showed that Pt clusters can recover the bone marrow DNA level and superoxide dismutase activities via scavenging free radicals. Importantly, the ultrasmall Pt clusters can be excreted rapidly by kidney and do not cause long-term toxicity.

Ultrasmall WS2 Quantum Dots with Visible Fluorescence for Protection of Cells and Animal Models from Radiation-Induced Damages.

Two-dimensional WS2 materials have attracted wide attention in condensed physics and materials science due to its unique geometric and electronic structures. Particularly, WS2 shows extraordinary catalytic activities when its size decreases to ultrasmall, which provides potential opportunities for medical applications. In this work, WS2 quantum dots with strong catalytic properties were used for in vitro and in vivo protection from ionizing radiation induced cell damages. WS2 quantum dots possess unique optical properties of blue photoluminescence emission and excitation-wavelength dependent emission profiles. In vitro studies showed that cell viability can be considerably improved and cellular reactive oxygen species (ROS) can be removed by WS2 quantum dots. In vivo studies showed WS2 quantum dots can effectively protect the hematopoietic system and DNA from damages caused by high-energy radiation through removing whole-body excessive ROS. Furthermore, WS2 quantum dots showed nearly 80% renal clearance within 24 h post injection and did not cause any obvious toxicities in up to 30 days after treatment.

Learning to rapidly re-contact the lost plume in chemical plume tracing.

Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification.

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.