Edge Graphitized Oxygen-Rich Carbon Based on Stainless Steel-Assisted High-energy Ball Milling for High-Capacity and Ultrafast Sodium Storage.

Oxygen doping is an effective strategy for constructing high-performance carbon anodes in Na ion batteries; however, current oxygen-doped carbons always exhibit low doping levels and high-defect surfaces, resulting in limited capacity improvement and low initial Coulombic efficiency (ICE). Herein, a stainless steel-assisted high-energy ball milling is exploited to achieve high-level oxygen doping (19.33%) in the carbon framework. The doped oxygen atoms exist dominantly in the form of carbon-oxygen double bonds, supplying sufficient Na storage sites through an addition reaction. More importantly, it is unexpected that the random carbon layers on the surface are reconstructed into a quasi-ordered arrangement by robust mechanical force, which is low-defect and favorable for suppressing the formation of thick solid electrolyte interfaces. As such, the obtained carbon presents a large reversible capacity of 363 mAh g-1 with a high ICE up to 83.1%. In addition, owing to the surface-dominated capacity contribution, an ultrafast Na storage is achieved that the capacity remains 139 mAh g-1 under a large current density of 100 A g-1 . Such good Na storage performance, especially outstanding rate capability, has rarely been achieved before.

Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle-Computer Interface.

In the field of the muscle-computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models.

Recent advances in nanomaterial-based biosensors for the detection of exosomes.

Exosomes are a type of extracellular vesicle actively secreted by almost all eukaryotic cells. They are ideal candidates for reliable next-generation biomarkers in the early diagnosis and therapeutic response evaluation of cancer. Thus, the quantification of exosomes is crucial in facilitating clinical research and application. Compared with traditional materials, nanomaterials have better optical, magnetic, electrical, and catalytic properties due to their small size, high specific surface area, and variable structure. The incorporation of nanomaterials into sensing systems is an attractive approach towards improving sensitivity and can provide improved sensor selectivity and stability. In this paper, we summarize the progress in nanomaterial-based exosome detection methods, including electrochemical biosensors, photoelectrochemical biosensors, colorimetric biosensors, fluorescence biosensors, chemiluminescence biosensors, electrochemiluminescence biosensors, surface plasmon resonance biosensors, and surface-enhanced Raman spectroscopy biosensors. Moreover, future research directions and challenges in exosome detection methods are discussed. We hope that this article will offer an overview of nanomaterial-based exosome detection techniques and open new avenues in disease research.Graphical abstract.

Laboratory and simulation study on the Cd(â ¡) adsorption by lake sediment: Mechanism and influencing factors.

Sediments are the major sinks for Cd(Ⅱ) in the aquatic environment. Here, the detailed binding mechanisms and effects of environmental factors on Cd(Ⅱ) adsorption onto lake sediment were tested by a batch of adsorption and characteristic experiments. Sediment samples and sediment-Cd complexes were characterized using Scanning electron microscopy, Energy dispersive spectroscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction spectral analyses. The interactive and main effect of parameters such as pH, flow velocity, Cd(II) concentration, sediment particle size, humic acid, fulvic acid and adsorption time involved in the adsorption process were determined using two models based on response surface methodology (RSM) and a back-propagation neural network with genetic algorithm (GABP). Results showed that Cd(II) adsorption onto sediment was mainly achieved through surface complexation with O-containing groups and precipitation with carbonate and sulfide. RSM was favorable for modeling Cd(II) adsorption in lake systems because it intuitively reflected the influence of the factors and had a good fitting precision (R2 = 0.8838, RSME = 2.5496) close to that of the GABP model (R2 = 0.8959, RSME = 2.5410). pH, sediment particle size, and humic acid exerted strong influences on Cd(II) immobilized by the sediment. Overall, our findings facilitate a better understanding of Cd(II) mobility in lakes and provide a reference for controlling heavy metals derived from both aqueous and sediment sources.

Discovery of novel indazole-acylsulfonamide hybrids as selective Mcl-1 inhibitors.

Overexpressing myeloid cell leukemia sequence 1 (Mcl-1) protein is an important way to confer the resistance of cancer cells to conventional anti-cancer treatments. Therefore, developing Mcl-1 inhibitors has become an attractive strategy for cancer therapy. In the studies, a series of new indazole-acylsulfonamide hybrids were designed, synthesized and evaluated as potent Mcl-1 inhibitors. Among them, the most potent compound 17 (Ki = 0.43 µM) showed a little better inhibitory activity against Mcl-1 protein than positive control AT-101 (Ki = 0.45 µM). Pleasingly, it displayed > 40-fold selectivity over Bcl-2 (Ki = 18 µM) and Bcl-xL (no activity). Furthermore, compound 17 had good inhibitory activities against PC-3, MDA-MB-231 and K562 cells (IC50 = 12.3, 10.6 and 6.62 µM, respectively) and could effectively induce apoptosis and the activation of caspase-3 in a dose-dependent manner in K562 cells.

Portable glucose meter: trends in techniques and its potential application in analysis.

A blood glucose meter is an electronic medical device used for determining the concentration of glucose in blood. These meters have undergone five phases of development: washed blood glucose meters, wiped blood glucose meters, colorimetric blood glucose meters, electrochemical blood glucose meters, and micro, multiple site blood glucose meters. Thanks to their speed, portability, low cost, and easy operation, blood glucose meters have been widely available for use in clinical diagnosis. Recently, coupling of target recognition elements (antibody-antigen recognition, nucleic acid hybridization, enzyme recognition, and click chemistry) with signal transduction and amplification strategies (glucose-generating enzymes, nicotinamide adenine dinucleotide (NADH)-generating enzymes, encapsulated glucose, nanomaterials, and cyclic amplification of DNA) has allowed various targets to be determined via the relationship between the signal of the blood glucose meter and the concentration of targets. In this paper, a brief review of the development and mechanism of blood glucose meters is given first. Then, more details on the application of blood glucose meters in analysis are described, including biomedical analysis, food analysis, and environmental analysis. Finally, the prospect of future development of blood glucose meters is also discussed. Graphical abstract ᅟ.

Hollow and porous nickel sulfide nanocubes prepared from a metal-organic framework as an efficient enzyme mimic for colorimetric detection of hydrogen peroxide.

Hollow, porous NiS nanocubes were prepared by a hydrothermal method starting from Ni-Co Prussian blue analogue nanocubes as the template. The morphology and structure of the NiS nanocubes were tuned by adjustment of the ion-exchange rate and the degree of chemical etching, and they were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, X-ray diffraction, and nitrogen sorption measurements. The NiS nanocubes are shown to act as a peroxidase mimic that can catalyze the oxidization of 3,3',5,5'-tetramethylbenzidine by hydrogen peroxide (H2O2), producing a visible color change, for which the absorbance is best measured at 652 nm. The outstanding activity may result from the unique structure of the NiS nanocubes. The catalytic oxidation follows Michaelis-Menten kinetics and shows a ping-pong mechanism of enzyme action. The findings were used to develop a rapid, sensitive, and selective colorimetric H2O2 assay with a response that is linear in the 4-40 µM range with a detection limit of 1.72 µM (signal-to-noise ratio of 3). Graphical abstarct ᅟ.

Binding characteristics of cadmium and zinc onto soil organic matter in different water managements and rhizosphere environments.

Soil organic matter (SOM) could immobilize most of metals, but it could promote the migration of a small part of metals in special environments. Heavy rainfall and drought makes wetlands affected by the alternation of drought and flood, altering the mobility of metals. Few studies have been conducted on the changes of binding characteristics of metals onto SOM which derived from different water conditions and rhizospheric environments. The objective of this paper was to explore the sequential differences of spectral variations of fluorescent groups and UV-Vis groups of metals onto SOM which derived from different water managements and rhizospheric environments. The method adopted was mainly two-dimensional correlation analysis (2DCOS). The results showed that flooding samples contained more aromatic substances compared to draining samples, which could promote metal binding. The binding characteristics were shown in the following: (1) Cd2+ and Zn2+ could react with aromatic substances, react with functional groups in SOM, and promote the formation of new groups such as carboxyl; (2) both Zn2+ and Cd2+ could bind with functional groups on proteins but relatively reductive environment can weaken the binding ability of Cd2+; (3) the protein-like or fulvic-like groups gave the fastest responses and then came the amide and carboxyl groups in nearly all flooding samples; (4) in flooding samples, Cd2+ was most easily to bind with fulvic-like groups, while Zn2+ was most easily to bind with protein-like groups. This work is conducive to the long-term management of heavy metal pollutants in wetlands.

Copper(II)-coated Fe3O4 nanoparticles as an efficient enzyme mimic for colorimetric detection of hydrogen peroxide.

The authors describe the preparation of Cu(II)-coated Fe3O4) nanoparticles (NPs) that possess excellent peroxidase-like activity. The NPs were formed by chelation between Cu(II) ions and the oxygen functional groups of sodium ligninsulfonate. The morphology and structure of the NPs were characterized by scanning electron microscopy, transmission electron microscopy, X-ray powder diffraction, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy. The NPs have an average diameter of 220 nm. They are shown to be viable peroxidase mimics that can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine by hydrogen peroxide to produce a blue coloration. The findings were used to design a colorimetric assay that has a linear response in the 2.5 to 100 µM H2O2 concentration range and a 0.2 µM detection limit. The assay excels by its selectivity, high sensitivity, good selectivity, portability and cost efficiency. Graphical abstract Fe3O4-Cu2+ nanoparticles with excellent peroxidase-like activity were successfully prepared via a facile strategy, and then used to design a facile as well as sensitive colorimetric H2O2 sensor. The linear range and the detection limit were 2.5~100 µM and 0.212 µM.

Passivating effect of dehydrated sludge and sepiolite on arsenic contaminated soil.

Exploring an efficient and economical method to remove arsenic from soil is of great practical significance but there were few studies on the combined use of sepiolite and dehydrated sludge as a repair agent to passivate heavy metals. Through soil passivation experiments, arsenic sequential extractions, and analysis of basic physicochemical properties of contaminated soils and repair agents, this study was to explore the applicability of dehydrated sludge-sepiolite compound repair agents and dehydrated sludge individual repair agents to passivate soil arsenic and its passivating effect. After passivation experiment, the best remediation period was 1-10 days. The best cultivated time was 10 day using DS2 repair agent. With a comparison of passivation effect of different repair agents, it was found that the best treatment group in individual repair agents was DS2 (10 days), and the best treatment group in compound repair agents was S1 (1 day). The passivation effect of individual repair agents was better than compound repair agents in 10-days cultivation. In the short term, the repair effect was increasing and then decreasing, thus this experiment was only suitable for use as a short-term repair method. The application of dehydrated sludge combined with sepiolite as repair agents provided a new way for both making full use of dehydrated sludge and controlling metal mobility.

Investigating organic matter properties affecting the binding behavior of heavy metals in the rhizosphere of wetlands.

Soil organic matter (SOM) is a crucial factor affecting the immobilization of heavy metal in wetlands. Recent studies have shown that the rhizosphere SOM has great ability to immobilize heavy metals. However, there existed few works on studying molecular characteristics of SOM to explore the mechanisms. Electrospray ionization-Fourier transform ion cyclotron resonance-mass spectrometry (ESI-FTICR-MS) combined with FTIR spectroscopy were applied to investigate the characteristics of SOM in rhizosphere and nonrhizosphere samples and to find out what characteristics the rhizosphere SOM embodies conducive to metal binding in this paper. The rhizosphere contained higher C, P, Mn, and other metal concentrations. The adsorption of Cr on rhizosphere SOM was greater than that on nonrhizosphere SOM. Compared to nonrhizosphere SOM, rhizosphere SOM contained less saturated and more oxidized compounds, greater overall molecular weights (MW), more condensed aromatic structures (56.59% VS 51.56% by peak intensity), less carboxylate and N-containing COO functional groups (25.98% VS 56.63% by peak intensity), more hydrophilicity, and the latter four are conducive to metal binding. This study showed that the rhizosphere SOM had unique compositional and structural characteristics. These results provided evidence for the phytoremediation technologies of heavy metal contaminated wetlands.

The complexation of rhizosphere and nonrhizosphere soil organic matter with chromium: Using elemental analysis combined with FTIR spectroscopy.

Complexation is a main mechanism controlling the reactions between soil organic matter (SOM) and heavy metals, which still have not been fully understood up to date. The objective of this study was to compare the SOM composition of nonrhizosphere and rhizosphere in low Cr treatment with that in high Cr treatment and to find out how metal concentrations affect the complexation with SOM. The results revealed that both the hydroxyl and the carboxyl were significantly different under different Cr treatment groups. For nonrhizosphere samples, the high Cr treatment tended to have less hydroxyl contents and more structural changes on hydroxyl (3389-3381 cm-1) than the low Cr treatment (3389-3388 cm-1), while in the rhizosphere samples the reverse happened. The gap of the different Cr treated band area in the rhizosphere samples (44 a.u of the gap) was greatly smaller than that in the nonrhizosphere samples (576 a.u of the gap). In both the rhizosphere and nonrhizosphere samples, the high Cr treatment showed greater structural changes on carboxylic acids (11, 12 a.u changes based on the control) than the low Cr treatment (4, 6 a.u). The unsaturated carboxylic acids could account for downward frequency shift and the contents in the nonrhizosphere samples were slightly greater than that in the rhizosphere samples. This study used elemental analysis combined with FTIR spectroscopy to explore the effects of metal concentrations on the complexation of Cr with SOM and the composition of SOM. These findings give a way to understanding part of the complexation mechanisms between the metal and SOM.

Enhanced robust spatial feature selection and correlation filter learning for UAV tracking.

Spatial boundary effect can significantly reduce the performance of a learned discriminative correlation filter (DCF) model. A commonly used method to relieve this effect is to extract appearance features from a wider region of a target. However, this way would introduce unexpected features from background pixels and noises, which will lead to a decrease of the filter's discrimination power. To address this shortcoming, this paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises. Unlike the ℓ2-norm-based tracking approaches that are prone to non-Gaussian noises, the proposed method imposes the ℓ2,1-norm on the loss term to enhance the robustness against the training outliers. To enhance the discrimination further, a jointly sparse feature selection scheme based on the ℓ2,1 -norm is designed to regularize the filter in rows and columns simultaneously. To the best of the authors' knowledge, this has been the first work exploring the structural sparsity in rows and columns of a learned filter simultaneously. The proposed model can be efficiently solved by an alternating direction multiplier method. The proposed EFSCF is verified by experiments on four challenging unmanned aerial vehicle datasets under severe noise and appearance changes, and the results show that the proposed method can achieve better tracking performance than the state-of-the-art trackers.

Maximal Margin Support Vector Machine for Feature Representation and Classification.

High-dimensional small sample size data, which may lead to singularity in computation, are becoming increasingly common in the field of pattern recognition. Moreover, it is still an open problem how to extract the most suitable low-dimensional features for the support vector machine (SVM) and simultaneously avoid singularity so as to enhance the SVM's performance. To address these problems, this article designs a novel framework that integrates the discriminative feature extraction and sparse feature selection into the support vector framework to make full use of the classifiers' characteristics to find the optimal/maximal classification margin. As such, the extracted low-dimensional features from high-dimensional data are more suitable for SVM to obtain good performance. Thus, a novel algorithm, called the maximal margin SVM (MSVM), is proposed to achieve this goal. An alternatively iterative learning strategy is adopted in MSVM to learn the optimal discriminative sparse subspace and the corresponding support vectors. The mechanism and the essence of the designed MSVM are revealed. The computational complexity and convergence are also analyzed and validated. Experimental results on some well-known databases (including breastmnist, pneumoniamnist, colon-cancer, etc.) show the great potential of MSVM against classical discriminant analysis methods and SVM-related methods, and the codes can be available on https://www.scholat.com/laizhihui.

High-Energy Ball Milling Promoted Sulfur Immobilization for Constructing High-Performance Na-Storage Carbon Anodes.

Sulfur (S) doping is an effective method for constructing high-performance carbon anodes for sodium-ion batteries. However, traditional designs of S-doped carbon often exhibit low initial Coulombic efficiency (ICE), poor rate capability, and impoverished cycle performance, limiting their practical applications. This study proposes an innovative design strategy to fabricate S-doped carbon using sulfonated sugar molecules as precursors via high-energy ball milling. The results show that the high-energy ball milling can immobilize S for sulfonated sugar molecules by modulating the chemical state of S atoms, thereby creating a S-rich carbon framework with a doping level of 15.5 wt %. In addition, the S atoms are present mainly in the form of C-S bonds, facilitating a stable electrochemical reaction; meanwhile, S atoms expand the spacing between carbon layers and contribute sufficient capacitance-type Na-storage sites. Consequently, the S-doped carbon exhibits a large capacity (>600 mAh g-1), a high ICE (>90%), superior cycling stability (490 mAh g-1 after 1100 cycles at 5 A g-1), and outstanding rate performance (420 mAh g-1 at a high current density of 50 A g-1). Such excellent Na-storage properties of S-doped carbon have rarely been reported in the literatures before.

Fe oxides and fulvic acids together promoted the migration of Cd(II) to the root surface of Phragmites australis.

Dissolved organic matter (DOM) or iron/manganese (hydro)oxides were important factors in the migration of Cd in sediments of wetlands. DOM and Fe oxides simultaneously affect the longitudinal and transverse migration of Cd in wetlands sediments of plants was still unclear. In this study, a 14-day rhizobox experiment was conducted and the result showed that the rhizosphere effect of Cd migration was only limited to the upper layer of sediments (- 2 to - 4 cm). Fe with fulvic acid (FA) simultaneously existed can precipitate Cd(II) from supernatant to sediments downward. Fe oxides at sediment concentration could effectively prevent Cd(II) from migrating to root surface (0.21 vs 0.02 at%). While Fe oxides with FA together at sediment concentration could effectively promoted the migration of Cd(II) to root surface (0.07 vs 0.08 at%). The formation of organo-metallic complexes of Fe in the presence of FA profoundly proved this finding (increased by ~33.0%). And the polysaccharides and aromatics in organic matter were the chief functional groups participating in the incorporation of Cd and Fe oxides. The findings reveal the migration rules of Cd(II) in sediments by FA and Fe oxides and give an insight into the mechanisms of Cd(II) migration to the root surface around wetland plants.

Effects of pyrolysis temperature on proton and cadmium binding properties onto biochar-derived dissolved organic matter: Roles of fluorophore and chromophore.

Understanding the environmental behavior of biochar-derived dissolved organic matter (BDOM) is crucial for promoting the extensive utilization of biochar and meeting the carbon neutrality targets. However, limited studies focused on the binding mechanism of protons and Cd with DOM released from biochar produced at different pyrolysis temperatures. By combining excitation-emission matrix spectroscopy and parallel factor analysis, we found that the humic-like fluorophores in BDOM had higher aromaticity, molecular weight, and contents of carboxylic and phenolic groups relative to the protein-like fluorophores. Conversely, the protein-like fluorophores exhibited a stronger binding affinity for Cd than humic-like fluorophores. With the pyrolysis temperature increased from 300 °C to 500 °C, the quenching effects of Cd on the protein-like components were enhanced significantly. Their fluorescence intensities could be quenched up to 51.64%. The results of ultraviolet-visible absorbance spectroscopy and differential absorbance spectroscopy showed that the carboxylic-like and phenolic-like chromophores were involved in the protons and Cd binding process of BDOM. The binding ability of phenolic-like chromophores with Cd was reduced as a function of increasing pyrolysis temperature. These findings implied that these carboxylic and phenolic groups were mainly contained in the non-fluorescent components. Besides, protons and Cd could also induce inter-chromophore interactions in BDOM, and the interaction was proportional to the pyrolysis temperature. These results clearly demonstrated the pyrolysis temperature-dependent changes in the protons and Cd binding properties of BDOM. More importantly, the possible risk of Cd mobility caused by the protein-like components in BDOM cannot be ignored when the biochar was applied in contaminated soils. This research extends our knowledge of the application potentiality of biochar in heavy metal polluted soil.

[Characteristics and Sources of DOM in Lake Sediments Under Different Inundation Environments].

The characteristics and sources of DOM in sediments are significantly affected by fluctuations in lake water levels. However, the impact of spatial differences on water levels remain unclear. Here, 36 sediment samples were collected from the flood passage and coastal beach of East Dongting Lake. The differences in the composition and source of DOM in sediments under perennial inundation and seasonal inundation were studied using UV-visible absorbance (UV-Vis) and fluorescent excitation-emission matrix (EEM)-parallel factor analysis (PARAFAC). Three fluorescent components of DOM in the sediment were identified. The relative abundance of protein-like components was as high as (72.95±8.94)%, including tryptophan (C2) and tyrosine (C3). However, the humic-like component (C1) abundance was (27.05±8.94)%. Compared with that in perennial inundation, DOM in seasonal inundation had a higher and lower relative abundance of protein-like components and humic-like components, respectively. Further, the aromatic and hydrophobic components were higher in perennial inundation, showing a spatial pattern of the middle>entrance>outlet of the lake, which was more conducive to the migration of pollutants. The high FI (1.93) and BIX (0.91) and low HIX (1.57) indicated that the DOM in sediments had the mixed characteristics of being mainly endogenic and relatively weakly terrigenous. This was mainly influenced by human input and sediment characteristics. The direct effect of sewage discharge was intensified by sediment exposure in the seasonal inundation zone. Additionally, the contents of clay and total nitrogen (TN) were significantly positively correlated with FI, indicating that high nutrients and clay in sediments enhanced the endogenous input of DOM (FI>1.9). The perennial inundation zone was influenced by external runoff input. At the same time, the pH and C/N were significantly positively correlated with HIX and C1, indicating that DOM in the sediments had higher terrigenic characteristics (HIX=1.38±0.57) than those in the seasonal inundation zone owing to the alkaline environment (pH>7.5) and runoff input. The results above revealed the relevant theories of the response of DOM in sediment to water quality and pollution in the process of hydrology and human activities and provide a scientific basis for the prevention and control of sediment pollution in lakes.

How do drying-wetting cycles influence availability of heavy metals in sediment? A perspective from DOM molecular composition.

Investigating the influence mechanism of drying-wetting cycles on the availability and mobility of heavy metals in sediment from the perspective of the molecular composition of dissolved organic matter (DOM) may gain a new understanding, but little current information exists. Here, we used spectral technologies, high-resolution mass spectrometry, and elemental stoichiometry method to trace the change rules of the molecular composition of DOM in the riparian sediment of the river. Results showed that the drying-wetting cycles could benefit the degradation of labile fractions (e.g., proteins, aliphatics, and lipids) of DOM and retain the fractions with high aromaticity and molecular size (e.g., lignin). The decrease in the availability of Cd after drying-wetting alternation processes was highly related to these changes in DOM composition. However, the availability of Zn and Cu remained almost unchanged, which probably resulted from the release and depletion of N and S in sediment-derived DOM under drying-wetting alternation conditions. As for Cr, its exchangeable fraction was unchanged during the drying-wetting alternation process, likely due to its high stability in the sediment. These results have implications on the environmental geochemical cycling of heavy metals in the riparian sediment with frequent drying-wetting alternation.

Comparative effectiveness of biologics and targeted therapies for psoriatic arthritis.

OBJECTIVE: To quantify comparative effectiveness of interleukin (IL)-12/23 antagonist (ustekinumab), IL-17A antagonists (secukinumab and ixekizumab), PDE4 inhibitor (apremilast) and tumour necrosis factor-alpha (TNF-α) inhibitors (infliximab, etanercept, adalimumab, certolizumab pegol and golimumab) for psoriatic arthritis (PsA). METHODS: We adapted a deidentified claims-based algorithm validated for inflammatory arthritis treatments to compare treatments among a retrospective cohort of commercially insured and Medicare Advantage beneficiaries with PsA from October 2013 to April 2019 in the OptumLabs Data Warehouse. Main outcomes include (1) treatment effectiveness, based on: adherence, adding or switching biologic or PDE4, addition of new non-biologic disease-modifying antirheumatic drug, increase in biologic or PDE4 dose or frequency and glucocorticoid use and (2) percentage of each group fulfilling the effectiveness algorithm. We used Poisson regression with robust variance stratified by prior PsA biologic exposure and adjusted for potential confounders. RESULTS: Of 2730 individuals with PsA, 327 received IL-12/23, 138 IL-17A's, 624 PDE4 and 1641 TNF-α's. Effectiveness criteria were fulfilled among 63 (19.3%) IL-12/23 recipients, 40 (29.0%) IL-17A recipients, 160 (25.6%) PDE4 recipients and 530 (32.3%) TNF-α recipients. Among biologic-naïve individuals, IL-12/23 was less effective than TNF-α's with fully adjusted relative risk (aRR) compared with TNF-α's of 0.63 (95% CI 0.45 to 0.89). Among biologic-experienced individuals, PDE4 recipients were less effective than TNF-α's (aRR 0.67, 95% CI 0.46 to 0.96). CONCLUSIONS: TNF-α's appeared more effective than IL-12/23's for biologic-naïve individuals, and PDE4's for biologic-experienced individuals. These results may help inform treatment choice for individuals with PsA.