Updating vehicle VOCs emissions characteristics under clean air actions in a tropical city of China.

In the context of clean air actions in China, vehicle emission limits have been continuously tightened, which has facilitated the reduction of volatile organic compounds (VOCs) emissions. However, the characteristics of VOC emissions from vehicles with strict emission limits are poorly understood. This study investigated the characteristics of VOCs emitted from vehicles under the latest standards and identified future directions for vehicle controls based on tunnel measurements. The results showed that the highest percentage of VOCs from vehicle consisted of alkanes (80.9 %), followed by aromatics (15.8 %) and alkenes (3.1 %). Alkanes had the most significant ozone formation potential due to their high concentrations, in contrast to the aromatics that have been dominant in previous studies. The measured VOC emission factor was 71.3 mg·km-1, including tailpipe emissions of 39.6 mg·km-1 and evaporative emissions of 31.7 mg·km-1. The VOC emission factors of the subgroups were obtained. The emission of evaporated VOCs accounted for 44.5 % of the total vehicle VOC emissions, which have increased substantially from previous studies. In addition, the emission characteristics of vehicles that are under the latest emission threshold values have changed significantly, and the mixing ratio of toluene/benzene (T/B) has been updated to 3:1. This study updates the VOCs emission factors of vehicles under clean air actions and highlights the future mitigation policies should focus on reducing evaporative VOC emissions.

Semirational Design Strategy To Enhance the Thermostability and Catalytic Activity of Cytochrome P450 105D7 for the Degradation of the Pharmaceutically Active Compounds: Diclofenac.

Pharmaceutically active compounds are an important category of emerging pollutants, and their biological transformation processes in the environment are crucial for understanding and evaluating the migration, transformation, and environmental fate of emerging pollutants. The cytochrome P450 105 enzyme family has been proven to play an important role in the degradation of exogenous environmental pollutants. However, its thermostability and catalytic activity still need to be improved to better adapt to complex environmental conditions. This work elucidates the key mechanisms and important residues of the degradation reaction through multiple computational strategies, establishes a mutation library, and obtains 21 single-point mutation designs. Experimental verification showed that 16 single mutants had enhanced thermostability, with the R89F and L197Y mutants showing the highest increases in thermostability at 135 and 119% relative to the wild-type enzyme, respectively. Additionally, as a result of the higher specific activity of D390Q, it was selected for combination mutagenesis, ultimately resulting in three combination mutants (R89F/L197Y, R89F/D390Q, and R89F/L197Y/D390Q) with enhanced thermostability and catalytic activity. This study provides a modification approach for constructing efficient enzyme variants through semirational design and can contribute to the development of control technologies for emerging pollutants.

Climate-related naturally occurring epimutation and their roles in plant adaptation in A. thaliana.

DNA methylation has been proposed to be an important mechanism that allows plants to respond to their environments sometimes entirely uncoupled from genetic variation. To understand the genetic basis, biological functions and climatic relationships of DNA methylation at a population scale in Arabidopsis thaliana, we performed a genome-wide association analysis with high-quality single nucleotide polymorphisms (SNPs), and found that ~56% on average, especially in the CHH sequence context (71%), of the differentially methylated regions (DMRs) are not tagged by SNPs. Among them, a total of 3235 DMRs are significantly associated with gene expressions and potentially heritable. 655 of the 3235 DMRs are associated with climatic variables, and we experimentally verified one of them, HEI10 (HUMAN ENHANCER OF CELL INVASION NO.10). Such epigenetic loci could be subjected to natural selection thereby affecting plant adaptation, and would be expected to be an indicator of accessions at risk. We therefore incorporated these climate-related DMRs into a gradient forest model, and found that the natural A. thaliana accessions in Southern Europe that may be most at risk under future climate change. Our findings highlight the importance of integrating DNA methylation that is independent of genetic variations, and climatic data to predict plants' vulnerability to future climate change.

A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model.

Aim: A high percentage of the elderly suffer from knee osteoarthritis (KOA), which imposes a certain economic burden on them and on society as a whole. The purpose of this study is to examine the risk of KOA and to develop a KOA nomogram model that can timely intervene in this disease to decrease patient psychological burdens. Methods: Data was collected from patients with KOA and without KOA at our hospital from February 2021 to February 2023. Initially, a comparison was conducted between the variables, identifying statistical differences between the two groups. Subsequently, the risk of KOA was evaluated using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to determine the most effective predictive index and develop a prediction model. The examination of the disease risk prediction model in KOA includes the corresponding nomogram, which encompasses various potential predictors. The assessment of disease risk entails the application of various metrics, including the consistency index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, the calibration chart, the GiViTi calibration band, and the model for predicting KOA. Furthermore, the potential clinical significance of the model is explored through decision curve analysis (DCA) and clinical influence curve analysis. Results: The study included a total of 582 patients, consisting of 392 patients with KOA and 190 patients without KOA. The nomogram utilized age, haematocrit, platelet count, apolipoprotein a1, potassium, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, and estimated glomerular filtration rate as predictors. The C index, AUC, calibration plot, Giviti calibration band, DCA and clinical influence KOA indicated the ability of nomogram model to differentiate KOA. Conclusion: Using nomogram based on disease risk, high-risk KOA can be identified directly without imaging.

Characterization of VOC emissions and health risk assessment in the plastic manufacturing industry.

Volatile organic compounds (VOCs) significantly contribute to ozone pollution formation, and many VOCs are known to be harmful to human health. Plastic has become an indispensable material in various industries and daily use scenarios, yet the VOC emissions and associated health risks in the plastic manufacturing industry have received limited attention. In this study, we conducted sampling in three typical plastic manufacturing factories to analyze the emission characteristics of VOCs, ozone formation potential (OFP), and health risks for workers. Isopropanol was detected at relatively high concentrations in all three factories, with concentrations in organized emissions reaching 322.3 µg/m3, 344.8 µg/m3, and 22.6 µg/m3, respectively. Alkanes are the most emitted category of VOCs in plastic factories. However, alkenes and oxygenated volatile organic compounds (OVOCs) exhibit higher OFP. In organized emissions of different types of VOCs in the three factories, alkenes and OVOCs contributed 22.8%, 67%, and 37.8% to the OFP, respectively, highlighting the necessity of controlling them. The hazard index (HI) for all three factories was less than 1, indicating a low non-carcinogenic toxic risk; however, there is still a possibility of non-cancerous health risks in two of the factories, and a potential lifetime cancer risk in all of the three factories. For workers with job tenures exceeding 5 years, there may be potential health risks, hence wearing masks with protective capabilities is necessary. This study provides evidence for reducing VOC emissions and improving management measures to ensure the health protection of workers in the plastic manufacturing industry.

Ultrasensitive 3D Stacked Silicon Nanosheet Field-Effect Transistor Biosensor with Overcoming Debye Shielding Effect for Detection of DNA.

Silicon nanowire field effect (SiNW-FET) biosensors have been successfully used in the detection of nucleic acids, proteins and other molecules owing to their advantages of ultra-high sensitivity, high specificity, and label-free and immediate response. However, the presence of the Debye shielding effect in semiconductor devices severely reduces their detection sensitivity. In this paper, a three-dimensional stacked silicon nanosheet FET (3D-SiNS-FET) biosensor was studied for the high-sensitivity detection of nucleic acids. Based on the mainstream Gate-All-Around (GAA) fenestration process, a three-dimensional stacked structure with an 8 nm cavity spacing was designed and prepared, allowing modification of probe molecules within the stacked cavities. Furthermore, the advantage of the three-dimensional space can realize the upper and lower complementary detection, which can overcome the Debye shielding effect and realize high-sensitivity Point of Care Testing (POCT) at high ionic strength. The experimental results show that the minimum detection limit for 12-base DNA (4 nM) at 1 × PBS is less than 10 zM, and at a high concentration of 1 µM DNA, the sensitivity of the 3D-SiNS-FET is approximately 10 times higher than that of the planar devices. This indicates that our device provides distinct advantages for detection, showing promise for future biosensor applications in clinical settings.

New structure transistors for advanced technology node CMOS ICs.

Over recent decades, advancements in complementary metal-oxide-semiconductor integrated circuits (ICs) have mainly relied on structural innovations in transistors. From planar transistors to the fin field-effect transistor (FinFET) and gate-all-around FET (GAAFET), more gate electrodes have been added to three-dimensional (3D) channels with enhanced control and carrier conductance to provide higher electrostatic integrity and higher operating currents within the same device footprint. Beyond the 1-nm node, Moore's law scaling is no longer expected to be applicable to geometrical shrinkage. Vertical transistor stacking, e.g. in complementary FETs (CFET), 3D stack (3DS) FETs and vertical-channel transistors (VFET), for enhanced density and variable circuit or system design represents a revolutionary scaling approach for sustained IC development. Herein, innovative works on specific structures, key process breakthroughs, shrinking cell sizes and design methodologies for transistor structure research and development are reviewed. Perspectives on future innovations in advanced transistors with new channel materials and operating theories are also discussed.

Reinforced hydrogel building via formation of alginate-chitosan double network with pH & salt-responsiveness and electric conductivity for soft actuators.

Aiming at green and friendly environmental protection, polyvinyl alcohol/sodium alginate/chitosan (PSCS) double network hydrogel was successfully prepared through diffusing the high molecular weight chitosan into PVA/sodium alginate (PS) hydrogel without any other toxic reagents. The polyanion hydrogels could be significantly enhanced by immersing the polyanion hydrogel in high molecular weight chitosan solution without requiring specific structure. The PSCS hydrogel had a compact and rough surface structure with the smaller porosities and larger crystallization degree compared with polyvinyl alcohol/sodium alginate hydrogels and polyvinyl alcohol/sodium alginate/Ca2+ (PSCa) hydrogels. The PSCS hydrogel possessed excellent hydrolysis resistance, the significant pH-sensitive and salt-sensitive swelling. In addition, the flexibility, Young's modulus and mechanical properties of PSCS hydrogel can be adjusted through the changing the content of sodium alginate. Moreover, PS, PSCa and PSCS had electric conductivity, and PSCS showed twice the conductivity compared to PS hydrogel. Based on differences of swelling ratio, a PSCS bilayer hydrogel was designed and showed excellent pH-driven deformation ability. The PSCS hydrogel is expected to expand the application of hydrogels in conditions involving stimulus response, and might serve as a promising intelligent actuators or soft robots.

Optimization of CRISPR-Cas9 system in Eustoma grandiflorum.

The optimization of the CRISPR-Cas9 system for enhancing editing efficiency holds significant value in scientific research. In this study, we optimized single guide RNA and Cas9 promoters of the CRISPR-Cas9 vector and established an efficient protoplast isolation and transient transformation system in Eustoma grandiflorum, and we successfully applied the modified CRISPR-Cas9 system to detect editing efficiency of the EgPDS gene. The activity of the EgU6-2 promoter in E. grandiflorum protoplasts was approximately three times higher than that of the GmU6 promoter. This promoter, along with the EgUBQ10 promoter, was applied in the CRISPR-Cas9 cassette, the modified CRISPR-Cas9 vectors that pEgU6-2::sgRNA-2/pEgUBQ10::Cas9-2 editing efficiency was 37.7%, which was 30.3% higher than that of the control, and the types of mutation are base substitutions, small fragment deletions and insertions. Finally we obtained an efficient gene editing vector for E. grandiflorum. This project provides an important technical platform for the study of gene function in E. grandiflorum.

New solution for predicting the removal efficiency of organic contaminants by UV/ H2O2: From a case study of 1H-benzotriazole.

Rapid prediction of the removal efficiency and energy consumption of organic contaminants under various operating conditions is crucial for advanced oxidation processes (AOPs) in industrial application. In this study, 1H-Benzotriazole (BTZ, CAS: 95-14-7) is selected as a model micropollutant, a validated incorporated Computational Fluid Dynamics (CFD) model is employed to comprehensively investigate the impacts of initial concentrations of H2O2, BTZ and dissolved organic carbon (DOC) (i.e., [DOC]0, [BTZ]0 and [DOC]0), as well as the effective UV lamp power P and volumetric flow rate Qv. Generally, the operation performance depends on [DOC]0 and [BTZ]0 in similar trends, but with quantitatively different ways. The increase in [H2O2]0 and P/Qv can promote •OH generation, leading to the elimination of BTZ. It is worth noting that P/Qv is found to be linearly correlated with the removal order of BTZ (ROBTZ) under specific conditions. Based on this finding, the degradation of other potential organic contaminants with a wide range of rate constants by UV/H2O2 is further investigated. A model for predicting energy consumption for target removal rates of organic pollutants is established from massive simulation data for the first time. Additionally, a handy Matlab app is first developed for convenient application in water treatment. This work proposes a new operable solution for fast predicting operation performance and energy consumption for the removal of organic contaminants in industrial applications of advanced oxidation processes.

PM2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning.

Numerous studies have linked ozone (O3) production to its precursors and fine particulate matter (PM2.5), while the complex interaction effects of PM2.5 and volatile organic compounds (VOCs) on O3 remain poorly understood. A systematic approach based on an interpretable machine learning (ML) model was utilized to evaluate the primary driving factors that impact O3 and to elucidate how changes in PM2.5, VOCs from different sources, NOx, and meteorological conditions either promote or inhibit O3 formation through their individual and synergistic effects in a tropical coastal city, Haikou, from 2019 to 2020. The results suggest that under low PM2.5 levels, alongside the linear O3-PM2.5 relationship observed, O3 formation is suppressed by PM2.5 with higher proportions of traffic-derived aerosol. Vehicle VOC emissions contributed maximally to O3 formation at midday, despite the lowest concentration. VOCs from fossil fuel combustion and industry emissions, which have opposing effects on O3, act as inhibitors and promoters by inducing diverse photochemical regimes. As PM2.5 pollution escalates, the impact of these VOCs reverses, becoming more pronounced in shaping O3 variation. Sensitivity analysis reveals that the O3 formation regime is VOC-limited, and effective regional O3 mitigation requires prioritizing substantial VOC reductions to offset enhanced VOC sensitivity induced by the co-reduction in PM2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O3-NOx-VOCs-PM2.5 interplay, considering both meteorological and atmospheric compositional variations.

New mechanism for the participation of aromatic oxidation products in atmospheric nucleation.

Oxygenated organic molecules (OOMs) are recognized as important precursors for new particle formation (NPF) in the urban atmosphere. The paper theoretically studied the formation of OOMs by styrene oxidation processes initiated by OH radicals, focusing on the OOMs nucleation mechanism. The results found that in the presence of an H2SO4 molecule, lowly oxygenated organic molecules containing a benzene ring (LOMBs) can form stable clusters and grow to the scale of a critical nucleus through pi-pi stacking and OH hydrogen bonding. In addition, LOMBs are more readily generated in a styrene-oxidized system in the presence/absence of NOx than highly oxygenated organic molecules (HOMs). The reaction of OH radicals with other aromatics containing a branched chain on the benzene ring produces LOMBs to varying degrees, with pi-pi stacking playing an essential role. This result suggests that, in the presence of H2SO4 molecules, LOMBs may play a more significant role in promoting nucleation than HOMs. Our findings serve as a pivotal foundation for future investigations into the oxidation and nucleation processes of diverse aromatics in urban environments.

[Ozone Pollution Characteristics and Formation Mechanism in a Typical Tropical Seaside City].

To investigate the pollution characteristics and formation mechanism of ambient air ozone(O3) in a typical tropical seaside city, we conducted an observational experiment on O3 and its precursors at an urban site in Haikou, Hainan Province, from June to October 2019. The O3 pollution characteristics were analyzed comprehensively; the O3-NOx-VOCs sensitivities and key precursors were determined, and the control strategies for O3 pollution were carried out. The results were as follows:1 O3 pollution in Haikou mainly occurred in September and October, with daily maximum 8-h O3 concentrations in the range of 39-190 µg·m-3, and the daily variation in O3 was unimodal, peaking at approximately 14:00. 2 The concentrations of NO2 and VOCs were higher during O3 pollution episodes than their respective mean values in Haikou City. The increased O3 precursor concentrations were an important factor leading to O3 pollution, whereas O3 pollution was also influenced by regional transport, with pollutants mainly transported from the northeastern part of Haikou City. 3 O3-NOx-VOCs sensitivity in Haikou City was in the VOCs and NOx transitional regime, and the most sensitive precursors in various months were different. O3 formation in September was sensitive to anthropogenic VOCs the most; however, in October it was sensitive to NOx. 4 In the future, the reduction ratio of VOCs to NOx should be 1:1-4:1 to control O3 pollution effectively in Haikou.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism.

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure - activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

Impacts of QM region sizes and conformation numbers on modelling enzyme reactions: a case study of polyethylene terephthalate hydrolase.

A quantum mechanics/molecular mechanics (QM/MM) approach is a broadly used tool in computational enzymology. Treating the QM region with a high-level DFT method is one of the important branches. Here, taking leaf-branch compost cutinase-catalyzed polyethylene terephthalate depolymerization as an example, the convergence behavior of energy barriers as well as key structural and charge features with respect to the size of the QM region (up to 1000 atoms) is systematically investigated. BP86/6-31G(d)//CHARMM and M06-2X/6-311G(d,p)//CHARMM level of theories were applied for geometry optimizations and single-point energy calculations, respectively. Six independent enzyme conformations for all the four catalytic steps (steps (i)-(iv)) were considered. Most of the twenty-four cases show that at least 500 QM atoms are needed while only two rare cases show that ∼100 QM atoms are sufficient for convergence when only a single conformation was considered. This explains why most previous studies showed that 500 or more QM atoms are required while a few others showed that ∼100 QM atoms are sufficient for DFT/MM calculations. More importantly, average energy barriers and key structural/charge features from six conformations show an accelerated convergence than that in a single conformation. For instance, to reach energy barrier convergence (within 2.0 kcal mol-1) for step (ii), only ∼100 QM atoms are required if six conformations are considered while 500 or more QM atoms are needed with a single conformation. The convergence is accelerated to be more rapid if hundreds and thousands of conformations were considered, which aligns with previous findings that only several dozens of QM atoms are required for convergence with semi-empirical QM/MM MD simulations.

Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province.

Primary emissions of particulate matter and gaseous pollutants, such as SO2 and NOx have decreased in China following the implementation of a series of policies by the Chinese government to address air pollution. However, controlling secondary inorganic aerosol pollution requires attention. This study examined the characteristics of the secondary conversion of nitrate (NO3-) and sulfate (SO42-) in three coastal cities of Shandong Province, namely Binzhou (BZ), Dongying (DY), and Weifang (WF), and an inland city, Jinan (JN), during December 2021. Furthermore, the Shapley Additive Explanation (SHAP), an interpretable attribution technique, was adopted to accurately calculate the contributions of secondary formations to PM2.5. The nitrogen oxidation rate exhibited a significant dependence on the concentration of O3. High humidity facilitates sulfur oxidation. Compared to BZ, DY, and WF, the secondary conversion of NO3- and SO42- was more intense in JN. The light-gradient boosting model outperformed the random forest and extreme-gradient boosting models, achieving a mean R2 value of 0.92. PM2.5 pollution events in BZ, DY, and WF were primarily attributable to biomass burning, whereas pollution in Jinan was contributed by the secondary formation of NO3- and vehicle emissions. Machine learning and the SHAP interpretable attribution technique offer a precise analysis of the causes of air pollution, showing high potential for addressing environmental concerns.

Unexpected enhancement of sulfuric acid-driven new particle formation by alcoholic amines: The role of ion-induced nucleation.

New particle formation (NPF) contributes more than half of the global aerosol. Diethanolamine (DEA) and methyldiethanolamine (MDEA) are the most common amines used to remove CO2 and H2S, which are lost to the atmosphere from CO2 chemical absorbers, livestock and consumer products and are involved in sulfuric acid (SA)-driven NPF. Ion-induced nucleation (IIN) is an important nucleation pathway for NPF. We investigated the role of IIN on DEA and MDEA enhancing SA-driven NPF using density functional method (DFT), molecular dynamics (MD) simulation and atmospheric cluster dynamics code (ACDC). The effects of SO42-, H3O+, NH4+, HSO4-, NO3-, ammonia, methylamine, dimethylamine, trimethylamine and water (W) on the nucleation of SA-DEA were further investigated. The enhancement ability of DEA is greater than that of dimethylamine (DMA) and MDEA. Participation in SA-based NPF is a removal pathway for DEA and MDEA. DEA-SA clusters are generated that not only aggregate DEA and SA molecules, but also increase further growth of atmospheric ions. The very low Gibbs formation free energy highlights the importance of ion-induced nucleation for SA-based NPF. The order of the ability of common atmospheric ions to increase the (SA)(DEA) cluster nucleation is SO42- > H3O+ > NH4+ > HSO4- > NO3-. The addition of 20 water molecules increases the (SA)(DEA)9 cluster from 1.882 nm to 2.053 nm, promoting SA-based NPF. The atmospheric ions accelerate the aggregation rate of the (SA)5(DEA)5 cluster within 15 ns?

The effects of the gas-liquid interface and gas phase on Cl/ClO radical interaction with water molecules.

In the marine boundary layer (MBL), chlorine (Cl) and chlorine monoxide (ClO) are powerful oxidants with high concentrations. The gas-liquid interface is also ubiquitous in the MBL as a favorable site for atmospheric reactions. Understanding the role of water in Cl/ClO radical chemistry is essential for predicting their behavior in the atmosphere and developing effective strategies for mitigating their harmful effects. However, the research studies on the system of Cl/ClO radicals on the surface of water droplets are still insufficient. In previous studies, we have found unique results related to the hydroxyl radical at the interface using ab initio molecular dynamics (AIMD). In this work, we have used AIMD to investigate interactions between Cl/ClO radicals and water molecules at the gas-liquid interface. Radical mobility, radial distribution functions, coordination, and population analyses were conducted to investigate the surface preference, bonding pattern, and track Cl/ClO radicals in the water droplets. In addition, density functional theory (DFT) analysis was conducted to compare the results at the gas-liquid interface with those in the gas phase. We found that Cl/ClO radicals tend to remain near the gas-liquid interface in water droplet systems and outside of water clusters in gas phase systems. The ClO radical can form O*-H and Cl-O bonds with water molecules; however, neither the O*-O hemibond nor the Cl-H bond was detected in all systems. Different dominant structures were obtained for ClO in the interface and gas phase. The ClO radical can be bonded to one water molecule from its oxygen side, (H2O)0-Cl-O*-(H2O)1 at the interface, or to two water molecules from the chlorine and oxygen sides, (H2O)1-Cl-O*-(H2O)1 in the gas phase. Meanwhile, the Cl radical can only form a dominant structure like Cl*-(H2O)1 at the gas-liquid interface by making a Cl*-O hemibond. Providing a thorough explanation of the Cl/ClO radical behavior at the gas-liquid interface, this study will improve our understanding of the MBL's oxidizing capacity and pollution causes.

The development of a clinical nomogram to predict medication nonadherence in patients with knee osteoarthritis.

Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.