Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept, entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants (WWTPs) and sewage networks, hardware (i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software (i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.

Antibacterial resistance is a global health threat that requires further concrete action on the part of all countries. In this context, one of the biggest concerns is whether enough new antibacterial drugs are being discovered and developed. Although several high-quality reviews on clinical antibacterial drug pipelines from a global perspective were published recently, none provides comprehensive information on original antibacterial drugs at clinical stages in China. In this review, we summarize the latest progress of novel antibacterial drugs approved for marketing and under clinical evaluation in China since 2019. Information was obtained by consulting official websites, searching commercial databases, retrieving literature, asking personnel from institutions or companies, and other means, and a considerable part of the data covered here has not been included in other reviews. As of June 30, 2023, a total of 20 antibacterial projects from 17 Chinese pharmaceutical companies or developers were identified and updated. Among them, two new antibacterial drugs that belong to traditional antibiotic classes were approved by the National Medical Products Administration (NMPA) in China in 2019 and 2021, respectively, and 18 antibacterial agents are in clinical development, with one under regulatory evaluation, five in phase-3, six in phase-2, and six in phase-1. Most of the clinical candidates are new analogs or mono-components of traditional antibacterial pharmacophore types, including two dual-acting hybrid antibiotics and a recombinant antibacterial protein. Overall, despite there being 17 antibacterial clinical candidates, our analysis indicates that there are still relatively few clinically differentiated antibacterial agents in stages of clinical development in China. Hopefully, Chinese pharmaceutical companies and institutions will develop more innovative and clinically differentiated candidates with good market potential in the future research and development (R&D) of original antibacterial drugs.

The issue of opacity within data-driven artificial intelligence (AI) algorithms has become an impediment to these algorithms’ extensive utilization, especially within sensitive domains concerning health, safety, and high profitability, such as chemical engineering (CE). In order to promote reliable AI utilization in CE, this review discusses the concept of transparency within AI utilizations, which is defined based on both explainable AI (XAI) concepts and key features from within the CE field. This review also highlights the requirements of reliable AI from the aspects of causality (i.e., the correlations between the predictions and inputs of an AI), explainability (i.e., the operational rationales of the workflows), and informativeness (i.e., the mechanistic insights of the investigating systems). Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE. Furthermore, a comprehensive transparency analysis case study is provided as an example to enhance understanding. Overall, this work provides a thorough discussion of this subject matter in a way that-for the first time-is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization. With this vital missing link, AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.

Antibiotic resistant bacteria (ARB) with antibiotic resistance genes (ARGs) can reduce or eliminate the effectiveness of antibiotics and thus threaten human health. The United Nations Environment Programme considers antibiotic resistance the first of six emerging issues of concern. Advanced oxidation processes (AOPs) that combine ultraviolet (UV) irradiation and chemical oxidation (primarily chlorine, hydrogen peroxide, and persulfate) have attracted increasing interest as advanced water and wastewater treatment technologies. These integrated technologies have been reported to significantly elevate the efficiencies of ARB inactivation and ARG degradation compared with direct UV irradiation or chemical oxidation alone due to the generation of multiple reactive species. In this study, the performance and underlying mechanisms of UV/chlorine, UV/hydrogen peroxide, and UV/persulfate processes for controlling ARB and ARGs were reviewed based on recent studies. Factors affecting the process-specific efficiency in controlling ARB and ARGs were discussed, including biotic factors, oxidant dose, UV fluence, pH, and water matrix properties. In addition, the cost-effectiveness of the UV-based AOPs was evaluated using the concept of electrical energy per order. The UV/chlorine process exhibited a higher efficiency with lower energy consumption than other UV-based AOPs in the wastewater matrix, indicating its potential for ARB inactivation and ARG degradation in wastewater treatment. Further studies are required to address the trade-off between toxic byproduct formation and the energy efficiency of the UV/chlorine process in real wastewater to facilitate its optimization and application in the control of ARB and ARGs.

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

Industries such as non-ferrous metal smelting discharge billions of gallons of highly toxic heavy metal wastewater (HMW) worldwide annually, posing a severe challenge to conventional wastewater treatment plants and harming the environment. HMW is traditionally treated via chemical precipitation using lime, caustic, or sulfide, but the effluents do not meet the increasingly stringent discharge standards. This issue has spurred an increase in research and the development of innovative treatment technologies, among which those using nanoparticles receive particular interest. Among such initiatives, treatment using nanoscale zero-valent iron (nZVI) is one of the best developed. While nZVI is already well known for its site-remediation use, this perspective highlights its application in HMW treatment with metal recovery. We demonstrate several advantages of nZVI in this wastewater application, including its multifunctionality in sequestrating a wide array of metal(loid)s (> 30 species); its capability to capture and enrich metal(loid)s at low concentrations (with a removal capacity reaching 500 mg·g⁻¹ nZVI); and its operational convenience due to its unique hydrodynamics. All these advantages are attributable to nZVI’s diminutive nanoparticle size and/or its unique iron chemistry. We also present the first engineering practice of this application, which has treated millions of cubic meters of HMW and recovered tons of valuable metals (e.g., Cu and Au). It is concluded that nZVI is a potent reagent for treating HMW and that nZVI technology provides an eco-solution to this toxic waste.

Municipal solid waste (MSW) is an important destination for abandoned plastics. During the waste disposal process, large plastic debris is broken down into microplastics (MPs) and released into the leachate. However, current research only focuses on landfill leachates, and the occurrence of MPs in other leachates has not been studied. Therefore, herein, the abundance and characteristics of MPs in three types of leachates, namely, landfill leachate, residual waste leachate, and household food waste leachate, were studied, all leachates were collected from the largest waste disposal center in China. The results showed that the average MP abundances in the different types of leachates ranged from (129 ± 54) to (1288 ± 184) MP particles per liter (particles·L⁻¹) and the household food waste leachate exhibited the highest MP abundance (p < 0.05). Polyethylene (PE) and fragments were the dominant polymer type and shape in MPs, respectively. The characteristic polymer types of MPs in individual leachates were different. Furthermore, the conditional fragmentation model indicated that the landfilling process considerably affected the size distribution of MPs in leachates, leading to a higher percentage (> 80%) of small MPs (20-100 μm) in landfill leachates compared to other leachates. To the best of our knowledge, this is the first study discussing the sources of MPs in different leachates, which is important for MP pollution control during MSW disposal.

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

The potential for reducing greenhouse gas (GHG) emissions and energy consumption in wastewater treatment can be realized through intelligent control, with machine learning (ML) and multimodality emerging as a promising solution. Here, we introduce an ML technique based on multimodal strategies, focusing specifically on intelligent aeration control in wastewater treatment plants (WWTPs). The generalization of the multimodal strategy is demonstrated on eight ML models. The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control, exhibiting exceptional performance and interpretability. Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models, with a mean absolute percentage error of 4.4% and a coefficient of determination of 0.948. Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8% compared to traditional fuzzy control methods. The potential application of these strategies in critical water science domains is discussed. To foster accessibility and promote widespread adoption, the multimodal ML models are freely available on GitHub, thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

The anticancer potential of quassinoids has attracted a great deal of attention for decades, and scientific data revealing their possible applications in cancer management are continuously increasing in the literature. Aside from the potent cytotoxic and antitumor properties of these degraded triterpenes, several quassinoids have exhibited synergistic effects with anticancer drugs. This article provides an overview of the potential anticancer properties of quassinoids, including their cytotoxic and antitumor activities, mechanisms of action, safety evaluation, and potential benefits in combination with anticancer drugs.

Infectious diseases are a global public health problem, with emerging and re-emerging infectious diseases on the rise worldwide. Therefore, their prevention and treatment are still major challenges. Bile acids are common metabolites in both hosts and microorganisms that play a significant role in controlling the metabolism of lipids, glucose, and energy. Bile acids have historically been utilized as first-line, valuable therapeutic agents for related metabolic and hepatobiliary diseases. Notably, bile acids are the major active ingredients of cow bezoar and bear bile, which are commonly used traditional Chinese medicines (TCMs) with the therapeutic effects of clearing heat, detoxification, and relieving wind and spasm. In recent years, the promising performance of bile acids against infectious diseases has attracted attention from the scientific community. This paper reviews for the first time the biological activities, possible mechanisms, production routes, and potential applications of bile acids in the treatment and prevention of infectious diseases. Compared with previous reviews, we comprehensively summarize existing studies on the use of bile acids against infectious diseases caused by pathogenic microorganisms that are leading causes of global morbidity and mortality. In addition, to ensure a stable supply of bile acids at affordable prices, it is necessary to clarify the biosynthesis of bile acids in vivo, which will assist scientists in elucidating the accumulation of bile acids and discovering how to engineer various bile acids by means of chemosynthesis, biosynthesis, and chemoenzymatic synthesis. Finally, we explore the current challenges in the field and recommend a development strategy for bile-acid-based drugs and the sustainable production of bile acids. The presented studies suggest that bile acids are potential novel therapeutic agents for managing infectious diseases and can be artificially synthesized in a sustainable way.

Tracing the contamination origins in water sources and identifying the impacts of natural and human processes are essential for ecological safety and public health. However, current analysis approaches are not ideal, as they tend to be laborious, time-consuming, or technically difficult. Disinfection byproducts (DBPs) are a family of well-known secondary pollutants formed by the reactions of chemical disinfectants with DBP precursors during water disinfection treatment. Since DBP precursors have various origins (e.g., natural, domestic, industrial, and agricultural sources), and since the formation of DBPs from different precursors in the presence of specific disinfectants is distinctive, we argue that DBPs and DBP precursors can serve as alternative indicators to assess the contamination in water sources and identify pollution origins. After providing a retrospective of the origins of DBPs and DBP precursors, as well as the specific formation patterns of DBPs from different precursors, this article presents an overview of the impacts of various natural and anthropogenic factors on DBPs and DBP precursors in drinking water sources. In practice, the DBPs (i.e., their concentration and speciation) originally present in source water and the DBP precursors determined using DBP formation potential tests—in which water samples are dosed with a stoichiometric excess of specific disinfectants in order to maximize DBP formation under certain reaction conditions—can be considered as alternative metrics. When jointly used with other water quality parameters (e.g., dissolved organic carbon, dissolved organic nitrogen, fluorescence, and molecular weight distribution) and specific contaminants of emerging concern (e.g., certain pharmaceuticals and personal care products), DBPs and DBP precursors in drinking water sources can provide a more comprehensive picture of water pollution for better managing water resources and ensuring human health.

DNA guanine (G)-quadruplexes (G4s) are unique secondary structures formed by two or more stacked G-tetrads in G-rich DNA sequences. These structures have been found to play a crucial role in highly transcribed genes, especially in cancer-related oncogenes, making them attractive targets for cancer therapeutics. Significantly, targeting oncogene promoter G4 structures has emerged as a promising strategy to address the challenge of undruggable and drug-resistant proteins, such as MYC, BCL2, KRAS, and EGFR. Natural products have long been an important source of drug discovery, particularly in the fields of cancer and infectious diseases. Noteworthy progress has recently been made in the discovery of naturally occurring DNA G4-targeting drugs. Numerous DNA G4s, such as MYC-G4, BCL2-G4, KRAS-G4, PDGFR-β-G4, VEGF-G4, and telomeric-G4, have been identified as potential targets of natural products, including berberine, telomestatin, quindoline, sanguinarine, isaindigotone, and many others. Herein, we summarize and evaluate recent advancements in natural and nature-derived DNA G4 binders, focusing on understanding the structural recognition of DNA G4s by small molecules derived from nature. We also discuss the challenges and opportunities associated with developing drugs that target DNA G4s.

Bear bile has been a valuable and effective medicinal material in traditional Chinese medicine (TCM) for over 13 centuries. However, the current practice of obtaining it through bear farming is under scrutiny for its adverse impact on bear welfare. Here, we present a new approach for creating artificial bear bile (ABB) as a high-quality and sustainable alternative to natural bear bile. This study addresses the scientific challenges of creating bear bile alternatives through interdisciplinary collaborations across various fields, including resources, chemistry, biology, medicine, pharmacology, and TCM. A comprehensive efficacy assessment system that bridges the gap between TCM and modern medical terminology has been established, allowing for the systematic screening of therapeutic constituents. Through the utilization of chemical synthesis and enzyme engineering technologies, our research has achieved the environmentally friendly, large-scale production of bear bile therapeutic compounds, as well as the optimization and recomposition of ABB formulations. The resulting ABB not only closely resembles natural bear bile in its composition but also offers advantages such as consistent product quality, availability of raw materials, and independence from threatened or wild resources. Comprehensive preclinical efficacy evaluations have demonstrated the equivalence of the therapeutic effects from ABB and those from commercially available drained bear bile (DBB). Furthermore, preclinical toxicological assessment and phase I clinical trials show that the safety of ABB is on par with that of the currently used DBB. This innovative strategy can serve as a new research paradigm for developing alternatives for other endangered TCMs, thereby strengthening the integrity and sustainability of TCM.

In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.

As the scale of urban rail transit (URT) networks expands, the study of URT resilience is essential for safe and efficient operations. This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research. First, URT resilience is defined by three primary abilities: absorption, resistance, and recovery, and four properties: robustness, vulnerability, rapidity, and redundancy. Then, the metrics and assessment approaches for URT resilience were summarized. The metrics are divided into three categories: topology-based, characteristic-based, and performance-based, and the assessment methods are divided into four categories: topological, simulation, optimization, and data-driven. Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods, such as conventional complex network analysis and operations optimization theory, with new techniques like big data and intelligent computing technology, to accurately assess URT resilience. Finally, five potential trends and directions for future research were identified: analyzing resilience based on multisource data, optimizing train diagram in multiple scenarios, accurate response to passenger demand through new technologies, coupling and optimizing passenger and traffic flows, and optimal line design.

The Stockholm Convention on Persistent Organic Pollutants (POPs) is a legally binding instrument for 186 Parties (status: April 2023). Accordingly, among other responsibilities, countries are obliged to report the production, import, or export of the POPs listed in Annexes A, B, or C; provide information to registers; maintain inventories; and monitor the presence of POPs in the environment. In the broader context of international chemicals and waste management, producer responsibilities, harmonized reporting, and compliance with national and international regulations, Ecuador has addressed the newly listed group of perfluorinated alkyl substances (PFAS) in its national implementation plan and sent selected products from its national market for PFAS analysis. The products analyzed came from the initially listed fields of specific exemptions and acceptable purposes, including: fire-fighting foams; photographic aids; greasers/degreasers; various kinds of paper/packaging; textiles; and leather, coatings, cleaners, metal plating, and pesticides. Our results showed that the three PFAS presently listed in the Stockholm Convention could be quantified in only a few samples; additional PFAS, not yet listed in the Convention also had low detection frequencies. Although the number of samples was limited, the samples covered a large spectrum of sample matrices, making it possible to conclude that—once these products become waste and are regulated under the Basel Convention—they would not constitute a disposal problem. Nevertheless, verification of the presence of PFAS in products on the market is expected to pose an analytical challenge for both, developed and developing countries.

The first pandemic wave of coronavirus disease 2019 (COVID-19) induced a considerable increase in several antivirals and antibiotics in surface water. The common symptoms of COVID-19 are viral and bacterial infections, while comorbidities (e.g., hypertension and diabetes) and mental shock (e.g., insomnia and anxiety) are nonnegligible. Nevertheless, little is known about the long-term impacts of comorbidities and mental shock on organic micropollutants (OMPs) in surface waters. Herein, we monitored 114 OMPs in surface water and wastewater treatment plants (WWTPs) in Wuhan, China, between 2019 and 2021. The pandemic-induced OMP pollution in surface water was confirmed by significant increases in 26 OMP concentrations. Significant increases in four antihypertensives and one diabetic drug suggest that the treatment of comorbidities may induce OMP pollution. Notably, cotinine (a metabolite of nicotine) increased 155 times to 187 ng·L⁻¹, which might be associated with increased smoking. Additionally, the increases in zolpidem and sulpiride might be the result of worsened insomnia and depression. Hence, it is reasonable to note that mental-health protecting drugs/behavior also contributed to OMP pollution. Among the observed OMPs, telmisartan, lopinavir, and ritonavir were associated with significantly higher ecological risks because of their limited WWTP-removal rate and high ecotoxicity. This study provides new insights into the effects of comorbidities and mental shock on OMPs in surface water during a pandemic and highlights the need to monitor the fate of related pharmaceuticals in the aquatic environment and to improve their removal efficiencies in WWTPs.

The steel industry is considered an important basic sector of the national economy, and its high energy consumption and carbon emissions make it a major contributor to climate change, especially in China. The majority of crude steel in China is produced via the energy- and carbon-intensive blast furnace-basic oxygen furnace (BF-BOF) route, which greatly relies on coking coal. In recent years, China’s steel sector has made significant progress in energy conservation and emission reduction, driven by decarbonization policies and regulations. However, due to the huge output of crude steel, the steel sector still produces 15% of the total national CO₂ emissions. The direct reduced iron (DRI) plus scrap-electric arc furnace (EAF) process is currently considered a good alternative to the conventional route as a means of reducing CO₂ emissions and the steel industry’s reliance on iron ore and coking coal, since the gas-based DRI plus scrap-EAF route is expected to be more promising than the coal-based one. Unfortunately, almost no DRI is produced in China, seriously restricting the development of the EAF route. Here, we highlight the challenges and pathways of the future development of DRI, with a focus on China. In the short term, replacing natural gas with coke oven gas (COG) and byproduct gas from the integrated refining and chemical sector is a more economically feasible and cleaner way to develop a gas-based route in China. As the energy revolution proceeds, using fossil fuels in combination with carbon capture, utilization, and storage (CCUS) and hydrogen will be a good alternative due to the relatively low cost. In the long term, DRI is expected to be produced using 100% hydrogen from renewable energy. Both the development of deep processing technologies and the invention of a novel binder are required to prepare high-quality pellets for direct reduction (DR), and further research on the one-step gas-based process is necessary.

Despite the extensive application of advanced oxidation processes (AOPs) in water treatment, the efficiency of AOPs in eliminating various emerging contaminants such as halogenated antibiotics is constrained by a number of factors. Halogen moieties exhibit strong resistance to oxidative radicals, affecting the dehalogenation and detoxification efficiencies. To address these limitations of AOPs, advanced reduction processes (ARPs) have been proposed. Herein, a novel nucleophilic reductant—namely, the carbon dioxide radical anion ($\mathrm{CO}_{2}^{·-}$) —is introduced for the simultaneous degradation, dehalogenation, and detoxification of florfenicol (FF), a typical halogenated antibiotic. The results demonstrate that FF is completely eliminated by $ \mathrm{CO}_{2}^{·-}$, with approximately 100% of Cl⁻ and 46% of F⁻ released after 120 min of treatment. Simultaneous detoxification is observed, which exhibits a linear response to the release of free inorganic halogen ions (R² = 0.97, p < 0.01). The formation of halogen-free products is the primary reason for the superior detoxification performance of this method, in comparison with conventional hydroxyl-radical-based AOPs. Products identification and density functional theory (DFT) calculations reveal the underlying dehalogenation mechanism, in which the chlorine moiety of FF is more susceptible than other moieties to nucleophilic attack by $ \mathrm{CO}_{2}^{·-}$. Moreover, $ \mathrm{CO}_{2}^{·-}$- based ARPs exhibit superior dehalogenation efficiencies (> 75%) in degrading a series of halogenated antibiotics, including chloramphenicol (CAP), thiamphenicol (THA), diclofenac (DLF), triclosan (TCS), and ciprofloxacin (CIP). The system shows high tolerance to the pH of the solution and the presence of natural water constituents, and demonstrates an excellent degradation performance in actual groundwater, indicating the strong application potential of $ \mathrm{CO}_{2}^{·-}$-based ARPs in real life. Overall, this study elucidates the feasibility of $ \mathrm{CO}_{2}^{·-}$ for the simultaneous degradation, dehalogenation, and detoxification of halogenated antibiotics and provides a promising method for their regulation during water or wastewater treatment.

Theβ-1-6 -linked poly- N -acetylglucosamine (PNAG) polymer is a conserved surface polysaccharide produced by many bacteria, fungi, and protozoan (and even filarial) parasites. This wide-ranging expression makes PNAG an attractive target for vaccine development, as it potentially encompasses a broad range of microorganisms. Significant progress has been made in discovering important properties of the biology of PNAG expression in recent years. The molecular characterization and regulation of operons for the production of PNAG biosynthetic proteins and enzymes have been studied in many bacteria. In addition, the physiological function of PNAG has been further elucidated. PNAG-based vaccines and PNAG-targeting antibodies have shown great efficacy in preclinical research. Furthermore, clinical tests for both vaccines and antibodies have been carried out in humans and economically important animals, and the results are promising. Although it is not destined to be a smooth road, we are optimistic about new vaccines and immunotherapeutics targeting PNAG becoming validated and eventually licensed for clinical use against multiple infectious agents.