Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

A Novel Method to Characterize the Damping Capacity of EPDM/CIIR Blends Using Vibrating Rubber Balls.

An experimental device fixed with a laser displacement sensor was assembled to investigate the rebound behaviors and damping mechanism of rubber balls prepared with ethylene-propylene-diene monomer (EPDM)/chlorinated butyl rubber (CIIR) blends. The result showed that a prediction model was proposed to characterize the damping capacity by using the rebound height of the rubber balls. The lower rebound height corresponded to better damping capacity. A modified equation relating to the rebound height has been obtained from the theoretical derivation on the basis of the dynamic mechanical analysis, showing that the rebound height was affected by the deformation frequency, the external excitation, and the nature of rubber blends. Furthermore, the energy dissipation rate (EDR), defined by the ratio of the height loss to the rebound time, was proposed to further characterize the damping capacity. The EDR value was shown to be highest for the pure CIIR and lowest for the pure EPDM, exhibiting a decreasing trend with the increase in EPDM content in the rubber blends. It can be expected that the damping capacity of the EPDM/CIIR blends decreases with the decrease in external excitation, the conclusion of which plays a key role in the formulation design of viscoelastic damping rubber materials.

Phosphorus adsorption from aqueous solutions using different types of cement: kinetics, isotherms, and mechanisms.

Exploring low-cost and high-performance phosphorus (P) adsorbents is key to controlling P contamination in water. This study evaluated the P adsorption performance of three types of cement: Ordinary Portland cement (OPC), Portland slag cement (PSC), and Portland pozzolana cement (PPC). Furthermore, SEM-EDS, XRD, XPS, and FTIR were employed to reveal the adsorption mechanism. The results showed that the pseudo-second-order model exhibited higher regression coefficients than the pseudo-first-order model, indicating that chemisorption dominated the adsorption process. The Langmuir equation fitted the P adsorption data well, with maximum P adsorption capacities of 245.8, 226.1, and 210.0 mg g-1 for OPC, PSC, and PPC at 25 °C, respectively. P adsorption capacities decreased gradually with increasing initial pH and reached their maximum values at pH 3. The anions of F-, CO32-, and SO42- negatively affected P adsorption due to the competitive adsorption with Ca2+. The results of XPS, XRD, and FTIR confirmed that Ca-P precipitates (i.e., hydroxyapatite) were the main removal mechanism. A real domestic sewage experiment showed that 0.6 g L-1 OPC effectively reduced the P concentration from 2.4 to below 0.2 mg L-1, with a dosage cost of 0.034 $ per ton. This study indicated that cement, as a low-cost and efficient P adsorbent, has great potential for application in removing P from acidic and neutral wastewater.

Gastrointestinal bleeding risk factors in type A aortic dissection post-aortic arch replacement.

Background: Gastrointestinal bleeding (GIB) is a notable complication in patients diagnosed with aortic dissection (AD). We evaluated the outcomes and identified the risk factors associated with GIB in patients with AD. Methods: A retrospective case-control study was conducted on patients diagnosed with type A aortic dissection (TAAD) who underwent total aortic arch replacement (TAAR) at our institution from July 2021 to July 2023. Comprehensive clinical data, laboratory findings, and imaging results were meticulously gathered and analyzed to identify potential risk factors linked to GIB in this patient cohort. Results: Of the 198 AD patients who underwent TAAR, 38 (19.2%) developed postoperative GIB (GIB group), with a median interval of 7 days between surgery and bleeding onset. The GIB group exhibited significantly higher mortality (26.3% vs. 3.1%, P<0.001), prolonged intensive care unit (ICU) stay {15 [interquartile range (IQR), 8-25] vs. 7 (IQR, 5-12) days, P<0.001}, and extended duration of ventilation [168 (IQR, 120-372) vs. 71 (IQR, 34-148) hours, P<0.001] compared to the control group (n=160, 80.8%). Logistic regression analysis identified age >54 years [odds ratio (OR): 3.529], intraoperative red blood cell (RBC) transfusion >600 mL (OR: 3.865), and concomitant celiac trunk and superior mesenteric artery (SMA) hypoperfusion (OR: 15.974) as independent risk factors for GIB in AD patients. Conclusions: GIB subsequent to TAAR in AD patients is linked to adverse prognosis. Factors such as advanced age, extensive intraoperative transfusion, and gastrointestinal (GI) perfusion abnormalities may heighten the risk of GIB in this patient population.

Phase and detuning control of the unidirectional reflection amplification based on the broken spatial symmetry.

In order to achieve the tunable unidirectional reflection amplification in a uniform atomic medium that is of vital importance to design high-quality nonreciprocal photonic devices, we propose a coherent closed three-level Δ-type atomic system by applying a microwave field, and a strong coupling field of linear variation along the x direction to control a probe field. In our scheme, the linearly increased coupling field destroys the spatial symmetry of probe susceptibility and effectively suppresses the reflection of one side; the microwave field constructs closed loop transitions to amplify the probe field and causes phase changes. The numerical simulation indicates that the unidirectional reflection amplification is sensitive to the relative phase ϕ and the coupling detuning Δc. Our results will open a new route toward harnessing optical non-reciprocity, which can provide more convenience and possibilities in the experimental realization.

Platycodin D facilitates antiviral immunity through inhibiting cytokine storm via targeting K63-linked TRAF6 ubiquitination.

Influenza virus infection is a worldwide challenge that causes heavy burdens on public health. The mortality rate of severe influenza patients is often associated with hyperactive immunological abnormalities characterized by hypercytokinemia. Due to the continuous mutations and the occurrence of drug-resistant influenza virus strains, the development of host-directed immunoregulatory drugs is urgently required. Platycodon grandiflorum is among the top 10 herbs of traditional Chinese medicine used to treat pulmonary diseases. As one of the major terpenoid saponins extracted from Platycodon grandiflorum, Platycodin D (PD) has been reported to play several roles, including anti-inflammation, analgesia, anti-cancer, hepatoprotection, and immunoregulation. However, the therapeutic roles of PD to treat influenza virus infection remains unknown. Here, we show that PD can protect the body weight loss in severely infected influenza mice, alleviate lung damage, and thus improve the survival rate. More specifically, PD protects flu mice via decreasing the immune cell infiltration into lungs and downregulating the overactivated inflammatory response. Western blot and immunofluorescence assays exhibited that PD could inhibit the activation of TAK1/IKK/NF-κB and MAPK pathways. Besides that, CETSA, SPR and immunoprecipitation assays indicated that PD binds with TRAF6 to decrease its K63 ubiquitination after R837 stimulation. Additionally, siRNA interference experiments exhibited that PD could inhibit the secretion of IL-1ß and TNF-α in TRAF6-dependent manner. Altogether, our results suggested that PD is a promising drug candidate for treating influenza. Our study also offered a scientific explanation for the commonly used Platycodon grandiflorum in many anti-epidemic classic formulas. Due to its host-directed regulatory role, PD may serve as an adjuvant therapeutic drug in conjunction with other antiviral drugs to treat the flu.

Strong associations between dissolved organic matter and microbial communities in the sediments of Qinghai-Tibetan Plateau lakes depend on salinity.

In aquatic ecosystems, dissolved organic matter (DOM) plays a vital role in microbial communities and the biogeochemical cycling of elements. However, little is known about the associations between DOM and microbial communities in lake sediments. This study investigated the composition of water-extractable organic matter and microbial communities in surface sediments of lakes with different salinities on the Qinghai-Tibet Plateau. Ultrahigh-resolution mass spectrometry and high-throughput microbial sequencing techniques were employed to assess the associations between molecular diversity and microbial diversity and the effects of salinity in 19 lakes spanning a salinity range from 0.22 ‰ to 341.87 ‰. Our results show that increasing salinity of lake water led to higher molecular diversity of DOM in surface sediments. High-salinity lakes exhibited distinct DOM characteristics, such as lower aromaticity, smaller molecular weight, and higher oxidation degree, compared to freshwater lakes. The complexity of the microbial network composition of sediments first increased and then decreased with the increase of salinity. Moreover, as salinity increases, the dominant species transitioned from Gammaproteobacteria to Bacteroidia, and this transition was accompanied by a decrease in microbial diversity and an increase in molecular diversity. Microbial factors accounted for 34.68 % of the variation in the molecular composition of DOM. Overall, this study emphasizes the significant effects of salinity on both molecular and microbial diversity in lake sediments. Furthermore, our findings underscore the importance of microbes in controlling the range of organic compounds present in lakes and deepen our knowledge of the biogeochemical cycling of DOM.

The causal relationship of female infertility and psychiatric disorders in the European population: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Infertility affects many couples globally, causing physical, emotional, and financial burdens. While observational studies suggest a link between psychiatric disorders and female infertility, causal relationships remain uncertain. Mendelian randomization analysis, using genome-wide association studies data, minimizes confounding factors and reverse causation, providing valuable insights into causal associations. METHODS: We conducted Mendelian randomization analysis to explore the potential causal relationship between female infertility and psychiatric disorders. Genome-wide association studies summary data for female infertility (112,105 individuals of European ancestry, comprising 11,442 cases and 100,663 controls), depression (807,553 individuals of European ancestry, comprising 246,363 cases and 561,190 controls), anxiety (21,763 individuals of European ancestry, comprising 7,016 cases and 14,745 controls), bipolar disorder (51,710 individuals of European ancestry, comprising 20,352 cases and 31,358 controls), and eating disorders (72,517 individuals of European ancestry, comprising 16,992 cases and 55,525 controls) were utilized. Instrumental variables were selected based on significant single nucleotide polymorphisms associated with each phenotype. We assessed instrumental variable strength, examined confounding factors, and employed inverse variance weighting, weighted median, and MR-Egger approaches for analysis. RESULTS: Our analysis included 85 single nucleotide polymorphisms for female infertility and 62 single nucleotide polymorphisms for psychiatric disorders. Results suggest a potential causal relationship between depression and female infertility, with both inverse variance weighting and weighted median methods showing increased infertility risk in depressed patients. Evidence is weak regarding bipolar disorder not increasing female infertility risk. We found no evidence supporting causal links between anxiety, eating disorders, and female infertility. Similarly, no causal relationship was found between female infertility and psychiatric disorders in the opposite direction. Sensitivity analyses and tests for heterogeneity and polymorphism supported result robustness. CONCLUSIONS: This analysis provides evidence for a potential causal relationship between depression and female infertility. Addressing depression in infertile women may improve fertility outcomes. Further research is needed to explore underlying mechanisms and potential interventions for improving fertility outcomes in women with psychiatric disorders.

Affinity-Guided Coevolution of Aptamers for Guanine, Xanthine, Hypoxanthine, and Adenine.

The simultaneous evolution of multiple aptamers can drastically increase the speed of aptamer discovery. Most previous studies used the same concentration for different targets, leading to the dominance of the libraries by one or a few aptamers and a low success rate. To foster the best aptamers to grow independently in the sequence space, it is important to (1) use low target concentrations close to their dissociation constants and (2) stop at an early round before any sequence starts to dominate. In this study, we demonstrate this affinity-guided selection concept using the capture-SELEX method to isolate aptamers for four important purines: guanine (5 µM), xanthine (50 µM), hypoxanthine (10 µM), and adenine (10 µM). The round 9 library was split, and in round 10, the four targets were individually used to elute the binding sequences. Using thioflavin T fluorescence spectroscopy and isothermal titration calorimetry, we confirmed highly selective aptamers for xanthine, guanine, and adenine. These aptamers have Kd values below 1 µM and around 100-fold selectivity against most competing analytes, and they compare favorably with existing RNA aptamers and riboswitches. A separate selection was performed using hypoxanthine alone, and no selective aptamer was achieved, even with negative selection, explaining the lack of its aptamer in our mixed selection. This affinity-guided multiplex SELEX study offers fundamental insights into aptamer selection and provides high-quality aptamers for three important purines.

Therapeutic targets for endometriosis: Genome-wide Mendelian randomization and colocalization analyses.

BACKGROUND: Endometriosis (EM) greatly affects women's reproductive health, identifying new drug targets for EM is urgently needed. This study utilizes comprehensive genome-wide Mendelian randomization (MR) and colocalization analyses, using genomic data, to identify potential therapeutic approaches for EM. METHODS: Genome-wide cis-expression quantitative trait loci (cis-eQTL) data were obtained from GTEx V8, which included 838 participants across 49 tissues or cells, and the eQTLGen consortium, which included 31,684 participants. Genome-wide association analysis (GWAS) data for EM were sourced from the FinnGen study, which consisted of 8,288 cases and 68,969 controls, as well as the UK Biobank study, which included 1,496 cases and 359,698 controls. This study utilized MR analysis to assess the correlation between genes and the risk of EM. Subsequently, colocalization analysis was conducted to investigate potential shared causal variants between the identified genes and EM. RESULTS: After conducting MR and colocalization analyses, we identified a total of 13 genes that showed significant evidence of colocalization. These genes are considered promising therapeutic candidates for treating EM. Among them, inner membrane mitochondrial protein (IMMT), src kinase associated phosphoprotein 1 (SKAP1), lysine methyltransferase 5A (KMT5A), KLF transcription factor 12 (KLF12), GRB10 interacting GYF protein 1 (GIGYF1), Wnt family member 7A (WNT7A), Sad1 and UNC84 domain containing 1 (SUN1), and poly (ADP-ribose) polymerase family member 3 (PARP3) were found to have positive associations with the risk of EM. On the other hand, progestin and adipoQ receptor family member 8 (PAQR8), adaptor related protein complex 3 subunit mu 1 (AP3M1), surfeit 6 (SURF6), TUB bipartite transcription factor (TUB), and DNA polymerase delta interacting protein 2 (POLDIP2) were found to have inverse relationships with the risk of EM. CONCLUSIONS: Through genome-wide MR studies, a comprehensive set of genes associated with EM has been identified. Among them, IMMT, PAQR8, SKAP1, KMT5A, AP3M1, SURF6, KLF12, GIGYF1, TUB, WNT7A, SUN1, POLDIP2, and PARP3 show potential as therapeutic targets for EM treatment. Nonetheless, it is crucial to conduct further rigorous investigations to validate these prospects.

CMTM6 promotes hepatocellular carcinoma progression through stabilizing ß-catenin.

CMTM6, a regulator of PD-L1 stability, has been implicated in the development of various cancers. However, the expression and role of CMTM6 in hepatocellular carcinoma (HCC) remains controversial. Our study revealed a negative correlation between CMTM6 expression and HCC prognosis through bioinformatics analysis and immunofluorescence staining. CMTM6 expression was also positively associated with alpha-fetoprotein (AFP) levels, supporting its potential as a prognostic marker for HCC. Using Cmtm6 knockout mice, we found that Cmtm6 deficiency inhibited HCC formation and cell proliferation in primary liver cancer models induced by DEN and DEN/CCl4. In HCC cell lines, CMTM6 promoted cell proliferation and interacted with ß-catenin, stabilizing it by preventing ubiquitination. In conclusion, our study suggested that CMTM6 upregulation promotes HCC cell proliferation through the ß-catenin pathway, making it a potential therapeutic target for HCC treatment.

Inactivation of harmful cyanobacteria Microcystis aeruginosa by Cu2+ doped corn stalk biochar treated with different pyrolysis temperatures.

In this study, biochars (BCs) derived from corn stalk treated at various pyrolysis temperatures (350-950 °C) were prepared and then loaded with Cu2+ to form highly efficient algaecide, i.e. Cu2+-doped BC composites (Cu-BCs). The results showed BCs pyrolyzed at higher temperatures suppressed the growth of Microcystis aeruginosa in the order of BC550 ≫ BC750 > BC950, while BC350 accelerated cell growth due to the release of inorganic nutrients. The difference could be attributed to the physicochemical characteristics, including specific surface area, adsorption capacity of nutrients and the presence of particularly persistent free radicals. Furthermore, Cu-BCs exhibited the improved inactivation performance, but the 72 h growth inhibition rates and reaction activities of Cu-BCs were still influenced by the Cu2+ loading ratio and pyrolysis temperature. These results, reported for the first time, demonstrated the algae inactivation efficiency of pristine BCs, and Cu-BCs were principally manipulated by the biochar pyrolysis temperature.

Arsenic Removal via the Biomineralization of Iron-Oxidizing Bacteria Pseudarthrobacter sp. Fe7.

Arsenic (As) is a highly toxic metalloid, and its widespread contamination of water is a serious threat to human health. This study explored As removal using Fe(II)-oxidizing bacteria. The strain Fe7 isolated from iron mine soil was classified as the genus Pseudarthrobacter based on 16S rRNA gene sequence similarities and phylogenetic analyses. The strain Fe7 was identified as a strain of Gram-positive, rod-shaped, aerobic bacteria that can oxidize Fe(II) and produce iron mineral precipitates. X-ray diffraction, X-ray photoelectron spectroscopy, and energy-dispersive X-ray spectroscopy patterns showed that the iron mineral precipitates with poor crystallinity consisted of Fe(III) and numerous biological impurities. In the co-cultivation of the strain Fe7 with arsenite (As(III)), 100% of the total Fe and 99.9% of the total As were removed after 72 h. During the co-cultivation of the strain Fe7 with arsenate (As(V)), 98.4% of the total Fe and 96.9% of the total As were removed after 72 h. Additionally, the iron precipitates produced by the strain Fe7 removed 100% of the total As after 3 h in both the As(III) and As(V) pollution systems. Furthermore, enzyme activity experiments revealed that the strain Fe7 oxidized Fe(II) by producing extracellular enzymes. When 2% (v/v) extracellular enzyme liquid of the strain Fe7 was added to the As(III) or As(V) pollution system, the total As removal rates were 98.6% and 99.4%, respectively, after 2 h, which increased to 100% when 5% (v/v) and 10% (v/v) extracellular enzyme liquid of the strain Fe7 were, respectively, added to the As(III) and As(V) pollution systems. Therefore, iron biomineralized using a co-culture of the strain Fe7 and As, iron precipitates produced by the strain Fe7, and the extracellular enzymes of the strain Fe7 could remove As(III) and As(V) efficiently. This study provides new insights and strategies for the efficient remediation of arsenic pollution in aquatic environments.

Spatial susceptibility modulation and controlled unidirectional reflection amplification via four-wave mixing.

Control of unidirectional light propagation is of paramount importantance to optical signal processing and optical communication. Especially, the amplified optical signal can isolate noise well that may provide more applications. In this work, we propose a dynamically modulated regime to realize unidirectional reflection amplification in a short and dense uniform atomic medium, and all atoms are driven into four-level double-Λ type by two coupling fields with linearly varied intensities along x direction and two weak probe fields. Based on four-wave mixing resonance and the broken spatial symmetry, the complete nonreciprocal reflection (unidirectional reflection) can be amplified with reflectivity more than 2.0, even to 6.0. In addition, the width, height, and position of the unidirectional reflection bands can be tunable. Thus, our regime is feasible and may inspire further applications in all-optical networks that require controllable unidirectional light amplification.

Enabling AI in synthetic biology through Construction File specification.

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.

PURPOSE: To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results. MATERIALS AND METHODS: A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions. RESULTS: The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively. CONCLUSIONS: The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

[Detection methods for polyethylene terephthalate degrading enzymes: a review].

Polyethylene terephthalate (PET) is one of the most widely used synthetic polyester. It poses serious threat to terrestrial, aquatic ecosystems and human health since it is difficult to be broken down and deposited in the environment. The biodegradation based on enzymatic catalysis offers a sustainable method for recycling PET. A number of PET hydrolases have been discovered in the last 20 years, and protein engineering has increased their degradation capabilities. However, no PET hydrolases that are practical for widespread industrial use have been identified. Screening of PET hydrolase using conventional detection techniques is laborious and inefficient process. Effective detection techniques are required to promote the commercialization of PET hydrolases. Using efficient detection techniques to screen potent industrial enzymes is essential for supporting the widespread industrial implementation of PET hydrolases. To define PET hydrolase, scientists have created a number of analytical techniques recently. The detection techniques that can be used to screen PET hydrolase, including high performance liquid chromatography, ultraviolet absorption spectrometric, and fluorescence activated droplet sorting method, are summarized in this study along with their potential applications.