Comprehensive review of emerging contaminants: Detection technologies, environmental impact, and management strategies.

Emerging contaminants (ECs) are a diverse group of unregulated pollutants increasingly present in the environment. These contaminants, including pharmaceuticals, personal care products, endocrine disruptors, and industrial chemicals, can enter the environment through various pathways and persist, accumulating in the food chain and posing risks to ecosystems and human health. This comprehensive review examines the chemical characteristics, sources, and varieties of ECs. It critically evaluates the current understanding of their environmental and health impacts, highlighting recent advancements and challenges in detection and analysis. The review also assesses existing regulations and policies, identifying shortcomings and proposing potential enhancements. ECs pose significant risks to wildlife and ecosystems by disrupting animal hormones, causing genetic alterations that diminish diversity and resilience, and altering soil nutrient dynamics and the physical environment. Furthermore, ECs present increasing risks to human health, including hormonal disruptions, antibiotic resistance, endocrine disruption, neurological effects, carcinogenic effects, and other long-term impacts. To address these critical issues, the review offers recommendations for future research, emphasizing areas requiring further investigation to comprehend the full implications of these contaminants. It also suggests increased funding and support for research, development of advanced detection technologies, establishment of standardized methods, adoption of precautionary regulations, enhanced public awareness and education, cross-sectoral collaboration, and integration of scientific research into policy-making. By implementing these solutions, we can improve our ability to detect, monitor, and manage ECs, reducing environmental and public health risks.

Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations.

Thermal power plants are significant contributors to nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, known for their continuity and global coverage, have become an effective means of quantifying power plant emissions. Previous studies, often accumulating long temporal data into integrated plumes, resulted in substantial errors in annual emissions at the individual power plant level due to neglecting variations in emissions and diffusion conditions. This study presents, for the first time, the quantification of instantaneous NOx emissions based on single overpass observations from the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. By addressing the temporal variability of power plant emissions, it effectively reduces annual estimation errors. Comparative analysis between the Exponentially-Modified Gaussian (EMG) and Gaussian Plume Model (GPM) simulations demonstrates the capability of EMG to provide instantaneous emission estimates based on actual plumes, exhibiting closer proximity to actual monitoring values than GPM. Applying the EMG method, we quantify the instantaneous emission rates of six power plants in the United States. Comparing annual emission estimations at individual power plants with traditional integrated plume results, our method demonstrates a 63.7 % improvement in annual emission estimations. This study offers more detailed data on power plant emissions, providing a new avenue for better understanding the emission behavior of thermal power plants.

Neglected Spleen Transcriptional Profile Reveals Inflammatory Disorder Conferred by Rabbit Hemorrhagic Disease Virus 2 Infection.

Rabbit hemorrhagic disease (RHD) is an acute fatal disease caused by the rabbit hemorrhagic disease virus (RHDV). Since the first outbreaks of type 2 RHDV (RHDV2) in April 2020 in China, the persistence of this virus in the rabbit population has caused substantial economic losses in rabbit husbandry. Previous failures in preventing RHDV2 prompted us to further investigate the immune mechanisms underlying the virus's pathogenicity, particularly concerning the spleen, a vital component of the mononuclear phagocyte system (MPS). For this, a previous RHDV2 isolate, CHN/SC2020, was utilized to challenge naive adult rabbits. Then, the splenic transcriptome was determined by RNA-Seq. This study showed that the infected adult rabbits had 3148 differentially expressed genes (DEGs), which were associated with disease, signal transduction, cellular processes, and cytokine signaling categories. Of these, 100 upregulated DEGs were involved in inflammatory factors such as IL1α, IL-6, and IL-8. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these DEGs were significantly enriched in the cytokine-cytokine receptor interaction signaling pathway, which may play a vital role in CHN/SC2020 infection. At the same time, proinflammatory cytokines and chemokines were significantly increased in the spleen at the late stages of infection. These findings suggested that RHDV2 (CHN/SC2020) might induce dysregulation of the cytokine network and compromise splenic immunity against viral infection, which expanded our understanding of RHDV2 pathogenicity.

A Metastructure Based on Amorphous Carbon for High Efficiency and Selective Solar Absorption.

Efficient solar thermal conversion is crucial for renewable clean energy technologies such as solar thermal power generation, solar thermophotovoltaic and seawater desalination. To maximize solar energy conversion efficiency, a solar selective absorber with tailored absorption properties designed for solar applications is indispensable. In this study, we propose a broadband selective absorber based on amorphous carbon (a-C) metamaterials that achieves high absorption in the ultraviolet (UV), visible (Vis) and near-infrared (NIR) spectral ranges. Additionally, through metal doping, the optical properties of carbon matrix materials can be modulated. We introduce Ti@a-C thin film into the nanostructure to enhance light absorption across most of the solar spectrum, particularly in the NIR wavelength band, which is essential for improving energy utilization. The impressive solar absorptivity and photothermal conversion efficiency reach 97.8% and 95.6%, respectively. Notably, these superior performances are well-maintained even at large incident angles with different polarized states. These findings open new avenues for the application of a-C matrix materials, especially in fields related to solar energy harvesting.

Discovery of a Novel Chromone Enantiomer and the Precursors of Nonactic Acid from the Coral-Reef-Derived Streptomyces sp. SCSIO 66814.

Three pairs of enantiomers (1-3)-the new 12R-aloesol (1a) and two new fatty acids (2 and 3)-and one new natural product (4) together three known compounds (5-7) were isolated from a coral-reef-derived Streptomyces sp. SCSIO 66814. Their structures were determined through extensive spectroscopic analysis, chiral analysis, and single-crystal X-ray diffraction data. Compounds 2 and 3 were presumed to be intermediates for further generating homononactic acid (5) and nonactic acid, and the latter two molecules were able to act as precursors to form macrotetrolides with remarkable biological activity. The isolation of related precursors, compounds 2-5, provided more evidence to support the proposal of a plausible biosynthetic pathway for nonactic acid and its homologs. Additionally, (+)-1 exhibited a weak activity against DPPH radicals.

2,3-Dimethoxycinnamic Acid from a Marine Actinomycete, a Promising Quorum Sensing Inhibitor in Chromobacterium violaceum.

An ethyl acetate extract of a marine actinomycete strain, Nocardiopsis mentallicus SCSIO 53858, isolated from a deep-sea sediment sample in the South China Sea, exhibited anti-quorum-sensing (QS) activity against Chromobacterium violaceum CV026. Guided by the anti-QS activity, a novel active compound was isolated and purified from the extract and was identified as 2,3-dimethoxycinnamic acid (2,3-DCA) through spectral data analysis. At a concentration of 150 µg/mL, 2,3-DCA exhibited robust inhibitory effects on three QS-regulated traits of C. violaceum CV026: violacein production, swarming motility, and biofilm formation, with inhibition rates of 73.9%, 65.9%, and 37.8%, respectively. The quantitative reverse transcription polymerase chain reaction results indicated that 2,3-DCA can disrupt the QS system in C. violaceum CV026 by effectively suppressing the expression of QS-related genes, including cviR, vioA, vioB, and vioE. Molecular docking analysis revealed that 2,3-DCA hinders the QS system by competitively binding to the same binding pocket on the CviR receptor as the natural signal molecule N-hexanoyl-L-homoserine lactone. Collectively, these findings suggest that 2,3-DCA exhibits promising potential as an inhibitor of QS systems, providing a potential solution to the emerging problem of bacterial resistance.

Single-chain fluorescent integrators for mapping G-protein-coupled receptor agonists.

G protein-coupled receptors (GPCRs) transduce the effects of many neuromodulators including dopamine, serotonin, epinephrine, acetylcholine, and opioids. The localization of synthetic or endogenous GPCR agonists impacts their action on specific neuronal pathways. In this paper, we show a series of single-protein chain integrator sensors that are highly modular and could potentially be used to determine GPCR agonist localization across the brain. We previously engineered integrator sensors for the mu- and kappa-opioid receptor agonists called M- and K-Single-chain Protein-based Opioid Transmission Indicator Tool (SPOTIT), respectively. Here, we engineered red versions of the SPOTIT sensors for multiplexed imaging of GPCR agonists. We also modified SPOTIT to create an integrator sensor design platform called SPOTIT for all GPCRs (SPOTall). We used the SPOTall platform to engineer sensors for the beta 2-adrenergic receptor (B2AR), the dopamine receptor D1, and the cholinergic receptor muscarinic 2 agonists. Finally, we demonstrated the application of M-SPOTIT and B2AR-SPOTall in detecting exogenously administered morphine, isoproterenol, and epinephrine in the mouse brain via locally injected viruses. The SPOTIT and SPOTall sensor design platform has the potential for unbiased agonist detection of many synthetic and endogenous neuromodulators across the brain.

Research Note: Rapid generation of a novel fowl adenovirus serotype 11 reverse genetic platform.

Fowl adenovirus serotype 11 (FAdV-11) is one of the main causative agents of inclusion body hepatitis (IBH) in broilers. Outbreaks of FAdV-11-related IBH have been increasingly reported in China and many other geographical areas worldwide. However, the critical virulence factors of FAdV-11 remain uncertain due to the lack of technical platforms for efficient manipulation of FAdV-11 genome. Here, we reported the establishment of a FAdV-11 reverse genetic system based on a novel FAdV-11 Chinese isolate FJSW/2021 using the exonuclease combined with RecET (ExoCET), Redαß recombineering and ccdB counter-selection techniques for the first time. A recombinant FAdV-11 was rescued efficiently by using the established reverse genetic platform through swapping the ORF11 gene of the FAdV-11 FJSW/2021 with the ZsGreen fluorescent protein expression cassette. This study provides an effective technical platform for identifying virulence factors of FAdV-11 and developing recombinant FAdV-11-vectored vaccine candidates.

Genome-guided discovery of two undescribed 6,6-spiroketal polyketides and stereochemical correction of bafilomycins P and Q from the marine-derived Streptomyces sp. SCSIO 66814.

Bafilomycins are macrocyclic polyketides with intriguing structures and therapeutic value. Genomic analysis of Streptomyces sp. SCSIO 66814 revealed a type I polyketide synthase biosynthetic gene cluster (BGC), namely blm, which encoded bafilomycins and featured rich post-modification genes. The One strain many compounds (OSMAC) strategy led to the discovery of six compounds related to the blm BGC from the strain, including two previously undescribed 6,6-spiroketal polyketides, streptospirodienoic acids D (1) and E (2), and four known bafilomycins, bafilomycins P (3), Q (4), D (5), and G (6). The structures of 1 and 2 were determined by extensive spectroscopic analysis, quantum calculation, and biosynthetic analysis. Additionally, the absolute configurations of the 6/5/5 tricyclic ring moiety containing six consecutive chiral carbons in the putative structures of 3 and 4 were corrected through NOE analysis, DP4+ calculation, and single-crystal X-ray diffraction data. Bioinformatic analysis uncovered a plausible biosynthetic pathway for compounds 1-6, indicating that both streptospirodienoic acids and bafilomycins were derived from the same blm BGC. Additionally, sequence analysis revealed that the KR domains of module 2 from blm BGC was B1-type, further supporting the configurations of 1-4. Notably, compounds 3 and 4 displayed significant cytotoxic activities against A-549 human non-small cell lung cancer cells and HCT-116 human colon cancer cells.

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model.

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.

Short-term effectiveness and potential factors of ustekinumab based on real-world data in Chinese psoriasis patients.

BACKGROUND: As one of the most effective biologic treatments for psoriasis, the short-term effectiveness of ustekinumab has yet to be studied extensively. OBJECTIVE: The purpose of this study was to evaluate the short-term effectiveness and potential factors within four weeks after the first-dose ustekinumab treatment based on real-world data. METHODS: The study enrolled 98 patients with moderate-to-severe psoriasis, given ustekinumab 45 mg at week 0, week 4, and then every 12 weeks. Based on clinical data collected at baseline and week 4, we investigated the short-term effectiveness of ustekinumab after the first dose and potential factors associated with the treatment. For evaluation, we collected demographic information, body data, medical history, laboratory examination results, Psoriasis Area and Severity Index (PASI), body surface area (BSA), and dermatology life quality index (DLQI). Response rates were calculated based on the number of patients that achieved a 75/90/100% reduction in PASI (PASI 75/90/100), and the primary treatment goal was to achieve PASI 75. RESULTS: The response rates for PASI 75/90/100 at week 4 were 30.5%, 18.9%, and 16.8%, respectively. For PASI 75, the response rate was higher in patients without metabolic syndrome (MS) (without MS vs. with MS: 36.9% vs. 5.9%, p = 0.013); the serum triglyceride (TG) level was significantly lower in patients achieving PASI 75 (expressed as mean ± standard deviation, achieved vs. unachieved: 1.82 ± 1.79 vs. 3.59 ± 8.89, p = 0.010). For PASI 100, the response rates were higher in female patients (female vs. male: 26.3% vs. 10.5%, p = 0.044) and patients with a family history of psoriasis (with family history vs. without family history: 44.4% vs. 13.9%, p = 0.042). In addition, the possibility of achieving PASI 75/90/100 went up along with the serum high-density lipoprotein cholesterol (HDL-C) level (expressed as adjusted odds ratio < 95% confidence interval>: PASI 75: 28.484 < 2.035-248.419>, p = 0.011; PASI 90: 28.226 < 2.828-281.729>, p = 0.004; PASI 100: 12.175 < 1.876-79.028>, p = 0.009). CONCLUSION: In this study, nearly one-third of patients achieved PASI 75 after only the first-dose ustekinumab treatment. Sex, family history of psoriasis, MS, serum TG level might affect the short-term effectiveness, and serum HDL-C level may be a potential factor. The possibility of achieving treatment goals (PASI 75/90/100) at week 4 increased along with serum HDL-C levels.

Research progress of anaerobic ammonium oxidation (Anammox) process based on integrated fixed-film activated sludge (IFAS).

The integrated fixed-film activated sludge (IFAS) process is considered one of the cutting-edge solutions to the traditional wastewater treatment challenges, allowing suspended sludge and attached biofilm to grow in the same system. In addition, the coupling of IFAS with anaerobic ammonium oxidation (Anammox) can further improve the efficiency of biological denitrification. This paper summarises the research progress of IFAS coupled with the anammox process, including partial nitrification anammox, simultaneous partial nitrification anammox and denitrification, and partial denitrification anammox technologies, and describes the factors that limit the development of related processes. The effects of dissolved oxygen, influent carbon source, sludge retention time, temperature, microbial community, and nitrite-oxidising bacteria inhibition methods on the anammox of IFAS are presented. At the same time, this paper gives an outlook on future research focus and engineering practice direction of the process.

Hydraulic characterization and start-up of a novel circulating flow bio-carriers.

High-quality biofilm carriers are crucial for the formation of biofilm, but problems such as slow biofilm growth on the carrier surface have been troubling a large number of researchers. The addition of a carrier changes the flow state in the reactor, which in turn affects the microbial attachment and the quantity of microorganisms. Also, aerobic microorganisms need to use dissolved oxygen in the water to remove water pollutants. In this paper, a novel recirculating flow carrier with a hollow cylinder structure is proposed, with a certain number of hollow inverted circular plates placed at equal distances inside. In this paper, the hydraulic residence time, aeration volume, and the spacing of the inflow plates of the recirculating flow biofilm carrier, which are three important factors affecting the hydraulic characteristics of the reactor, are first investigated. At the same time, it was compared with the common combined carrier to find the optimal operating conditions for the hydraulic characteristics. Secondly, a reactor start-up study was carried out to confirm that the new recirculating flow biofilm carrier could accelerate the biofilm growth by changing the hydraulic characteristics. The results showed that under the same conditions, the hydraulic properties of the reactor were better with the addition of the recirculating flow carrier, with an effective volume ratio of 98% and a significant reduction in short flows and dead zones. The stabilized removal of COD, NH3-N, and TN in the reactor with the addition of the recirculating flow carrier reached about 94%, 99%, and 91% respectively, at the beginning of the 15th day, which effectively proved the feasibility of the recirculating flow carrier.

Genomic characterization and pathogenicity of a novel fowl adenovirus serotype 11 isolated from chickens with inclusion body hepatitis in China.

Fowl adenovirus serotype 11 (FAdV-11) is one of the primary causative agents of inclusion body hepatitis (IBH), which causes substantial economic losses in the world poultry industry. In this study, we characterized the genome of the fowl adenovirus serotype 11 (FAdV-11) isolate FJSW/2021. The full genome of FJSW/2021 was 44, 154 base pairs (bp) in length and had a similar organization to that of previously reported FAdV-11 isolates. Notably, compared with those of other reported FAdV-11 strains, the preterminal protein (pTP) of FAdV-11 FJSW/2021 has six amino acid (aa) insertions (S-L-R-I-I-C) between 470 and 475 and one aa mutation of L476F; moreover, the tandem repeat (TR) regions of TR1 and TR2 were 33 bp (1 repeat) and 1,080 bp (8 repeats) shorter than those of the Canadian nonpathogenic isolate ON NP2, respectively. The pathogenicity of FJSW/2021 was studied in 10-day-old specific pathogen-free chicken embryos following allantoic cavity inoculation and in 1-day-old, 1-wk-old and 2-wk-old SPF chickens following intramuscular inoculation with 107 TCID50 of the virus. The results showed that FJSW/2021 can induce typical severe IBH in chicks less than 2 wk old. These findings highlighted the genetic differences between the pathogenic and non-pathogenic FAdV-11 isolates. The data will provide guidance for identifying the virulence factors of FAdV-11 strains. The animal challenge model developed in our study will allow precise evaluation of the efficacy of potential FAdV-11 vaccine candidates.

Progress in the treatment of diabetic cardiomyopathy, a systematic review.

Diabetic cardiomyopathy (DCM) is a condition characterized by myocardial dysfunction that occurs in individuals with diabetes, in the absence of coronary artery disease, valve disease, and other conventional cardiovascular risk factors such as hypertension and dyslipidemia. It is considered a significant and consequential complication of diabetes in the field of cardiovascular medicine. The primary pathological manifestations include myocardial hypertrophy, myocardial fibrosis, and impaired ventricular function, which can lead to widespread myocardial necrosis. Ultimately, this can progress to the development of heart failure, arrhythmias, and cardiogenic shock, with severe cases even resulting in sudden cardiac death. Despite several decades of both fundamental and clinical research conducted globally, there are currently no specific targeted therapies available for DCM in clinical practice, and the incidence and mortality rates of heart failure remain persistently high. Thus, this article provides an overview of the current treatment modalities and novel techniques pertaining to DCM, aiming to offer valuable insights and support to researchers dedicated to investigating this complex condition.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

The impacts of biologic treatment on metabolic profiling in psoriasis.

Psoriasis is an immune-mediated inflammatory disease commonly accompanied by various metabolic disorders. It is widely known that biologics could affect the metabolic status and comorbidities in psoriasis patients, however, the effects of biologics on metabolism in psoriasis patients remain poorly understood. The aim of this study was to elucidate the characteristic changes of metabolic profiling in psoriasis vulgaris (PsV) patients before and after applying biologics. Plasma samples were collected from a retrospective cohort of 43 PsV patients. Non-targeted metabolomics analyses were performed using liquid chromatography-mass spectrometry (LC-MS) to compare the metabolic profiles before and after applying adalimumab (ADA) or ixekizumab (IXE) for 4 weeks. Additionally, correlation analyses were conducted to investigate the associations between metabolite expression levels and clinical characteristics. The biologics significantly affected the metabolic profiles of PsV patients especially in glycerophospholipids (GPs). First, phosphatidylcholine (PC), unsaturated lysophosphatidylcholine (LPC), unsaturated lysophosphatidic acid (LPA) and unsaturated lysophosphatidylethanolamine (LPE) were significantly up-regulated, whereas phosphatidylethanolamine (PE), saturated LPC, saturated LPA and saturated LPE were predominantly down-regulated after biologic treatment. What is more, the changes in PE and LPA were mainly observed after applying IXE instead of ADA. Second, we also found GPs including PC, unsaturated LPC, unsaturated LPA and unsaturated LPE were primarily negatively correlated with disease severity, whereas, PE, saturated LPC, saturated LPA and saturated LPE displayed inverse correlations. Biologics could affect GP metabolism and facilitate the transition of metabolic status from a pro-inflammatory to an anti-inflammatory phenotype in PsV patients.

Profiles of phenolics and their synthetic pathways in Asparagus officinalis L.

The synthetic pathways of some phenolics compounds in asparagus have been reported, however, the diversified phenolics compounds including their modification and transcription regulation remains unknown. Thus, multi-omics strategies were applied to detect the phenolics profiles, contents, and screen the key genes for phenolics biosynthesis and regulation in asparagus. A total of 437 compounds, among which 204 phenolics including 105 flavonoids and 82 phenolic acids were detected with fluctuated concentrations in roots (Rs), spears (Ss) and flowering twigs (Fs) of the both green and purple cultivars. Based on the detected phenolics profiles and contents correlated to the gene expressions of screened synthetic enzymes and regulatory TFs, a full phenolics synthetic pathway of asparagus was proposed for the first time, essential for future breeding of asparagus and scaled healthy phenolics production using synthetic biological strategies.

Arc plasma for high-efficiency ionization and scavenging of plasticizers in wrap films.

Herein, ambient electric arc ionization mass spectrometry was used to examine 16 plasticizers in various wrap films, demonstrating high sensitivity (detection limit: <0.2 ng/mg) and precision (intra-/inter-day precision: <12 %). The ease of operation helps in the identification of wrap film and plasticizer analysis. In addition, the introduction of a cold arc plasma treatment presents a practical and innovative method for effectively eliminating plasticizers. This innovative strategy has implications for both environmental protection and food safety.

Circulating blood biomarkers correlated with the prognosis of advanced triple negative breast cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) can improve survivals of metastatic triple negative breast cancer (mTNBC); however, we still seek circulating blood biomarkers to predict the efficacy of ICIs. MATERIALS AND METHODS: In this study, we analyzed the data of ICIs treated mTNBC collected in Anhui Medical University affiliated hospitals from 2018 to 2023. The counts of lymphocytes, monocytes, platelets, and ratio indexes (NLR, MLR, PLR) in peripheral blood were investigated via the Kaplan-Meier curves and the Cox proportional-hazards model. RESULTS: The total of 50 mTNBC patients were treated with ICIs. High level of peripheral lymphocytes and low level of NLR and MLR at baseline and post the first cycle of ICIs play the predictable role of immunotherapies. Lymphocytes counts (HR = 0.280; 95% CI: 0.095-0.823; p = 0.021) and NLR (HR = 1.150; 95% CI: 1.052-1.257; p = 0.002) are significantly correlated with overall survival. High NLR also increases the risk of disease progression (HR = 2.189; 95% CI:1.085-4.414; p = 0.029). When NLR at baseline ≥ 2.75, the hazard of death (HR = 2.575; 95% CI:1.217-5.447; p = 0.013) and disease progression (HR = 2.189; 95% CI: 1.085-4.414; p = 0.029) significantly rise. HER-2 expression and anti-tumor therapy lines are statistically correlated with survivals. CONCLUSIONS: Before the initiation of ICIs, enriched peripheral lymphocytes and poor neutrophils and NLR contribute to the prediction of survivals.