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Bivalves are often employed for biomonitoring contaminants in marine environments; however, in these large-scale programs, unavoidably, using multiple species presents a significant challenge. Interspecies differences in contaminant bioaccumulation can complicate data interpretation, and direct comparisons among species may result in misleading conclusions. Here, we propose a robust framework based on toxicokinetic measurements that accounts for interspecies differences in bioaccumulation. Specifically, via a recently developed double stable isotope tracer technique, we determined the toxicokinetics of cadmium (Cd)─a metal known for its high concentrations in bivalves and significant interspecies bioaccumulation variability─in six widespread bivalve species including mussels (Perna viridis, Mytilus unguiculatus, Mytilus galloprovincialis) and oysters (Magallana gigas, Magallana hongkongensis, Magallana angulata). Results show that oysters generally have higher Cd uptake rate constants (ku: 1.18-3.09 L g-1 d-1) and lower elimination rate constants (ke: 0.008-0.017 d-1) than mussels (ku: 0.21-0.64 L g-1 d-1; ke: 0.018-0.037 d-1). The interspecies differences in tissue Cd concentrations are predominantly due to Cd uptake rather than elimination. Utilizing toxicokinetic parameters to back-calculate Cd concentrations in seawater, we found that the ranking of Cd contamination levels at the six sites markedly differs from those based on tissue Cd concentrations. We propose that this approach will be useful for interpreting data from past and future biomonitoring programs.

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Silver nanoparticles (AgNPs) have drawn a lot of attention from a variety of fields, particularly the biological and biomedical sciences. As a result, it is acknowledged that AgNPs' direct interactions with macromolecules such as DNA, proteins, and enzymes are essential for both therapeutic and nanotoxicological applications. Enzymes as important catalysts may interact with AgNP surfaces in a variety of ways. Therefore, mechanistic investigation into the molecular effects of AgNPs on enzyme conformation and function is necessary for a comprehensive assessment of their interactions. In this overview, we aimed to overview the various strategies for producing AgNPs. We then discussed the enzyme activity inhibition (EAI) mechanism by nanostructured particles, followed by an in-depth survey of the interaction of AgNPs with different enzymes. Furthermore, various parameters influencing the interaction of NPs and enzymes, as well as the antibacterial and anticancer effects of AgNPs in the context of the enzyme inhibitors, were discussed. In summary, useful information regarding the biological safety and possible therapeutic applications of AgNPs-enzyme conjugates may be obtained from this review.

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Ferroptosis, a unique form of regulated cell death driven by iron-dependent lethal lipid peroxidation, is implicated in various stress-related diseases like neurodegeneration, vasculopathy, and metabolic disturbance. Stress-related diseases encompass widespread medical disorders that are influenced or exacerbated by stress. These stressors can manifest in various organ or tissue systems and have significant implications for human overall health. Understanding ferroptosis in these diseases offers insights for therapeutic strategies targeting relevant pathways. This review explores ferroptosis mechanisms, its role in pathophysiology, its connection to stress-related diseases, and the potential of ferroptosis-targeted nanomedicines in treating conditions. This monograph also delves into the engineering of ferroptosis-targeted nanomedicines for tackling stress-related diseases, including cancer, cardia-cerebrovascular, neurodegenerative, metabolic and inflammatory diseases. Anyhow, nanotherapy targeting ferroptosis holds promise by both promoting and suppressing ferroptosis for managing stress-related diseases.

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BACKGROUND: Allergic Rhinitis (AR), a prevalent condition in otorhinolaryngology, is mediated by Type 1 hypersensitivity through IgE, characterized by Type 2 inflammatory response and eosinophil infiltration in the nasal mucosa. Since AR disease exhibits significant heterogeneity in symptom severity, an objective assessment of AR severity may facilitate better individualized treatment. OBJECTIVE: To explore the changes in peripheral blood IL-9, Th9, and BAFF levels of allergic rhinitis (AR) in patients and the clinical significance associated with it. METHODS: A retrospective study selected 80 AR patients admitted from January 2022 to October 2022 as the case group, dividing them into mild and moderate-to-severe groups based on symptom scores. Concurrently, 50 patients without AR, who were treated for nasal bone fractures or underwent septoplasty, were selected as the group for comparison. Alterations in the expression levels of peripheral blood IL-9, Th9, and BAFF were analyzed and compared among the different groups. The diagnostic value of serum BAFF for the severity of AR was analyzed using the receiver operating characteristic (ROC) curve. RESULTS: Noticeable variations were observed in clinical variables among the three groups such as, total IgE levels, peripheral blood eosinophil count and proportion, TNSS, and VAS (P< 0.05), while no statistically significant differences were observed in other variables (P> 0.05). The comparison of IL-9, Th9, and BAFF among the three groups revealed statistically significant differences (P< 0.05). Analysis using multivariate logistic regression revealed that IL-9 (OR = 2.365), Th9 (OR = 2.186), BAFF (OR = 2.307) were influencing factors of moderate-to-severe AR (P< 0.05). The ROC curve indicated that the AUC for the diagnosis of moderate-to-severe AR by IL-9, Th9, BAFF were 0.770, 0.734, 0.761, respectively, and the combined detection AUC was 0.888, an area under the curve higher than individual testing. CONCLUSION: Changes in peripheral blood IL-9, Th9, and BAFF levels in AR patients may function as indicators to assess the level of severity in diagnostic procedures.

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Cardiovascular diseases, particularly stroke, are a leading cause of morbidity and mortality worldwide. Genetic variations in genes associated with inflammation have been implicated in the pathogenesis of stroke. Interleukin-6 (IL-6), a pleiotropic cytokine with diverse biological functions, has been linked to cardiovascular diseases and stroke. The relationship between cerebral ischemia and inflammation is well-established, suggesting a potential role for IL-6 polymorphisms in stroke susceptibility. In the context of this study, the focus is on evaluating a pleiotropic cytokinin polymorphism, specifically IL-6-572GC, and its association with cerebral infarction in a Chinese male population. The investigation aims to elucidate the genetic correlation between IL-6 polymorphisms and stroke risk, particularly in the context of hemorrhagic subtype of stroke. The study utilizes a case-control design, comparing stroke patients with healthy controls while adjusting for classic risk factors associated with stroke. The methodology employed includes the detection of IL-6 polymorphisms using Real Time Taq Man Probe and PCR-RFLP methods. The results suggest an association between the IL-6-572GC genotype and an increased risk of stroke, particularly in the hemorrhagic subtype. However, the relationship between another IL-6 polymorphism, IL-6-174GC, and stroke remains inconclusive, except for a potential correlation with one allele. The findings underscore the potential role of IL-6-572GC genotype as a genetic risk factor for stroke in the Chinese male population under study. Further research involving larger cohorts is warranted to validate these results and clarify the role of IL-6-174GC polymorphism in stroke susceptibility. Understanding the genetic underpinnings of stroke can provide valuable insights for risk assessment and personalized treatment strategies in affected populations.

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Characterization studies of the plant metabolome are crucial for revealing plant physiology, developing functional foods, and controlling quality. Mass spectrometry-based metabolite profiling allows unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte, which makes it challenging to filter out redundant signals and organize the signals corresponding to abundant constituents. This study proposed a strategy integrating in-source fragments elimination, diagnostic ions recognition, and feature-based molecular networking (ISFE-DIR-FBMN) to simultaneously characterize cycloartane triterpenoids (CTs) from three medicinal Cimicifuga species. The results showed that 63.1 % of the measured ions were redundant. A total of 184 CTs were annotated, with 27.1 % being reported for the first time. It presents a promising approach to assess the composition of natural extracts, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns. Besides, chemometrics analysis of the three Cimicifuga species identified 32 species-specific markers, highlighting significant differences among them. The valuable information can enhance the sustainable utilization and further development of Cimicifuga resources. The codes involved in ISFE-DIR-FBMN are freely available on GitHub (https://github.com/LHJ-Group/ISFE-DIR-FBMN.git).

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Advanced-stage cutaneous T-cell lymphomas (CTCLs) are notorious for their highly aggressive behavior, resistance to conventional treatments, and poor prognosis, particularly when large-cell transformation occurs. PEG10 has been recently proposed as a potent driver for large-cell transformation in CTCL. However, the targeting of PEG10 continues to present a formidable clinical challenge that has yet to be addressed. In this study, we report an important post-translational regulatory mechanism of PEG10 in CTCL. USP9X, a deubiquitinase, interacted with and deubiquitinated PEG10, thereby stabilizing PEG10. Knockdown of USP9X or pharmacological targeting of USP9X resulted in a prominent downregulation of PEG10 and its downstream pathway in CTCL. Moreover, USP9X inhibition conferred tumor cell growth disadvantage and enhanced apoptosis in vitro, an effect that occurred in part through its regulation on PEG10. Furthermore, we demonstrated that inhibition of USP9X obviously restrained CTCL tumor growth in vivo and that high expression of USP9X is associated with poor survival in patients with CTCL. Collectively, our findings uncover USP9X as a key post-translational regulator in the stabilization of PEG10 and suggest that targeting PEG10 stabilization through USP9X inhibition may represent a promising therapeutic strategy for advanced-stage CTCL.

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The conventional therapeutic treatment of triple-negative breast cancer (TNBC) is negatively influenced by the development of tumor cell drug resistant, and systemic toxicity of therapeutic agents due to off-target activity. In accordance with research findings, nanoparticles (NPs) responsive to the tumor microenvironment (TME) have been discovered for providing opportunities to selectively target tumor cells via active targeting or Enhanced Permeability and Retention (EPR) effect. The combination of the TME control and therapeutic NPs offers promising solutions for improving the prognosis of the TNBC because the TME actively participates in tumor growth, metastasis, and drug resistance. The NP-based systems leverage stimulus-responsive mechanisms, such as low pH value, hypoxic, excessive secretion enzyme, concentration of glutathione (GSH)/reactive oxygen species (ROS), and high concentration of Adenosine triphosphate (ATP) to combat TNBC progression. Concurrently, NP-based stimulus-responsive introduces a novel approach for drug dosage design, administration, and modification of the pharmacokinetics of conventional chemotherapy and immunotherapy drugs. This review provides a comprehensive examination of the strengths, limitations, applications, perspectives, and future expectations of both novel and traditional stimulus-responsive NP-based drug delivery systems for improving outcomes in the medical practice of TNBC. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

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BACKGROUND: This study extends from the 2015 Shandong Province Epidemiological Survey of Mental Disorders in adults aged 18 and above. Over five years, it investigates pain characteristics and influencing factors in individuals with depressive disorders in Shandong Province. METHODS: The study encompasses 871 individuals who met DSM-IV criteria for depressive disorders in 2015. Using 1:1:1 matching by gender, age, and residence, 825 non-afflicted individuals were selected as high-risk controls, and 825 screening-negative individuals became low-risk controls. A follow-up study in 2020 involved 1848 participants. Survey tools included a general information questionnaire, General Health Questionnaire-12 (GHQ-12), SCID-I/P, Global Pain Scale (GPS), Quality of Life Questionnaire (QLQ), PSQI, MoCA, and clinical data questionnaire. RESULTS: GPS scores in the current depressive group were higher than in non-current depressive group (Z = 14.36, P < 0.01). GPS scores in study group exceeded those in high-risk and low-risk control groups (H = 93.71, P < 0.01). GPS scores in non-remission group were higher than in the remission group (Z = 8.90, P < 0.01). Regression analysis revealed positive correlations between GPS scores and physical illnesses, current depression, incumbency, GHQ-12 total score, and PSQI total score. Negative correlations were observed with QLQ total score and MoCA total score. LIMITATIONS: The study could not assess pain during the 2015 survey, limiting controlled pain analysis before and after five years. CONCLUSION: Depression sufferers may experience prolonged heightened pain, potentially relieved when depression subsides. Individual pain is influenced by depression, physical illnesses, sleep quality, quality of life, cognitive function, gender, residence, and occupation.

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Suspended particulate matter (SPM) carries a major fraction of metals in turbid coastal waters, markedly influencing metal bioaccumulation and posing risks to marine life. However, its effects are often overlooked in current water quality criteria for metals, primarily due to challenges in quantifying SPM's contribution. This contribution depends on the SPM concentration, metal distribution coefficients (Kd), and the bioavailability of SPM-bound metals (assimilation efficiency, AE), which can collectively be integrated as a modifying factor (MF). Accordingly, we developed a new stable isotope method to measure metal AE by individual organisms from SPM, employing the widely distributed filter-feeding clam Ruditapes philippinarum as a representative species. Assessing SPM from 23 coastal sites in China, we found average AEs of 42% for Zn, 26% for Cd, 20% for Cu, 8% for Ni, and 6% for Pb. Moreover, using stable isotope methods, we determined metal Kd of SPM from these sites, which can be well predicted by the total organic carbon and iron content (R2 = 0.977). We calculated MFs using a Monte Carlo method. The calculated MFs are in the range 9.9-43 for Pb, 8.5-37 for Zn, 2.9-9.7 for Cu, 1.4-2.7 for Ni, and 1.1-1.6 for Cd, suggesting that dissolved-metal-based criteria values should be divided by MFs to provide adequate protection to aquatic life. This study provides foundational guidelines to refine water quality criteria in turbid waters and protect coastal ecosystems.

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Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from ultra-high performance liquid chromatography triple quadrupole mass spectrometry-based pseudotargeted metabolomics analysis. As a result, a total of 16 compounds were screened out to be the specific markers, which were successfully applied to distinguish five FBs by using discriminant analysis model. Besides, partial least squares regression models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with regression coefficient of prediction (R2p) > 0.9 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

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BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

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Background: Discriminating between cutaneous anaplastic large cell lymphoma (cALCL) and CD30-positive transformed mycosis fungoides (CD30+ TMF) is challenging, particularly when they arise in the context of pre-existing mycosis fungoides. The development of molecular diagnostic tools was hampered by the rarity of both diseases and the limited understanding of their pathogenesis. Methods: In this study, we established a cohort comprising 25 cALCL cases and 25 CD30+ TMF cases, with transcriptomic data obtained from 31 samples. We compared the clinicopathological information and investigated the gene expression profiling between these two entities. Furthermore, we developed an immunohistochemistry (IHC) algorithm to differentiate these two entities clinically. Results: Our investigation revealed distinct clinicopathological features and unique gene expression programs associated with cALCL and CD30+ TMF. cALCL and CD30+ TMF displayed marked differences in gene expression patterns. Notably, CD30+ TMF demonstrated enrichment of T cell receptor signaling pathways and an exhausted T cell phenotype, accompanied by infiltration of B cells, dendritic cells, and neurons. In contrast, cALCL cells expressed high levels of HLA class II genes, polarized towards a Th17 phenotype, and exhibited neutrophil infiltration. An IHC algorithm with BATF3 and TCF7 staining emerged as potential diagnostic markers for identifying these two entities. Conclusions: Our findings provide valuable insights into the differential molecular signatures associated with cALCL and CD30+ TMF, which contribute to their distinct clinicopathological behaviors. An appropriate IHC algorithm could be used as a potential diagnostic tool.

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Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images. Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance. Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance. Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.

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Insect egg deposition can induce plant defenses against their larvae. Previous studies have primarily focused on herbaceous plant defenses; however, little is known about how the Salicaceae respond to insect egg deposition and defend themselves against herbivores. By combining plant defense gene studies and bioassays, we investigated the effect of the coleoptera Plagiodera versicolora egg deposition on willow (Salix matsudana cv. 'Zhuliu') and examined the interactions at the plant resistance and transcriptome levels. RNA-seq data were utilized to analyze changes in the leaf transcriptome with and without oviposition, and also the changes in the leaf transcriptome of feeding-damaged leaves with and without prior oviposition. P. versicolora oviposition on willow leaves resulted in altered expression levels of transcripts associated with plant stress and metabolic responses. Compared with leaves with no oviposition, leaves with egg deposition showed a slight increase in phenylpropanoid biosynthesis and phytohormone signaling genes after larval feeding. The RNA-seq analysis revealed alterations in willow transcripts in response to leaf beetle infestations. Bioassays indicated that oviposition by P. versicolora on willows reduced subsequent larvae performance, suggesting that prior oviposition by P. versicolora could increase willows' resistance to larvae. This study advances our knowledge of how oviposition by coleoptera insects induces changes in the resistance of leaves to herbivory in the Salicaceae family.