Different extractable pools of Cd and Pb in agricultural soil under amendments: Water-soluble concentration sensitively indicates metal availability.

Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive, hindering accurate assessment on environmental risks and effectiveness of remediation strategies. This study evaluated the feasibility of European Community Bureau of Reference (BCR) sequential extraction, Ca(NO3)2 extraction, and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime, magnesium hydroxide, corn stover biochar, and calcium dihydrogen phosphate. Moreover, the enriched isotope tracing technique (112Cd and 206Pb) was employed to evaluate the aging process of newly introduced Cd and Pb within 56 days' incubation. Results demonstrated that extractable pools by BCR and Ca(NO3)2 extraction were little impacted by amendments and showed little correlation with soil pH. This is notable because soil pH is closely linked to metal availability, indicating these extraction methods may not adequately reflect metal availability. Conversely, water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH (Pearson's r: -0.908 to -0.825, P < 0.001), suggesting water extraction as a more sensitive approach. Furthermore, newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools. Additionally, water-soluble concentrations of essential metals were impacted by soil amendments, raising caution on their potential effects on plant growth. These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability, which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Disturbances of thalamus and prefrontal cortex contribute to cognitive aging: A structure-function coupling analysis based on KL divergence.

Normal aging is accompanied by changes in brain structure and function associated with cognitive decline. Structural and functional abnormalities, particularly the prefrontal cortex (PFC) and subcortical regions, contributed to cognitive aging. However, it remains unclear how the synchronized changes in structure and function of individual brain regions affect the cognition in aging. Using 3D T1-weighted structural data and movie watching functional magnetic resonance imaging data in a sample of 422 healthy individuals (ages from 18 to 87 years), we constructed regional structure-function coupling (SFC) of cortical and subcortical regions by quantifying the distribution similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF). Further, we investigated age-related changes in SFC and its relationship with cognition. With aging, increased SFC localized in PFC, thalamus and caudate nucleus, decreased SFC in temporal cortex, lateral occipital cortex and putamen. Moreover, the SFC in the PFC was associated with executive function and thalamus was associated with the fluid intelligence, and partially mediated age-related cognitive decline. Collectively, our results highlight that tighter structure-function synchron of the PFC and thalamus might contribute to age-related cognitive decline, and provide insight into the substrate of the thalamo-prefrontal pathway with cognitive aging.

Unintentionally-produced persistent organic pollutants in the aquatic environment contaminated from historical chlor-alkali production.

Historical chlor-alkali production has led to substantial concentrations of persistent organic pollutant residues in the environment. This study systematically investigated the distribution of polycyclic aromatic hydrocarbons (PAHs), chlorinated/brominated-PAHs (Cl/Br-PAHs), polychlorinated naphthalenes (PCNs), and hexachlorobutadiene (HCBD) in sediment, lotus (Nelumbo nucifera), and fish samples from Ya-Er Lake, which is a site in China with historical chlor-alkali contamination. The average concentrations [(4.97-1.47) × 103 ng/g dry weight (dw)] of these pollutants in backfill sediments, which were dredged from the lake after chlor-alkali production stopped, were 2.68-70.87 times those in fresh lake sediments (0.622-218 ng/g dw) and reported concentrations in other areas. Correlation analyses indicated that Cl-PAHs, Br-PAHs, and PCNs likely originated from halogenation of parent PAHs in the study area, and the chlorination ratios were larger than those of bromination. The Cl(1/2/3)-PAHs/PAHs and Br(1)-PAHs/PAHs ratios were higher than those for PAHs with more halogen atoms. This contamination extended into the biota, with notable pollutant burdens found in lotus (Nelumbo nucifera, 0.305-77.3 ng/g dw) and even higher concentrations in fish (2.20-345 ng/g lipid weight). Estimated biological soil accumulation factors revealed significant enrichment in lotus organs (mean: 7.19) and fish muscle (mean: 10.65), especially the latter, which highlighted bioaccumulation and potential food chain transfer risks. The estimated daily intakes of PAHs, Cl/Br-PAHs, and HCBD through fish consumption currently pose negligible risks, while dietary intake of PCNs may present health concerns. Continuous monitoring and impact assessments are crucial for developing appropriate risk management strategies to safeguard public health.

Nickel-Catalyzed Narasaka-Heck Cyclization Carbonylation of Unsaturated Oxime Esters with Arylboronic Acids.

The Narasaka-Heck reaction is one of the most straightforward methods for constructing pyrroline derivatives. Herein, we report a novel nickel-catalyzed three-component carbonylation reaction, which cleverly realizes the continuous construction of C(sp3)-N bonds and C(sp3)-C(sp2) bonds and effectively promotes the synthesis of acyl-substituted pyrroline derivatives. Furthermore, this strategy not only expands the conversion pathway of γ,δ-unsaturated oxime esters but also provides a new method for the synthesis of nitrogen-containing heterocyclic compounds.

Water-extractable metals as indicators of wheat metal accumulation: Insights from Cd, Pb, Mn, Cu, and Zn.

There is a long-standing debate over the effectiveness of chemical extraction methods in assessing soil metal phytoavailability. This study addresses the limitations of widely-used chemical extraction methods and presents the water-extractable pool as a more reliable indicator based on wheat pot experiments using homogenized agricultural soil amended with lime materials, phosphate, and biochar. Over 120 days' pot experiments, Cd accumulation in whole wheat plants and tissues exhibited positive relationships with water-extractable Cd concentrations at heading and maturity stage (Spearman's rho: 0.521-0.851; P < 0.05), revealing that the water-extractable pool instead of other pools better indicates wheat metal accumulation. Water-extractable metal concentrations are effective in assessing phytoavailability of metals primarily in ionic forms in soil solution (e.g, Zn, Cd), but less reliable for metals strongly complexed with dissolved organic matter (DOM) or sensitive to redox conditions. It demonstrated that water-extractable metal concentrations and chemical forms are key factors, fundamentally determined by metal properties and impacted by environmental factors. This study clarifies a more direct link between chemical extraction and plant metal uptake mechanisms. Given the extensive application of chemical extraction methods over several decades, this study will help advance soil metal risk assessment and remediation practices.

Persistent organic pollutants and metabolic diseases: From the perspective of lipid droplets.

The characteristic of semi-volatility enables persistent organic pollutants (POPs) almost ubiquitous in the environment. There is increasing concern about the potential risks of exposure to POPs due to their lipophilicity and readily bioaccumulation. Lipid droplets (LDs) are highly dynamic lipid storage organelles, alterations of intracellular LDs play a vital role in the progression of many prevalent metabolic diseases, such as type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). This article systematically reviewed the biological processes involved in LDs metabolism, the role of LDs proteins and LDs in metabolic diseases, and summarized updating researches on involvement of POPs in the progression of LDs-related metabolic diseases and potential mechanisms. POPs might change the physiological functions of LDs, also interfere the processes of adipogenesis and lipolysis by altering LDs synthesis, decomposition and function. However, further studies are still needed to explore the underlying mechanism of POPs-induced metabolic diseases, which can offer scientific evidences for metabolic disease prevention.

Palladium-Catalyzed Four-Component Radical Cascade Carbonylation Access to 2,3-Disubstituted Benzofuran Derivatives.

Multicomponent radical tandem reactions have emerged as a crucial technique for synthesizing complex molecules in organic chemistry. In this study, we report a palladium-catalyzed four-component difluoroalkylative carbonylation of enynes and ethyl difluoroiodoacetate. This transformation proceeds through a multistep sequential reaction that introduces reactive difluoro and carbonyl groups while constructing the benzofuran skeleton. Moreover, a variety of valuable 2,3-disubstituted benzofuran derivatives were obtained in respectable yields with excellent functional group compatibility.

A switch strategy for the synthesis of C4-ethylamine indole and C7-aminoindoline via controllable carbon elimination.

Controllable ß-carbon elimination to extrude norbornene remains a long-standing challenge in palladium and norbornene chemistry. Herein, this manuscript describes a switchable synthesis of biologically active C4-ethylaminoindole and C7-aminoindoline scaffolds by controlling ß-carbon elimination, utilizing aziridine as a C-H ethylamination reagent through a C-N bond cleavage reaction. Furthermore, the protecting groups of the product can be easily removed, offering an unusual method for the synthesis of dopamine receptor agonists.

Trace rare earth elements analysis in atmospheric particulates and cigar smoke by ICP-MS after pretreatment with magnetic polymers.

BACKGROUND: Some heavy metals could be ingested into human body through breathing besides diet and drinking. Atmospheric particulates and smoke are main sources of this kind for the metals' exposure to human. Compared with environmental water, the methodologies for trace metals in particulates and smoke samples with more complex matrix are much less. Magnetic functional sorbents can be designed to remove complex matrix and enrich target analytes. The combination of magnetic solid phase extraction (MSPE) with highly sensitive inductively coupled plasma mass spectrometry (ICP-MS) detection is a good alternative for the analytical purpose. (92). RESULTS: Magnetic polymers were synthesized through free radical polymerization with Fe3O4 nanoparticles as the core and 2-methyl-2-hydroxyethyl 2-acrylate-2-hydroxyethyl ester phosphate as external modifier. The sorbent showed a high phosphorus content (2.7 wt%) and good selectivity to target REEs, along with good reusability (at least 45 times) and chemical stability. With the consumption of 150 mL aqueous solution, an enrichment factor of 300 was obtained by the proposed method, leading to low detection limits (0.001-0.2 ng L-1) for 15 REEs. The application potential of the method was further evaluated by analyzing local atmospheric particulate and cigar smoke samples. Recovery of 86.3-107 % in digested total suspended particulate (TSP) was obtained for 15 REEs, demonstrating a good anti-interference ability of the method. Target REEs in TSP, PM2.5 and PM10 samples were found to be 0.01-2.81, 0.006-1.09 and 0.009-2.46 ng m-3, respectively, and none of them were detected in the collected cigar smoke. (148) SIGNIFICANCE: The method of MSPE-ICP-MS was demonstrated with good potential for trace analysis in complex sample matrix, probably due to the good selectivity of the functionalized polymers. With the design and fabrication of specific functionalized magnetic sorbents, other heavy metals can be monitored in those samples which would be intake by human breathing. It provided an efficient strategy for the evaluation of metals' health risk in particulates and smoke samples. (69).

Main-group compounds selectively activate natural gas alkanes under room temperature and atmospheric pressure.

Most C-H bond activations of natural gas alkanes rely on transition metal complexes. Activations by using main-group systems have been reported but required heating or photo-irradiation under high atmospheric pressure with rather low regioselectivity. Here we report that Lewis acid-carbene adducts facilely undergo oxidative additions to C-H bonds of ethane, propane and n-butane with high selectivity under room temperature and atmospheric pressure. The Lewis acids can be moved by the addition of a base and the carbene-derived products can be easily converted into aldehydes. This work offers a route for main-group element compounds to selectively functionalise C-H bonds of natural gas alkanes and other small molecules.

Development and applications of graduate outcome-based curriculum for basic medical education.

Introduction: Outcome-based education (OBE) leads to revolutionary change in medical education, but each medical school is acknowledged to develop suited framework based on specific positioning, educational philosophy and expected outcomes. Methods: Based on the graduate outcomes of basic medical education in China released by Working Committee for the Accreditation of Medical Education (WCAME) which officially recognized by World Federation for Medical Education (WFME), Taizhou University re-documented the overall organization of the curriculum and classified the courses as "Crucial, Supporting and Associated (CSA)" categories to connect the graduate outcomes and course objectives. Results: We redefined the standard for graduates in Taizhou University Medical School including 34 items in four domains and redesigned the curriculum with 76 courses classified as CSA. Empirical data reveals a substantial improvement in students' performance on Standardized Competence Test for Clinical Medicine Undergraduates in China (SCTCMU) by implementing the CSA system between 2022 and 2023. Notably, in 2023, Taizhou University's students achieved pass rates more than 20 percentage points higher than the national average, demonstrating a profound and unprecedented impact. Conclusion: The CSA classification system provides a notably clear and structured framework for categorizing courses based on their direct or indirect relevance to educational objectives, which provides clarity to educators and empowers students with a more purposeful approach to their studies.

Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning.

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into tumor molecular subtyping. However, the high dimensionality and small sample size of the data may result in ambiguous and overlapping cancer subtypes during clustering. In this study, we propose a novel contrastive-learning-based approach to address this issue. The proposed end-to-end deep learning method can extract crucial information from the multi-omics features by self-supervised learning for patient clustering. RESULTS: By applying our method to nine public cancer datasets, we have demonstrated superior performance compared to existing methods in separating patients with different survival outcomes (p < 0.05). To further evaluate the impact of various omics data on cancer survival, we developed an XGBoost classification model and found that mRNA had the highest importance score, followed by DNA methylation and miRNA. In the presented case study, our method successfully clustered subtypes and identified 14 cancer-related genes, of which 12 (85.7%) were validated through literature review. CONCLUSIONS: Our findings demonstrate that our method is capable of identifying cancer subtypes that are both statistically and biologically significant. The code about COLCS is given at: https://github.com/Mercuriiio/COLCS .

Lymphotoxin-ß promotes breast cancer bone metastasis colonization and osteolytic outgrowth.

Bone metastasis is a lethal consequence of breast cancer. Here we used single-cell transcriptomics to investigate the molecular mechanisms underlying bone metastasis colonization-the rate-limiting step in the metastatic cascade. We identified that lymphotoxin-ß (LTß) is highly expressed in tumour cells within the bone microenvironment and this expression is associated with poor bone metastasis-free survival. LTß promotes tumour cell colonization and outgrowth in multiple breast cancer models. Mechanistically, tumour-derived LTß activates osteoblasts through nuclear factor-κB2 signalling to secrete CCL2/5, which facilitates tumour cell adhesion to osteoblasts and accelerates osteoclastogenesis, leading to bone metastasis progression. Blocking LTß signalling with a decoy receptor significantly suppressed bone metastasis in vivo, whereas clinical sample analysis revealed significantly higher LTß expression in bone metastases than in primary tumours. Our findings highlight LTß as a bone niche-induced factor that promotes tumour cell colonization and osteolytic outgrowth and underscore its potential as a therapeutic target for patients with bone metastatic disease.

Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement.

Recently, vision-language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is that the rich knowledge embedded in radiology reports can effectively assist and guide the learning process, reducing the need for additional labels. However, these reports tend to be complex and sometimes even consist of redundant descriptions that make the representation learning too challenging to capture the key semantic information. This paper develops a novel iterative vision-language representation learning framework by proposing a key semantic knowledge-emphasized report refinement method. Particularly, raw radiology reports are refined to highlight the key information according to a constructed clinical dictionary and two model-optimized knowledge-enhancement metrics. The iterative framework is designed to progressively learn, starting from gaining a general understanding of the patient's condition based on raw reports and gradually refines and extracts critical information essential to the fine-grained analysis tasks. The effectiveness of the proposed framework is validated on various downstream medical image analysis tasks, including disease classification, region-of-interest segmentation, and phrase grounding. Our framework surpasses seven state-of-the-art methods in both fine-tuning and zero-shot settings, demonstrating its encouraging potential for different clinical applications.

Synthesis of C-Alkyl Glycosides from Alkyl Bromides and Glycosyl Carboxylic Acids via Ni/Photoredox Dual Catalysis.

C-Alkyl glycosides, an important class of C-glycosides, are widely found in various drugs and natural products. The synthesis of C-alkyl glycosides has attracted considerable attention. Herein, we developed a Ni/photoredox catalyzed decarboxylative C(sp3)-C(sp3) coupling reaction of stable glycosylcarboxylic acids with simple aliphatic bromides to generate C-alkyl glycosides. The method successfully linked several functional molecular fragments (natural products or drugs) to a sugar moiety, showing the extensive application prospects of this transformation. Controlled experiments and DFT calculations demonstrated that the reaction pathway contains a free radical process, and a possible mechanism is proposed.

COPD-Like Phenotypes in TBC-Treated Mice Can be Effectively Alleviated via Estrogen Supplement.

Tris(2,3-dibromopropyl) isocyanurate (TBC), recognized as an endocrine disruptor, can cause inflammatory injury to the lung tissue of mice. To investigate the specific respiratory effects of TBC, male C57BL/6J mice were administered a daily dose of 20 mg/kg of TBC over 14 days. Postexposure, these mice developed chronic obstructive pulmonary disease (COPD)-like symptoms characterized by inflammatory lung damage and functional impairment. In light of the antiestrogenic properties of TBC, we administrated estradiol (E2) to investigate its potential protective role against TBC-induced damage and found that the coexposure of E2 notably mitigated the COPD-like phenotypes. Immunohistochemical analysis revealed that TBC exposure reduced estrogen receptor alpha (ERα) expression and increased nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) expression, while E2 treatment rebalanced the expression levels of ERα and NF-κB to their normative states. Our findings indicate that TBC, as an antiestrogenic agent, may contribute to the pathogenesis of COPD through an ERα-mediated inflammatory pathway, but that E2 treatment could reverse the impairment, providing a potentially promising remedial treatment. Given the lung status as a primary target of air pollution, the presence of antiestrogenic compounds like TBC in atmospheric particulates presents a significant concern, with the potential to exacerbate respiratory conditions such as COPD and pneumonia.

[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.

Inverted Sandwich-Type e-LCR Aided by Lambda Exonuclease-RecJf Combination Enables Ultrasensitive Detection of Low-Frequency EGFR-L858R Mutation in NSCLC.

Highly sensitive detection of low-frequency EGFR-L858R mutation is particularly important in guiding targeted therapy of nonsmall-cell lung carcinoma (NSCLC). To this end, a ligase chain reaction (LCR)-based electrochemical biosensor (e-LCR) with an inverted sandwich-type architecture was provided by combining a cooperation of lambda exonuclease-RecJf exonuclease (λ-RecJf exo). In this work, by designing a knife-like DNA substrate (an overhang ssDNA part referred to the "knife arm") and introducing the λ-RecJf exo, the unreacted DNA probes in the LCR were specially degraded while only the ligated products were preserved, after which the ligated knife-like DNA products were hybridized with capture probes on the gold electrode surface through the "knife arms", forming the inverted sandwich-type DNA structure and bringing the methylene blue-label close to the electrode surface to engender the electrical signal. Finally, the sensitivity of the e-LCR could be improved by 3 orders of magnitude with the help of the λ-RecJf exo, and due to the mutation recognizing in the ligation site of the employed ligase, this method could detect EGFR-L858R mutation down to 0.01%, along with a linear range of 1 fM-10 pM and a limit detection of 0.8 fM. Further, the developed method could distinguish between L858R positive and negative mutations in cultured cell samples, tumor tissue samples, and plasma samples, whose accuracy was verified by the droplet digital PCR, holding a huge potential in liquid biopsy for precisely guiding individualized-treatment of NSCLC patients with advantages of high sensitivity, low cost, and adaptability to point-of-care testing.