G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data.

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments. OBJECTIVES: Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients. METHODS: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer. RESULTS: Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu. CONCLUSION: IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation.

Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.

Alteration of fractional anisotropy in preterm-born individuals: a systematic review and meta-analysis.

BACKGROUD: Neurological disorders are common in preterm (PT) born individuals. Diffusion tensor imaging (DTI) studies using tract-based spatial statistics (TBSS) effectively detect microstructural white matter (WM) abnormalities in the brain. We conducted this systematic review to integrate the findings of TBSS studies to determine the most consistent WM alterations in PT born individuals. METHODS: PubMed, Embase, Web of Science and Science Direct were searched. DTI studies using TBSS in PT born individuals were screened up to October 2022. The systematic review included studies reporting alterations in FA values for the entire brain in a stereotactic space, with three coordinates (x, y, z), according to the seed-based d mapping method. RESULTS: The search strategy identified seventeen studies that fulfilled our inclusion criteria, with a total of 911 PT-born individuals and 563 matched controls were analysed. Of the seventeen studies, eight were dedicated to 650 adults, five to 411 children and four to 413 infants. Ten studies recruited 812 individuals born very prematurely (GA <29 weeks), six studies recruited 386 moderately premature individuals (GA = 29-32 weeks) and one study recruited 276 individuals born late prematurely (GA >32 weeks). This meta-analysis of six studies including 388 individuals highlighted four brain regions in which fractional anisotropy (FA) was lower in PT group than in people born at term. The quantitative meta-analysis found that the most robust WM alterations were located in the corpus callosum (CC), the bilateral thalamus and the left superior longitudinal fasciculus (SLF) II. Significant changes in FA reflect WM abnormalities in PT born individuals from infant to young adulthood. CONCLUSIONS: Significant changes in FA reflect WM abnormalities in individuals born PT from infancy to young adulthood. The abnormal development of the CC, bilateral thalamus and left SLF may play a vital role in the neurodevelopment of PT individuals.

Neurological disorders are prevalent in preterm (PT) born individuals. The use of tract-based spatial statistics (TBSS) in diffusion tensor imaging (DTI) studies has proven effective in detecting microstructural abnormalities of the white matter (WM) of the brain. In order to determine the most consistent alterations in WM among those born prematurely, we have screened DTI studies using TBSS in this PT born population up until October 2022. The meta-analysis identified four brain regions where fractional anisotropy (FA) was lower in the PT group than in those born at term. The quantitative meta-analysis identified the corpus callosum, the bilateral thalamus and the left superior longitudinal fasciculus II. As the most robust WM alterations. Various studies have demonstrated the links between PT birth, intelligence quotient, gestational age and subject age.

CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal.

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction.

Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provides a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a parameter-efficient fine-tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pretrained PLMs. We integrated low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum Matthews correlation coefficient (MCC) gain of 87.3% for SPs with small training samples and an overall MCC gain of 6.1%. Furthermore, we also employed two other PEFT methods, prompt tuning and adapter tuning, in ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using adapter tuning can also improve the state-of-the-art results by up to 28.1% MCC gain for SPs with small training samples and an overall MCC gain of 3.8%. LoRA requires fewer computing resources and less memory than the adapter during the training stage, making it possible to adapt larger and more powerful protein models for SP prediction.

PAI-1 Regulates the Cytoskeleton and Intrinsic Stiffness of Vascular Smooth Muscle Cells.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

TIF1-γ Positive Dermatomyositis with Spontaneous Muscular Hematoma in the Context of Ovarian Cancer: A Novel Survival Case Report.

Background: Dermatomyositis (DM) represents a group of inflammatory myopathies, with TIF1-γ positive DM strongly associated with malignancies. Spontaneous muscular hematoma in DM patients is exceedingly rare and often prognosticates a severe clinical outcome, especially in the context of concurrent malignancy. Case Presentation: We describe a novel survival case of a patient with TIF1-γ positive DM and an underlying ovarian tumor who developed a spontaneous muscular hematoma. Despite the traditionally poor prognosis of these conditions, the patient survived through a comprehensive treatment regimen. This included targeted chemotherapy for ovarian cancer (Carboplatin and Paclitaxel), alongside corticosteroids, immunoglobulins, and immunosuppressants for DM, as well as component blood transfusions, coagulation correction therapy to control hematoma, and integrated management: nutritional support, lung function exercise, volume management. Results: The integrated treatment strategy stabilized the patient's condition and resolved the hematoma, a significant achievement given the usual high mortality rate of such complications. Conclusion: This case underscores the importance of a multidisciplinary approach in the early diagnosis and treatment of TIF1-γ positive DM with complex comorbidities, including spontaneous muscular hematoma and ovarian cancer. It highlights the potential for favorable outcomes with aggressive and coordinated treatment strategies.

The effect of COVID-19 vaccine to the Omicron variant in children and adolescents: a systematic review and meta-analysis.

Background: Omicron (B.1.1.529), a variant of SARS-CoV-2, has emerged as a dominant strain in COVID-19 pandemic. This development has raised concerns about the effectiveness of vaccination to Omicron, particularly in the context of children and adolescents. Our study evaluated the efficacy of different COVID-19 vaccination regimens in children and adolescents during the Omicron epidemic phase. Methods: We searched PubMed, Cochrane, Web of Science, and Embase electronic databases for studies published through March 2023 on the association between COVID-19 vaccination and vaccine effectiveness (VE) against SARS-CoV-2 infection in children and adolescents at the Omicron variant period. The effectiveness outcomes included mild COVID-19 and severe COVID-19. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was prospectively registered in PROSPERO (CRD42023390481). Results: A total of 33 studies involving 16,532,536 children were included in the analysis. First, in children and adolescents aged 0-19 years, the overall VE of the COVID-19 vaccine is 45% (95% confidence interval [CI]: 40 to 50%). Subgroup analysis of VE during Omicron epidemic phase for different dosage regimens demonstrated that the VE was 50% (95% CI: 44 to 55%) for the 2-dose vaccination and 61% (95% CI: 45 to 73%) for the booster vaccination. Upon further analysis of different effectiveness outcomes during the 2-dose vaccination showed that the VE was 41% (95% CI: 35 to 47%) against mild COVID-19 and 71% (95% CI: 60 to 79%) against severe COVID-19. In addition, VE exhibited a gradual decrease over time, with the significant decline in the efficacy of Omicron for infection before and after 90 days following the 2-dose vaccination, registering 54% (95% CI: 48 to 59%) and 34% (95% CI: 21 to 56%), respectively. Conclusion: During the Omicron variant epidemic, the vaccine provided protection against SARS-CoV-2 infection in children and adolescents aged 0-19 years. Two doses of vaccination can provide effective protection severe COVID-19, with booster vaccination additionally enhancing VE.

Unveiling the multicomponent phase separation through molecular dynamics simulation and graph theory.

Biomolecular condensates formed by multicomponent phase separation play crucial roles in diverse cellular processes. Accurate assessment of individual-molecule contributions to condensate formation and precise characterization of their spatial organization within condensates are crucial for understanding the underlying mechanism of phase separation. Using molecular dynamics simulations and graph theoretical analysis, we demonstrated quantitatively the significant roles of cation-π and π-π interactions mediated by aromatic residues and arginine in the formation of condensates in polypeptide systems. Our findings reveal temperature and chain length-dependent alterations in condensate network parameters, such as the number of condensate network layers, and changes in aggregation and connectivity. Notably, we observe a transition between assortativity and disassortativity in the condensate network. Moreover, polypeptides W, Y, F, and R consistently promote condensate formation, while the contributions of other charged and two polar polypeptides (Q and N) to condensate formation depend on temperature and chain length. Furthermore, polyadenosine and polyguanosine can establish stable connections with aromatic and R polypeptides, resulting in the reduced involvement of K, E, D, Q, and N in phase separation. Overall, this study provides a distinctive, precise, and quantitative approach to characterize the multicomponent phase separation.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years.

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

The value of fibrinogen combined with D-dimer and neonatal weight in predicting postpartum hemorrhage in vaginal delivery.

OBJECTIVES: The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. METHODS: A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mild PPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. RESULTS: Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined with D-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313-0.0587). CONCLUSIONS: Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH.

S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure.

Large protein language models (PLMs) present excellent potential to reshape protein research by encoding the amino acid sequences into mathematical and biological meaningful embeddings. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily depending on 3D structures. To address this issue, we introduce S-PLM, a 3D structure-aware PLM utilizing multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinate space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, we provide a library of lightweight tuning tools to adapt S-PLM for diverse protein property prediction tasks. Our results demonstrate S-PLM's superior performance over sequence-only PLMs, achieving competitiveness in protein function prediction compared to state-of-the-art methods employing both sequence and structure inputs.

Topology- and size-dependent binding of DNA nanostructures to the DNase I.

The susceptibility of DNA nanomaterials to enzymatic degradation in biological environments is a significant obstacle limiting their broad applications in biomedicine. While DNA nanostructures exhibit some resistance to nuclease degradation, the underlying mechanism of this resistance remains elusive. In this study, the interaction of tetrahedral DNA nanostructures (TDNs) and double-stranded DNA (dsDNA) with DNase I is investigated using all-atom molecular dynamics simulations. Our results indicate that DNase I can effectively bind to all dsDNA molecules, and certain key residues strongly interact with the nucleic bases of DNA. However, the binding of DNase I to TDNs exhibits a non-monotonic behavior based on size; TDN15 and TDN26 interact weakly with DNase I (∼ - 75 kcal/mol), whereas TDN21 forms a strong binding with DNase I (∼ - 110 kcal/mol). Furthermore, the topological properties of the DNA nanostructures are analyzed, and an under-twisting (∼32°) of the DNA helix is observed in TDN15 and TDN26. Importantly, this under-twisting results in an increased width of the minor groove in TDN15 and TDN26, which primarily explains their reduced binding affinity to DNase I comparing to the dsDNA. Overall, this study demonstrated a novel mechanism for local structural control of DNA at the nanoscale by adjusting the twisting induced by length.

Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations.

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.

Structural transition of parenthood among Chinese nulliparous couples with planned pregnancies, 2013-2019.

BACKGROUND: The postponement of parenthood is a global public health issue that has received attention of many public health experts. However, few studies have investigated the postponement in marriage age, marriage and conception interval, and pregnancy age in terms of demographic and regional heterogenicities. METHODS: This is a cross-sectional, registry-based study, and a total of 13 894 601 nulliparous couples who participated in the National Free Pre-Pregnancy Check-ups Project and became pregnant during 2013-2019 were included. We calculated annual percentage change and forest plots for marriage age, marriage and conception interval, and pregnancy age. RESULTS: Late marriage (marriage age ≥ 35 years), long marriage and conception interval (marriage and conception interval ≥ 2 years), and advanced pregnancy (pregnancy age ≥ 35 years) increased from 1.20%, 22.01%, and 1.88% in 2013 to 1.69%, 32.75%, and 2.79% in 2019, respectively. The corresponding annual percentage changes were 6.55%, 8.44%, and 8.17%. Participants without higher education had a higher annual percentage change, but comparable prevalence for long marriage and conception interval with participants with higher education. Participants residing in second- or new first-tier cities, and the northeast of China who had a higher prevalence of parenthood postponement also had higher corresponding annual percentage changes. CONCLUSIONS: Structural postponement of parenthood with demographic and regional heterogenicities was observed among Chinese nulliparous couples with planned pregnancies during 2013-2019. Inclusive and comprehensive parenting support should be developed and implemented in mainland China to minimize the negative health effects arising from the postponement, especially for couples without higher education and living in new first/second-tier cities or the northeast China.

Effect of Terahertz Waves on the Structure of the Aß42 Monomer, Dimer, and Protofibril: Insights from Molecular Dynamics Simulations.

Amyloid-ß (Aß) and its assemblies play important roles in the pathogenesis of Alzheimer's disease (AD). Recent studies conducted by experimental and computational researchers have extensively explored the structure, assembly, and influence of biomolecules and cell membranes on Aß. However, the impact of terahertz waves on the structures of Aß monomers and aggregates remains largely unexplored. In this study, we systematically investigate the molecular mechanisms by which terahertz waves affect the structure of the Aß42 monomer, dimer, and tetramer through all-atom molecular dynamics (MD) simulations. Our findings indicate that terahertz waves at a specific frequency (42.55 THz) can enhance intramolecular and intermolecular interactions in the Aß42 monomer and dimer, respectively, by resonating with the symmetric stretching mode of the -COO- groups and the symmetric bending/stretching mode of -CH3 groups. Consequently, the ß-structure content of the Aß42 monomer is greatly increased, and the binding energy between the monomers in the Aß42 dimer is significantly enhanced. Additionally, our observations suggest that terahertz waves can mildly stabilize the structure of tetrameric protofibrils by enhancing the interactions among peripheral peptides. Furthermore, we also investigated the effect of the frequency of terahertz waves on the structure of Aß42. The present study contributes to a better understanding of the impact of external fields on the biobehavior of Aß42 peptides and may shed some light on the potential risks associated with electromagnetic field radiation.

Investigating the oxidation mechanism of facet-dependent pyrite: implications for the environment and sulfur evolution.

The oxidation of pyrite (FeS2) not only adversely affects the environment, but also plays a critical role in the geochemical evolution of Fe and S elements. However, the oxidation rate of FeS2 is often controlled by its exposed crystal facets. Herein, the oxidation behaviors and mechanisms of naturally existing FeS2(100) and FeS2(210) crystals are investigated. The adsorption models of O2 on FeS2(100) and FeS2(210) facets are established, additionally, their corresponding surface energies, O2 adsorption sites and energies are also obtained using Density Functional Theory (DFT) calculations. These results suggest that the FeS2(210) facet more readily reacts with O2 because it has more unsaturated coordination of Fe atoms compared with the FeS2(100) facet. Moreover, electrochemical results such as EIS, Tafel and CV curves further prove that FeS2(210) possesses a higher oxidation rate than that of FeS2(100). The results of chemical oxidation experiments and XPS analyses show that FeS2(210) can produce more total Fe, SO42- and H+ than FeS2(100). Furthermore, various intermediate S species such as SO32-, S2O32-, S3O62-, S4O62- and S5O62- are also detected. This work can provide a basis for understanding the oxidation mechanism of facet-dependent FeS2 and the geochemical evolution of Fe and S elements.

Multiple miRNA Detection through a Suspended Microbead Array Encoded by Triple-Color Upconversion Luminescent Nanotags via Bi-Beam Splitter Hybrid-Multitrap Optical Tweezers.

In recent years, optical tweezers have become a novel tool for biodetection, and to improve the inefficiency of a single trap, the development of multitraps is required. Herein, we constructed a set of hybrid multitrap optical tweezers with the balance of stability and flexibility by the combination of two different beam splitters, a diffraction optical element (DOE) and galvano mirrors (GMs), to capture polystyrene (PS) microbeads in aqueous solutions to create an 18-trap suspended array. A sandwich hybridization strategy of DNA-miRNA-DNA was adopted to detect three kinds of target miRNAs associated with triple negative breast cancer (TNBC), in which different upconversion nanoparticles (UCNPs) with red, green, and blue emissions were applied as luminescent tags to encode the carrier PS microbeads to further indicate the levels of the targets. With encoded luminescent microbeads imaged by a three-channel microscopic system, the biodetection displayed high sensitivity with low limits of detection (LODs) of 0.27, 0.32, and 0.33 fM and exceptional linear ranges of 0.5 fM to 1 nM, 0.7 fM to 1 nM, and 1 fM to 1 nM for miR-343-3p, miR-155, and miR-199a-5p, respectively. In addition, this bead-based assay method was demonstrated to have the potential for being applied in patients' serum by satisfactory standard addition recovery experiment results.

Role of leptin in the pathophysiology of preeclampsia.

Preeclampsia (PE) is a severe pregnancy complication. The exact pathogenesis of PE remains unclear, but it is related to immune, inflammatory, circulatory, and oxidative stress factors. Leptin is a protein involved in these processes and is essential for maintaining a normal pregnancy and healthy fetal growth. Abnormal increases in leptin levels have been observed in the peripheral blood and placenta of patients with PE. Disturbances in leptin can affect the proliferation and hypertrophy of vascular smooth muscle cells, which are important for placentation. Leptin also regulates arterial tension and trophoblast function in pregnant women. In addition, consistently high levels of leptin are linked to hyperactive inflammation and oxidative stress reactions in both patients with PE and animal models. This review focuses on the role of leptin in the pathophysiology of PE and elucidates its potential mechanisms.