Coastal phytoplankton blooms expand and intensify in the 21st century.

Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems1,2-yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly (P < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.

MOTIVATION: Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins. RESULTS: A novel sequence-based method (SAAMBE-MEM) for predicting binding free energy changes (ΔΔG) in membrane protein-protein complexes due to mutations has been developed. This method utilized the MPAD database, which contains binding affinities for wild-type and mutant membrane protein complexes. A machine learning model was developed to predict ΔΔG by leveraging features such as amino acid indices and position-specific scoring matrices (PSSM). Through extensive dataset curation and feature extraction, SAAMBE-MEM was trained and validated using the XGBoost regression algorithm. The optimal feature set, including PSSM-related features, achieved a Pearson correlation coefficient of 0.64, outperforming existing methods trained on the SKEMPI database. Furthermore, it was demonstrated that SAAMBE-MEM performs much better when utilizing evolution-based features in contrast to physicochemical features. AVAILABILITY AND IMPLEMENTATION: The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. The cleaned MPAD database is available at the website.

The global succinylation of SARS-CoV-2-infected host cells reveals drug targets.

SARS-CoV-2, the causative agent of the COVID-19 pandemic, undergoes continuous evolution, highlighting an urgent need for development of novel antiviral therapies. Here we show a quantitative mass spectrometry-based succinylproteomics analysis of SARS-CoV-2 infection in Caco-2 cells, revealing dramatic reshape of succinylation on host and viral proteins. SARS-CoV-2 infection promotes succinylation of several key enzymes in the TCA, leading to inhibition of cellular metabolic pathways. We demonstrated that host protein succinylation is regulated by viral nonstructural protein (NSP14) through interaction with sirtuin 5 (SIRT5); overexpressed SIRT5 can effectively inhibit virus replication. We found succinylation inhibitors possess significant antiviral effects. We also found that SARS-CoV-2 nucleocapsid and membrane proteins underwent succinylation modification, which was conserved in SARS-CoV-2 and its variants. Collectively, our results uncover a regulatory mechanism of host protein posttranslational modification and cellular pathways mediated by SARS-CoV-2, which may become antiviral drug targets against COVID-19.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule.

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

A Bioinspired Copper-Pair Catalyst in Metal-Organic Frameworks for Molecular Dioxygen Activation and Aerobic Oxidative C-N Coupling.

Open Cu sites were loaded to the UiO-67 metal-organic framework (MOF) skeleton by introduction of flexible Cu-binding pyridylmethylamine (pyma) side chains to the biphenyldicarboxylate linkers. Distance between Cu centers in the MOF pores was tuned by controlling the density of metal-binding side chains. "Interacted" Cu-pair or "isolated" monomeric Cu sites were achieved with high and low (pyma)Cu side chain loading, respectively. Spectroscopic and theoretical studies indicate that "interacted" Cu pairs can effectively bind and activate molecular dioxygen to form Cu2O2 clusters, which showed high catalytic activity for aerobic oxidative C-N coupling. On the contrary, MOF catalyst bearing isolated monomeric Cu sites only showed modest catalytic activity. Enhancement in catalytic performance for the Cu-pair catalyst is attributed to the remote synergistic effect of the paired Cu site, which binds molecular dioxygen and cleaves the O═O bond in a collaborative manner. This work demonstrates that noncovalently interacted metal-pair sites can effectively activate inert small molecules and promote heterogeneous catalytic processes.

Biological evaluation of 9-thioansamitocin P3.

Highly cytotoxic maytansine derivatives are widely used in targeted tumor delivery. Structure-activity studies published earlier suggested the C9 carbinol to be a key element necessary to retain the potency. However, in 1984 a patent was published by Takeda in which the synthesis of 9-thioansamitocyn (AP3SH) was described and its activity in xenograft models was shown. In this article we summarize the results of an extended study of the anti-tumor properties of AP3SH. Like other maytansinoids, it induces apoptosis and arrests the cell cycle in the G2/M phase. It is metabolized in liver microsomes predominately by C3A4 isoform and doesn't inhibit any CYP isoforms except CYP3A4 (midazolam, IC50 7.84 µM). No hERG inhibition, CYP induction or mutagenicity in Ames tests were observed. AP3SH demonstrates high antiproliferative activity against 25 tumor cell lines and tumor growth inhibition in U937 xenograft model. Application of AP3SH as a cytotoxic payload in drug delivery system was demonstrated by us earlier.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study.

BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Interaction of species A rotavirus VP4 with the cellular proteins vimentin and actin related protein 2 discovered by a proximity interactome assay.

IMPORTANCE: Rotavirus (RV) is an important zoonosis virus, which can cause severe diarrhea and extra-intestinal infection. To date, some proteins or carbohydrates have been shown to participate in the attachment or internalization of RV, including HGBAs, Hsc70, and integrins. This study attempted to indicate whether there were other proteins that would participate in the entry of RV; thus, the RV VP4-interacting proteins were identified by proximity labeling. After analysis and verification, it was found that VIM and ACTR2 could significantly promote the proliferation of RV in intestinal cells. Through further viral binding assays after knockdown, antibody blocking, and recombinant protein overexpression, it was revealed that both VIM and ACTR2 could promote RV replication.

IFITM3 inhibits severe fever with thrombocytopenia syndrome virus entry and interacts with viral Gc protein.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne hemorrhagic fever disease with high fatality rate of 10%-20%. Vaccines or specific therapeutic measures remain lacking. Human interferon inducible transmembrane protein 3 (hIFITM3) is a broad-spectrum antiviral factor targeting viral entry. However, the antiviral activity of hIFITM3 against SFTS virus (SFTSV) and the functional mechanism of IFITM3 remains unclear. Here we demonstrate that endogenous IFITM3 provides protection against SFTSV infection and participates in the anti-SFTSV effect of type Ⅰ and Ⅲ interferons (IFNs). IFITM3 overexpression exhibits anti-SFTSV function by blocking Gn/Gc-mediated viral entry and fusion. Further studies showed that IFITM3 binds SFTSV Gc directly and its intramembrane domain (IMD) is responsible for this interaction and restriction of SFTSV entry. Mutation of two neighboring cysteines on IMD weakens IFITM3-Gc interaction and attenuates the antiviral activity of IFITM3, suggesting that IFITM3-Gc interaction may partly mediate the inhibition of SFTSV entry. Overall, our data demonstrate for the first time that hIFITM3 plays a critical role in the IFNs-mediated anti-SFTSV response, and uncover a novel mechanism of IFITM3 restriction of SFTSV infection, highlighting the potential of clinical intervention on SFTS disease.

CDK4/6 inhibitors plus endocrine therapy vs. placebo plus endocrine therapy for HR+/HER2- advanced breast cancer: a phase III RCTs based meta-analysis.

BACKGROUND: Does incorporating Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors into endocrine therapy (ET) effectively enhance survival outcomes, notably overall survival (OS), among individuals with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer? This remains a clinical controversy. We compared the antitumor efficacy and adverse effects (AEs) between CDK4/6 inhibitors + ET (CET) and placebo + ET (PET) by conducting a phase III randomized controlled trials (RCTs) based meta-analysis. METHODS: Seven databases were searched to identify eligible studies, comprising Phase III RCTs comparing CET to PET. The primary endpoints were OS and progression-free survival (PFS), with secondary endpoints including responses and adverse events (AEs). RESULTS: Seven RCTs (DAWNA-2, MONALEESA-2, MONALEESA-3, MONALEESA-7, MONARCH-3, PALOMA-2, and PALOMA-4) were included. The CET group exhibited significantly improved OS (HR: 0.81 [0.74, 0.88]), PFS (HR: 0.57 [0.52, 0.63]), objective response rate (RR: 1.31 [1.20, 1.43]), and clinical benefit rate (RR: 1.11 [1.07, 1.15]). These benefits were consistent across almost all subgroups. Additionally, the CET group showed better overall survival rates (OSR) from 24 to 60 months (OSR 24-60 m) and progression-free survival rates (PFSR) from 6 to 60 months (PFSR 6-60 m). However, more total AEs, grade 3-5 AEs, and serious AEs were found in CET group. The top 5 grade 3-5 AEs in the CET group were neutropenia (59.39%), leukopenia (24.11%), decreased white blood cell count (12.99%), hypertension (7.03%), and increased alanine aminotransferase (5.91%). CONCLUSIONS: The superiority of CET over PET in HR+/HER2- advanced breast cancer is evident, showing improved survival and responses. Nonetheless, the higher incidence of AEs, specifically hematologic AEs, requires cautious attention.

Indirect Impact of Eutrophication on Occurrence, Air-Water Exchange, and Vertical Sinking Fluxes of Antibiotics in a Subtropical River.

A significant challenge that warrants attention is the influence of eutrophication on the biogeochemical cycle of emerging contaminants (ECs) in aquatic environments. Antibiotics pollution in the eutrophic Pearl River in South China was examined to offer new insights into the effects of eutrophication on the occurrence, air-water exchange fluxes (Fair-water), and vertical sinking fluxes (Fsinking) of antibiotics. Antibiotics transferred to the atmosphere primarily through aerosolization controlled by phytoplankton biomass and significant spatiotemporal variations were observed in the Fair-water of individual antibiotics throughout all sites and seasons. The Fsinking of ∑AB14 (defined as a summary of 14 antibiotics) was 750.46 ± 283.19, 242.71 ± 122.87, and 346.74 ± 249.52 ng of m-2 d-1 in spring, summer, and winter seasons. Eutrophication indirectly led to an elevated pH, which reduced seasonal Fair-water of antibiotics, sediment aromaticity, and phytoplankton hydrophobicity, thereby decreasing antibiotic accumulation in sediments and phytoplankton. Negative correlations were further found between Fsinking and the water column daily loss of antibiotics with phytoplankton biomass. The novelty of this study is to provide new complementary knowledge for the regulation mechanisms of antibiotics by phytoplankton biological pump, offering novel perspectives and approaches to understanding the coupling between eutrophication and migration and fate of antibiotics in a subtropical eutrophic river.

Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions.

BACKGROUND: This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions. METHODS: 178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency. RESULTS: Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values. CONCLUSION: SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.

The novel circFKBP8/miR-432-5p/E2F7 cascade functions as a regulatory network in breast cancer.

BACKGROUND: Circular RNAs (circRNAs) are capable of affecting breast cancer (BC) development. However, the role and underneath mechanism of circFKBP8 (also known as hsa_circ_0000915) in BC remain largely unknown. METHODS: Expression analyses were performed using quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC) assays. Effects on cell functional phenotypes were determined by assessing cell proliferation, migratory capacity, invasion, and stemness in vitro. The relationship between microRNA (miR)-432-5p and circFKBP8 or E2F transcription factor 7 (E2F7) was examined by RNA pull-down, dual-luciferase reporter, and RNA immunoprecipitation (RIP) assays. Xenograft assays were used to identify the function of circFKBP8 in vivo. RESULTS: CircFKBP8 was presented at high levels in BC tissues and cells. High circFKBP8 expression was associated with worse overall survival in BC patients. CircFKBP8 suppression inhibited BC cell proliferation, migratory capacity, invasion and stemness in vitro. CircFKBP8 suppression blocked xenograft tumor growth in vivo. Mechanistically, circFKBP8 functioned as a miR-432-5p sponge to modulate E2F7 expression. CircFKBP8 modulated BC cell malignant behaviors by miR-432-5p, and miR-432-5p affected these cell phenotypes through E2F7. CONCLUSION: Our observations prove that circFKBP8 promotes BC malignant phenotypes through the miR-432-5p/E2F7 cascade, offering a promising therapeutic and prognostic target for BC.

Interaction of Nipah Virus F and G with the Cellular Protein Cortactin Discovered by a Proximity Interactome Assay.

Nipah virus (NiV) is a highly lethal zoonotic virus with a potential large-scale outbreak, which poses a great threat to world health and security. In order to explore more potential factors associated with NiV, a proximity labeling method was applied to investigate the F, G, and host protein interactions systematically. We screened 1996 and 1524 high-confidence host proteins that interacted with the NiV fusion (F) glycoprotein and attachment (G) glycoprotein in HEK293T cells by proximity labeling technology, and 863 of them interacted with both F and G. The results of GO and KEGG enrichment analysis showed that most of these host proteins were involved in cellular processes, molecular binding, endocytosis, tight junction, and other functions. Cytoscape software (v3.9.1) was used for visual analysis, and the results showed that Cortactin (CTTN), Serpine mRNA binding protein 1 (SERBP1), and stathmin 1 (STMN1) were the top 20 proteins and interacted with F and G, and were selected for further validation. We observed colocalization of F-CTTN, F-SERBP1, F-STMN1, G-CTTN, G-SERBP1, and G-STMN1 using confocal fluorescence microscopy, and the results showed that CTTN, SERBP1, and STMN1 overlapped with NiV F and NiV G in HEK293T cells. Further studies found that CTTN can significantly inhibit the infection of the Nipah pseudovirus (NiVpv) into host cells, while SERBP1 and STMN1 had no significant effect on pseudovirus infection. In addition, CTTN can also inhibit the infection of the Hendra pseudovirus (HeVpv) in 293T cells. In summary, this study revealed that the potential host proteins interacted with NiV F and G and demonstrated that CTTN could inhibit NiVpv and HeVpv infection, providing new evidence and targets for the study of drugs against these diseases.

The "LLQY" Motif on SARS-CoV-2 Spike Protein Affects S Incorporation into Virus Particles.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) glycoprotein mediates viral entry and membrane fusion. Its cleavage at S1/S2 and S2' sites during the biosynthesis in virus producer cells and viral entry are critical for viral infection and transmission. In contrast, the biological significance of the junction region between both cleavage sites for S protein synthesis and function is less understood. By analyzing the conservation and structure of S protein, we found that intrachain contacts formed by the conserved tyrosine (Y) residue 756 (Y756) with three α-helices contribute to the spike's conformational stability. When Y756 is mutated to an amino acid residue that can provide hydrogen bonds, S protein could be expressed as a cleaved form, but not vice versa. Also, the L753 mutation linked to the Y756 hydrogen bond prevents the S protein from being cleaved. Y756 and L753 mutations alter S protein subcellular localization. Importantly, Y756 and L753 mutations are demonstrated to reduce the infectivity of the SARS-CoV-2 pseudoviruses by interfering with the incorporation of S protein into pseudovirus particles and causing the pseudoviruses to lose their sensitivity to neutralizing antibodies. Furthermore, both mutations affect the assembly and production of SARS-CoV-2 virus-like particles in cell culture. Together, our findings reveal for the first time a critical role for the conserved L753-LQ-Y756 motif between S1/S2 and S2' cleavage sites in S protein synthesis and processing as well as virus assembly and infection. IMPORTANCE The continuous emergence of SARS-CoV-2 variants such as the delta or lambda lineage caused the continuation of the COVID-19 epidemic and challenged the effectiveness of the existing vaccines. Logically, the spike (S) protein mutation has attracted much concern. However, the key amino acids in S protein for its structure and function are still not very clear. In this study, we discovered for the first time that the conserved residues Y756 and L753 at the junction between the S1/S2 and S2' sites are very important, like the S2' cleavage site R815, for the synthesis and processing of S protein such as protease cleavage, and that the mutations severely interfered with the incorporation of S protein into pseudotyped virus particles and SARS-CoV-2 virus-like particles. Consequently, we delineate the novel potential target for the design of broad-spectrum antiviral drugs in the future, especially in the emergence of SARS-CoV-2 variants.

Three-Pronged Flower-like Nanoplatforms for the Photothermal/Photodynamic/Quaternary Ammonium Salt Synergistic Antibacterial Method and Bioimaging.

Bacterial infections caused by pathogenic bacteria are extremely threatening to human health. Currently, the treatment of bacterial infections relies heavily on antibiotics, leading to a high incidence of antibiotic abuse. Bacterial resistance appeared along with the misuse of antibiotics that produced growing harm to human beings. Therefore, a cutting-edge strategy for treating bacterial infections is indeed needed. Here we prepared QCuRCDs@BMoS2 nanocomposites (QBs) for an efficient bacterial trapping and triple quaternary ammonium salt/photothermal/photodynamic bactericidal method. Copper-doped carbon quantum dots were first prepared by using a solvothermal method, modified with quaternary ammonium salts, and then combined with grafted MoS2 nanoflowers. The long alkyl chains of QBs and the sharp surface of MoS2 facilitate the destruction of bacterial structures, while the electrostatic adsorption binds closely to bacteria, shortening the bactericidal distance of the reactive oxygen species (ROS). Moreover, the excellent photothermal performance under 808 nm irradiation in the near-infrared (NIR) region and deep penetrating heat can accelerate oxidative stress and achieve a multisynergistic bactericidal purpose. Consequently, QBs with ideal antibacterial properties and inherent brightness hold great promise in the biomedical field.

Association of STAT3 gene polymorphisms with systemic lupus erythematosus in a Chinese Han population.

OBJECTIVE: Evidence supports the important role of STAT3 in SLE; however, association between STAT3 gene polymorphisms and SLE risk needs discussion. METHODS: Three hundred SLE patients and 380 healthy controls from Chinese Han population were included. DNA is extracted from peripheral blood mononuclear cells and the clinical characteristics of patients are collected. STAT3 gene polymorphisms (rs6503695, rs744166, rs9912773, and rs12601982) were genotyped by the Kompetitive Allele-Specific PCR (KASP) method. SPSS 26.0 was utilized to analyze the genetic susceptibility of SLE and STAT3 gene polymorphisms. RESULTS: Frequencies of genotypes CT, TT, and TT+CT were significantly lower in SLE patients compared with those in healthy controls with respect to rs6503695 (p = .007, p < .001, p = .001). Frequencies of rs744166 genotypes AG, AA, and AA+AG were decreased in SLE patients as compared to those in healthy controls (p = .034, p = .006, p = .009). The recessive models (CC vs GG+GC) for rs9912773 and (AA vs GG+GA) for rs12601982 were significantly related to SLE patients (p = .014, p = .035). Moreover, allele C of rs6503695 was related to optic nerve damage in SLE patients (p = .036). rs744166 allele G was correlated with positive rash and albuminuria in SLE patients (p = .006, p = .014). For rs9912773, SLE patients carrying genotype GG had higher serum C3 and C4 levels compared to genotype GC+CC (p = .029, p = .028). The rs12601982 allele G was strongly associated with positive hypocomplementemia in SLE patients (p = .034). SLE patients carrying genotypes GG, GC, and CC had different SLEDAI score for rs12601982 (GG vs GC vs CC, p = .003). CONCLUSION: STAT3 gene polymorphisms associated with SLE susceptibility.

Systemic lupus erythematosus with high disease activity identification based on machine learning.

OBJECTIVE: Clinical evaluation of systemic lupus erythematosus (SLE) disease activity is limited and inconsistent, and high disease activity significantly, seriously impacts on SLE patients. This study aims to generate a machine learning model to identify SLE patients with high disease activity. METHOD: A total of 1014 SLE patients with low disease activity and 453 SLE patients with high disease activity were included. A total of 94 clinical, laboratory data and 17 meteorological indicators were collected. After data preprocessing, we use mutual information and multisurf to evaluate and select the importance of features. The selected features are used for machine learning modeling. Performance of the model is evaluated and verified by a series of binary classification indicators. RESULTS: We screened out hematuria, proteinuria, pyuria, low complement, precipitation, sunlight and other features for model construction by integrated feature selection. After hyperparameter optimization, the LGB has the best performance (ROC: AUC = 0.930; PRC: AUC = 0.911, APS = 0.913; balance accuracy: 0.856), and the worst is the naive bayes (ROC: AUC = 0.849; PRC: AUC = 0.719, APS = 0.714; balance accuracy: 0.705). Finally, the selection of features has good consistency in the composite feature importance bar plot. CONCLUSION: We identify SLE patients with high disease activity by a simple machine learning pipeline, especially the LGB model based on the characteristics of proteinuria, hematuria, pyuria and other feathers screened out by collective feature selection.

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis.

OBJECTIVE: Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. METHODS: A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. RESULTS: Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. CONCLUSION: We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.