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Albeit microemulsion systems have emerged as efficient platforms for fabricating tunable nano/microstructures, lack of understanding on the emulsion-interfacial assembly hindered the control of fabrication. Herein, a nucleation-inhibited microemulsion interfacial assembly method is proposed, which deviates from conventional interfacial nucleation approaches, for the synthesis of polydopamine microvesicles (PDA MVs). These PDA MVs exhibit an approximate diameter of 1 µm, showcasing a pliable structure reminiscent of cellular morphology. Through modifications of antibodies on the surface of PDA MVs, their capacity as artificial antigen presentation cells is evaluated. In comparison to solid nanoparticles, PDA MVs with cell-like structures show enhanced T-cell activation, resulting in a 1.5-fold increase in CD25 expression after 1 day and a threefold surge in PD-1 positivity after 7 days. In summary, the research elucidates the influence of nucleation and interfacial assembly in microemulsion polymerization systems, providing a direct synthesis method for MVs and substantiating their effectiveness as artificial antigen-presenting cells.
BACKGROUND: The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date. OBJECTIVE: The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms. METHODS: We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above. RESULTS: Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours. CONCLUSIONS: By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.
Purpose: Early detection of hepatocellular carcinoma (HCC) through surveillance could reduce this cancer-associated mortality. We aimed to develop and validate algorithms using panel serum biomarkers to identify HCC in a real-world multi-center study in China. Patients and Methods: A total of 10,359 eligible subjects, including HCCs and benign liver diseases (BLDs), were recruited from six Chinese medical centers. The three nomograms were built using logistic regression and their sensitivities and specificities were carefully assessed in training and validation cohorts. HCC patients after surgical resection were followed to determine the prognostic values of these algorithms. Prospective surveillance performance was assessed in a cohort of chronic hepatitis B patients during 144 weeks follow-up. Results: Independent risk factors such as alpha-fetoprotein (AFP), lens cuinaris agglutinin-reactive fraction of AFP (AFP-L3), des-gamma-carboxy prothrombin (DCP), albumin (ALB), and total bilirubin (TBIL) obtained from train cohort were used to construct three nomograms (LAD, C-GALAD, and TAGALAD) using logistic regression. In the training and two validation cohorts, their AUCs were all over 0.900, and the higher AUCs appeared in TAGALAD and C-GALAD. Furthermore, the three nomograms could effectively stratify HCC into two groups with different survival and recurrence outcomes in follow-up validation. Notably, TAGALAD could predict HCC up to 48 weeks (AUC: 0.984) and 24 weeks (AUC: 0.900) before clinical diagnosis. Conclusion: The proposed nomograms generated from real-world Chinese populations are effective and easy-to use for HCC surveillance, diagnosis, as well as prognostic evaluation in various clinical scenarios based on data feasibility.
The prevalence rate of acute respiratory distress syndrome (ARDS) is estimated at approximately 10% in critically ill patients worldwide, with the mortality rate ranging from 17% to 39%. Currently, ARDS mortality is usually higher in patients with COVID-19, giving another challenge for ARDS treatment. However, the treatment efficacy for ARDS is far from satisfactory. The relationship between the gut microbiota and ARDS has been substantiated by relevant scientific studies. ARDS not only changes the distribution of gut microbiota, but also influences intestinal mucosal barrier through the alteration of gut microbiota. The modulation of gut microbiota can impact the onset and progression of ARDS by triggering dysfunctions in inflammatory response and immune cells, oxidative stress, cell apoptosis, autophagy, pyroptosis, and ferroptosis mechanisms. Meanwhile, ARDS may also influence the distribution of metabolic products of gut microbiota. In this review, we focus on the impact of ARDS on gut microbiota and how the alteration of gut microbiota further influences the immune function, cellular functions and related signaling pathways during ARDS. The roles of gut microbiota-derived metabolites in the development and occurrence of ARDS are also discussed.
Gastrointestinal Microbiome , Respiratory Distress Syndrome , Humans , Oxidative Stress , Apoptosis , Autophagy
BACKGROUND: The large soft-tissue defect after total or high sacrectomy for giant sacral tumor induces high incidence of wound complications. It remains a huge challenge to reconstruct the soft-tissue defect and achieve the preferred clinical outcome. METHODS: A total of 27 patients undergoing one-stage total or high sacrectomy for giant sacral tumors between 2016 and 2021 in a tertiary university hospital were retrospectively reviewed. Participants were divided into two groups. Thirteen patients underwent a pedicled vertical rectus abdominis myocutaneous (VRAM) flap reconstruction, whereas 14 patients underwent a conventional wound closure. Patient's clinical characteristics, surgical duration, postoperative complications, and outcomes were compared between the two groups. RESULTS: Patients in VRAM and non-VRAM groups were similar in baseline characteristics. The mean tumor size was 12.85 cm (range: 10-17 cm) in VRAM group and 11.79 cm (range: 10-14.5 cm) in non-VRAM group (P = 0.139). The most common giant sacral tumor is chordoma. Patients in VRAM group had a shorter length of drainage (9.85 vs 17.14 days), postoperative time in bed (5.54 vs 17.14 days), and total length of stay (19.46 vs 33.36 days) compared with patients in non-VRAM group. Patients in the VRAM group had less wound infection and debridement than patients in non-VRAM group (15.4% vs 57.1%, P < 0.001). CONCLUSIONS: This study demonstrates the advantages of pedicled VRAM flap reconstruction of large soft-tissue defects after high or total sacrectomy using the anterior-posterior approach. This choice of reconstruction is better than direct wound closure in terms of wound infection, length of drainage, and total length of stay.
Chordoma , Myocutaneous Flap , Plastic Surgery Procedures , Wound Infection , Humans , Rectus Abdominis/transplantation , Retrospective Studies , Postoperative Complications/etiology , Postoperative Complications/surgery , Chordoma/surgery , Wound Infection/surgery , Perineum/surgery
Background: Frontotemporal dementia (FTD) is the most common cause of early-onset dementia with 10-20% of cases caused by mutations in one of three genes: GRN, C9orf72, or MAPT. To effectively develop therapeutics for FTD, the identification and characterization of biomarkers to understand disease pathogenesis and evaluate the impact of specific therapeutic strategies on the target biology as well as the underlying disease pathology are essential. Moreover, tracking the longitudinal changes of these biomarkers throughout disease progression is crucial to discern their correlation with clinical manifestations for potential prognostic usage. Methods: We conducted a comprehensive investigation of biomarkers indicative of lysosomal biology, glial cell activation, synaptic and neuronal health in cerebrospinal fluid (CSF) and plasma from non-carrier controls, sporadic FTD (symptomatic non-carriers) and symptomatic carriers of mutations in GRN, C9orf72, or MAPT, as well as asymptomatic GRN mutation carriers. We also assessed the longitudinal changes of biomarkers in GRN mutation carriers. Furthermore, we examined biomarker levels in disease impacted brain regions including middle temporal gyrus (MTG) and superior frontal gyrus (SFG) and disease-unaffected inferior occipital gyrus (IOG) from sporadic FTD and symptomatic GRN carriers. Results: We confirmed glucosylsphingosine (GlcSph), a lysosomal biomarker regulated by progranulin, was elevated in the plasma from GRN mutation carriers, both symptomatic and asymptomatic. GlcSph and other lysosomal biomarkers such as ganglioside GM2 and globoside GB3 were increased in the disease affected SFG and MTG regions from sporadic FTD and symptomatic GRN mutation carriers, but not in the IOG, compared to the same brain regions from controls. The glial biomarkers GFAP in plasma and YKL40 in CSF were elevated in asymptomatic GRN carriers, and all symptomatic groups, except the symptomatic C9orf72 mutation group. YKL40 was also increased in SFG and MTG regions from sporadic FTD and symptomatic GRN mutation carriers. Neuronal injury and degeneration biomarkers NfL in CSF and plasma, and UCHL1 in CSF were elevated in patients with all forms of FTD. Synaptic biomarkers NPTXR, NPTX1/2, and VGF were reduced in CSF from patients with all forms of FTD, with the most pronounced reductions observed in symptomatic MAPT mutation carriers. Furthermore, we demonstrated plasma NfL was significantly positively correlated with disease severity as measured by CDR+NACC FTLD SB in genetic forms of FTD and CSF NPTXR was significantly negatively correlated with CDR+NACC FTLD SB in symptomatic GRN and MAPT mutation carriers. Conclusions: In conclusion, our comprehensive investigation replicated alterations in biofluid biomarkers indicative of lysosomal function, glial activation, synaptic and neuronal health across sporadic and genetic forms of FTD and unveiled novel insights into the dysregulation of these biomarkers within brain tissues from patients with GRN mutations. The observed correlations between biomarkers and disease severity open promising avenues for prognostic applications and for indicators of drug efficacy in clinical trials. Our data also implicated a complicated relationship between biofluid and tissue biomarker changes and future investigations should delve into the mechanistic underpinnings of these biomarkers, which will serve as a foundation for the development of targeted therapeutics for FTD.
Chronic liver diseases caused by various factors may develop into liver fibrosis (LF). Early stage of LF could be reversible. Tanshinone IIA (Tan IIA), an extract from Salvia miltiorrhiza, has been reported to be hepatoprotective. However, the potential targets and mechanism of Tan IIA in the treatment of LF are still unclear. Our study aims at the anti-LF mechanism of Tan IIA through network pharmacological analysis combined with LF-related experiments. Serum biochemical indicators and histopathological examination showed that Tan IIA could ameliorate the process of LF in the CCl4 -induced mouse model. Western blot and immunohistochemical assays showed that Tan IIA decreased the expression of Kirsten rat sarcoma viral oncogene homolog (KRAS), phosphatidylinositide 3-kinases/protein kinase B (PI3K/Akt), and nuclear factor erythroid 2-related factor/heme oxygenase-1 (Nrf2/HO-1). Compared with the model group, the Tan IIA groups increased the decreased superoxide dismutase activity and glutathione content, while decreasing the increased malondialdehyde content. These results indicate that Tan IIA may play an antioxidant role by inhibiting the expression of KRAS, PI3K/Akt, and Nrf2/HO-1 to ameliorate the progression of LF, which to some extent explains the pharmacological mechanism of Tan IIA in LF. In conclusion, our study demonstrates that Tan IIA could regulate LF via PI3K/Akt and Nrf2/HO-1 signaling pathways. It may be an effective therapeutic compound for the treatment of LF.
Abietanes , NF-E2-Related Factor 2 , Proto-Oncogene Proteins c-akt , Animals , Mice , Heme Oxygenase (Decyclizing)/metabolism , Liver Cirrhosis/chemically induced , Liver Cirrhosis/drug therapy , Mice, Inbred C57BL , NF-E2-Related Factor 2/metabolism , Phosphatidylinositol 3-Kinases/metabolism , Proto-Oncogene Proteins c-akt/metabolism , Proto-Oncogene Proteins p21(ras)/metabolism , Signal Transduction
Bone metastasis is of common occurrence in renal cell carcinoma with poor prognosis, but no optimal treatment approach has been established for bone metastatic renal cell carcinoma. To explore the potential therapeutic targets for bone metastatic renal cell carcinoma, we profile single cell transcriptomes of 6 primary renal cell carcinoma and 9 bone metastatic renal cell carcinoma. We also include scRNA-seq data of early-stage renal cell carcinoma, late-stage renal cell carcinoma, normal kidneys and healthy bone marrow samples in the study to better understand the bone metastasis niche. The molecular properties and dynamic changes of major cell lineages in bone metastatic environment of renal cell carcinoma are characterized. Bone metastatic renal cell carcinoma is associated with multifaceted immune deficiency together with cancer-associated fibroblasts, specifically appearance of macrophages exhibiting malignant and pro-angiogenic features. We also reveal the dominance of immune inhibitory T cells in the bone metastatic renal cell carcinoma which can be partially restored by the treatment. Trajectory analysis showes that myeloid-derived suppressor cells are progenitors of macrophages in the bone metastatic renal cell carcinoma while monocytes are their progenitors in primary tumors and healthy bone marrows. Additionally, the infiltration of immune inhibitory CD47+ T cells is observed in bone metastatic tumors, which may be a result of reduced phagocytosis by SIRPA-expressing macrophages in the bone microenvironment. Together, our results provide a systematic view of various cell types in bone metastatic renal cell carcinoma and suggest avenues for therapeutic solutions.
Bone Neoplasms , Carcinoma, Renal Cell , Kidney Neoplasms , Humans , Carcinoma, Renal Cell/genetics , Kidney Neoplasms/genetics , Bone Neoplasms/genetics , Macrophages/metabolism , Tumor Microenvironment
Curcumin (CUR) exhibits a definite curative effect in the treatment of depression. To identify potential antidepressant targets and mechanisms of action of CUR. This study used network pharmacology to explore the signaling pathways and CUR-related targets in depression. C57BL/6 J mice (male,12-14 weeks old) were randomly divided into four groups (n = 8): saline-treated (control mice), lipopolysaccharide (LPS, 2 mg/kg/day, intraperitoneally), LPS + CUR (50 mg/kg/day, intragastrically), and LPS + CUR + LY294002 (7.5 mg/kg/day, intraperitoneally). After 1 week, behavioral tests were performed. Then, neuronal damage in the prefrontal cortex of mice was evaluated by hematoxylin-eosin (HE) staining. We uncovered the main active mechanism of CUR against depression using Western blotting and enzyme-linked immunosorbent assay (ELISA). Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the most significantly enriched pathway in CUR against depression was the PI3K-Akt pathway. Moreover, 52 targets were significantly correlated with the PI3K-Akt signaling pathway and CUR-related targets. In addition, among the top 50 targets ranked by degree in the protein-protein interaction (PPI) network, there were 23 targets involved in the 52 intersection targets. Administration of LPS alone extended immobility time in the open field test (OFT) and tail suspension test (TST) and decreased sucrose consumption in the sucrose preference test (SPT). Pretreatment with CUR relieved LPS-induced changes in the behavioral tests, activity of the PI3K-Akt signaling pathway, neuronal damage in the prefrontal cortex (PFC), and inflammatory response. Moreover, inhibition of the PI3K-Akt signaling pathway by LY294002 blocked the therapeutic effects of CUR. Our study indicates that CUR may be an effective antidepressant agent in an LPS-induced mouse model, partly because of its anti-inflammatory action through the PI3K-Akt signaling pathway.
Curcumin , Mice , Male , Animals , Curcumin/pharmacology , Curcumin/therapeutic use , Lipopolysaccharides/pharmacology , Proto-Oncogene Proteins c-akt/metabolism , Network Pharmacology , Phosphatidylinositol 3-Kinases/metabolism , Mice, Inbred C57BL , Antidepressive Agents/pharmacology , Antidepressive Agents/therapeutic use , Sucrose
Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired objectives directly while ignoring the explanations for predictions. Although several techniques can provide interpretations for machine learning-based methods such as feature attribution, there are still gaps between these interpretations and the principles commonly adopted by medicinal chemists when designing and optimizing molecules. Here, we propose an interpretation framework, named MolSHAP, for quantitative structure-activity relationship analysis by estimating the contributions of R-groups. Instead of attributing the activities to individual input features, MolSHAP regards the R-group fragments as the basic units of interpretation, which is in accordance with the fragment-based modifications in molecule optimization. MolSHAP is a model-agnostic method that can interpret activity regression models with arbitrary input formats and model architectures. Based on the evaluations of numerous representative activity regression models on a specially designed R-group ranking task, MolSHAP achieved significantly better interpretation power compared with other methods. In addition, we developed a compound optimization algorithm based on MolSHAP and illustrated the reliability of the optimized compounds using an independent case study. These results demonstrated that MolSHAP can provide a useful tool for accurately interpreting the quantitative structure-activity relationships and rationally optimizing the compound activities in drug discovery.
Drug Discovery , Quantitative Structure-Activity Relationship , Reproducibility of Results , Drug Discovery/methods , Algorithms , Machine Learning
Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.
BACKGROUND: Visceral sarcoma is a rare malignancy with a poor prognosis. However, there is no recommended prognostic staging system for the malignant disease. METHOD: We analyzed the data of patients diagnosed with primary soft tissue sarcoma (STS) of the abdomen and thoracic visceral organs between 2006 and 2017 at our hospital. Prognostic factors (size, tumor grade, and lymph node metastasis) were analyzed in our cohort (n = 203) and the SEER validation cohort (n = 5826). RESULTS: Tumor size, grade, and lymph node metastasis were important prognostic factors for visceral sarcoma in both our and the SEER cohorts. Based on these prognostic factors, we established a new staging system for visceral sarcoma, by which patients could be stratified into clinically meaningful and non-overlapping stages in both our cohort and the SEER validation series. Moreover, the area under the curve (AUC) value of the staging system for 5-year survival was 0.84 (95% CI: 0.78-0.89) in our series and 0.80 (95% CI: 0.79-0.81) in SEER series, respectively. In addition, compared with the widely used FIGO staging system for female genital sarcoma, the visceral sarcoma staging system could more effectively and reliably stratify patients into four different prognostic groups. CONCLUSIONS: The visceral sarcoma staging system is applicable for STS of the abdomen and thoracic visceral organs and is better than the current FIGO staging system for female genital sarcoma and should be incorporated into the AJCC Cancer Staging Manual.
Background: Current knowledge on apolipoprotein A1 (APOA1) in hepatocellular carcinoma (HCC) is fragmented and even contradictory. Multi-dimensional analyses are required to comprehensively elucidate its value and underlying mechanism. Methods: We collected 49 RNA-seq datasets, 40 cell line types data and 70 scRNA pan-cancer datasets public available, including 17 HCC datasets (1754 tumor samples), and enrolled 73 pairs of HCC tissue and 516 blood samples independently from our clinics. APOA1 impacting on the HCC tumor microenvironment (TME) was analyzed using intensive data mining. Methylation sequencing, flow cytometry, quantitative PCR, western blot, immunohistochemistry and clinical chemistry assays were conducted for wet experimental investigation. Results: The APOA1 ontology fingerprint indicated that it played various crucial biological roles in HCC, primarily involved in cholesterol efflux. Consistent findings at histology, serology, and clinical follow-up revealed that high APOA1 was a good prognosis indicator of HCC. Hypermethylation in the APOA1 promoter region was found in clinical samples which is in accordance with the reduction of APOA1 in HCC. The cell cycle, DNA replication, mismatch repair pathways, and tumor cell proliferation were less observed in the HCC APOA1high subgroup. The favorable immunoregulatory abilities of APOA1 showed interesting findings: a positive correlation between APOA1 and anti-tumor immune cells (NK, CD8+ T cells) and a negative association with immune cells exerting immunosuppressive effects, including M2 macrophages. Conclusion: This is an integrative multidimensional exploration of APOA1 using bioinformatics and experiments. Both the prognostic value and anti-tumor effects based on APOA1 panoramic exploration in the HCC TME demonstrate a new potential clinical target for HCC assessment and intervention in the future.
Background: The clinical dangers of asymptomatic hyperuricemia to human health have become increasingly prominent over the past 20 years. Previous studies have shown the potential benefits of acupuncture on uric acid levels in the body. However, definitive evidence is lacking. Our objective is to evaluate the efficacy and safety of acupuncture on serum uric acid (SUA) in individuals with asymptomatic hyperuricemia. Methods: This is a randomized, single-blind, sham-controlled trial. A total of 180 eligible patients with asymptomatic hyperuricemia will be recruited at three hospitals in China. Patients will be randomly assigned in a 1:1 ratio to receive 16 sessions of manual acupuncture or sham acupuncture for 8 weeks. Patients will be followed up for 12 weeks. The primary outcome will be the change in SUA levels at week 8 after randomization. Secondary outcomes will include dynamic changes in SUA levels, efficacy rates, proportion of gout flare, body weight, and acute medication intake. The MGH Acupuncture Sensation Scale and adverse events related to acupuncture will be measured after each treatment. A blinding assessment will be performed on patients who receive at least one session of acupuncture. Data analyses will be performed on a full analysis set and a per-protocol set. Ethics and dissemination: Ethics approval has been obtained from the Clinical Trial Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (approval no. 2021-S135). Written informed consent will be obtained from enrolled patients. The findings will be disseminated in a peer-reviewed journal. Clinical trial registration: ClinicalTrials.gov identifier, NCT05406830.
Acupuncture Therapy , Gout , Hyperuricemia , Humans , Uric Acid , Single-Blind Method , Symptom Flare Up , Acupuncture Therapy/adverse effects , Randomized Controlled Trials as Topic
Photosynthetic efficiency is the primary determinant of crop yield, including vegetative biomass and grain yield. Manipulation of key transcription factors known to directly control photosynthetic machinery can be an effective strategy to improve photosynthetic traits. In this study, we identified an Arabidopsis gain-of-function mutant, cogwheel1-3D, that shows a significantly enlarged rosette and increased biomass compared with wild-type plants. Overexpression of COG1, a Dof transcription factor, recapitulated the phenotype of cogwheel1-3D, whereas knocking out COG1 and its six paralogs resulted in a reduced rosette size and decreased biomass. Transcriptomic and quantitative reverse transcription polymerase chain reaction analyses demonstrated that COG1 and its paralogs were required for light-induced expression of genes involved in photosynthesis. Further chromatin immunoprecipitation and electrophoretic mobility shift assays indicated that COG1 can directly bind to the promoter regions of multiple genes encoding light-harvesting antenna proteins. Physiological, biochemical, and microscopy analyses revealed that COG1 enhances photosynthetic capacity and starch accumulation in Arabidopsis rosette leaves. Furthermore, combined results of bioinformatic, genetic, and molecular experiments suggested that the functions of COG1 in increasing biomass are conserved in different plant species. These results collectively demonstrated that COG1 acts as a key regulator of plant biomass by promoting photosynthesis and starch accumulation. Manipulating COG1 to optimize photosynthetic capacity would create new strategies for future crop yield improvement.
Arabidopsis Proteins , Arabidopsis , Transcription Factors/genetics , Transcription Factors/metabolism , Arabidopsis/metabolism , Biomass , Starch/metabolism , Photosynthesis , Plants/metabolism , Plant Leaves/metabolism , Arabidopsis Proteins/genetics , Arabidopsis Proteins/metabolism
Bacterial infections and inflammation pose a severe threat to human health and the social economy. The existence of super-bacteria and the increasingly severe phenomenon of antibiotic resistance highlight the development of new antibacterial agents. Due to low cytotoxicity, high biocompatibility, and different antibacterial mechanisms from those for antibiotics, functionalized carbon dots (FCDs) promise a new platform for the treatment of bacterial infectious diseases. However, few articles have systematically sorted out the available antibacterial mechanisms for FCDs and their application in the treatment of bacterial inflammation. This review focuses on the available antibacterial mechanisms for FCDs, including covalent and non-covalent interactions, reactive oxygen species, photothermal therapy, and size effect. Meanwhile, the design of antibacterial FCDs is introduced, including surface modification, doping, and combination with other nanomaterials. Furthermore, this review specifically concentrates on the research advances of antibacterial FCDs in the treatment of bacterial inflammation. Finally, the advantages and challenges of applying FCDs in practical antimicrobial applications are discussed.
Bacterial Infections , Carbon , Humans , Bacterial Infections/drug therapy , Bacteria , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Inflammation/drug therapy
BACKGROUND: Biomedical relation extraction (RE) is of great importance for researchers to conduct systematic biomedical studies. It not only helps knowledge mining, such as knowledge graphs and novel knowledge discovery, but also promotes translational applications, such as clinical diagnosis, decision-making, and precision medicine. However, the relations between biomedical entities are complex and diverse, and comprehensive biomedical RE is not yet well established. OBJECTIVE: We aimed to investigate and improve large-scale RE with diverse relation types and conduct usability studies with application scenarios to optimize biomedical text mining. METHODS: Data sets containing 125 relation types with different entity semantic levels were constructed to evaluate the impact of entity semantic information on RE, and performance analysis was conducted on different model architectures and domain models. This study also proposed a continued pretraining strategy and integrated models with scripts into a tool. Furthermore, this study applied RE to the COVID-19 corpus with article topics and application scenarios of clinical interest to assess and demonstrate its biological interpretability and usability. RESULTS: The performance analysis revealed that RE achieves the best performance when the detailed semantic type is provided. For a single model, PubMedBERT with continued pretraining performed the best, with an F1-score of 0.8998. Usability studies on COVID-19 demonstrated the interpretability and usability of RE, and a relation graph database was constructed, which was used to reveal existing and novel drug paths with edge explanations. The models (including pretrained and fine-tuned models), integrated tool (Docker), and generated data (including the COVID-19 relation graph database and drug paths) have been made publicly available to the biomedical text mining community and clinical researchers. CONCLUSIONS: This study provided a comprehensive analysis of RE with diverse relation types. Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.
COVID-19 , Humans , Data Mining , Databases, Factual , Knowledge , Precision Medicine
Bacterial infectious diseases are severe threats to human health and increase substantial financial burdens. Nanomaterials have shown great potential in timely and accurate bacterial identification, detection, and monitoring to improve the cure rate and reduce mortality. Recently, carbon dots have been evidenced to be ideal candidates for bacterial identification and detection due to their superior physicochemical properties and biocompatibility. This review outlines the detailed recognition elements and recognition strategies with functionalized carbon dots (FCDs) for bacterial identification and detection. The advantages and limitations of different kinds of FCDs-based sensors will be critically discussed. Meanwhile, the ongoing challenges and perspectives of FCDs-based sensors for bacteria sensing are put forward.
Bacteria , Nanostructures , Humans , Carbon
