Comparative transcriptomics reveals the mechanism of antibacterial activity of fruit-derived dihydrochalcone flavonoids against Porphyromonas gingivalis.

Porphyromonas gingivalis causes various health issues through oral infections. This study investigates the antibacterial activities of food-derived dihydrochalcone flavonoids against Porphyromonas gingivalis and their mechanisms of antibacterial action through comparative transcriptome profiling. Susceptibility tests showed that two typical dihydrochalcone flavonoids (phloretin and phlorizin) had much lower minimum inhibitory concentrations (12.5 µg mL-1 and 50 µg mL-1, respectively) than the common flavanone naringenin (100 µg mL-1). SEM observations and the LDH activity assay indicated obvious anomalies in cell morphology and increased cell membrane permeability, indicating the destructive effect of those compounds on the cell structure. These compounds might also induce apoptosis in P. gingivalis, as shown by the CLSM fluorescence images. Transcriptomic analysis revealed that the flavonoid treatment impacted DNA function and oxidative damage. These flavonoids may activate antioxidant-related pathways that are lethal to anaerobic bacteria like P. gingivalis. Additionally, the compounds resulted in the silencing of transposition-related genes, potentially inhibiting resistance-gene acquisition and expression. Phloretin regulated fatty acid metabolism pathways, which are related to the construction and maintenance of the cell membrane. This suggests a relationship between the structure and antibacterial activities of the tested compounds that share a flavonoid skeleton but differ in the C-ring and glucose moiety. This is the first report of the antibacterial activities and mechanisms of action of food-derived dihydrochalcone flavonoids at the transcriptome level, offering a promising approach for the development of new antibacterial agents from natural products and enhancing their applicability in treating diseases associated with oral pathogens as a substitute for antibiotics.

Rational design of a DNA-launched live attenuated vaccine against human enterovirus 71.

Human Enterovirus 71 (EV71) has emerged as one of the predominant causative agents of hand, foot and mouth disease (HFMD) with global impact. Despite the inactivated vaccine being licensed, other vaccine candidates based on advanced technology platforms are under development. In this report, we rationally designed and constructed two DNA-launched live attenuated vaccine candidates (pDL-EV71) under the control of specific promoters. In vitro and in vivo transfection with pDL-EV71 driven by the CMV promoter successfully yielded fully infectious EV71. More importantly, the administration of pDL-EV71 did not cause clinical symptoms following intracranial or intramuscular inoculation in neonatal and IFNα/ßR-/- mice, demonstrating its safety profile. Moreover, a single-dose or two-dose immunization with pDL-EV71 elicited robust neutralizing antibodies against EV71 as well as an antigen-specific cellular response in mice. A single-dose immunization with 10 µg of pDL-EV71 conferred complete protection against lethal EV71 infection in neonates born to immunized maternal mice. Overall, our present results demonstrate that pDL-EV71 is a safe and effective vaccine candidate against EV71 for further development.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis.

BACKGROUND: Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear. METHOD: This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells. RESULTS: One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels. CONCLUSION: ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

The risk of dyslipidemia on PLHIV associated with different antiretroviral regimens in Huzhou.

BACKGROUND: Dyslipidemia is increasingly common in people living with HIV (PLHIV), thereby increasing the risk of cardiovascular events and diminishing the quality of life for these individuals. The study of blood lipid metabolism of PLHIV has great clinical significance in predicting the risk of cardiovascular disease. Therefore, this study aims to examine the blood lipid metabolism status of HIV-infected patients in Huzhou before and after receiving highly active antiretroviral therapy (HAART) and to explore the impact of different HAART regimens on dyslipidemia. METHOD: PLHIV confirmed in Huzhou from June 2010 to June 2022 was included. The baseline characteristics and clinical data during the follow-up period were collected, including some blood lipid indicators (total cholesterol and triglycerides) and HAART regimens. A multivariate logistic regression model and the generalized estimating equation model were used to analyze the independent effects of treatment regimens on the risk of dyslipidemia. RESULT: The overall prevalence of dyslipidemia among PLHIV after HAART was 70.11%. PLHIV receiving lamivudine (3TC) + efavirenz (EFV) + zidovudine (AZT) had a higher prevalence of dyslipidemia compared to those receiving 3TC+EFV+tenofovir disoproxil fumarate (TDF). In a logistic analysis adjusted for important covariates such as BMI, age, diabetes status, etc., we found that the risks of dyslipidemia were higher with 3TC+EFV+AZT (dyslipidemia: odds ratio [OR] = 2.09, 95% confidence interval [Cl]: 1.28-3.41; TG ≥1.7: OR = 2.40, 95%Cl:1.50-3.84) than with 3TC+EFV+TDF. Furthermore, on PLHIV that was matched 1:1 by the HAART regimens, the results of the generalized estimation equation again showed that 3TC+EFV+AZT (TG ≥1.7: OR = 1.84, 95%Cl: 1.10-3.07) is higher for the risk of marginal elevations of TG than 3TC+EFV+TDF. CONCLUSION: The prevalence of dyslipidemia varies according to different antiretroviral regimens. Using both horizontal and longitudinal data, we have repeatedly demonstrated that AZT has a more adverse effect on blood lipids than TDF from two perspectives. Therefore, we recommend caution in using the 3TC+EFV+AZT regimen for people at clinical risk of co-occurring cardiovascular disease.

Systematic pharmacology and experimental validation to elucidate the inflammation-associated mechanism of Huanglian Wendan (HLWD) decoction in the treatment of MAFLD associated with atherosclerosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Metabolic-associated fatty liver disease (MAFLD) and atherosclerosis are very common disorders that frequently coexist. The therapeutic efficacy of Huanglian Wendan (HLWD) decoction, a traditional Chinese medicine (TCM) prescription, is satisfactory in treating MAFLD associated with atherosclerosis. However, the underlying mechanisms through which HLWD exerts its effects need to be elucidated. Given the complex composition of HLWD and its multiple therapeutic targets, pharmacological investigation is challenging. AIM OF THIS STUDY: This study aimed to identify the effective compounds in HLWD and elucidate the mechanisms involved in its therapeutic effect on MAFLD associated with atherosclerosis. MATERIALS AND METHODS: We used a systematic pharmacology method to identify effective compounds present in HLWD and determine the mechanism by which it affects MAFLD associated with atherosclerosis. The effective components of HLWD were identified through ultrahigh-performance liquid chromatography-q exactive-orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). Next, a comprehensive in silico method was used to predict potential related targets and disease targets for these compounds to establish corresponding pathways. The accuracy of our assumed systemic pharmacology results was determined by conducting follow-up experiments. RESULTS: By conducting UHPLC-Q-Orbitrap HRMS combined with network analysis, we identified 18 potentially active components of HLWD and assessed the inflammatory regulatory mechanism by which it affects MAFLD associated with atherosclerosis on the basis of 52 key targets. We used a high-fat, high-cholesterol (HFHC)-induced mice model of MAFLD associated with atherosclerosis to confirm our results. We found that administering HLWD significantly improved the appearance of their liver and reduced their body weight, liver weight, blood lipids, hepatic damage, and hepatic pathology. HLWD also decreased atherosclerotic lesion areas, foam cells, and inflammatory cells in the aorta. HLWD showed anti-inflammatory effects, suppressed M1 polarization, and promoted M2 polarization in the liver and aorta. HLWD might also regulate peroxisome proliferator-activated receptor-γ (PPARγ)/nuclear factor kappa-B (NF-κB) signaling to influence macrophage polarization and inflammation. CONCLUSIONS: Our results showed that HLWD protected against HFHC diet-induced MAFLD associated with atherosclerosis by regulating PPARγ/NF-κB signaling, thus adjusting macrophage polarization and inflammation. Additionally, pharmacochemistry research, network pharmacology analysis, and experimental verification can be combined to form a comprehensive model used in studies on TCM.

Association of hemoglobin with plasma neurofilament light and white matter hyperintensities in Alzheimer's disease continuum.

Objective: This study aimed to investigate the association of hemoglobin (Hb) with axonal injury marker plasma neurofilament light (PNFL) and brain structure measurements in the Alzheimer's disease (AD) continuum. Methods: The data used in this study were collected from the Alzheimer's Disease Neuroimaging Initiative database. Participants with cognitively normal, mild cognitive impairment, and mild dementia were included in the data analyses. All participants had available data on blood tests, PNFL levels, neuropsychological assessments, brain structure measurements (including volumes of white matter hyperintensities [WMH], hippocampus, gray matter, and total brain), and Aß positron emission tomography standardized uptake value ratio (SUVR) at baseline. Aß-positive was defined as SUVR threshold value > 1.11. Linear regression, restricted cubic spline, and causal mediation analyses were conducted to investigate the association of Hb concentration with PNFL levels and brain structure measurements. Stratified analyses were also employed to evaluate the association between Hb concentration and PNFL levels across different APOE genotypes and sex. Results: In the Aß-positive group, Hb concentration was associated with PNFL levels (ß = -0.022, p = 0.002). Stratified analyses suggested an association between Hb concentration and PNFL in APOE ɛ4 carriers (ß = -0.031, p < 0.001) and males (ß = -0.030, p < 0.001) but not in non-carriers and females (p > 0.05). Hb concentration was also associated with WMH volume (ß = -0.04, p = 0.028), especially in APOE ɛ4 carriers, with mediation analysis revealing that PNFL mediated the association between Hb concentration and WMH volume. The association of Hb concentration with other brain structure measurements was minimal. Conclusion: In the AD continuum, Hb was associated with axonal injury marker PNFL and WMH volume, particularly in APOE ɛ4 carriers.

The Potential of Gemini and GPTs for Structured Report Generation based on Free-Text 18F-FDG PET/CT Breast Cancer Reports.

RATIONALE AND OBJECTIVE: To compare the performance of large language model (LLM) based Gemini and Generative Pre-trained Transformers (GPTs) in data mining and generating structured reports based on free-text PET/CT reports for breast cancer after user-defined tasks. MATERIALS AND METHODS: Breast cancer patients (mean age, 50 years ± 11 [SD]; all female) who underwent consecutive 18F-FDG PET/CT for follow-up between July 2005 and October 2023 were retrospectively included in the study. A total of twenty reports from 10 patients were used to train user-defined text prompts for Gemini and GPTs, by which structured PET/CT reports were generated. The natural language processing (NLP) generated structured reports and the structured reports annotated by nuclear medicine physicians were compared in terms of data extraction accuracy and capacity of progress decision-making. Statistical methods, including chi-square test, McNemar test and paired samples t-test, were employed in the study. RESULTS: The structured PET/CT reports for 131 patients were generated by using the two NLP techniques, including Gemini and GPTs. In general, GPTs exhibited superiority over Gemini in data mining in terms of primary lesion size (89.6% vs. 53.8%, p < 0.001) and metastatic lesions (96.3% vs 89.6%, p < 0.001). Moreover, GPTs outperformed Gemini in making decision for progress (p < 0.001) and semantic similarity (F1 score 0.930 vs 0.907, p < 0.001) for reports. CONCLUSION: GPTs outperformed Gemini in generating structured reports based on free-text PET/CT reports, which is potentially applied in clinical practice. DATA AVAILABILITY: The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ultrafast near-infrared pyroelectric detector based on inhomogeneous plasmonic metasurface.

Pyroelectric (PE) detection technologies have attracted extensive attention due to the cooling-free, bias-free, and broadband properties. However, the PE signals are generated by the continuous energy conversion processes from light, heat, to electricity, normally leading to very slow response speeds. Herein, we design and fabricate a PE detector which shows extremely fast response in near-infrared (NIR) band by combining with the inhomogeneous plasmonic metasurface. The plasmonic effect dramatically accelerates the light-heat conversion process, unprecedentedly improving the NIR response speed by 2-4 orders of magnitude to 22 µs, faster than any reported infrared (IR) PE detector. We also innovatively introduce the concept of time resolution into the field of PE detection, which represents the detector's ability to distinguish multiple fast-moving targets. Furthermore, the spatially inhomogeneous design overcomes the traditional narrowband constraint of plasmonic systems and thus ensures a wideband response from visible to NIR. This study provides a promising approach to develop next-generation IR PE detectors with ultrafast and broadband responses.

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood.

BACKGROUND: Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes. METHODS: A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data. RESULTS: The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA. CONCLUSION: Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Differential immunological profiles in seronegative versus seropositive rheumatoid arthritis: Th17/Treg dysregulation and IL-4.

Background: Rheumatoid arthritis (RA) is an autoimmune disease with various subtypes. Among these, seronegative rheumatoid arthritis (SnRA), distinguished by its distinctive seronegative antibody phenotype, presents clinical diagnosis and treatment challenges. This study aims to juxtapose the immunological features of SnRA with seropositive rheumatoid arthritis (SpRA) to investigate potential mechanisms contributing to differences in antibody production. Methods: This study included 120 patients diagnosed with RA and 78 patients diagnosed with psoriatic arthritis (PsA), comprising 41 cases of SnRA and 79 cases of SpRA. Clinical, serological, and immune data were collected from all participants to systematically identify and confirm the most pivotal immunological distinctions between SnRA and SpRA. Results: (1) SpRA demonstrates more pronounced T-helper 17 cells (Th17)/Regulatory T cells (Treg) dysregulation, vital immunological differences from SnRA. (2) SpRA exhibits higher inflammatory cytokine levels than SnRA and PsA. (3) Lymphocyte subset ratios and cytokine overall distribution in SnRA close to PsA. (4) Interleukin-4 (IL-4) emerges as the central immunological disparity marker between SnRA and SpRA. Conclusion: Th17/Treg imbalance is one of the vital immunological disparities between SnRA and SpRA. Interestingly, PsA and SnRA display similar peripheral blood immunological profiles, providing immunological evidence for these two diseases' clinical and pathological similarities. Furthermore, IL-4 emerges as the central immunological disparity marker between SnRA and SpRA, suggesting its potential role as a triggering mechanism for differential antibody production.

Liquid-liquid phase separation-related features of PYGB/ACTR3/CCNA2/ITGB1/ATP8A1/RAP1GAP2 predict the prognosis of pancreatic cancer.

Background: The growth and metastasis of pancreatic cancer (PC) has been found to be closely associated with liquid-liquid phase separation (LLPS). This study sought to identify LLPS-related biomarkers in PC to construct a robust prognostic model. Methods: Transcriptomic data and clinical information related to PC were retrieved from publicly accessible databases. The PC-related data set was subjected to differential expression, Mendelian randomization (MR), univariate Cox, and least absolute selection and shrinkage operator analyses to identify biomarkers. Using the biomarkers, we subsequently constructed a risk model, identified the independent prognostic factors of PC, established a nomogram, and conducted an immune analysis. Results: The study identified four genes linked with an increased risk of PC; that is, PYGB, ACTR3, CCNA2, and ITGB1. Conversely, ATP8A1, and RAP1GAP2 were found to provide protection against PC. These findings contributed significantly to the development of a highly precise risk model in which risk, age, and pathology N stage were categorized as independent factors in predicting the prognosis of PC patients. Using these factors, a nomogram was established to predict survival outcomes accurately. An immune analysis revealed varying levels of eosinophils, gamma delta T cells, and other immune cells between the distinct risk groups. The high-risk patients exhibited increased potential for immune escape, while the low-risk patients showed a higher response to immunotherapy. Conclusions: Six genes were identified as having potential causal relationships with PC. These genes were integrated into a prognostic risk model, thereby serving as unique prognostic signatures. Our findings provide novel insights into predicting the prognosis of PC patients.

A predictive model for L-T4 dose in postoperative DTC after RAI therapy and its clinical validation in two institutions.

Purpose: To develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions. Methods: A total of 266 DTC patients who received RAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was validated through a two-center prospective study (n=263). Results: Six significant clinical and biochemical features were selected, including body surface area (BSA), weight, hemoglobin (HB), height, body mass index (BMI), and age. Cross-validation showed that the support vector regression (SVR) model was with the highest accuracy (53.4%) for prediction of L-T4 dose among the established eight models. In the two-center prospective validation study, a total of 263 patients were included. The TSH targeting rate based on constructed SVR model were dramatically higher than that based on empirical administration (Rate 1 (first rate): 52.09% (137/263) vs 10.53% (28/266); Rate 2 (cumulative rate): 85.55% (225/263) vs 53.38% (142/266)). Furthermore, the model significantly shortens the time (days) to achieve target TSH level (62.61 ± 58.78 vs 115.50 ± 71.40). Conclusions: The constructed SVR model can effectively predict the L-T4 dose for postoperative DTC after RAI therapy, thus shortening the time to achieve TSH target level and improving the quality of life for DTC patients.

Genetically predicted the causal relationship between gut microbiota and the risk of polymyositis/dermatomyositis: a Mendelian randomization analysis.

Introduction: Observational studies suggest associations between gut microbiota and polymyositis (PM) and dermatomyositis (DM), but causal relationships are unclear. We investigate the causal effects of gut microbiota on PM and DM, providing insights hoping to provide insights for future treatment and prevention. Methods: Summary statistics of gut microbiota were obtained from a multi-ethnic Genome Wide Association Studies (GWAS) meta-analysis, including 119 taxa from 18,340 Europeans. PM/DM statistics were extracted from GWAS analyses. Mendelian randomization (MR) with IVW, MR-Egger, and weighted median methods was performed. Sensitivity analyses addressed heterogeneity and pleiotropy. Of the 119 bacterial genera studied, six showed causal links. Results: Alloprevotella (OR: 3.075, 95% CI: 1.127-8.386, p = 0.028), Ruminococcaceae UCG003 (OR: 4.219, 95% CI: 1.227-14.511, p = 0.022), Dialister (OR: 0.273, 95% CI: 0.077-0.974, p = 0.045) were associated with PM. Anaerotruncus (OR: 0.314, 95% CI: 0.112-0.882, p = 0.028), Ruminococcaceae UCG002 (OR: 2.439, 95% CI: 1.173-5.071, p = 0.017), Sutterella (OR: 3.392, 95% CI: 1.302-8.839, p = 0.012) were related to DM. Sensitivity analyses validated these associations. Discussion: We establish causal relationships between Ruminococcaceae, Sutterella, Anaerotruncus with DM, Alloprevotella, Ruminococcaceae UCG003, and Dialister with PM. Common microbiota, like Ruminococcaceae, have significant clinical implications. These findings open up greater possibilities for the gut microbiota to contribute to the development of PM/DM and for future monitoring of the gut microbiota in patients with PM/DM.

Phenotypic variation in hypocotyl elongation among elite sand rice (Agriophyllum squarrosum) lines.

Sand rice (Agriophyllum squarrosum), widely distributed in Central Arid Asia and prevalent in the sand dunes of northern China, presents a promising potential as a climate-resilient crop. The plasticity of hypocotyl growth is the key trait for sand rice to cope with wind erosion and sand burial, ensure seedling emergence, and determine plant architecture. In this study, we assessed the overall hypocotyl phenotype of six sand rice elite lines, which were collected from different regions of northern China, and selected by our group over past decade through common garden trials. Significant phenotypic variations were observed in thousand-seed weight (TSW), seedling emergence percentage, hypocotyl length and diameter, and seedling fresh weight among the lines. The elite line Aerxiang (AEX) exhibited excellent agronomic performance with superior and synchronous emergence, and high survival percentage, distinguishing itself as a prime candidate for further large-scale cultivation. Contrastingly, the lines from the arid regions showed markedly lower performance. Partial Least Squares Path Modeling (PLSPM) was used to assess the impact of seed provenance climate factors, including annual mean temperature (AMT) and annual mean precipitation (AMP), on trait variability among lines. The findings indicate a significant correlation between climate factors and hypocotyl length, highlighting the intricate adaptation of sand rice to local climate. The comprehensive understanding of the mechanisms behind phenotypic variations offers valuable insights for sand rice de novo domestication and innovative germplasm resources, and lays the foundation for ecological restoration in sandy areas.

Solvent-Activated Transformation of Polymer Configurations for Advancing the Interfacial Reliability of Perovskite Photovoltaics.

Organic materials have been widely used as the charge transport layers in perovskite solar cells due to their structural versatility and solution processability. However, their low surface energy usually causes unsatisfactory thin-film wettability in contact with the perovskite solution, which limits the interfacial performance of the photovoltaic devices. Although solvent post-treatment could occasionally regulate the wetting behavior of organic films, the mechanism of the solid-liquid interaction is still unclear. Here, we present evidence of a possible correlation between the solvent and the wettability of a conventional polymer, poly[bis(4-phenyl) (2,4,6-trimethylphenyl) amine] (PTAA), and reveal the critical roles of Hansen solubility parameters (HSPs) of solvents in wetting mechanisms. Our results suggest that the conventional solvent N,N-dimethylformamide (DMF) improves the wettability of PTAA by the morphological disruption mechanism but negatively impacts interfacial charge collection and stability. In contrast, 2-methoxyethanol (2-Me) with an appropriate HSP value induces the transformation of the PTAA configuration in an orderly manner, which simultaneously improves the wetting property and maintains the film topography. After careful optimization of the surface conformation of the PTAA film, both perovskite crystallization and interfacial compatibility have been enhanced. Benefiting from superior interfacial properties, the perovskite solar cells based on 2-Me deliver a champion efficiency of 24.15% compared to 21.4% for DMF-based ones. More encouragingly, the use of 2-Me minimizes the perovskite buried interfacial defects, enabling the unencapsulated devices to maintain about 95% of their initial efficiencies after light illumination for 1100 h. The present study demonstrates the high effectiveness of solvent-polymer interaction for adjusting interfacial properties and strengthening the robustness of perovskite solar cells.

Research progress on the correlation between intestinal flora and colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies in the world. With the rapid pace of life and changes in diet structure, the incidence and mortality of CRC increase year by year posing a serious threat to human health. As the most complex and largest microecosystem in the human body, intestinal microecology is closely related to CRC. It is an important factor that affects and participates in the occurrence and development of CRC. Advances in next-generation sequencing technology and metagenomics have provided new insights into the ecology of gut microbes. It also helps to link intestinal flora with CRC, and the relationship between intestinal flora and CRC can be continuously understood from different levels. This paper summarizes the relationship between intestinal flora and CRC and its potential role in the diagnosis of CRC providing evidence for early screening and treatment of CRC.

Recent advances in identifying protein targets of bioactive natural products.

Background: Natural products exhibit structural complexity, diversity, and historical therapeutic significance, boasting attractive functions and biological activities that have significantly influenced drug discovery endeavors. The identification of target proteins of active natural compounds is crucial for advancing novel drug innovation. Currently, methods for identifying targets of natural products can be categorized into labeling and label-free approaches based on whether the natural bioactive constituents are modified into active probes. In addition, there is a new avenue for rapidly exploring the targets of natural products based on their innate functions. Aim: This review aimed to summarize recent advancements in both labeling and label-free approaches to the identification of targets for natural products, as well as the novel target identification method based on the natural functions of natural products. Methods: We systematically collected relevant articles published in recent years from PubMed, Web of Science, and ScienceDirect, focusing on methods employed for identifying protein targets of bioactive natural products. Furthermore, we systematically summarized the principles, procedures, and successful cases, as well as the advantages and limitations of each method. Results: Labeling methods allow for the direct labeling of target proteins and the exclusion of indirectly targeted proteins. However, these methods are not suitable for studying post-modified compounds with abolished activity, chemically challenging synthesis, or trace amounts of natural active compounds. Label-free methods can be employed to identify targets of any natural active compounds, including trace amounts and multicomponent mixtures, but their reliability is not as high as labeling methods. The structural complementarity between natural products and their innate receptors significantly increase the opportunities for finding more promising structural analogues of the natural products, and natural products may interact with several structural analogues of receptors in humans. Conclusion: Each approach presents benefits and drawbacks. In practice, a combination of methods is employed to identify targets of natural products. And natural products' innate functions-based approach is a rapid and selective strategy for target identification. This review provides valuable references for future research in this field, offering insights into techniques and methodologies.

Synthesis of Carboxyl-Functionalized COFs with Alternate Stable ß-Ketoenamine and Benzimidazole Linkages: Unraveling Exceptional Solvent Effects for Efficient Uranium Separation.

The prevalent π-π interactions in 2D covalent organic frameworks (COFs) impart a certain flexibility to the structures, making the stacking of COF layers susceptible to external stimuli and introducing some structural disorder. Recent research indicates that the flexibility between COF layers and the associated disorder significantly influence their selective adsorption performance toward gas molecules. However, the adsorption process in a solution environment is more complex compared to gas-phase adsorption, involving interactions between adsorbents and adsorbates, as well as the solvation effects of flexible 2D COFs. Therefore, the inherent flexibility and disorder in 2D COFs under solution conditions and their impact on the adsorption performance of metal ions have not been observed yet. Herein, the synthesis of a novel carboxyl-functionalized COF featuring stable ß-ketoenamine and benzimidazole linkages, named DMTP-COOH, is presented. DMTP-COOH exhibits excellent selective adsorption capability for uranium, with significantly different adsorption capacities observed after treatment with different solvents. This notable difference in adsorption capacity is observed under varying pH, concentration, time, and even in the presence of multiple competing ions. This work represents the first observation of the significant impact of solvent soaking treatment on the selective adsorption performance of COFs for uranium under liquid conditions.

Genomic characterization of SNW-1, a novel prophage of the deep-sea vent chemolithoautotroph Sulfurimonas indica NW79.

The globally widespread genus Sulfurimonas are playing important roles in different habitats, including the deep-sea hydrothermal vents. However, phages infecting Sulfurimonas have never been isolated and characterized to date. In the present study, a novel prophage SNW-1 was identified from Sulfurimonas indica NW79. Whole genome sequencing resulted in a circular, double-stranded DNA molecule of 37,096 bp with a mol% G+C content of 37. The genome includes 64 putative open reading frames, 33 of which code for proteins with predicted functions. Presence of hallmark genes associated with Caudoviricetes and genes involved in lysis and lysogeny indicated that SNW-1 should be a temperate, tailed phage. Phylogenetic and comparative proteomic analyses suggested that Sulfurimonas phage SNW-1 was distinct from other double stranded DNA phages and might represent a new viral genus.

Exploring the diagnostic and prognostic significance of circulating tumor cells in stage II-IV colorectal cancer using a nano-based detection method.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer mortality globally, underscoring the urgency for a noninvasive and effective biomarker to enhance patient prognosis. Circulating tumor cells (CTCs), a potential marker for real-time tumor monitoring, are limited in clinical utility due to the low sensitivity of existing detection methods. Previously, we introduced a novel Nano-based CTCs detection method that relies on the electrical properties of cell surfaces, thus eliminating thereby obviating the need for specific molecular biomarkers. In this study, we employed this technique to evaluate the diagnostic and prognostic value of CTCs in stage II-IV CRC. METHODS: A total of 194 participants were included, consisting of 136 CRC patients and 58 healthy individuals. The peripheral blood of the participants was collected, and CTCs enumeration was performed utilizing the Nano-based detection method that we newly developed. The Receiver Operating Characteristic (ROC) curve and multivariate Cox proportional-hazards analysis were employed to assess the effectiveness of CTCs for diagnosing CRC and predicting patient prognosis. RESULTS: The Nano-based method demonstrated an ability to differentiate CRC patients from healthy individuals with a sensitivity of 84.6% and a specificity of 94.8%. Furthermore, baseline CTCs levels were predictive of progression-free survival (PFS) in CRC patients, with lower levels associated with longer PFS compared to higher levels (4.5 months vs. 8.0 months at 15 CTCs/mL, p = 0.016; 4.4 months vs. 8.0 months at 20 CTCs/mL, p = 0.028). We also explored the dynamic changes in the number of CTCs after 1-5 cycles of chemotherapy. Patients with increasing CTCs levels typically experienced disease progression (PD), while those with decreasing levels often achieved a partial response (PR) or maintained stable disease (SD). These findings suggest that the dynamic fluctuations in CTCs counts are closely tied to the clinical course of the disease. CONCLUSION: Our study indicates the potential of Nano-based CTCs detection in diagnosing and predicting outcomes for patients with stage II-IV CRC.