Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up.

BACKGROUND: Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability. METHODS: Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence. RESULTS: The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively. CONCLUSIONS: The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.

Label-free and highly-sensitive detection of calcium ions using a silicon-on-sapphire light-addressable potentiometric sensor.

BACKGROUND: Ionic calcium (Ca2+) plays a crucial role in maintaining normal physiological and biochemical functions within the human body. Detecting the concentration of Ca2+ is of utmost significance for various purposes, including disease screening, cellular metabolism research, and evaluating drug effectiveness. However, current detection approaches such as fluorescence and colorimetry face limitations due to complex labeling techniques and the inability to track changes in Ca2+ concentration. In recent years, extensive research has been conducted in this field to explore label-free and efficient approaches. RESULTS: In this study, a novel light-addressed potentiometric sensor (LAPS) using silicon-on-sapphire technology, has been successfully developed for Ca2+ sensing. The Ca2+-sensitive LAPS achieved a wide-range detection of Ca2+, ranging from 10-2 M to 10-7 M, with an impressive detection limit of 100 nM. These advancements are attributed to the ultra-thin silicon layer, silicon dioxide layer, and solid-state silicon rubber sensitive membrane around 6 µm. Furthermore, the sensor demonstrated the ability to dynamically monitor fluctuations in Ca2+ concentration ranging from 10-9 M to 10-2 M within a solution. Its remarkable selectivity, specificity, and long-term stability have facilitated its successful application in the detection of Ca2+ in human serum and urine. SIGNIFICANCE AND NOVELTY: This work presents a Ca2+-sensitive sensor that combines a low detection limit and a wide detection range. The development represents the emergence of a label-free and rapid Ca2+ detection tool with immense prospects in home-based health monitoring, community disease screening, as well as cellular metabolism, and drug screening evaluations.

Simultaneous Detection of Citric Acid and Oxalic Acid Based on Dual Spectrum and Biomimetic Peroxidase for Urolithiasis Screening with a Fully Automatic Urine Analyzer.

Urolithiasis stands as a prevalent ailment within the urinary system, with hyperoxaluria and hypocitraturia being the most frequent manifestations characterized by excessive oxalic acid (OA) and deficient citric acid (CA) levels in urine. Detecting these compounds in urine quantitatively holds paramount importance for early urolithiasis screening. Existing methodologies fall short in achieving simultaneous and on-site identification of OA and CA, posing challenges for accurate urolithiasis screening. Addressing this concern, the study successfully accomplishes the concurrent identification of OA and CA in urine through a combination of dual-spectral analysis and biomimetic peroxidase utilization. Bovine serum albumin and dithiothreitol-modified copper nanoclusters (BSA-DTT-CuNCs) are employed as biomimetic peroxidases, effectively mitigating interference and enabling the simultaneous determination of OA and CA. The quantification range spans from 0 to 12 mm for OA and 0.5 to 2.5 mm for CA, with detection limits of 0.18 and 0.11 mm, respectively. To facilitate swift and on-location urine analysis, a fully automated urine analyzer (FAUA) is introduced that streamlines the process of biomarker pretreatment and identification within urine samples. Validation with real urine samples from urolithiasis patients demonstrates the method's diagnostic precision, highlighting the dual-spectral technique and analyzer's promising role in urolithiasis screening.

Progress in optical sensors-based uric acid detection.

The escalating number of patients affected by various diseases, such as gout, attributed to abnormal uric acid (UA) concentrations in body fluids, has underscored the need for rapid, efficient, highly sensitive, and stable UA detection methods and sensors. Optical sensors have garnered significant attention due to their simplicity, cost-effectiveness, and resistance to electromagnetic interference. Notably, research efforts have been directed towards UA on-site detection, enabling daily monitoring at home and facilitating rapid disease screening in the community. This review aims to systematically categorize and provide detailed descriptions of the notable achievements and emerging technologies in UA optical sensors over the past five years. The review highlights the advantages of each sensor while also identifying their limitations in on-site applications. Furthermore, recent progress in instrumentation and the application of UA on-site detection in body fluids is discussed, along with the existing challenges and prospects for future development. The review serves as an informative resource, offering technical insights and promising directions for future research in the design and application of on-site optical sensors for UA detection.

Urinary microbiota and serum metabolite analysis in patients with diabetic kidney disease.

Background: Diabetic kidney disease (DKD) is a common and potentially fatal consequence of diabetes. Chronic renal failure or end-stage renal disease may result over time. Numerous studies have demonstrated the function of the microbiota in health and disease. The use of advanced urine culture techniques revealed the presence of resident microbiota in the urinary tract, undermining the idea of urine sterility. Studies have demonstrated that the urine microbiota is related with urological illnesses; nevertheless, the fundamental mechanisms by which the urinary microbiota influences the incidence and progression of DKD remain unclear. The purpose of this research was to describe key characteristics of the patients with DKD urinary microbiota in order to facilitate the development of diagnostic and therapeutic for DKD. Methods: We evaluated the structure and composition of the microbiota extracted from urine samples taken from DKD patients (n = 19) and matched healthy controls (n = 15) using 16S rRNA gene sequencing. Meanwhile, serum metabolite profiles were compared using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Associations between clinical characteristics, urine microbiota, and serum metabolites were also examined. Finally, the interaction between urine microbiota and serum metabolites was clarified based on differential metabolite abundance analysis. Results: The findings indicated that the DKD had a distinct urinary microbiota from the healthy controls (HC). Taxonomic investigations indicated that the DKD microbiome had less alpha diversity than a control group. Proteobacteria and Acidobacteria phyla increased in the DKD, while Firmicutes and Bacteroidetes decreased significantly (P < 0.05). Acidobacteria was the most prevalent microbiota in the DKD, as determined by the Linear discriminant analysis Effect Size (LEfSe) plot. Changes in the urinary microbiota of DKD also had an effect on the makeup of metabolites. Short-chain fatty acids (SCFAs) and protein-bound uremic toxins (PBUTs) were shown to be specific. Then we discovered that arginine and proline metabolism was the primary mechanism involved in the regulation of diabetic kidney disease. Conclusions: This study placed the urinary microbiota and serum metabolite of DKD patients into a functional framework and identified the most abundant microbiota in DKD (Proteobacteria and Acidobacteria). Arginine metabolites may have a major effect on DKD patients, which correlated with the progression of DKD.

Serum metabolomics analysis reveals metabolite profile and key biomarkers of idiopathic membranous nephropathy.

Background: Idiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease with multiple and complex pathogenic mechanisms. Currently, renal biopsy is considered the gold standard for diagnosing membranous nephropathy. However, there were limitations to the renal puncture biopsy, such as the relatively high cost, longer time consuming, and the risk of invasive procedures. We investigated the profile of serum metabolites in IMN patients based on the UHPLC-QE-MS metabolomics technique for exploring the potential disease biomarkers and clinical implementation. Methods: In our research, we collected serum samples from healthy control (n = 15) and IMN patients (n = 25) to perform metabolomics analysis based on the UHPLC-QE-MS technique. Result: We identified 215 differentially expressed metabolites (DEMs) between the IMN and healthy control (HC) groups. Furthermore, these DEMs were significantly identified in histidine metabolism, arginine and proline metabolism, pyrimidine metabolism, purine metabolism, and steroid hormone biosynthesis. Several key DEMs were significantly correlated with the level of clinical parameters, such as serum albumin, IgG, UTP, and cholesterol. Among them, dehydroepiandrosterone sulfate (DHEAS) was considered the reliable diagnostic biomarker in the IMN group. There was an increased abundance of actinobacteria, phylum proteobacteria, and class gammaproteobacterial in IMN patients for host-microbiome origin analysis. Conclusion: Our study revealed the profiles of DEMs from the IMN and HC groups. The result demonstrated that there were disorders of amino acids, nucleotides, and steroids hormones metabolism in IMN patients. The down-regulation of DHEAS may be associated with the imbalance of the immune environment in IMN patients. In host-microbiome origin analysis, the gut microbiota and metabolite disturbances were present in IMN patients.

Individual and combined effects of arbuscular mycorrhizal fungi and phytohormones on the growth and physiobiochemical characteristics of tea cutting seedlings.

Both arbuscular mycorrhizal fungi (AMF) and phytohormones collectively regulate plant growth and root development, but their individual and combined effects on tea [Camellia sinensis (L.) O. Kuntze] cutting seedings remain unclear. This study examined the individual and combined effects of two species of AMF (Rhizophagus intraradices, RI and Funneliformis mosseae, FM) and two types of palnt hormones (strigolactones, SLs; polyamines, PAs) on tea cutting seedings, by evaluating the growth and physiobiochemical characteristics of plants treated with the AMFs and/or hormones. The results showed that inoculation with either AMF individually or hormones treatment alone could significantly enhanced mycorrhizal colonization, growth target and physiobiochemical characteristics of tea cutting seedlings. Interestingly, the addition of a combination of AMFs and hormones showed superior effects, while SL and RI exhibited the most improvements to the colonization rate, plant growth, root-morphological traits, root DHA activity, photosynthesis, chlorophyll content, soluble sugar content in leaves, and the activities of antioxidant enzymes (SOD, POD, and CAT), compared to other treatment combinations (SL + FM, PA + RI, and PA + FM). Correlation analyses revealed a significantly (p < 0.05) positive correlation of root AMF colonization with root-related traits (e.g., DHA, root total length, surface area, and volume) and leaf-related traits (e.g., leaf area, shoot biomass, total chlorophyll, and antioxidant enzyme activities). This study demonstrated that while the apllication of individual AMF or plant hormones had a certain good effects on most growth and physiobiochemical characteristics parameters of tea cutting seedings, the additive effect was from specific combined of AMF and plant hormones. These results highlight the possibility for combined of AMF and plant hormones to improve the asexual reproduction of tea plants via cuttings.

Serum proteome and metabolome uncover novel biomarkers for the assessment of disease activity and diagnosing of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting thousands of people. There are still no effective biomarkers for SLE diagnosis and disease activity assessment. We performed proteomics and metabolomics analyses of serum from 121 SLE patients and 106 healthy individuals, and identified 90 proteins and 76 metabolites significantly changed. Several apolipoproteins and the metabolite arachidonic acid were significantly associated with disease activity. Apolipoprotein A-IV (APOA4), LysoPC(16:0), punicic acid and stearidonic acid were correlated with renal function. Random forest model using the significantly changed molecules identified 3 proteins including ATRN, THBS1 and SERPINC1, and 5 metabolites including cholesterol, palmitoleoylethanolamide, octadecanamide, palmitamide and linoleoylethanolamide, as potential biomarkers for SLE diagnosis. Those biomarkers were further validated in an independent cohort with high accuracy (AUC = 0.862 and 0.898 for protein and metabolite biomarkers respectively). This unbiased screening has led to the discovery of novel molecules for SLE disease activity assessment and SLE classification.

Single-stranded DNA binding protein coupled aptasensor with carbon-gold nanoparticle amplification for marine toxins detection assisted by a miniaturized absorbance reader.

Okadaic acid (OA), one of the most widely distributed marine toxins worldwide poses a severe threat to human health. Previous sensing methods for OA detection are usually based on antigen-antibody binding mechanism. However, the drawbacks of antibodies especially the enzyme-labeled antibodies, such as the harsh storage condition and high cost, lead to significant challenges to OA detection in biological samples. To overcome these limitations, a single-stranded DNA binding protein (SSB) coupled aptasensor was developed for OA detection. SSB was incubated on the microplate as a substitute for conventional OA-protein conjugations. Carbon-gold nanoparticles were synthesized and labeled with horseradish peroxidase and thiol-modified aptamers to obtain a capture probe (CGNs@HRP-Apt) instead of the enzyme-labeled antibody for signal amplification. OA and SSB competed to bind with limited aptamers on CGNs@HRP-Apt probes followed by colorimetric assay to obtain the optical signals correlated to OA concentration. To achieve on-site detection, a miniaturized and multichannel absorbance reader (Smart-plate reader) was self-designed with full automation for OA detection. Utilizing the SSB coupled aptasensor and the Smart-plate reader, our approach enables cost-effective and on-site OA sensing with a detection range of 2.5-80 ppb and an ultra-low limit of detection of 0.68 ppb. Moreover, novel OA detection kits based on the SSB coupled aptasensor were prepared which can effectively reduce the cost by 15 times lower than that of commercial ELISA kits. Therefore, the developed platform provides a favorable and promising avenue for marine toxin detection in aquaculture and food safety.

Multifunctional AuNPs@HRP@FeMOF immune scaffold with a fully automated saliva analyzer for oral cancer screening.

Delayed diagnosis of cancer-causing death is a worldwide concern. General diagnosis methods are invasive, time-consuming, and operation complicated, which are not suitable for preliminary screening. To address these challenges, the sensing platform based on immune scaffold and fully automated saliva analyzer (FASA) was proposed for oral cancer screening for the first time by non-invasive detection of Cyfra21-1 in saliva. Through one-step synthesis method with unique covalent and electrostatic adsorption strategy, AuNPs@HRP@FeMOF immune scaffold features multiple functions including antibody carrier, catalytic activity, and signal amplification. Highly integrated FASA with the immune scaffold provides automatic testing to avoid false-positive results and reduce pretreatment time without any user intervention. Compared with the commercial analyzer, FASA has comparable performance for Cyfra21-1 detection with a detection range of 3.1-50.0 ng/mL and R2 of 0.971, and superior features in full automation, high integration, time saving and low cost. Oral cancer patients could be distinguished accurately by the platform with an excellent correlation (R2 of 0.904) and average RSD (5.578%) without sample dilution. The proposed platform provides an effective and promising tool for cancer screening in point-of-care applications, which can be further extended for biomarker detection in universal body fluids, disease screening, prognosis review and homecare monitoring.

Enzyme-free and wide-range portable colorimetric sensing system for uric acid and hydrogen peroxide based on copper nanoparticles.

Uric acid (UA) is the final product of purine metabolism. A high concentration of UA in body fluid may lead to kidney stones, gout, and some cardiovascular diseases. Therefore, the non-invasive daily monitoring of UA is of great significance for both hyperuricemia patients and fit people. However, most of the current detection methods for UA are enzyme-dependent which limits the application scenarios and lacks portable instruments for on-site detection, including optics and electrochemistry. In this work, an enzyme-free and wide-range colorimetric sensor for UA and H2O2 detection was developed based on a mercaptosuccinic acid (MSA)-modified Cu nanoparticles (CuNPs). Under the action of UA or H2O2, with the cleavage of MSAs on the CuNPs surface, small Cu particles are further aggregated into larger particles with a lightning violet color. With the employment of the multi-channel handheld automatic photometer (MHAP), the concentration of UA and H2O2 can be determined on-site according to the absorbance measurement by the photodiodes. The linear range of UA was 5 µM-4.5 mM with the limit of detection (LOD) of 3.7 µM, while the linear range of H2O2 was 5 mM-500 mM and 5 µM-5 mM with the LOD of 4.3 µM. This approach has been applied to the detection of UA in human urine, providing more possibilities for non-invasive home health monitoring, community medical diagnosis, and broader prospects of on-site disease detection.

A Silicon-Based Field-Effect Biosensor for Drug-Induced Cardiac Extracellular Calcium Ion Change Detection.

Calcium ions participate in the regulation of almost all biological functions of the body, especially in cardiac excitation-contraction coupling, acting as vital signaling through ion channels. Various cardiovascular drugs exert their effects via affecting the ion channels on the cell membrane. The current strategies for calcium ion monitoring are mainly based on fluorescent probes, which are commonly used for intracellular calcium ion detection (calcium imaging) and cannot achieve long-term monitoring. In this work, an all-solid-state silicone-rubber ion-sensitive membrane was fabricated on light-addressable potentiometric sensors to establish a program-controlled field-effect-based ion-sensitive light-addressable potentiometric sensor (LAPS) platform for extracellular calcium ion detection. L-type calcium channels blocker verapamil and calcium channel agonist BayK8644 were chosen to explore the effect of ion channel drugs on extracellular calcium ion concentration in HL-1 cell lines. Simultaneously, microelectrode array (MEA) chips were employed to probe the HL-1 extracellular field potential (EFP) signals. The Ca2+ concentration and EFP parameters were studied to comprehensively evaluate the efficacy of cardiovascular drugs. This platform provides more dimensional information on cardiovascular drug efficacy that can be utilized for accurate drug screening.

Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.

Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge. Methods: We performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers. Results: Screening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq. Discussion: Unbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

Microbiome and metabolome analysis to clarify the interaction between the urine microbiota and serum metabolites in Chinese patients with immunoglobulin A nephropathy.

Background: The bacterial and metabolic networks in immunoglobin A nephropathy (IgAN), the most common type of primary chronic glomerulonephritis worldwide, have not been extensively studied. To help develop better methods for the diagnosis, treatment, and prognosis of IgAN, we characterized the alterations of the urinary microbiome and serum metabolome in patients with IgAN. Methods: We analyzed serum and urine samples from Chinese patients with IgAN and healthy controls (HCs) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 16S ribosomal RNA gene sequencing. Results: Patients with IgAN had a higher relative abundance of Actinomyces and a lower relative abundance of Lactobacillus. The elements of metabolism have been affected, including free amino acids, polyunsaturated fatty acids, and oligopeptides. We also identified the 9 metabolites that might be the core metabolites, including guanidinoacetic acid, apo-[3-methylcrotonoyl-CoA:carbon-dioxide ligase (ADP-forming)], and diethanolamine, which linked the metabolic networks between the urinary tract (UT) and blood. Other core metabolites, such as homocitrulline, apo-[3-methylcrotonoyl-CoA:carbon-dioxide ligase (ADP-forming)], butyrylcarnitine, formiminoglutamic acid (FIGLU), diethanolamine, and prolylhydroxyproline, were positively correlated with urinary mili-total protein (MTP). Conversely, Lactobacillus was negatively correlated with MTP. Conclusions: We verified the connection between the disruption of the microbiota and serum metabolites, along with the clinical parameters, in patients with IgAN, which may help provide a tool for IgAN interventions.

A robust colorimetric aptasensor for the label-free detection of marine toxins based on tyrosine-capped gold nanoparticles.

PbTx-2 and okadaic acid (OA) are two typical marine toxins that are highly toxic and harmful to human health. The approach based on citrate-capped gold nanoparticles (Cit-AuNPs) and specific aptamers to construct label-free colorimetric sensors is a widely used method for marine toxin detection. However, the potential interactions between Cit-AuNPs and target molecules have always been ignored, which may result in wrong analytical results due to shortcomings in the Cit-AuNPs. To overcome these shortfalls, in this work, AuNPs were synthesized using tyrosine as a reducing and capping agent, and a robust colorimetric aptasensor based on tyrosine-capped AuNPs (Tyr-AuNPs) was constructed for the label-free detection of marine toxins. Tyr-AuNPs presented better stability compared to Cit-AuNPs due to the stronger binding of amine groups on tyrosine to AuNPs through the Au-N bond. Interactions between Tyr-AuNPs and PbTx-2 were analyzed through UV-vis and isothermal titration calorimetry methods and the results validated the robustness of the Tyr-AuNPs. Colorimetric aptasensors were established for PbTx-2 and OA detection with a linear range of 0.05-4 ppm and limits of detection of 2.25 ppb and 5.19 ppb, respectively. These results demonstrate that the developed colorimetric aptasensor can be a robust and promising method for marine toxin detection.

Absolute quantification and characterization of oxylipins in lupus nephritis and systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with multi-organ inflammation and defect, which is linked to many molecule mediators. Oxylipins as a class of lipid mediator have not been broadly investigated in SLE. Here, we applied targeted mass spectrometry analysis to screen the alteration of oxylipins in serum of 98 SLE patients and 106 healthy controls. The correlation of oxylipins to lupus nephritis (LN) and SLE disease activity, and the biomarkers for SLE classification, were analyzed. Among 128 oxylipins analyzed, 92 were absolutely quantified and 26 were significantly changed. They were mainly generated from the metabolism of several polyunsaturated fatty acids, including arachidonic acid (AA), linoleic acid (LA), docosahexanoic acid (DHA), eicosapentanoic acid (EPA) and dihomo-γ-linolenic acid (DGLA). Several oxylipins, especially those produced from AA, showed different abundance between patients with and without lupus nephritis (LN). The DGLA metabolic activity and DGLA generated PGE1, were significantly associated with SLE disease activity. Random forest-based machine learning identified a 5-oxylipin combination as potential biomarker for SLE classification with high accuracy. Seven individual oxylipin biomarkers were also identified with good performance in distinguishing SLE patients from healthy controls (individual AUC > 0.7). Interestingly, the biomarkers for differentiating SLE patients from healthy controls are distinct from the oxylipins differentially expressed in LN patients vs. non-LN patients. This study provides possibilities for the understanding of SLE characteristics and the development of new tools for SLE classification.

Synthesis and Bioactivity of Novel Sulfonate Scaffold-Containing Pyrazolecarbamide Derivatives as Antifungal and Antiviral Agents.

Novel pyrazolecarbamide derivatives bearing a sulfonate fragment were synthesized to identify potential antifungal and antiviral agents. All the structures of the key intermediates and target compounds were confirmed by nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). The single-crystal X-ray diffraction of the compound T22 showed that pyrazole carbamide is a sulfonate. The in vitro antifungal activities of the target compounds against Colletotrichum camelliae, Pestalotiopsis theae, Gibberella zeae, and Rhizoctonia solani were evaluated at 50 µg/ml. Among the four pathogens, the target compounds exhibited the highest antifungal activity against Rhizoctonia solani. The compound T24 (EC50 = 0.45 mg/L) had higher antifungal activity than the commercial fungicide hymexazol (EC50 = 10.49 mg/L) against R. solani, almost similar to bixafen (EC50 = 0.25 mg/L). Additionally, the target compounds exhibited protective effects in vivo against TMV. Thus, this study reveals that pyrazolecarbamide derivatives bearing a sulfonate fragment exhibit potential antifungal and antiviral activities.

A Review on the Extraction, Bioactivity, and Application of Tea Polysaccharides.

Tea is a non-alcoholic drink containing various active ingredients, including tea polysaccharides (TPSs). TPSs have various biological activities, such as antioxidant, anti-tumor, hypoglycemic, and anti-cancer activities. However, TPSs have a complex composition, which significantly limits the extraction and isolation methods, thus limiting their application. This paper provides insight into the composition, methodological techniques for isolation and extraction of the components, biological activities, and functions of TPSs, as well as their application prospects.

Multi-Platform-Based Analysis Characterizes Molecular Alterations of the Nucleus in Human Colorectal Cancer.

Background: The disturbed molecular alterations of nucleus may promote the development of colorectal cancer (CRC). A multi-platform-based analysis of nucleus of CRC patients helps us to better understand the underlying mechanism of CRC and screen out the potential drug targets for clinical treatment. However, such studies on nucleus in human CRC are still lacking. Methods: We collected the cancerous and para-cancerous tissues from eight CRC patients and performed a multiplex analysis of the molecular changes of the nucleus, including structural variations (SVs), DNA methylation, chromatin accessibility, proteome and phosphorproteome. Results: In our study, we revealed a significant molecular change of nucleus of CRC patients using our original proteomic and phosphorylomic datasets. Subsequently, we characterized the molecular alterations of nucleus of CRC patients at multiple dimensionalities, including DNA, mRNA, protein and epigenetic modification. Next, we found that the great molecular changes of nucleus might affect the biological processes named endocytosis and ubiquitin-mediated proteolysis. Besides, we identified DYNC1LI2 and TPR as the potentially hub proteins within the network of nuclear genes in CRC cells. Furthermore, we identified 1905 CRC-specific SVs, and proclaimed 17 CRC-specific SVs were probably associated with the disturbance of immune microenvironment of CRC patients. We also revealed that the SVs of CXCL5, CXCL10 and CXCL11 might be the core SVs among all the immune-relevant SVs. Finally, we identified seven genes as the upstream transcriptional factors potentially regulating the expression of nuclear genes, such as YY1 and JUN, using a multi-omics approach. Conclusion: Here, we characterized the molecular changes of nucleus of CRC patients, disclosed the potentially core nuclear genes within the network, and identified the probable upstream regulator of nucleus. The findings of this study are helpful to understand the pathogenic molecular changes of nucleus in CRC patients and provide a functional context for drug development in future.

Development of QDs-based nanosensors for heavy metal detection: A review on transducer principles and in-situ detection.

Heavy metal pollution has severe threats to the ecological environment and human health. Thus, it is urgent to achieve the rapid, selective, sensitive and portable detection of heavy metal ions. To overcome the defects of traditional methods such as time-consuming, low sensitivity, high cost and complicated operation, QDs (Quantum dots)-based nanomaterials have been used in sensors to significantly improve the sensing performance. Due to their excellent physicochemical properties, high specific surface area, high adsorption and reactive capacity, nanomaterials could act as potential probes or offer enhanced sensitivity and create a promising nanosensors platform. In this review, the rapidly advancing types of QDs for heavy metal ions detection are first summarized. Modified with ligands, nanomaterials, or biomaterials, QDs are assembled on sensors by the interaction of electrostatic adsorption, chemical bonding, steric hindrance, and base-pairing. The stability of QDs-based nanosensors is improved by doping the elements to QDs, providing the reference substance, optimizing the assemble strategies and so on. Then, according to transducer principles, the two most typical sensor categories based on QDs: optical and electrochemical sensors are highlighted to be discussed. In the meanwhile, portable devices combining with QDs to adapt the practical detection in complex situations are summarized. The deficiencies and future challenges of QDs in toxicity, specificity, portability, multi-metal co-detection and degradation during the detection are also pointed out. In the end, the development trends of QDs-based nanosensors for heavy metal ions detection are discussed. This review presents an overall understanding, recent advances, current challenges and future outlook of QDs-based nanosensors for heavy metal detection.