Improved PAA algorithm for breast mass detection in mammograms.

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Synthesis and Characterization of Slow-Release Chitosan Oligosaccharide Pyridine Schiff Base Copper Complexes with Antifungal Activity.

Herein, a series of chitosan oligosaccharide copper complexes modified with pyridine groups (CPSx-Cu complexes) were successfully prepared via the Schiff base reaction and ion complexation reaction for slow-release fungicide. The structures of the synthesized derivatives were characterized via Fourier transform infrared spectroscopy and 1H and 13C nuclear magnetic resonance spectroscopy, and the unit configuration of the complexes was calculated using Gaussian software. The slow-release performance experiment demonstrated that the cumulative copper ion release rate of CPSx-Cu complexes was dependent on the type of substituents on the pyridine ring. Furthermore, the in vitro and in vivo antifungal activities of the CPSx-Cu complexes were investigated. At a concentration of 0.4 mg/mL, CPSx-Cu complexes completely inhibited the growth of Pythium vexans and Phytophthora capsici. Results indicated that CPSx-Cu complexes with slow-release ability exhibited better antifungal activity than thiodiazole-copper and copper sulfate basic. This study confirmed that combining chitosan oligosaccharide with bioactive pyridine groups and copper ions is an effective approach to further developing slow-release copper fungicides, providing new possibilities for the application of copper fungicides in green agriculture. This study lays the foundation for further studies on biogreen copper fungicides.

Comparison of six machine learning methods for differentiating benign and malignant thyroid nodules using ultrasonographic characteristics.

BACKGROUND: Several machine learning (ML) classifiers for thyroid nodule diagnosis have been compared in terms of their accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the receiver operating curve (AUC). A total of 525 patients with thyroid nodules (malignant, n = 228; benign, n = 297) underwent conventional ultrasonography, strain elastography, and contrast-enhanced ultrasound. Six algorithms were compared: support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), logistic regression (LG), GlmNet, and K-nearest neighbors (K-NN). The diagnostic performances of the 13 suspicious sonographic features for discriminating benign and malignant thyroid nodules were assessed using different ML algorithms. To compare these algorithms, a 10-fold cross-validation paired t-test was applied to the algorithm performance differences. RESULTS: The logistic regression algorithm had better diagnostic performance than the other ML algorithms. However, it was only slightly higher than those of GlmNet, LDA, and RF. The accuracy, sensitivity, specificity, NPV, PPV, and AUC obtained by running logistic regression were 86.48%, 83.33%, 88.89%, 87.42%, 85.20%, and 92.84%, respectively. CONCLUSIONS: The experimental results indicate that GlmNet, SVM, LDA, LG, K-NN, and RF exhibit slight differences in classification performance.

An Overview of Image Generation of Industrial Surface Defects.

Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence.

BACKGROUND: Rapid on-site triage is critical after mass-casualty incidents (MCIs) and other mass injury events. Unmanned aerial vehicles (UAVs) have been used in MCIs to search and rescue wounded individuals, but they mainly depend on the UAV operator's experience. We used UAVs and artificial intelligence (AI) to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue. METHODS: This was a preliminary experimental study. We developed an intelligent triage system based on two AI algorithms, namely OpenPose and YOLO. Volunteers were recruited to simulate the MCI scene and triage, combined with UAV and Fifth Generation (5G) Mobile Communication Technology real-time transmission technique, to achieve triage in the simulated MCI scene. RESULTS: Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs. Eight volunteers participated in the MCI simulation scenario. The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs. CONCLUSION: The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm.

In recent years, deep learning has been used to develop an automatic breast cancer detection and classification tool to assist doctors. In this paper, we proposed a three-stage deep learning framework based on an anchor-free object detection algorithm, named the Probabilistic Anchor Assignment (PAA) to improve diagnosis performance by automatically detecting breast lesions (i.e., mass and calcification) and further classifying mammograms into benign or malignant. Firstly, a single-stage PAA-based detector roundly finds suspicious breast lesions in mammogram. Secondly, we designed a two-branch ROI detector to further classify and regress these lesions that aim to reduce the number of false positives. Besides, in this stage, we introduced a threshold-adaptive post-processing algorithm with dense breast information. Finally, the benign or malignant lesions would be classified by an ROI classifier which combines local-ROI features and global-image features. In addition, considering the strong correlation between the task of detection head of PAA and the task of whole mammogram classification, we added an image classifier that utilizes the same global-image features to perform image classification. The image classifier and the ROI classifier jointly guide to enhance the feature extraction ability and further improve the performance of classification. We integrated three public datasets of mammograms (CBIS-DDSM, INbreast, MIAS) to train and test our model and compared our framework with recent state-of-the-art methods. The results show that our proposed method can improve the diagnostic efficiency of radiologists by automatically detecting and classifying breast lesions and classifying benign and malignant mammograms.

China rheumatoid arthritis registry of patients with Chinese medicine (CERTAIN): Rationale, design, and baseline characteristics of the first 11,764 enrollees.

BACKGROUND: Chinese medicine (CM) has become a popular interventional treatment for rheumatoid arthritis (RA). However, limited knowledge about general characteristics and long-term clinical outcomes hampers the development of CM for RA. PURPOSE: The main objectives of the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) were to describe the population of RA patients receiving CM treatment in multiple centers in China using different variables and compare these findings with internationally reported data. STUDY DESIGN: The CERTAIN is a prospective, multicenter, observational disease registry. METHODS: Adult RA patients who fulfilled the 2010 American College of Rheumatology/ European League Against Rheumatism classification criteria for RA and received CM treatment were recruited into the CERTAIN by rheumatologists from 145 hospitals across 30 provinces in China. Data on demographics, disease characteristics, comorbidities, treatments, and adverse events, with a 2-year follow-up, were collected and documented using a predefined protocol. RESULTS: In the 2 years since the study began in September 2019, 11,764 patients have been enrolled (enrolment is ongoing), and 13.10% of participants have completed the 6-month follow-up. We present the baseline characteristics of the first 11,764 enrollees. CONCLUSIONS: The CERTAIN is the first nationwide registry to document comprehensive data on CM treatment in patients with RA. The development of the CERTAIN resource is a significant step forward for Chinese RA patients, herbal medicine users, and research communities and will deepen our understanding of CM for RA. REGISTRATION: The study was registered at ClinicalTrials.gov (NCT05219214).

Synthesis and antifungal activity of slow-release pyridinylcarbonyl chitooligosaccharide copper complexes.

Copper fungicides are fungicides have a wide application, but their toxicity to plant growth and the damage they cause to the environment cannot be ignored. As such, the novel, low toxicity biogenic copper fungicide has strong industrial application prospects. Herein, pyridinylcarbonyl chitooligosaccharide ligands (pCOSx) and their copper complexes (pCOSx-Cu) were synthesized. The results showed that a p-π-π conjugated system was formed in pCOSx, resulting in the formation of a slowly dissociated coordination bond between the nitrogen atom of pyridyl and Cu2+ in pCOSx-Cu. The cumulative release rate of Cu2+ is positively correlated with the electron donating ability of pyridyl. Compared with the commercial copper fungicide thiodiazole­copper, pCOSx-Cu exhibited better antifungal activity, lower phytotoxicity and better biocompatibility. This work demonstrated that it was feasible to construct a conjugated system in a chitooligosaccharide copper complex to improve slow-release performance, which laid a foundation for the in-depth study of green copper fungicides. CHEMICAL COMPOUNDS: Chitooligosaccharide (PubChem CID: 3086191); Nicotinoyl chloride hydrochloride (PubChem CID: 88438); Isonicotinoyl chloride hydrochloride (PubChem CID: 12262826); 2-chloronicotinoyl chloride (PubChem CID: 2774541); Trimethylchlorosilane (PubChem CID: 6397); Tetrabutylammonium fluoride (PubChem CID: 2724141); Copper (II) acetate monohydrate (PubChem CID: 165397).

The Impact of Traditional Chinese Medicine QingreHuoxue Treatment and the Combination of Methotrexate and Hydroxychloroquine on the Radiological Progression of Active Rheumatoid Arthritis: A 52-Week Follow-Up of a Randomized Controlled Clinical Study.

Traditional Chinese medicine (TCM) has been used successfully to treat rheumatoid arthritis (RA). QingreHuoxue treatment (QingreHuoxue decoction [QRHXD]/QingreHuoxue external preparation [QRHXEP]) is a Chinese medicine treatment for RA. To date, very few studies have compared the long-term effects of QRHXD with those of conventional disease-modifying antirheumatic drugs on RA disease activity and radiological progression. QRHXD delayed the radiological progression and showed long-term clinical efficacy of RA. In clinical experiments, the clinical evidence of delaying the radiological progression of RA patients was obtained. A portion of the patients who participated in the "Traditional Chinese Medicine QingreHuoxue Treatment vs. the Combination of Methotrexate and Hydroxychloroquine for Active Rheumatoid Arthritis" study were followed up for 52 weeks, and intention-to-treat (ITT) and compliance protocol (PP) analyses were used to collect and compare the clinical indicators and imaging data between baseline and week 52. Two radiologists who were blind to treatment scored the images independently. Of the 468 subjects, 141 completed the 52-week follow-up. There were no significant differences among the three groups: the traditional Chinese medicine comprehensive treatment group, the Western medicine treatment group, and the integrated traditional Chinese and Western medicine treatment group. There were no differences in the total Sharp score, joint space stenosis score, and joint erosion score at baseline or 52 weeks. In the comparison of the estimated annual radiographic progression (EARP) and the actual annual Sharp total score changes among the three groups, the actual changes were much lower than the EARP at baseline. The radiological progress in all three groups was well controlled. Results of the ITT and PP data sets showed that the disease activity score 28 level of the three groups at 52 weeks was significantly lower than that at baseline. During the 52-week treatment period, the clearance of heat and promotion of blood circulation controlled disease activity and delayed the radiological progress of active RA.

[Biological connotation of four traditional Chinese medicine syndromes of rheumatoid arthritis based on "disease-syndrome-symptom" association network].

The present study explored the biological connotation of traditional Chinese medicine(TCM) syndromes of rheumatoid arthritis(RA) from the "disease-syndrome-symptom" association network. RA patients with four TCM syndromes(dampness-heat obstruction, phlegm-stasis obstruction, Qi-blood deficiency, and liver and kidney deficiency), three for each type, were assigned as the RA TCM syndrome group, and three healthy volunteers as the normal control group. The differential gene sets of four syndromes were screened out through transcriptome expression profiling and bioinformatics mining. The relevant gene sets of syndrome-related clinical symptoms were collected from TCMIP v2.0(http://www.tcmip.cn/). The "disease-syndrome-symptom" association networks of four RA syndromes were established by using the intersection genes of syndrome-related differential genes and symptom-related genes, and the key network target genes of each syndrome were screened out and the corresponding biological functions were mined through topological feature calculation and enrichment analysis. The genes associated with clinical symptoms such as vasculitis, joint pain, and fever in the damp-heat obstruction syndrome ranked the top, and the key network target genes of this syndrome were most significantly enriched in the pathways related to material and energy metabolism and thermal reaction biological processes. The clinical symptom-related genes of the phlegm-stasis obstruction syndrome were most significantly enriched in the pathways related to "immunity-inflammation", nervous system regulation, and sensory response. The clinical symptoms such as hypoglycemia, hypotension, weight loss, palpitation, and arrhythmia in Qi-blood deficiency syndrome were predominant, and its key network target genes were most significantly enriched in the pathways related to the nervous system and "immunity-inflammation" response. The abnormal symptoms in the liver and kidney in the liver and kidney deficiency syndrome were commonly seen, and its key network target genes were most significantly enriched in the "immunity-inflammation" regulatory pathways, and liver and kidney development and metabolic response. In conclusion, the differences and connections of the biological basis between different TCM syndromes of RA are in line with the theoretical interpretation of TCM on the etiology and pathogenesis of RA. This study summarized the objective essence of syndromes to a certain extent from the "disease-syndrome-symptom" association network and is expected to provide a theoretical basis for the discovery of serum biomarkers of RA syndromes.

Classification of Obsessive-Compulsive Disorder Using Distance Correlation on Resting-State Functional MRI Images.

Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies. Our goal was to use the multivariate pattern analysis to compare the ability of such three correlation methods to distinguish between the patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCSs), so as to find optimal correlation method. The main process includes four steps. First, the regions of interest are defined by automated anatomical labeling (AAL). Second, functional connectivity (FC) matrices are constructed by the three correlation methods. Third, the best discriminative features are selected by support vector machine recursive feature elimination (SVM-RFE) with a stratified N-fold cross-validation strategy. Finally, these discriminative features are used to train a classifier. We had a total of 128 subjects out of which 61 subjects had OCD and 67 subjects were normal. All the three correlation methods with SVM have achieved good results, among which distance correlation is the best [accuracy = 93.01%, specificity = 89.71%, sensitivity = 95.08%, and area under the receiver-operating characteristic curve (AUC) = 0.94], followed by Pearson correlation and partial correlation is the last. The most discriminative regions of the brain for distance correlation are right dorsolateral superior frontal gyrus, orbital part of left superior frontal gyrus, orbital part of right middle frontal gyrus, right anterior cingulate and paracingulate gyri, left the supplementary motor area, and right precuneus, which are the promising biomarkers of OCD.

Establishing an appropriate Z score regression equation for Chinese pediatric coronary artery echocardiography: a multicenter prospective cohort study.

BACKGROUND: Z score utility is emphasized in classifying coronary artery lesions in Kawasaki disease patients. The present study is the largest such multicenter Chinese pediatric study about coronary artery diameter reference values and Z score regression equation to date. It is useful in Chinese pediatric echocardiography. METHODS: A multicenter cohort was assembled, which consisted of 852 healthy children between 1 month and 17 years of age, ten children were excluded because their ultrasound images were not clear, or lost in following up. Diameters of the right coronary artery, left coronary artery, and left anterior descending coronary artery were assessed using echocardiography. Data were body surface area (BSA)-corrected using BSA calculated via either the Stevenson BSA formula or the Haycock BSA formula. Coronary artery diameter reference values and Z score regression equations were established for use in the Chinese pediatric population. RESULTS: No difference was observed between coronary artery diameter data corrected using BSAste or BSAhay. Of the five assessed regression models, the exponential model exhibited the best fit and was therefore selected as the basis for derivation of the SZ method. When comparing Z scores, those produced by the SZ method conformed to the standard normal distribution, while those produced by the D method did not. In addition, there was a statistically significant difference between Z scores produced by the SZ and D methods (P < 0.05). CONCLUSIONS: Coronary artery diameter reference values for echocardiography were successfully established for use in the Chinese pediatric population, and a Z score regression equation more suitable for clinical use in this population was successfully developed.

Design of Chitosan Sterilization Agents by a Structure Combination Strategy and Their Potential Application in Crop Protection.

Chitosan is the only cationic polysaccharide in nature. It is a type of renewable resource and is abundant. It has good biocompatibility, biodegradability and biological activity. The amino and hydroxyl groups in its molecules can be modified, which enables chitosan to contain a variety of functional groups, giving it a variety of properties. In recent years, researchers have used different strategies to synthesize a variety of chitosan derivatives with novel structure and unique activity. Structure combination is one of the main strategies. Therefore, we will evaluate the synthesis and agricultural antimicrobial applications of the active chitosan derivatives structure combinations, which have not been well-summarized. In addition, the advantages, challenges and developmental prospects of agricultural antimicrobial chitosan derivatives will be discussed.

Traditional Chinese Medicine Qingre Huoxue Treatment vs. the Combination of Methotrexate and Hydroxychloroquine for Active Rheumatoid Arthritis: A Multicenter, Double-Blind, Randomized Controlled Trial.

Traditional Chinese medicine (TCM) has been used successfully to treat rheumatoid arthritis (RA). Qingre Huoxue treatment (Qingre Huoxue decoction (QRHXD)/Qingre Huoxue external preparation (QRHXEP)) is a therapeutic scheme of TCM for RA. To date, there have been few studies comparing the efficacy and safety of QRHXD and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) for the treatment of active RA. This was investigated in a multicenter, double-blind, randomized controlled trial involving 468 Chinese patients with active RA [disease activity score (DAS)-28 > 3.2] treated with QRHXD/QRHXEP (TCM group), methotrexate plus hydroxychloroquine [Western medicine (WM) group], or both [integrative medicine (IM) group]. Patients were followed up for 24 weeks. The primary outcome measure was the change in DAS-28 from baseline to 24 weeks. The secondary outcome measures were treatment response rate according to American College of Rheumatology 20, 50, and 70% improvement criteria (ACR-20/50/70) and the rate of treatment-related adverse events (TRAEs). The trial was registered at ClinicalTrials.gov (NCT02551575). DAS-28 decreased in all three groups after treatment (p < 0.0001); the score was lowest in the TCM group (p < 0.05), while no difference was observed between the WM and IM groups (p > 0.05). At week 24, ACR-20 response was 73.04% with TCM, 80.17% with WM, and 73.95% with IM (based on the full analysis set [FAS], p > 0.05); ACR-50 responses were 40.87, 47.93, and 51.26%, respectively, (FAS, p > 0.05); and ACR-70 responses were 20.87, 22.31, and 25.21%, respectively, (FAS, p > 0.05). Thus, treatment efficacy was similar across groups based on ACR criteria. On the other hand, the rate of TRAEs was significantly lower in the TCM group compared to the other groups (p < 0.05). Thus, QRHXD/QRHXEP was effective in alleviating the symptoms of active RA-albeit to a lesser degree than csDMARDs-with fewer side effects. Importantly, combination with QRHXD enhanced the efficacy of csDMARDs. These results provide evidence that QRHXD can be used as an adjunct to csDMARDs for the management of RA, especially in patients who experience TRAEs with standard drugs. Clinical Trial Registration: ClinicalTrials.gov, identifier NCTNCT025515.

C-coordinated O-carboxymethyl chitosan Cu(II) complex exerts antifungal activity by disrupting the cell membrane integrity of Phytophthora capsici Leonian.

Damage to the cell membrane is an effective method to prevent drug resistance in plant fungal diseases. Here, we proposed a negative remodeling model of the cell membrane structure induced by the C-coordinated O-carboxymethyl chitosan Cu (II) complex (O-CSLn-Cu). FITC-labeled O-CSLn-Cu (FITC-O-CSLn-Cu) was first synthesized via a nucleophilic substitution reaction and confirmed by FT-IR. FITC-labeled O-CSLn-Cu could pass through the fungal cell membrane, as detected by confocal laser scanning microscopy (CLSM) coupled with fluorescein isothiocyanate (FITC)-fluorescence. O-CSLn-Cu treatment led to apparent morphological changes in the membranes of P. capsici Leonian and giant unilamellar vesicles (GUVs) by transmission electron microscopy (TEM). Then, we performed component analysis of the cell membrane from the P. capsici Leonian affected by O-CSLn-Cu with a particular interest in membrane physicochemical properties. Many unsaturated fatty acids (UFAs) and key enzymes promoting UFA synthesis of the cell membrane were downregulated. Similarly, a large number of membrane proteins responsible for substance transport and biochemical reactions were downregulated. Furthermore, O-CSLn-Cu treatments increased plasma membrane permeability with significant leakage of intercellular electrolytes, soluble proteins and sugars, and lipid peroxidation with decreasing membrane fluidity. Finally, aquaporin 10 was proven to be a potential molecular target sensitive to antimicrobial agents according to composition analysis of membrane structure and immunohistochemistry.

Proteoglycan from Bacillus sp. BS11 Inhibits the Inflammatory Response by Suppressing the MAPK and NF-κB Pathways in Lipopolysaccharide-Induced RAW264.7 Macrophages.

Inflammation is involved in the pathogenesis of many debilitating diseases. Proteoglycan isolated from marine Bacillus sp. BS11 (EPS11) was shown to have anticancer activity, but its anti-inflammatory potential remains elusive. In the present study, the anti-inflammatory effects and mechanism of EPS11 were evaluated using a lipopolysaccharide (LPS)-induced RAW264.7 macrophage model. Biochemical characterization showed that the total sugar content and protein content of EPS11 were 49.5% and 30.2% respectively. EPS11 was composed of mannose, glucosamine, galactosamine, glucose, galactose, rhamnose, and glucuronic acid. Its molecular weight was determined to be 3.06 × 105 Da. The protein determination of EPS11 was also performed. EPS11 displayed a strong anti-inflammatory effect on LPS-stimulated RAW264.7 macrophages in vitro, which significantly suppressed inflammatory cytokines and mediators (such as NO, TNF-α, IL-6 and IL-1ß, and COX-2). Western blot analysis indicated that EPS11 could downregulate the expression of many key proteins in mitogen-activated protein kinases (MAPKs) and transcription factor nuclear factor-κB (NF-κB) signaling pathways. In particular, EPS11 almost completely inhibited the expression of NF-κB P65, which indicated that EPS11 acted primarily on the NF-κB pathways. These findings offer new insights into the molecular mechanism underlying the anti-inflammatory effect of EPS11.

Automated mammographic mass detection using deformable convolution and multiscale features.

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract.

Integrated proteomics and metabolomics analysis reveals the antifungal mechanism of the C-coordinated O-carboxymethyl chitosan Cu(II) complex.

With wide application in agriculture, copper fungicides have undergone three stages of development: inorganic copper, synthetic organic copper, and natural organic copper. Using chitin/chitosan (CS) as a substrate, the natural organic copper fungicide C-coordinated O-carboxymethyl chitosan Cu(II) complex (O-CSLn-Cu) was developed in the laboratory. Taking Phytophthora capsici Leonian as an example, we explored the antifungal mechanism of O-CSLn-Cu by combining tandem mass tag (TMT)-based proteomics with non-targeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. A total of 1172 differentially expressed proteins were identified by proteomics analysis. According to the metabolomics analysis, 93 differentially metabolites were identified. Acetyl-CoA-related and membrane localized proteins showed significant differences in the proteomics analysis. Most of the differential expressed metabolites were distributed in the cytoplasm, followed by mitochondria. The integrated analysis revealed that O-CSLn-Cu could induce the "Warburg effect", with increased glycolysis in the cytoplasm and decreased metabolism in the mitochondria. Therefore, P. capsici Leonian had to compensate for ATP loss in the TCA cycle by increasing the glycolysis rate. However, this metabolic shift could not prevent the death of P. capsici Leonian. To verify this hypothesis, a series of biological experiments, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and enzyme activity measurements were carried out. The results suggest that O-CSLn-Cu causes mitochondrial injury, which consequently leads to excessive ROS levels and insufficient ATP levels, thereby killing P. capsici Leonian.

Detection of Simulated Periradicular Lesions in Porcine Bone by Optical Coherence Tomography.

INTRODUCTION: The accurate detection of periradicular lesions located under a nonperforated cortical plate poses a challenge in endodontic microsurgery. Optical coherence tomography (OCT) is a noninvasive imaging method that has been successfully used in many dental applications. In this study, we investigated if spectral-domain OCT (SD-OCT) could be used to determine simulated periradicular lesions. METHODS: Twenty-eight cavities with different depths were prepared on bone plates obtained from 5 porcine mandibles. Both 3-dimensional SD-OCT imaging and micro-computed tomographic (micro-CT) imaging were used to image the bottom of the air-filled cavity and the cavity filled with soft tissue for comparison. The residual bone thickness under the cavity was measured by SD-OCT and micro-CT imaging and compared using the Pearson correlation. RESULTS: The air-filled lesions were readily detected; yet, filling of the cavity with soft tissue diminished the appearance of the lesion boundaries in the SD-OCT images. The optical values of residual bone thickness obtained from SD-OCT ranged from 0.14-2.11 mm, which corresponded to the range of 0.26-1.18 mm from micro-CT imaging. A strong correlation was found between the 2 imaging modalities (r = 0.96; range, 0.94-0.98). The slope (1.56) of the linear regression matched the bulk refractive index of bone tissues. CONCLUSIONS: SD-OCT allows for visualization of the lesion boundaries via intact bone surfaces and may be a promising, practical, and nonirradiating adjunct tool for chairside localization of periradicular lesions in bone.

NormExpression: An R Package to Normalize Gene Expression Data Using Evaluated Methods.

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets.