ABSTRACT
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.
Subject(s)
Thyroid Nodule , Humans , Thyroid Nodule/diagnostic imaging , Thyroid Nodule/pathology , Sensitivity and Specificity , Diagnosis, Differential , Ultrasonography/methods , Machine LearningABSTRACT
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.
ABSTRACT
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.
Subject(s)
Arthritis, Rheumatoid , Medicine, Chinese Traditional , Arthritis, Rheumatoid/genetics , Hot Temperature , Humans , Kidney , SyndromeABSTRACT
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.
Subject(s)
Coronary Vessels , Echocardiography , Child , China , Cohort Studies , Coronary Vessels/diagnostic imaging , Humans , Infant , Prospective Studies , Reference ValuesABSTRACT
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.
Subject(s)
Anti-Infective Agents/chemistry , Biocompatible Materials/chemistry , Chitosan/chemistry , Crops, Agricultural , Polysaccharides/chemistry , Aldehydes/chemistry , Antifungal Agents/chemistry , Crop Protection , Ions , Ketones/chemistry , Phosphorus/chemistry , Plant Diseases/prevention & control , Schiff Bases/chemistry , Sulfur/chemistryABSTRACT
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.
Subject(s)
Anti-Inflammatory Agents/pharmacology , Bacillus/metabolism , Macrophages/drug effects , Mitogen-Activated Protein Kinases/metabolism , NF-kappa B/metabolism , Proteoglycans/pharmacology , Animals , Anti-Inflammatory Agents/isolation & purification , Cytokines/metabolism , Inflammation Mediators/metabolism , Lipopolysaccharides/toxicity , Macrophages/enzymology , Mice , Molecular Structure , Nitric Oxide/metabolism , Phosphorylation , Proteoglycans/isolation & purification , RAW 264.7 Cells , Signal Transduction , Structure-Activity RelationshipABSTRACT
In this study, 3-methyl-1,2,4-triazolyl chitosan (MTACS) and 3-chloromethyl-1,2,4-triazolyl chitosan (CMTACS) were prepared via cyclization of acyl thiourea chitosan (TUCS). Their structures were confirmed by FT-IR, ¹H-NMR, elemental analysis, DSC, XRD, and SEM. The conformations were predicted using the Gaussian 09 program. Additionally, the antifungal properties of MTACS and CMTACS against Stemphylium solani weber (S. solani), Alternaria porri (A. porri), and Gloeosporium theae-sinensis (G. theae-sinensis) were assayed in vitro and ranged from 250 µg/mL to 1000 µg/mL. The results showed that MTACS and CMTACS exhibited enhanced inhibitory effect on the selected fungi compared to the original chitosan and TUCS. In particular, they displayed better antifungal activities against A. porri and G. theae-sinensis than that of the positive control, Triadimefon. The findings described here may lead to them being used as antifungal agents for crop protection.
Subject(s)
Antifungal Agents/chemistry , Chitosan/analogs & derivatives , Chitosan/chemistry , Triazoles/chemistry , Antifungal Agents/chemical synthesis , Antifungal Agents/pharmacology , Chitosan/chemical synthesis , Chitosan/pharmacology , Cyclization , Triazoles/chemical synthesis , Triazoles/pharmacologyABSTRACT
BACKGROUND: Ultrasound transient elastography technology has found its place in elastography because it is safe and easy to operate. However, it's application in deep tissue is limited. The aim of this study is to design an ultrasound transient elastography system with coded excitation to obtain greater detection depth. METHODS: The ultrasound transient elastography system requires tissue vibration to be strictly synchronous with ultrasound detection. Therefore, an ultrasound transient elastography system with coded excitation was designed. A central component of this transient elastography system was an arbitrary waveform generator with multi-channel signals output function. This arbitrary waveform generator was used to produce the tissue vibration signal, the ultrasound detection signal and the synchronous triggering signal of the radio frequency data acquisition system. The arbitrary waveform generator can produce different forms of vibration waveform to induce different shear wave propagation in the tissue. Moreover, it can achieve either traditional pulse-echo detection or a phase-modulated or a frequency-modulated coded excitation. A 7-chip Barker code and traditional pulse-echo detection were programmed on the designed ultrasound transient elastography system to detect the shear wave in the phantom excited by the mechanical vibrator. Then an elasticity QA phantom and sixteen in vitro rat livers were used for performance evaluation of the two detection pulses. RESULTS: The elasticity QA phantom's results show that our system is effective, and the rat liver results show the detection depth can be increased more than 1 cm. In addition, the SNR (signal-to-noise ratio) is increased by 15 dB using the 7-chip Barker coded excitation. CONCLUSIONS: Applying 7-chip Barker coded excitation technique to the ultrasound transient elastography can increase the detection depth and SNR. Using coded excitation technology to assess the human liver, especially in obese patients, may be a good choice.
Subject(s)
Elasticity Imaging Techniques/methods , Animals , Elasticity , Liver/diagnostic imaging , Phantoms, Imaging , RatsABSTRACT
The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.
ABSTRACT
OBJECTIVE: To evaluate the effect of outbreaks control in school settings after a 2 dose varicella vaccine immunization strategy implemented in Beijing. METHODS: Epidemiological data of varicella outbreaks in school and kindergarten settings, which were reported by all 16 districts (county) during 2007-2013 according to the technical management norms of Beijing, was collected. The first dose and second dose varicella vaccine coverage rate of eligible children after the 2 dose varicella vaccine immunization strategy implementation were estimated through BJIIMS. Based on above we analyzed the changes of outbreak quantity, case quantity and the distribution characteristics between the pre-adjustment era (2007-2011 years) and late adjustment era (2013) of the 2 dose immunization strategy. RESULTS: In pre-adjustment era (2007-2011 years), an average of 74 (95% CI: 60-89) outbreaks was reported and 964 (95% CI: 812-1 116) cases were involved per year. In late adjustment era (2013): Outbreaks (35) declined 52.7%, involved cases (371) declined 61.5%; Outbreaks epidemic duration shortened from 22 days of pre-adjustment era to 18 days; Outbreaks involved 10-24 cases declined 64.7% (from 34 to 12); Outbreaks involved ≥ 25 cases declined 71.4% (from 7 to 2); Outbreaks of different school type as well as different regions without exception declined dramatically. Cumulative one-dose vaccine coverage in children of 2-6 yr of age was 89.6% (812 859/907 579), and cumulative second-dose vaccine coverage in children of 4-7 yr of age was 44.3% (289 764/647 732). CONCLUSION: Implementation of a 2 dose varicella vaccine immunization strategy effectively controlled outbreaks in school and kindergarten settings.
Subject(s)
Chickenpox Vaccine , Disease Outbreaks , Schools , Vaccination , Chickenpox , Child , Child, Preschool , Epidemics , Humans , StudentsABSTRACT
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.
Subject(s)
Chitosan , Coordination Complexes , Fungicides, Industrial , Copper/chemistry , Antifungal Agents/chemistry , Fungicides, Industrial/pharmacology , Chitosan/chemistry , Coordination Complexes/pharmacology , Coordination Complexes/chemistry , Schiff Bases , Spectroscopy, Fourier Transform Infrared , Pyridines/pharmacology , Oligosaccharides/pharmacologyABSTRACT
Thyroid cancer is the most common malignant tumor of the endocrine system, and evidence suggests that post-translational modifications (PTMs) and epigenetic alterations play an important role in its development. Recently, there has been increasing evidence linking dysregulation of ubiquitinating enzymes and deubiquitinases with thyroid cancer. This review aims to summarize our current understanding of the role of ubiquitination-modifying enzymes in thyroid cancer, including their regulation of oncogenic pathways and oncogenic proteins. The role of ubiquitination-modifying enzymes in thyroid cancer development and progression requires further study, which will provide new insights into thyroid cancer prevention, treatment and the development of novel agents.
ABSTRACT
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.
Subject(s)
Algorithms , Breast Neoplasms , Mammography , Humans , Mammography/methods , Breast Neoplasms/diagnostic imaging , Female , Deep Learning , Early Detection of Cancer/methods , False Positive Reactions , Probability , Radiographic Image Interpretation, Computer-Assisted/methods , Breast/diagnostic imaging , Databases, FactualABSTRACT
Background: Most primary bone tumors are often found in the bone around the knee joint. However, the detection of primary bone tumors on radiographs can be challenging for the inexperienced or junior radiologist. This study aimed to develop a deep learning (DL) model for the detection of primary bone tumors around the knee joint on radiographs. Methods: From four tertiary referral centers, we recruited 687 patients diagnosed with bone tumors (including osteosarcoma, chondrosarcoma, giant cell tumor of bone, bone cyst, enchondroma, fibrous dysplasia, etc.; 417 males, 270 females; mean age 22.8±13.2 years) by postoperative pathology or clinical imaging/follow-up, and 1,988 participants with normal bone radiographs (1,152 males, 836 females; mean age 27.9±12.2 years). The dataset was split into a training set for model development, an internal independent and an external test set for model validation. The trained model located bone tumor lesions and then detected tumor patients. Receiver operating characteristic curves and Cohen's kappa coefficient were used for evaluating detection performance. We compared the model's detection performance with that of two junior radiologists in the internal test set using permutation tests. Results: The DL model correctly localized 94.5% and 92.9% bone tumors on radiographs in the internal and external test set, respectively. An accuracy of 0.964/0.920, and an area under the receiver operating characteristic curve (AUC) of 0.981/0.990 in DL detection of bone tumor patients were for the internal and external test set, respectively. Cohen's kappa coefficient of the model in the internal test set was significantly higher than that of the two junior radiologists with 4 and 3 years of experience in musculoskeletal radiology (Model vs. Reader A, 0.927 vs. 0.777, P<0.001; Model vs. Reader B, 0.927 vs. 0.841, P=0.033). Conclusions: The DL model achieved good performance in detecting primary bone tumors around the knee joint. This model had better performance than those of junior radiologists, indicating the potential for the detection of bone tumors on radiographs.
ABSTRACT
Infection by bacteria leads to tissue damage and inflammation, which need to be tightly controlled by host mechanisms to avoid deleterious consequences. It is previously reported that TMEM16F, a calcium-activated lipid scramblase expressed in various immune cell types including T cells and neutrophils, is critical for the control of infection by bacterium Listeria monocytogenes (Lm) in vivo. This function correlated with the capacity of TMEM16F to repair the plasma membrane (PM) damage induced in T cells in vitro, by the Lm toxin listeriolysin O (LLO). However, whether the protective effect of TMEM16F on Lm infection in vivo is mediated by an impact in T cells, or in other cell types, is not determined. Herein, the immune cell types and mechanisms implicated in the protective effect of TMEM16F against Lm in vivo are elucidated. Cellular protective effects of TMEM16F correlated with its capacity of lipid scrambling and augment PM fluidity. Using cell type-specific TMEM16F-deficient mice, the indication is obtained that TMEM16F expressed in liver Kupffer cells (KCs), but not in T cells or B cells, is key for protection against Listeria in vivo. In the absence of TMEM16F, Listeria induced PM rupture and fragmentation of KCs in vivo. KC death associated with greater liver damage, inflammatory changes, and dysregulated liver metabolism. Overall, the results uncovered that TMEM16F expressed in Kupffer cells is crucial to protect the host against Listeria infection. This influence is associated with the capacity of Kupffer cell-expressed TMEM16F to prevent excessive inflammation and abnormal liver metabolism.
ABSTRACT
BACKGROUND: After the first monovalent 2009 pandemic influenza A (H1N1) vaccine became available in September 2009, Chinese officials conducted a mass vaccination program in Beijing. We evaluated the safety and effectiveness of the vaccine. METHODS: During a 5-day period in September 2009, a total of 95,244 children and adults received the PANFLU.1 vaccine (Sinovac Biotech), a monovalent split-virion vaccine of 15 µg of hemagglutinin antigen without adjuvant. We assessed adverse events after immunization through an enhanced passive-surveillance system and through active surveillance, using diary cards and telephone interviews. Active surveillance for neurologic diseases was implemented in hospitals citywide. To assess vaccine effectiveness, we compared the rates of reported laboratory-confirmed cases of 2009 H1N1 virus infection in students who received the vaccine with the rates in those who did not receive the vaccine, starting 2 weeks after the mass vaccination. RESULTS: As of December 31, 2009, adverse events were reported by 193 vaccine recipients. Through hospital-based active surveillance, 362 cases of incident neurologic diseases were identified within 10 weeks after the mass vaccination, including 27 cases of the Guillain-Barré syndrome. None of the neurologic conditions occurred among vaccine recipients. From 245 schools, 25,037 students participated in the mass vaccination and 244,091 did not. During the period from October 9 through November 15, 2009, the incidence of confirmed cases of 2009 H1N1 virus infection per 100,000 students was 35.9 (9 of 25,037) among vaccinated students and 281.4 (687 of 244,091) among unvaccinated students. Thus, the estimated vaccine effectiveness was 87.3% (95% confidence interval, 75.4 to 93.4). CONCLUSIONS: Among 95,244 children and adults in Beijing, the PANFLU.1 vaccine had a safety profile similar to those of seasonal influenza vaccines and appeared to be effective against confirmed H1N1 virus infection in school-age children. (Funded by the Beijing Municipal Health Bureau.).
Subject(s)
Influenza A Virus, H1N1 Subtype , Influenza Vaccines , Influenza, Human/prevention & control , Mass Vaccination , Adolescent , Adult , Child , Child, Preschool , China/epidemiology , Disease Outbreaks/prevention & control , Guillain-Barre Syndrome/epidemiology , Humans , Incidence , Influenza Vaccines/adverse effects , Influenza Vaccines/immunology , Influenza, Human/epidemiology , Middle Aged , Nervous System Diseases/epidemiology , Population Surveillance , Risk , Treatment Outcome , Young AdultABSTRACT
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.
ABSTRACT
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).
Subject(s)
Copper , Fungicides, Industrial , Antifungal Agents/pharmacology , Chitin/pharmacology , Chitosan , Chlorides , Copper/chemistry , Fungicides, Industrial/chemistry , Fungicides, Industrial/pharmacology , OligosaccharidesABSTRACT
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.
Subject(s)
Deep Learning , Neoplasms , Mammography , Breast Density , Research , AlgorithmsABSTRACT
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.