The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA.

Objective: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means. Materials and methods: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations. Results: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency. Conclusion: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

Automatic Localization of Soybean Seedlings Based on Crop Signaling and Multi-View Imaging.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.

Restoring Treg/Th17 cell balance in ulcerative colitis through HRas silencing and MAPK pathway inhibition.

This study investigates HRas-dependent mechanisms in the disruption of regulatory T (Treg) cells and T helper 17 (Th17) cells balance in ulcerative colitis (UC). Comprehensive RNA sequencing and bioinformatics analyses revealed elevated HRas and MAPK pathway-related protein expression in UC samples. Using a murine UC model induced by dextran sulfate sodium (DSS), HRas silencing was found to promote Treg cell differentiation and suppress Th17 cell production, effectively restoring balance. Inactivation of the MAPK pathway played a pivotal role in this rebalancing effect. In vivo experiments further confirmed that HRas silencing mitigated colon tissue damage in DSS-induced mice, emphasizing its potential as a therapeutic strategy for UC.

Dietary long-chain fatty acids promote colitis by regulating palmitoylation of STAT3 through CD36-mediated endocytosis.

The Western diet, characterized by its high content of long-chain fatty acids (LCFAs), is widely recognized as a significant triggering factor for inflammatory bowel disease (IBD). While the link between a high-fat diet and colitis has been observed, the specific effects and mechanisms remain incompletely understood. Our study provides evidence that the diet rich in LCFAs can disrupt the integrity of the intestinal barrier and exacerbate experimental colitis in mice. Mechanistically, LCFAs upregulate the signal transducer and activator of transcription-3 (STAT3) pathway in the inflammatory model, and STAT3 knockout effectively counters the pro-inflammatory effects of LCFAs on colitis. Specifically, palmitic acid (PA), a representative LCFA, enters intestinal epithelial cells via the cluster of differentiation 36 (CD36) pathway and participates in the palmitoylation cycle of STAT3. Inhibiting this cycle using pharmacological inhibitors like 2-Bromopalmitate (2-BP) and ML349, as well as DHHC7 knockdown, has the ability to alleviate inflammation induced by PA. These findings highlight the significant role of dietary LCFAs, especially PA, in the development and progression of IBD. Diet adjustments and targeted modulation offer potential therapeutic strategies for managing this condition. Model of LCFAs involvement in the palmitoylation cycle of STAT3 upon internalization into cells. Following cellular uptake through CD36, LCFAs are converted to palmitoyl-CoA. In the presence of DHHC7, palmitoyl-CoA binds to STAT3 at the C108 site, forming palmitoylated STAT3. Palmitoylation further promotes phosphorylation at the Y705 site of STAT3. Subsequently, palmitoylated STAT3 undergoes depalmitoylation by APT2 and translocates to the nucleus to exert its biological functions.

Clinical features and management of lymphoepithelial cyst.

The aim of the study was to analyze clinical features of lymphoepithelial cyst (LEC) to make a more comprehensive and deeper understanding of it. We retrospectively analyzed the hospital records of 201 patients who were diagnosed by pathology results. Clinical characteristics like demographic profiles, lesion characteristics, therapeutic schedule, and associated costs were analyzed. Patient's age ranged from 17 to 83 years old (52.6 ± 14.3, 120 males and 81 females). There were 12 cases of pancreatic LEC, 48 of oral LEC, and 141 of parotid LEC. Single lesion was found to be more than multiple lesions (147:54, 73.1%:26.9%). The majority of patients was primarily diagnosed by imaging test and endoscopy (171, 85.1%). All patients were finally confirmed by pathology results. Different treatment plans were selected according to personal situation, including dynamic observation (21, 10.5%), non-surgical treatment (24, 11.9%), and surgical treatment (156, 77.6%). No recurrence was found in surgical treatment patients for up to 24 months follow-up. To sum up, LEC is a rare and benign lesion, which is mostly located at parotid and oral, rarely located at pancreas. No typical symptoms could be found. EUS-FNA could be a reliable way to obtain pathological diagnosis. LEC could be cured by surgical resection with no recurrence.

Immunogenicity and Therapeutic Efficacy of a Sendai-Virus-Vectored HSV-2 Vaccine in Mouse and Guinea Pig Models.

BACKGROUND: To date, there is no licensed vaccine for preventing herpes simplex virus type 2 (HSV-2). The current treatment to address the infection and prevent its transmission is not always satisfactory. METHODS: We constructed two recombinant vectors, one encoding HSV-2 glycoprotein D (gD, SeV-dF/HSV-2-gD) and one encoding HSV-2-infected cell protein 27 (ICP27, SeV-dF/HSV-2-ICP27), based on a replication-defective Sendai virus through reverse genetics, collectively comprising a combinatorial HSV-2 therapeutic vaccine candidate. The immunogenicity and proper immunization procedure for this vaccine were explored in a murine model. The therapeutic effect that helps prevent recurrent HSV-2 disease was evaluated in HSV-2-infected guinea pigs. RESULTS: Both a robust humoral immune response and a cellular immune response, characterized by the neutralizing antibody titer and the IFN-γ level, respectively, were elicited in BALB/c mice. A further study of cellular immunogenicity in mice revealed that T lymphocytes were successfully enhanced with the desirable secretion of several cytokines. In HSV-2-seropositive guinea pigs, vaccination could reduce the severity of HSV-2 in terms of recurrent lesions, duration of recurrent outbreak, and frequency of recurrence by 58.66%, 45.34%, and 45.09%, respectively, while viral shedding was also significantly inhibited in the vaccine-treated group compared to the group treated with phosphate-buffered saline. CONCLUSIONS: The replication-defective recombinant Sendai viruses conveying HSV-2-gD and ICP27 proteins showed great immunogenicity and potential for preventing recurrent HSV-2 disease.

Ru(phen)3 Cl2 -Catalyzed Chlorotrifluoromethylation of Unactivated Alkenes.

A novel Ru(phen)3 Cl2 -catalyzed free-radical chlorotrifluoromethylation reaction of unactivated olefins was investigated. Substituted 8-aminoquinoline-derived inert enamides were reacted with the Togni regent to afford a broad range of Cl-containing trifluoromethyl derivatives in good yields. The reaction proceeded at 90 °C and experimental result shows that the chlorine source of the products originated from LiCl.

Examination of the cross-reactivity between vaccinia virus Tiantan strain and monkeypox virus.

AIM: To investigate the cross-reactivity between the sera collected from Vaccinia Virus Tiantan Strain vaccinated rabbits and viral antigens of monkeypox virus. METHODS: Vaccinia viruses were prepared on chicken embryo fibroblasts (CEF) and Vero cells respectively named as CEF-VTT NVSI-1 and Vero-VTT NVSI-1. Rabbits were inoculated with a total of three doses of adjuvanted 1.3 × 108 PFU CEF-VTT NVSI-1 each dose or adjuvanted 3.9 × 107 PFU Vero-VTT NVSI-1 (Freunds complete adjuvant) via the subcutaneous route. We then performed the enzyme-linked immunosorbent assay (ELISA) and bio-layer interferometry (BLI) for determination of the binding activity and affinity of immune sera to five crucial surface antigens on monkeypox virus including A35, B6R, H3 and to corresponding homologous antigens A33R, B5 and L1R of vaccinia virus. For comparison, plaque reduction neutralizing tests were used to evaluate the neutralization of immune sera against vaccinia virus. RESULTS: Both CEF-VTT NVSI-1 and Vero-VTT NVSI-1 vaccinations following planned schedule could induce neutralizing antibody titers greater than 1:2048 in rabbit sera. Binding antibodies targeting monkeypox viral antigens were confirmed by both indirect ELISA and BLI methods. Indirect ELISA for rabbit sera revealed 1:51200 binding antibody titers to A35/B6R/H3 monkeypox virus antigens while BLI tests yielded affinities at 2 × 10-6 to 8 × 10-7 between the sera and the three antigens. Similarly, such sera showed binding strength to vaccinia virus antigens A33R/B5/L1R consistent with that to three preceding monkeypox virus antigens. These results demonstrated the cross-reactivity between the sera of vaccinia virus vaccinated animals and monkeypox virus antigens. Traditional ELISA test and BLI method displayed a high consistency in antigen screening and they were further proved to correlate to the results of plaque reduction neutralizing test, which indicates that BLI could be utilized as an indirect alternative for assessment of neutralizing activity of samples in response to live virus. CONCLUSIONS: Sera of vaccinia virus-vaccinated rabbits exhibited cross-reactivity with viral antigens of monkeypox virus. Potential in improving the accuracy of antigen discovery while reducing the lengthy work needed for the screening as BLI method possesses, it contributes greatly to the rapid preliminary evaluation of immune response generated by vaccines.

Randomized Trial of Modified Chest Tube Placement vs Routine Placement After Lung Resection.

BACKGROUND: Chest tube placement after pulmonary resection is usually considered a mandatory procedure. However, peritubular leakage of pleural fluid and intrathoracic air is frequent after surgery. Therefore, we separated the chest tube from the intercostal space as a modified placement strategy. METHODS: Patients undergoing robotic and video-assisted lung resection were enrolled in this study at our medical center between February 2021 and August 2021. All patients were randomly divided into either the modified group (n = 98) or the routine group (n = 101). The incidence of peritubular leakage of pleural fluid and peritubular air leaking or entering after surgery were the primary end points of the study. RESULTS: A total of 199 patients were randomized. Patients in the modified group had lower incidence of peritubular leakage of pleural fluid (after surgery, 39.6% vs 18.4% [P = .001]; after chest tube removal, 26.7% vs 11.2% [P = .005]), lower incidence of peritubular air leaking or entering (14.9% vs 5.1% [P = .022]), and fewer dressing changes (5.02 ± 2.30 vs 3.48 ± 0.94 [P < .001]). In patients undergoing lobectomy and segmentectomy, the type of chest tube placement was associated with the severity of peritubular pleural fluid leakage (P < .05). CONCLUSIONS: The modified chest tube placement was safe and had better clinical efficacy than the routine type. The reduction of postoperative peritubular leakage of pleural fluid resulted in better wound recovery. This modified strategy should be popularized, especially in patients undergoing pulmonary lobectomy or segmentectomy.

A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control.

Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time.

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network.

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

The interaction between microbiota and immune in intestinal inflammatory diseases: Global research status and trends.

Aims: This study aimed to conduct a bibliometric analysis of the relevant literature on the interaction between microbiota and immune in intestinal inflammatory diseases, and show its current status, hotspots, and development trends. Methods: The related literature was acquired from the Web of Science Core Collection on October 12, 2022. Co-occurrence and cooperation relationship analysis of authors, institutions, countries, references, and keywords in the literature were carried out through CiteSpace 6.1.R3 software and the Online Analysis platform of Literature Metrology. At the same time, the relevant knowledge maps were drawn, and the keywords cluster analysis and emergence analysis were performed. Results: 3,608 related publications were included, showing that the number of articles in this field is increasing year by year. The results showed that Gasbarrini A and Sokol H were the authors with the highest cumulative number of articles with 25, and the institution with the most articles was Harvard University with 142 articles. The USA was far ahead in the article output, with 1,131 articles, and had a dominant role, followed by China with 707 articles. The journal Frontiers in Immunology contributed the most to this research field with 213 articles. In the cooperation network analysis, the USA, Harvard University, and Xavier RJ were the most widely collaborated country, institution, and author, respectively, which implied a high level of influence. Keywords analysis showed that there were 770 keywords, which were mainly classified as internal related diseases, such as "inflammatory bowel disease", "irritable bowel syndrome", "colorectal cancer", and the mechanism of interaction of microbiota and immune, such as "intestinal microbiota", "commensal microbiota", "regulatory T cell", "dendritic cell", "barrier function", "activation", "anti-inflammatory properties", "intestinal epithelium", and "diversity". Emerging analysis showed that future research hotspots and trends might be the short-chain fatty acid, gut dysbiosis, gut-liver axis, and fusobacterium nucleatum. Conclusion: This research was the first bibliometric analysis of publications in the field of interaction between microbiota and immune in intestinal inflammatory diseases using visualization software and data information mining, and obtained the current status, hotspots, and development of this field, which provides a theoretical basis for its scientific research.

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection.

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

Biodegradable, Flexible and Ultraviolet Blocking Nanocellulose Composite Film Incorporated with Lignin Nanoparticles.

The exploration of functional films using sustainable cellulose-based materials to replace plastics has been of much interest. In this work, two kinds of lignin nanoparticles (LNPs) were mixed with cellulose nanofibrils (CNFs) for the fabrication of composite films with biodegradable, flexible and ultraviolet blocking performances. LNPs isolated from p-toluenesulfonic acid hydrolysis was easily recondensed and deposited on the surface of composite film, resulting in a more uneven surface; however, the composite film consisting of CNFs and LNPs isolated from maleic acid hydrolysis exhibited a homogeneous surface. Compared to pure CNF film, the composite CNF/LNP films exhibited higher physical properties (tensile strength of 164 MPa and Young's modulus of 8.0 GPa), a higher maximal weight loss temperature of 310 °C, and a perfect UVB blocking performance of 95.2%. Meanwhile, the composite film had a lower environmental impact as it could be rapidly biodegraded in soil and manmade seawater. Overall, our results open new avenues for the utilization of lignin nanoparticles in biopolymer composites to produce functional and biodegradable film as a promising alternative to petrochemical plastics.

Patient-directed vs. fixed-volume PEG for colonoscopy preparation: a randomized controlled trial.

Background: Individualization using different volumes of polyethylene glycol is widely regarded as the optimal solution for bowel preparation, while the patient-directed regimen we propose may serve as a reliable individual solution. This study aimed to assess the efficacy, safety, and satisfaction of bowel preparation with a patient-directed regimen. Methods: Patients in the fixed-volume group ingested the same amount of PEG, while those in patient-directed group ingested different amount according to stool consistency or stool water content. Results: After filtering by exclusion criteria, 428 individuals in the fixed-volume group and 103 in the patient-directed group were successfully enrolled and analyzed. Eighty-three (80.6%) individuals in the patient-directed group had a reduced polyethylene glycol volume. There was no significant difference in the bowel preparation efficacy between the two groups (90.0% vs. 90.3%, χ² = 0.01; p = 0.918). Patients in the patient-directed group complained of fewer adverse effects (53.0% vs. 36.9%, χ² = 8.655; p = 0.003), especially vomiting (13.6% vs. 1.0%, χ² = 13.304; p < 0.001). Regarding comfort during bowel preparation, the degree of comfort was not significantly different between groups. Furthermore, the willingness rate for further colonoscopy in the patient-directed group was significantly higher than that in the fixed-volume group (90.3% vs. 77.1%, χ² = 8.912; p < 0.05). Multivariable logistic regression analysis showed that the body mass index served as an independent factor impacting quality of bowel preparation with the patient-directed regimen (OR 1.16, 95% CI 1.00-1.34; p = 0.043). Conclusions: Without decreasing the bowel preparation efficacy, the patient-directed regimen increased the safety and satisfaction of bowel preparation and is expected to be a regular and individual solution for bowel preparation. Individuals with a lower body mass index are more likely to undertake this new regimen. Trial registration number: ChiCTR1900022072 at ChiClinicalTrials.gov.

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.

Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review.

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Erratum: Fusobacterium nucleatum Promotes Metastasis in Colorectal Cancer by Activating Autophagy Signaling via the Upregulation of CARD3 Expression: Erratum.

[This corrects the article DOI: 10.7150/thno.38870.].

Systems biomarker characteristics of circulating alkaline phosphatase activities for 48 types of human diseases.

BACKGROUND: Most human diseases are accompanied by systems changes. Systems biomarkers should reflect such changes. The phosphorylation and dephosphorylation of biomolecules maintain human homeostasis. However, the systems biomarker characteristics of circulating alkaline phosphatase, a routine blood test conducted for many human diseases, have never been investigated. METHOD: This study retrieved the circulating alkaline phosphatase (ALP) activities from patients with 48 clinically confirmed diseases and healthy individuals from the database of our hospital during the past five years. A detailed analysis of the statistical characteristics of ALP was conducted, including quantiles, receiving operator curve (ROC), and principal component analysis. RESULTS: Among the 48 diseases, 45 had increased, and three had decreased median levels of ALP activities compared to the healthy control. Preeclampsia, hepatic encephalopathy, pancreatic cancer, and liver cancer had the highest median values, whereas nephrotic syndrome, lupus erythematosus, and nephritis had decreased median values compared to the healthy control. Further, area under curve (AUC) values were ranged between 0.61 and 0.87 for 19 diseases, and the ALP activities were the best systems biomarker for preeclampsia (AUC 0.87), hepatic encephalopathy (AUC 0.87), liver cancer (AUC 0.81), and pancreatic cancer (AUC 0.81). CONCLUSIONS: Alkaline phosphatase was a decent systems biomarker for 19 different types of human diseases. Understanding the molecular mechanisms of over-up-and-down-regulation of ALP activities might be the key to understanding the whole-body systems' reactions during specific disease progression.

Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN). METHODS: This study included three data sets. First, a CNN model was developed and tested on data set 1. Then, a hybrid prediction model was developed on data set 2 by multivariable binary logistic regression analysis. We combined the CNN model score and the selected epidemiological risk factors, and a risk prediction nomogram was presented. An independent multicenter cohort was used for model external validation. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: The final hybrid model included the CNN model score and the screened risk factors included age, gender, smoking status and family history of cancer. The nomogram showed good discrimination and calibration with an area under the curve (AUC) of 91.6% (95% CI: 89.4%-93.5%), compare with the CNN model, the improvement was significance. The performance of the nomogram still showed good discrimination and good calibration in the multicenter validation cohort, with an AUC of 88.3% (95% CI: 83.1%-92.3%). CONCLUSIONS: Our study showed that epidemiological characteristics should be considered in lung cancer screening, which can significantly improve the efficiency of the artificial intelligence (AI) model alone. We combined the CNN model score with Asian lung cancer epidemiological characteristics to develop a new nomogram to facilitate and accurately perform individualized lung cancer screening, especially for Asians.