Deep regression using 99mTc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate.

OBJECTIVES: To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background. METHODS: This study was a single-center retrospective analysis of the data of 14,634 patients who underwent 99mTc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFRa predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFRb directly predicts GFR from dynamic renal imaging without using other information. The root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 were used to evaluate the performance of our approach. RESULTS: sGFRa achieved an RMSE of 5.05, MAE of 4.03, MAPE of 6.07%, and R2 of 0.93 for total GFR while sGFRb achieved an RMSE of 7.61, MAE of 5.92, MAPE of 8.92%, and R2 of 0.85 for total GFR. The accuracy of sGFRa and sGFRb in determining the stage of chronic kidney disease was 87.41% and 82.44%, respectively. CONCLUSIONS: The findings of sGFRa show that automatic GFR calculation based on CNN and using dynamic renal imaging is feasible and efficient and, additionally, can aid clinical diagnosis. Furthermore, the promising results of sGFRb demonstrate that CNN can predict GFR from dynamic renal imaging without additional information. KEY POINTS: • Our CNN-based AI systems can automatically calculate GFR from dynamic renal imaging without manually delineating the ROIs of kidneys and the corresponding background. • sGFRa accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method. • sGFRb-predicted GFR directly without any parameters related to the Gates equation.

Ubiquitin protein E3 ligase ASB9 suppresses proliferation and promotes apoptosis in human spermatogonial stem cell line by inducing HIF1AN degradation.

BACKGROUND: Spermatogonial stem cells (SSCs) are critical for sustaining spermatogenesis. Even though several regulators of SSC have been identified in rodents, the regulatory mechanism of SSC in humans has yet to be discovered. METHODS: To explore the regulatory mechanisms of human SSCs, we analyzed publicly available human testicular single-cell sequencing data and found that Ankyrin repeat and SOCS box protein 9 (ASB9) is highly expressed in SSCs. We examined the expression localization of ASB9 using immunohistochemistry and overexpressed ASB9 in human SSC lines to explore its role in SSC proliferation and apoptosis. Meanwhile, we used immunoprecipitation to find the target protein of ASB9 and verified its functions. In addition, we examined the changes in the distribution of ASB9 in non-obstructive azoospermia (NOA) patients using Western blot and immunofluorescence. RESULTS: The results of uniform manifold approximation and projection (UMAP) clustering and pseudotime analysis showed that ASB9 was highly expressed in SSCs, and its expression gradually increased during development. The immunohistochemical and dual-color immunofluorescence results displayed that ASB9 was mainly expressed in nonproliferating SSCs. Overexpression of ASB9 in the SSC line revealed significant inhibition of cell proliferation and increased apoptosis. We predicted the target proteins of ASB9 and verified that hypoxia-inducible factor 1-alpha inhibitor (HIF1AN), but not creatine kinase B-type (CKB), has a direct interaction with ASB9 in human SSC line using protein immunoprecipitation experiments. Subsequently, we re-expressed HIF1AN in ASB9 overexpressing cells and found that HIF1AN reversed the proliferative and apoptotic changes induced by ASB9 overexpression. In addition, we found that ABS9 was significantly downregulated in some NOA patients, implying a correlation between ASB9 dysregulation and impaired spermatogenesis. CONCLUSION: ASB9 is predominantly expressed in human SSCs, it affects the proliferation and apoptotic process of the SSC line through HIF1AN, and its abnormal expression may be associated with NOA.

Arcuate NPY is involved in salt-induced hypertension via modulation of paraventricular vasopressin and brain-derived neurotrophic factor.

Chronic high salt intake is one of the leading causes of hypertension. Salt activates the release of the key neurotransmitters in the hypothalamus such as vasopressin to increase blood pressure, and neuropepetide Y (NPY) has been implicated in the modulation of vasopressin levels. NPY in the hypothalamic arcuate nucleus (Arc) is best known for its control in appetite and energy homeostasis, but it is unclear whether it is also involved in the development of salt-induced hypertension. Here, we demonstrate that wild-type mice given 2% NaCl salt water for 8 weeks developed hypertension which was associated with marked downregulation of NPY expression in the hypothalamic Arc as demonstrated in NPY-GFP reporter mice as well as by in situ hybridization analysis. Furthermore, salt intake activates neurons in the hypothalamic paraventricular nucleus (PVN) where mRNA expression of brain-derived neurotrophic factor (BDNF) and vasopressin was found to be upregulated, leading to elevated serum vasopressin levels. This finding suggests an inverse correlation between the Arc NPY level and expression of vasopressin and BDNF in the PVN. Specific restoration of NPY by injecting AAV-Cre recombinase into the Arc only of the NPY-targeted mutant mice carrying a loxP-flanked STOP cassette reversed effects of salt intake on vasopressin and BDNF expression, leading to a normalization of salt-dependent blood pressure. In summary, our study uncovers an important Arc NPY-originated neuronal circuitry that could sense and respond to peripheral electrolyte signals and thereby regulate hypertension via vasopressin and BDNF in the PVN.

Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm.

BACKGROUND AND AIMS: The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility. METHODS: In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (3:1:1) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise: expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience. RESULTS: The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001). CONCLUSIONS: The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.

Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination.

BACKGROUND: This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system's evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system's ability to improve FEQ during colonoscopy. METHODS: First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the system's performance in enhancing fold examination. RESULTS: The system's evaluations of FEQ of each endoscopist were significantly correlated with experts' scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27-0.30] vs. 0.23 [0.17-0.26]) and experts (14.00 [14.00-15.00] vs. 11.67 [10.00-13.33]) (both P < 0.001). CONCLUSION: The system's evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort. METHODS: Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output. RESULTS: We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively. CONCLUSION: Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably. KEY POINTS: • The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.

Role of Sp1 in atherosclerosis.

Specificity protein (Sp) is a famous family of transcription factors including Sp1, Sp2 and Sp3. Sp1 is the first one of Sp family proteins to be characterized and cloned in mammalian. It has been proposed that Sp1 acts as a modulator of the expression of target gene through interacting with a series of proteins, especially with transcriptional factors, and thereby contributes to the regulation of diverse biological processes. Notably, growing evidence indicates that Sp1 is involved in the main events in the development of atherosclerosis (AS), such as inflammation, lipid metabolism, plaque stability, vascular smooth muscle cells (VSMCs) proliferation and endothelial dysfunction. This review is designed to provide useful clues to further understanding roles of Sp1 in the pathogenesis of AS, and may be helpful for the design of novel efficacious therapeutics agents targeting Sp1.

Artificial intelligence for diagnosing gastric lesions under white-light endoscopy.

BACKGROUND: The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis. METHODS: Images of early gastric cancer, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa were retrospectively collected to train and test the system. The performance of the system was compared with that of 12 endoscopists. The performance of endoscopists with or without referring to the system was also evaluated. RESULTS: A total of 29,809 images from 8947 patients and 1579 images from 496 patients were used to train and test the system, respectively. For per-lesion analysis, the overall accuracy of the system was 85.7%, which was comparable to that of senior endoscopists (85.1%, P = 0.729) and significantly higher than that of junior endoscopists (78.8%, P < 0.001). With system assistance, the overall accuracies of senior and junior endoscopists increased to 89.3% (4.2%, P < 0.001) and 86.2% (7.4%, P < 0.001), respectively. Senior and junior endoscopists achieved varying degrees of improvement in the diagnostic performance of other types of lesions except for polyp. The diagnostic times of senior (3.8 vs 3.2 s per image, P = 0.500) and junior endoscopists (6.2 vs 4.6 s per image, P = 0.144) assisted by the system were both slightly shortened, despite no significant differences. CONCLUSIONS: The proposed AI system could be applied as an auxiliary tool to reduce the workload of endoscopists and improve the diagnostic accuracy of gastric lesions.

Exosomes from Adipose-Derived Stem Cells Can Prevent Medication-Related Osteonecrosis of the Jaw.

The treatment measures of medication-related osteonecrosis of the jaw (MRONJ) is a worldwide challenge in oral and maxillofacial surgery because of its unclear pathogenesis. Previous studies suggested that mesenchymal stem cells played important roles in promoting MRONJ lesion healing, but the detailed mechanisms were unknown. Increasing numbers of studies have demonstrated that exosomes derived from mesenchymal stem cells, especially adipose-derived stem cells, have key roles in stem cell-based therapies by accelerating bone remodeling, facilitating angiogenesis, and promoting wound healing. We hypothesized that exosomes derived from adipose-derived stem cells can prevent MRONJ by accelerating gingival healing and enhancing bone remodeling processes. Our results may provide a promising therapeutic option for MRONJ clinical therapy.

Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study.

BACKGROUND: 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. METHODS: We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model's performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. RESULTS: The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for 'diffusely increased,' 0.997 for 'diffusely decreased,' 0.998 for 'focal increased,' and 0.945 for 'heterogeneous uptake' in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. CONCLUSIONS: Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs). METHODS: We retrospectively collected the 99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve the outcomes of this study, we conducted subgroup analyses, including lesion burden number and tumor type for the classifying ability of the CNN. RESULTS: In the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4-6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer. CONCLUSION: The CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

Mitochondrial Translocation of DJ-1 Is Mediated by Grp75: Implication in Cardioprotection of Resveratrol Against Hypoxia/Reoxygenation-Induced Oxidative Stress.

Resveratrol (Res) was recently reported to ameliorate hypoxia/reoxygenation (H/R)-caused oxidative stress in H9c2 cardiomyocytes through promoting the mitochondrial translocation of DJ-1 protein and subsequently preserving the activity of mitochondrial complex I. However, it is noteworthy that DJ-1 possesses no mitochondria-targeting sequence. Therefore, how Res induces DJ-1 mitochondrial translocation is an important and interesting question for further exploration. Glucose-regulated protein 75 (Grp75), whose N-terminus contains a 51-amino acid long mitochondrial-targeting signal peptide, is a cytoprotective chaperone that partakes in mitochondrial import of several proteins. Here, the contribution of Grp75 to mitochondrial import of DJ-1 by Res was investigated in a cellular model of H/R. Our results showed that Res upregulated the expression of DJ-1 protein, enhanced the interaction of DJ-1 and Grp75, and promoted DJ-1 translocation to mitochondria from cytosol in H9c2 cardiomyocytes undergoing H/R. Importantly, knockdown of Grp75 markedly reduced the interaction of DJ-1 with Grp75 and subsequent DJ-1 mitochondrial translocation induced by Res. Furthermore, Res pretreatment promoted the association of DJ-1 with ND1 and NDUFA4 subunits of complex I, preserved the activity of complex I, decreased mitochondria-derived reactive oxygen species production, and eventually ameliorated H/R-caused oxidative stress damage. Intriguingly, these effects were largely prevented also by small interfering RNA targeting Grp75. Overall, these results suggested that Grp75 interacts with DJ-1 to facilitate its translocation from cytosol to mitochondria, which is required for Res-mediated preservation of mitochondria complex I and cardioprotection from H/R-caused oxidative stress injury.

[Textual research on origin and efficacy of Tibetan medicine "Shengdeng"].

"Shengdeng" is a traditional Tibetan medicine, which has many synonyms. It is complex in origin and serious in mi-xing. In order to clarify the origin of "Shengdeng" and its medicinal use, this paper makes a textual research on the name, variety and efficacy of "Shengdeng" by consulting Tibetan medicine classics such as The Four Medical Trantras and Jingzhu Materia Medica, combined with modern literature of Tibetan medicine. It is clear that the synonyms, primitive species, mainstream varieties and functions of Tibetan medicine "Shengdeng". The collation and analysis of the literature shows that "Shengdeng" has effects of treating rheumatism, drying "Huangshui", detumescence and relieving pain, and is mainly used for the treatment of rheumatoid arthritis in Tibetan medicine. Its original varieties include 14 species of plants belonging to 6 families: Spicaceae, Rhamnaceae, Cephalotaxus, Leguminosae, Hematoxylaceae and Taxaceae. Combined with the collection of legal standards at all levels, the distribution of resources and the application of clinical prescriptions, it is considered that the mainstream species of "Shengdeng" are Rhamnella gilgitica, Xanthoceras sorbifolia, Rhamnus parvifolia. As a substitute, Acacia catechu is also widely used in clinic. The literature review and variety textual research on Tibetan medicine "Shengdeng" is helpful to improve the safety, effectiveness and quality controllability of its clinical application, and provide scientific basis for its further standard setting, pharmacodynamics research, new drug development and so on.

International-focused Online Forum: A Good Way to Jointly Manage the COVID-19 Pandemic for Global Critical Care Community.

How to cite this article: Xiao-Bo H, Poonyathawon S, Semedi BP, Xiao-Yi Z, Wei F, Da-Wei W, et al. International-focused Online Forum: A Good Way to Jointly Manage the COVID-19 Pandemic for Global Critical Care Community. Indian J Crit Care Med 2020;24(4):283-284.

Tao-Hong-Si-Wu decoction reduces ischemia reperfusion rat myoblast cells calcium overloading and inflammation through the Wnt/IP3R/CAMKII pathway.

Limb ischemia reperfusion (LIRI) injury is associated with serious local and systemic effects. Reperfusion may augment tissue injury in excess of that produced by ischemia alone. Calcium overloading and inflammation are considered to be two of the pathological mechanisms of limb ischemia/reperfusion (I/R) injury. Tao-Hong-Si-Wu decoction (THSWD) is a traditional Chinese herbal medicine with a powerful anti-inflammatory properties. We studied the probable restorative effect of THSWD on limb I/R-induced calcium overloading and inflammation in myoblast obtained from gastrocnemius muscle tissues of Sprague-Dawley rats (Frizzled Z5,a wnt5a blocker; KN-93, a calmodulin-dependent protein kinase II (CamkII) blocker; XeC, a IP3R blocker as positive controls). The simulated ischemia and reperfusion(I/R) solutions were used to imitate LIRI environment. The results showed that after I/R treatment, the secretion of proinflammatory factors (TNF-α and IL-1ß) and Wnt5a/Ca2+ signal molecules (wnt5a, camkII, and IP3R) upregulated significantly, the Ca2+ concentration enhanced too in myoblast cells. THSWD pretreatment decreased the secretion of TNF-α and IL-1ß, Ca2+ concentration; and abated the Wnt5a/Ca2+ signal molecules of wnt5a, camkII and IP3R expression activated by I/R injury; but could not abated the Wnt11 and protein kinase C (PKC) expression significantly, the results was similar with Frizzled Z5 treatment cells. Our research illustrated that THSWD may have a mitigating effect on LIRI targeting Wnt/IP3R/CAMKII but not Wnt/IP3R/PKC signaling pathway for the first time. This study may encourage the use of THSWD in the critical clinical settings with LIRI.

Resveratrol attenuates myocardial hypoxia/reoxygenation-induced cell apoptosis through DJ-1-mediated SIRT1-p53 pathway.

Resveratrol, a multi-functional phytoalexin, has been well indicated to exert cardioprotective effects by weakening ischemia/reperfusion (I/R) injury, and cell apoptosis is a vital way in I/R injury. SIRT1-p53 pathway has strong significance in regulating cell apoptosis. DJ-1 can directly bind to SIRT1 and stimulate the activity of SIRT1-p53. Therefore, the current study was determined whether Resveratrol attenuates hypoxia/reoxygenation (H/R)-induced cell apoptosis, and whether DJ-1-mediated SIRT1 activation involves in the cardioprotective effects of Resveratrol. The results showed that remarkable decrease in the number of apoptotic cells along with reduction of lactate dehydrogenase release and restoration of cell viability emerged when Resveratrol was applied in the H9c2 cells exposed to H/R. Moreover, Resveratrol increased DJ-1 expression and promoted the interaction of DJ-1 with SIRT1, which further contributed to subsequent restoration of SIRT1 activity and decrease of acetylation level of p53. However, above cardioprotective effects of Resveratrol were abrogated by DJ-1 siRNA and SIRT1 specific inhibitor Sirtinol. In conclusion, the current study demonstrated that Resveratrol suppressed H/R-induced cell apoptosis, which may be conducted by up-regulating DJ-1, and later activating SIRT1 activity and subsequently inhibiting p53 acetylation level in the H9c2 cells.

Performance Evaluation of an Enzyme-Linked Immunosorbent Assay for Seven Autoantibodies in Lung Cancer.

BACKGROUND: To verify and evaluate the performance characteristics of an enzyme-linked immunosorbent assay (ELISA) assay kit (Hangzhou Cancer probe Biotech Company) for seven autoantibodies (7-AABS), including p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, and GBU4-5. METHODS: Evaluation was carried out according to "Guidelines for performance evaluation of in vitro diagnostic reagent". The performance parameters included detection limit, reportable range, precision, accuracy, and method comparison. RESULTS: The detection limit was less than 3.75 U/mL. Reportable range was from 3.75 U/mL to 60 U/mL. The coefficient of variations (CVs) of within-run of 7-AABS were 5.15% - 10.13%, and between-run of CVs were 3.41% - 8.80%. For accuracy verification, the relative deviations (Bias) were all lower than 15% in the indicated concentration range. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ac-curacy were 35.9%, 90.0%, 80.3%, 55.3%, 61.3%, respectively. CONCLUSIONS: Overall, the verification study demonstrated the performance of the kit meets the testing requirements. It is qualified for clinical applications.

Molecular Mechanism Underlying Hypoxic Preconditioning-Promoted Mitochondrial Translocation of DJ-1 in Hypoxia/Reoxygenation H9c2 Cells.

DJ-1 was recently reported to be involved in the cardioprotection of hypoxic preconditioning (HPC) against hypoxia/reoxygenation (H/R)-induced oxidative stress damage, by preserving mitochondrial complex I activity and, subsequently, inhibiting mitochondrial reactive oxygen species (ROS) generation. However, the molecular mechanism by which HPC enables mitochondrial translocation of DJ-1, which has no mitochondria-targeting sequence, to preserve mitochondrial complex I, is largely unknown. In this study, co-immunoprecipitation data showed that DJ-1 was associated with glucose-regulated protein 75 (Grp75), and this association was significantly enhanced after HPC. Immunofluorescence imaging and Western blot analysis showed that HPC substantially enhanced the translocation of DJ-1 from cytosol to mitochondria in H9c2 cells subjected to H/R, which was mimicked by DJ-1 overexpression induced by pFlag-DJ-1 transfection. Importantly, knockdown of Grp75 markedly reduced the mitochondrial translocation of DJ-1 induced by HPC and pFlag-DJ-1 transfection. Moreover, HPC promoted the association of DJ-1 with mitochondrial complex I subunits ND1 and NDUFA4, improved complex I activity, and inhibited mitochondria-derived ROS production and subsequent oxidative stress damage after H/R, which was also mimicked by pFlag-DJ-1 transfection. Intriguingly, these effects of HPC and pFlag-DJ-1 transfection were also prevented by Grp75 knockdown. In conclusion, these results indicated that HPC promotes the translocation of DJ-1 from cytosol to mitochondria in a Grp75-dependent manner and Grp75 is required for DJ-1-mediated protection of HPC on H/R-induced mitochondrial complex I defect and subsequent oxidative stress damage.

DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images.

Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of labeled data. However, currently available annotating systems are not suitable for annotating pulmonary nodules in CT images. We propose a web-based lung nodules annotating system named as DeepLNAnno. DeepLNAnno has a unique three-tier working process and loads of features like semi-automatic annotation, which not only make it much easier for doctors to annotate compared to some other annotating systems but also increase the accuracy of the labels. We invited a medical group from West China Hospital to annotate the CT images using our DeepLNAnno system, and collected a large number of labeled data. The results of our experiments demonstrated that a usable nodule-detection system is developed, and good benchmark scores on our evaluation data are obtained.

Plant Immunity Inducer Development and Application.

Plant immunity inducers represent a new and rapidly developing field in plant-protection research. In this paper, we discuss recent research on plant immunity inducers and their development and applications in China. Plant immunity inducers include plant immunity-inducing proteins, chitosan oligosaccharides, and microbial inducers. These compounds and microorganisms can trigger defense responses and confer disease resistance in plants. We also describe the mechanisms of plant immunity inducers and how they promote plant health. Furthermore, we summarize the current situation in plant immunity inducer development in China and the global marketplace. Finally, we also deeply analyze the development trends and application prospects of plant immunity inducers in environmental protection and food safety.