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Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss. Secondly, use the layer-wise relevance analysis method to measure relevance difference between neurons weight and outputting results. Deep-DPGI disturbs neuron weight by adaptive noising mechanism, protecting the safety of high-order gene interactions and balancing the privacy and utility. Specifically, more noise is added to gradients of neurons that is less relevance with the outputs, less noise to gradients that more relevance. Finally, Experiments on simulated and real datasets demonstrate that Deep-DPGI not only improve the power of high-order gene interactions detection in with marginal and without marginal effect of complex disease models, but also prevent the disclosure of sensitive information effectively.

Objective:. Medical data mining and sharing is an important process in E-Health applications. However, because these data consist of a large amount of personal private information of patients, there is the risk of privacy disclosure when sharing and mining. Therefore, ensuring the security of medical big data in the process of publishing, sharing, and mining has become the focus of current research. The objective of our study is to design a framework based on a differential privacy protection mechanism to ensure the secure sharing of medical data. We developed a privacy protection query language (PQL) that integrates multiple data mining methods and provides a secure sharing function. Methods:. This study is mainly performed in Xuzhou Medical University, China and designs three sub-modules: a parsing module, mining module, and noising module. Each module encapsulates different computing methods, such as a composite parser and a noise theory. In the PQL framework, we apply the differential privacy theory to the results of the computing between modules to guarantee the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. In addition, PQL is encapsulated in MNSSp3 that is a data mining and security sharing platform and the data comes from public data sets, such as UCBI. The public data set (NCBI database) was used as the experimental data, and the data collection time was January 2020. Results:. We designed and developed a query language that provides functions for medical data mining, sharing, and privacy preservation. We theoretically proved the performance of the PQL framework. The experimental results show that the PQL framework can ensure the security of each mining result and the availability of the output results is above 97%. Conclusion:. Our framework enables medical data providers to securely share health data or treatment data and develops a usable query language, based on a differential privacy mechanism, that enables researchers to mine information securely using data mining algorithms.

Purpose: Mental health assessments that combine patients’ facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students. Materials and Methods: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score. Results: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively. Conclusion: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students’ psychological problems.

To survive and thrive in harsh and ever-changing environments, intricate mechanisms have evolved for bacterial cells to monitor perturbations impacting the integrity of their envelope and to mount an appropriate response to contain or repair the damage. In this study, we report in Shewanella oneidensis a previously undescribed mechanism for the envelope defect resulting from the loss of Arc, a two-component transcriptional regulatory system crucial for respiration. We uncovered σE , a master regulator establishing and maintaining the integrity of the cell envelope in γ-proteobacteria, as the determining factor for the cell envelope defect of the arcA mutant. When ArcA is depleted, σE activity is compromised by enhanced production of anti-σE protein RseA. Surprisingly, S. oneidensis σE is not essential for viability, but becomes so in the absence of ArcA. Furthermore, we demonstrated that there is an interplay between these two regulators as arcA expression is affected by availability of σE . Overall, our results underscore functional interplay of regulatory systems for envelope stress response: although each of the systems may respond to perturbation of particular components of the envelope, they are functionally intertwined, working together to form an interconnected safety net.

How to transform the growing medical big data into medical knowledge is a global topic. However, medical data contains a large amount of personal privacy information, especially electronic medical records, gene data and electroencephalography data; the current methods and tools for data sharing are not efficient or cannot be applied in real-life applications. Privacy disclosure has become the bottleneck of medical big data sharing. In this context, we conducted research of medical data from the data collection, data transport and data sharing to solve the key problems of privacy protection and put forward a privacy protection sharing platform called MNSSp3 (medical big data privacy protection platform based on Internet of things), which attempts to provide an effective medical data sharing solution with the privacy protection algorithms for different data types and support for data analytics. The platform focuses on the transmission and sharing security of medical big data to provide users with mining methods and realizes the separation of data and users to ensure the security of medical data. Moreover, the platform also provides users with the capacity to upload privacy algorithms independently. We discussed the requirements and the design components of the platform, then three case studies were presented to verify the functionality of the platform, and the results of the experiments show clearly the benefit and practicality of the proposed platform.

Autoimmune hepatitis (AIH) is characterized by hepatocyte destruction, leading to lymphocyte and macrophage accumulation in the liver. Macrophages are key drivers of AIH. A membrane-permeable pan-caspase inhibitor, Z-Val-Ala-DL-Asp-fluoromethylketone (zVAD), induces macrophage necroptosis in response to certain stimuli. However, the function of zVAD in the pathogenesis of autoimmune hepatitis remains elusive. In this study, we aimed to evaluate the effect and explore the underlying mechanisms of zVAD against AIH.Murine acute autoimmune liver injury was established by concanavalin A (ConA) injection. Bone marrow-derived macrophages (BMDMs) were used in adoptive cell transfer experiments. The mechanism of action of zVAD was examined using macrophage cell lines and BMDMs. Phosphorylation of mixed lineage kinase domain-like proteins was used as a marker of necroptosis.Treatment with zVAD increased necroptosis, reduced inflammatory cytokine production, and alleviated liver injury in a ConA-induced liver injury mouse model. Regardless of zVAD treatment, macrophage deletion resulted in reduced neutrophil accumulation and relieved ConA-induced liver injury. In vitro studies have shown that zVAD pretreatment promotes lipopolysaccharide-induced macrophage necroptosis and leads to reduced pro-inflammatory cytokine and chemokine secretion. Transferring zVAD-pretreated BMDMs in mice notably reduced ConA-associated liver inflammation and injury, resulting in lower mortality than that observed after transferring normal BMDMs. Mechanistically, zVAD treatment increased the expression of tumour necrosis factor receptor (TNFR)-1 and interleukin (IL)-10 in macrophages. TNFR1 expression decreased upon transfection with IL-10-specific small interfering RNAs and blocking of TNFR1 decreased macrophage necroptosis.We found that zVAD alleviated ConA-induced liver injury by increasing the sensitivity of macrophages to necroptosis via IL-10-induced TNFR1 expression. This study provides new insights into the treatment of autoimmune hepatitis via zVAD-induced macrophage necroptosis.

BACKGROUND Medical data mining and sharing is an important process to realize the value of medical big data in E-Health applications. However, medical data contains a large amount of personal private information of patients, there is a risk of privacy disclosure when sharing and mining. Therefore, how to ensure the security of medical big data in the process of publishing, sharing and mining has become the focus of current researches. OBJECTIVE The objective of our study is to design a framework based on differential privacy protection mechanism to ensure the security sharing of medical data. We developed a privacy Protection Query Language (PQL) that can integrate multiple machine mining methods and provide secure sharing functions for medical data. METHODS This paper adopts a modular design method with three sub-modules, including parsing module, mining module and noising module. Each module encapsulates different computing devices, such as composite parser, noise jammer, etc. In the PQL framework, we apply the differential privacy mechanism to the results of the module collaborative calculation to optimize the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. RESULTS Designed and developed a query language framework that provides medical data mining, sharing and privacy preserving functions. We theoretically proved the performance of the PQL framework. The experimental results showed that the PQL framework can ensure the security of each mining result, and the average usefulness of the output results is above 97%. CONCLUSIONS We presented a security framework that enables medical data providers to securely share the health data or treatment data, and developed a usable query language based on differential privacy mechanism that enables researchers to mine potential information securely using data mining algorithms. CLINICALTRIAL

Ischemic stroke seriously threatens human health and creates a large social burden. The present study investigated whether tissue inhibitor of metalloproteinases-3 (TIMP3) prevented cerebral ischemia/reperfusion (I/R), with the aim to explore the underlying mechanism. A transient middle cerebral artery occlusion model was conducted in mice, and oxygen glucose deprivation and reoxygenation (OGD/R) was investigated in PC12 cells to mimic cerebral ischemia-reperfusion injury (CIRI). Western blotting was used to determine the expression of TIMP3, Bax, Bcl-2 and AKT. TUNEL was used to detect apoptosis in cerebral tissues or cultured PC12 cells. Expression levels of reactive oxygen species (ROS), superoxide dismutase (SOD) and malondialdehyde (MDA) were detected to reveal oxidative stress. The results demonstrated that TIMP3 expression was significantly decreased after I/R in vivo or OGD/R in vitro, and the number of TUNEL-positive cells was reduced by the overexpression of TIMP3. The attenuation of Bax/Bcl-2 ratio in OGD/R-induced PC12 cells suppressed the expression levels of ROS and MDA; while also elevating SOD activity in the OGD/R-induced neurocytes in vitro. In addition, TIMP3-overexpression reversed the downregulation of phosphorylated-AKT (Thr308 and Ser473) in OGD/R-treated PC12 cells. However, the anti-apoptotic and anti-oxidative stress roles of TIMP3 in OGD/R-induced PC12 cells were partially abolished after treatment with the AKT inhibitor, AZD5363. Overall, TIMP3 exerted an anti-apoptotic and anti-oxidative stress role in CIRI through the AKT pathway, which may be a potential therapeutic target for the treatment of CIRI.

As a bacteriostatic agent, nitrite has been used in food preservation for centuries. When used in combination with antibiotics, nitrite is reported to work either cooperatively or antagonistically. However, the mechanism underlying these effects remains largely unknown. Here we show that nitrite mediates tolerance to aminoglycosides in both Gram-negative and Gram-positive bacteria, but has little interaction with other types of antibiotics. Nitrite directly and mainly inhibits cytochrome heme-copper oxidases (HCOs), and by doing so, the membrane potential is compromised, blocking uptake of aminoglycosides. In contrast, reduced respiration (oxygen consumption rate) resulting from nitrite inhibition is not critical for aminoglycoside tolerance. While our data indicate that nitrite is a promising antimicrobial agent targeting HCOs, cautions should be taken when used with other antibiotics, aminoglycosides in particular., Zhang et al. show that nitrite mediates bacterial tolerance to aminoglycosides without affecting other antibiotics. They find that nitrite inhibits cytochrome heme-copper oxidases, blocking bacterial uptake of aminoglycosides. This study suggests nitrite as a promising antimicrobial agent while discouraging its combined use with aminoglycosides.

Background Transplantation of bone marrow-derived mesenchymal stem cells (BMSCs) has emerged as a novel therapy for acute liver failure (ALF). However, the homing efficiency of BMSCs to the injured liver sites appears to be poor. In this study, we aimed to determine if overexpression of c-Met in BMSCs could promote the homing ability of BMSCs to rat livers affected by ALF. Methods Overexpression of c-Met in BMSCs (c-Met-BMSCs) was attained by transfection of naive BMSCs with the lenti-c-Met-GFP. The impact of transplanted c-Met-BMSCs on both homing and repair of ALF was evaluated and compared with lenti-GFP empty vector transfected BMSCs (control BMSCs). Results After cells were transfected with the lenti-c-Met-GFP vector, the BMSCs displayed very high expression of c-Met protein as demonstrated by Western blot. In addition, in vitro transwell migration assays showed that the migration ability of c-Met-BMSCs was significantly increased in comparison with that of control BMSCs (P

To survive and thrive in harsh and ever-changing environments, intricate mechanisms have evolved for bacterial cells to monitor perturbations impacting the integrity of their envelope and to mount an appropriate response to contain or repair the damage. In this study, we report in Shewanella oneidensis a previously undescribed mechanism for the envelope defect resulting from the loss of Arc, a two-component transcriptional regulatory system crucial for respiration. We uncovered σ

In many γ-proteobacteria, FadR is recognized as a global transcriptional regulator: in addition to being the most prominent regulator for FA biosynthesis and degradation, the protein also mediates expression of many genes in diverse biological processes. In Shewanella oneidensis, a bacterium renowned for its respiratory versatility, FadR directly controls only a few genes. However, the FadR loss substantially increases BCFA contents and impairs growth. In this study, we showed that FadR is required to activate a number of important FA biosynthesis genes, including fabA, fabB, and fabH1. Although most of these genes are controlled by FadR in a direct manner, they are not critically responsible for the phenotypes resulting from the FadR depletion. Subsequent investigations identified BKD encoded by the bkd operon as the critical factor for enhanced BCFA production. In the absence of FadR, the bkd operon is derepressed, resulting in elevated conversion of 3MOP to 3-methylbutanoyl-CoA, one of the direct substrates for BCFA synthesis. We further showed that the growth defect of the fadR mutant is due to BCAA shortage, a scenario also attributable to excessive BKD: 3MOP, the common substrate for both BCFA and BCAA, is disproportionately used for BCFA synthesis, leading to reduced production of BCAA. Collectively, our findings reveal that the S. oneidensis FadR regulon is surely larger than previously proposed and a new mechanism by which FadR impacts bacterial physiology.

Intelligent 1 video monitoring technology is an integration of image processing, pattern recognition and computer vision technology, which has developed rapidly in recent years. Due to the special environment of the substation, the video processing capacity of the traditional substation monitoring system to the complex environment is weak. In this paper, an embedded image processing board based on gaussian model hybrid foreground detection algorithm is applied to video monitoring system of substation. This monitoring system can realize different real-time monitoring tasks, such as intrusion detection, smoke detection and legacy object detection. The experimental results show that the designed system has good anti-shadow performance, and the false alarm rate is significantly improved compared with the traditional video monitoring system, which has practical promotion value in the video monitoring system of substation.

BACKGROUND Propranolol is the first-line drug for treatment of infantile hemangioma. However, its mechanism of action remains unclear. Nuclear factor-κB is highly expressed in tumors, directly or indirectly promoting angiogenesis. Thrombospondin-1 is the most important antiangiogenesis protein in vivo. These proteins mediate signaling pathways, probably playing an important role in hemangioma treatment. This study explored the synergistic regulation of thrombospondin-1 and nuclear factor-κB signaling pathways in the treatment of hemangioma with propranolol. METHODS The hemangioma-derived endothelial cells were sorted out from the specimens of proliferative hemangioma by flow cytometry. Furthermore, a BALB/c nude mouse hemangioma model was established. Viability and proliferation of hemangioma-derived endothelial cells and the role of thrombospondin-1 and nuclear factor-κB signaling pathways were observed after propranolol administration in vitro and in vivo. RESULTS The expression of thrombospondin-1 and its receptor CD36 in hemangioma-derived endothelial cells gradually increased with the increase in propranolol concentration, whereas the expression of nuclear factor-κBp65, phosphorylated inhibitor of κB alpha (p-IκBα), and phosphorylated inhibitor of nuclear factor-κB kinase beta (p-IκKβ) weakened gradually (p < 0.05). In vivo, the tumors shrank gradually after propranolol treatment, with an increase in thrombospondin-1 and CD36 and a decrease in nuclear factor-κBp65, p-IκBα, and p-IκKβ (p < 0.05). Glucocorticoid improved the antiangiogenesis mediated by thrombospondin-1/CD36 and inhibited the angiogenesis mediated by nuclear factor-κB/IκB (p < 0.05). Negative regulation occurred between the two signaling pathways. CONCLUSION The treatment of infantile hemangioma with propranolol is promising to promote thrombospondin-1-mediated antiangiogenesis and to block nuclear factor-κB-mediated angiogenesis.

To explore the significance of the relative proteins in thrombin sensitive protein(TSP-1) and nuclear factor kappa B (NF-κB) signal pathways by detecting their expressions in different phases of hemangioma and the relations between them and microvessel density(MVD).Forty cases with hemangioma were randomly selected and divided into proliferative group(n =21) and involutional group(n =19) according to Mulliken standard.The expressions of TSP-1,CD36,p59fyn,Caspase-3,p38MAPK,NF-κBp65,p-IκBα,p-IKKβ,VEGF and CD34 in endothelial cells were detected by immunohistochemistry.The relevance between the proteins in TSP-1 and NF-κB signal pathways and MVD were compared and analyzed respectively.The expressions of TSP-1,CD36,p59fyn,Caspase-3,p38MAPK in proliferative hemangioma were all lower than those in involutional hemangioma (F=32.582,47.575,11.645,18.824,13.140;P =0.000,0.000,0.002,0.000,0.001).Conversely, the expressions of NF-κBp65,p-IκBα,p-IKKβ,VEGF in proliferative hemangioma were higher than those in involutional hemangioma(F =7.807,6.323,5.988,4.216;P =0.009,0.018,0.021,0.049).Meanwhile,MVD were different between the two groups(F =7.256,P =0.012).The expressions of TSP-1, CD36, p59fyn and MVD showed a negative relationship(rs =-0.420,-0.519,-0.388;P =0.021,0.003,0.034). Conversely, the expressions of NF-κBp65,pvIκBα,VEGF and MVD showed a positive relationship(rs =0.666,0.611,0.515;P =0.000,0.000,0.004).The expression of TSP-1 and NF-κBp65 showed a negative relationship (rs =-0.413,P =0.023).TSP-1 signal pathway and NF-κBp65 signal pathway may be interactive in evolution of infantile hemangioma, which has a potential role by effecting the progress of angiogenesis.

Objective The purpose of this study is to investigate the grinding patterns and discuss the factors influencing the position relationship between intercuspal position (ICP) and grinding area during sleep bruxism. Methods Lateral condylar inclination, inclination of lateral incisal path and freedom in long centric of thirty subjects were measured. The grinding patterns during sleep bruxism were recorded with a bruxism recording device, BruxChecker. The position relationship between ICP and the grinding area was examined. Spearman’s rank correlation coefficient was used for correlation analysis between grinding area and free factors (grinding patterns, freedom in long centric and discrepancy between lateral condylar inclination and inclination of lateral incisal path). Results All 12 subjects with 0 mm-freedom in long centric exhibited that ICP of both sides located within the grinding areas. 4 subjects showed that ICP of both sides located outside the grinding areas. There is a significant correlation between 0 mm-freedom in long centric and ICP within the grinding areas ( p Conclusions Freedom in long centric has a significant effect on position relationship between intercuspal position and the grinding area.